zarr-python

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 zarr, os, pprint
import numpy as np

Read remote local and remote OME-Zarrs

# path = "https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr"
path = "./6001240.zarr"
dataset = zarr.open_group(path, mode = 'r')

Inspect the group-level metadata

Print the data type

print(f"Type of the dataset: {type(dataset)}")

Summarize group-level metadata:

dataset.info

Note the store type, the number of arrays and groups. \ Note also the group named 'labels'.

Print the full metadata:

pprint.pprint(dict(dataset.attrs))

Get multiscales metadata:

meta = dict(dataset.attrs['multiscales'][0])

Print the axis ordering and the units

pprint.pprint(meta['axes'])
axis_order = ''.join(item['name'] for item in meta['axes'])
print(f"Axis order is {axis_order}")

Print the voxel scaling for each resolution level

for idx, transform in enumerate(meta['datasets']):
    print(f"\033[1mVoxel transform for the level {idx}:\033[0m")
    pprint.pprint(transform)

Inspect individual resolution layers

Get the top resolution array:

zarr_array0 = dataset[0]
print(f"Array type: {type(zarr_array0)}")
print(f"Shape of the top-level array: {zarr_array0.shape}")

Get a downscaled array:

zarr_array1 = dataset[1]
print(f"Array type: {type(zarr_array1)}")
print(f"Shape of the first-level downscaled array: {zarr_array1.shape}")

Summarize array-level metadata:

zarr_array0.info
zarr_array1.info

Print chunk size for the top layer:

print(f"Chunk size: {zarr_array0.chunks}")

Convert the zarr array to a numpy array:

numpy_array0 = zarr_array0[:]
print(f"Array type: {type(numpy_array0)}")
# or use numpy directly
numpy_array0 = np.array(zarr_array0)
print(f"Array type: {type(numpy_array0)}")