ome-zarr-py

Activate the conda environment ngff_workshop:

conda activate ngff_workshop

Browse into the example data directory and then access Python:

cd /path/to/example_omezarrs
python

Import the relevant tools

import ome_zarr, zarr, pprint, os
from ome_zarr.reader import Reader
from ome_zarr.io import parse_url

Read local and remote OME-Zarrs

# local_path = "./6001240.zarr"
remote_path = "https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr"
reader = Reader(parse_url(remote_path))
# Note here that 'parse_url' can parse both remote and local urls.

Note that ome-zarr-py uses the term 'node' for different zarr groups and reads them in a flat list.

Print the node information per resolution level:

nodes = list(reader())
for idx, node in enumerate(nodes):
    print(f"The node at the level {idx} is {node}")

Get the data and metadata of the top-level node

dataset = nodes[0].data
meta = nodes[0].metadata

Check the 'data' instance to examine the array shape and the chunks for each resolution layer

for idx, array in enumerate(dataset):
    print(f"The array {idx} is a {type(array)} and has shape {array.shape} and has chunks with shape {array.chunksize}")

Print the axis types and units of the arrays using the metadata instance

print(f"Axis properties of the dataset:")
pprint.pprint(meta['axes'])

Print the voxel sizes per resolution level (and any other voxel transforms that may exist)

for idx, transforms in enumerate(meta['coordinateTransformations']):
    print(f"\033[1mThe transform metadata for the level {idx}:\033[0m")
    print(f"{transforms}")