Note

Go to the end to download the full example code.

Writing a Large Array#

This script shows how to store a larger-than-RAM array to a OME-Zarr dataset. The same method works for HCS datasets too.

Warning

Executing this example will write a large file. Modify the store path manually.

import numpy as np
from tqdm import tqdm

from iohub.ngff import open_ome_zarr

store_path = ""

Shape and data type of the large array this array is about 10 GB, each time point is about 100 MB. It may not be actually larger than RAM but enough for demo. Monitor the memory usage of Python when the following runs and it should take significantly less than 10 GB.

shape = (100, 2, 25, 1024, 1024)
dtype = np.uint16

Note

This will not run if the Zarr store path is not set above

Store this array by looping through the time points

if store_path:
    with open_ome_zarr(
        store_path, layout="fov", mode="w-", channel_names=["DAPI", "GFP"]
    ) as dataset:
        img = dataset.create_zeros(
            name="0",
            shape=shape,
            dtype=dtype,
            chunks=(1, 1, 1, 1024, 1024),  # chunk by XY planes
        )
        for t in tqdm(range(shape[0])):
            # write 4D image data for the time point
            img[t] = np.ones(shape[1:]) * t
        dataset.print_tree()
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