Note

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Multi-FOV HCS OME-Zarr#

This script writes a high content screening (HCS) OME-Zarr dataset with a single FOV and a single scaling level per well, and adds an extra well-position to an existing dataset.

import os
from tempfile import TemporaryDirectory

import numpy as np

from iohub.ngff import open_ome_zarr

Set storage path

tmp_dir = TemporaryDirectory()
store_path = os.path.join(tmp_dir.name, "hcs.zarr")
print("Zarr store path", store_path)

Zarr store path /tmp/tmp5iaut2q0/hcs.zarr

Write 5D data to multiple wells. Integer path names will be automatically converted to strings. While the NGFF specification (and iohub) allows for arbitrary names, the ome-zarr-py library and the napari-ome-zarr viewer can only load positions and arrays with name ‘0’ for the whole plate.

position_list = (
    ("A", "1", "0"),
    ("H", 1, "0"),
    ("H", "12", "CannotVisualize"),
    ("Control", "Blank", 0),
)

with open_ome_zarr(
    store_path,
    layout="hcs",
    mode="w-",
    channel_names=["DAPI", "GFP", "Brightfield"],
) as dataset:
    # Create and write to positions
    # This affects the tile arrangement in visualization
    for row, col, fov in position_list:
        position = dataset.create_position(row, col, fov)
        position["0"] = np.random.randint(
            0, np.iinfo(np.uint16).max, size=(5, 3, 2, 32, 32), dtype=np.uint16
        )
    # Print dataset summary
    dataset.print_tree()

/
 ├── A
 │   └── 1
 │       └── 0
 │           └── 0 (5, 3, 2, 32, 32) uint16
 ├── Control
 │   └── Blank
 │       └── 0
 │           └── 0 (5, 3, 2, 32, 32) uint16
 └── H
     ├── 1
     │   └── 0
     │       └── 0 (5, 3, 2, 32, 32) uint16
     └── 12
         └── CannotVisualize
             └── 0 (5, 3, 2, 32, 32) uint16

Append a channel to all the positions

with open_ome_zarr(store_path, mode="r+") as dataset:
    for name, position in dataset.positions():
        print(f"Appending a channel to position: {name}")
        position.append_channel("Segmentation", resize_arrays=True)
        position["0"][:, 3] = np.random.randint(
            0, np.iinfo(np.uint16).max, size=(5, 2, 32, 32), dtype=np.uint16
        )
    dataset.print_tree()

Appending a channel to position: A/1/0
Appending a channel to position: Control/Blank/0
Appending a channel to position: H/1/0
Appending a channel to position: H/12/CannotVisualize
/
 ├── A
 │   └── 1
 │       └── 0
 │           └── 0 (5, 4, 2, 32, 32) uint16
 ├── Control
 │   └── Blank
 │       └── 0
 │           └── 0 (5, 4, 2, 32, 32) uint16
 └── H
     ├── 1
     │   └── 0
     │       └── 0 (5, 4, 2, 32, 32) uint16
     └── 12
         └── CannotVisualize
             └── 0 (5, 4, 2, 32, 32) uint16

Try viewing the images with napari-ome-zarr

Clean up

tmp_dir.cleanup()
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