Single-FOV OME-Zarr¶
This example shows how to create a single-FOV, single-scale OME-Zarr dataset, read data in read-only mode, append an extra time point to an existing dataset, and add a new channel to an existing dataset.
import os
from tempfile import TemporaryDirectory
import numpy as np
from iohub.ngff import open_ome_zarr
Write 5D data to a new Zarr store¶
tmp_dir = TemporaryDirectory()
store_path = os.path.join(tmp_dir.name, "ome.zarr")
tczyx = np.random.randint(
0, np.iinfo(np.uint16).max, size=(5, 2, 3, 32, 32), dtype=np.uint16
)
with open_ome_zarr(
store_path, layout="fov", mode="a", channel_names=["DAPI", "GFP"]
) as dataset:
dataset["img"] = tczyx
Read in read-only mode¶
with open_ome_zarr(store_path, layout="auto", mode="r") as dataset:
img = dataset["img"]
print(img)
print(img.numpy())
try:
img[0, 0, 0, 0, 0] = 0
except Exception as e:
print(f"Writing was rejected: {e}")
Append a new timepoint¶
new_1czyx = np.random.randint(
0, np.iinfo(np.uint16).max, size=(1, 2, 3, 32, 32), dtype=np.uint16
)
with open_ome_zarr(store_path, layout="fov", mode="r+") as dataset:
img = dataset["img"]
print(img.shape)
img.append(new_1czyx, axis=0)
print(img.shape)
Modify channels¶
dataset = open_ome_zarr(store_path, mode="r+")
dataset.print_tree()
# Append a new channel and write a Z-stack
new_zyx = np.random.randint(
0, np.iinfo(np.uint16).max, size=(3, 32, 32), dtype=np.uint16
)
dataset.append_channel("New", resize_arrays=True)
dataset["img"][0, 2] = new_zyx
print(dataset.channel_names)
dataset.print_tree()
# Rename the new channel
dataset.rename_channel("New", "Renamed")
print(dataset.channel_names)
# Write new data to the channel
new_tzyx = np.random.randint(
0, np.iinfo(np.uint16).max, size=(6, 3, 32, 32), dtype=np.uint16
)
c_idx = dataset.get_channel_index("Renamed")
dataset["img"][:, c_idx] = new_tzyx
dataset.close()