ome-zarr

import numpy as np import zarr import os import shutil from ome_zarr.io import parse_url from ome_zarr.writer import write_image from ome_zarr.reader import Reader # Define a path for the OME-Zarr store store_path = 'my_ome_zarr_image.zarr' # Clean up previous store if it exists for a fresh run if os.path.exists(store_path): shutil.rmtree(store_path) # 1. Create a dummy numpy array (e.g., a 5D image: T, C, Z, Y, X) # OME-Zarr typically uses this dimension order or a subset. # Here: 1 Time point, 2 Channels, 3 Z-slices, 64 Y, 64 X data = np.zeros((1, 2, 3, 64, 64), dtype=np.uint16) data[0, 0, 1, 10:20, 10:20] = 100 # Channel 0, Z-slice 1 data[0, 1, 2, 30:40, 30:40] = 200 # Channel 1, Z-slice 2 # 2. Write the numpy array to an OME-Zarr store # `parse_url` creates an FSSpec store object. # `zarr.group` creates the root Zarr group, overwriting if it exists. storage = parse_url(store_path, mode='w').store root_group = zarr.group(storage, overwrite=True) # `write_image` handles the OME-Zarr metadata and array layout. write_image( image=data, group=root_group, axes=['t', 'c', 'z', 'y', 'x'], # Specify axes order, crucial for OME-Zarr chunks=(1, 1, 1, 32, 32) # Optional: define Zarr chunking ) print(f"OME-Zarr image written to: {store_path}") # 3. Read the OME-Zarr image reader = Reader(parse_url(store_path)) # Get the list of nodes (images) in the Zarr store. Typically one at the root. nodes = list(reader())[0] # Access image data for the highest resolution (level 0) image_data_level_0 = nodes.data[0] # Access OME metadata (a dictionary parsed from the OME-XML JSON) ome_metadata = nodes.metadata print(f"Successfully read OME-Zarr image from: {store_path}") print(f"Shape of data (level 0): {image_data_level_0.shape}") print(f"OME metadata keys: {ome_metadata.keys()}") # Clean up the created Zarr store (optional but good practice for examples) shutil.rmtree(store_path) print(f"Cleaned up: {store_path}")