Comparison of OME-Zarr libs

[will-moore]

Some discussion about potential changes to ome-zarr-py at #402 inspired me to check out other OME-Zarr libs to understand alternative ways of structuring things...

Work in progres....

Inspired by https://docs.google.com/spreadsheets/d/1airVWkmQGaVopHgtW1tL8k4YJM55DO9I5QA6jnpu1nk/edit?gid=0#gid=0

ngff-zarr

https://github.com/thewtex/ngff-zarr
Testing example at https://ngff-zarr.readthedocs.io/en/latest/quick_start.html

import ngff_zarr as nz
import numpy as np
data = np.random.randint(0, 256, int(1e6)).reshape((1000, 1000))
multiscales = nz.to_multiscales(data)
nz.to_ngff_zarr('example.ome.zarr', multiscales)

• Pyramid generation is separate from writing to zarr 👍 Pyramid shapes are (1000,1000) and (500,500).
• 1 line to generate pyramid, 1 line to write to zarr
• We get array at example.ome.zarr/scale0/image/.zarray with example.ome.zarr/scale0/.zattrs for xarray _ARRAY_DIMENSIONS
• nz.to_multiscales(image, scale_factors=[2,4,8], chunks=64) generates a Multiscales data object with data as dask delayed pyramid.
• Can't pass in e.g. a 4D image with shape (1, 512, 512, 512) since it fails to downsample - trying to downsample all dimensions?
• No support for multi-C or multi-T images??

pydantic-ome-ngff

https://github.com/janeliascicomp/pydantic-ome-ngff

from pydantic_ome_ngff.v04.multiscale import MultiscaleGroup
from pydantic_ome_ngff.v04.axis import Axis
import numpy as np
import zarr

axes = [
    Axis(name='y', unit='nanometer', type='space'),
    Axis(name='x', unit='nanometer', type='space')
]
arrays = [np.zeros((512, 512)), np.zeros((256, 256))]

group_model = MultiscaleGroup.from_arrays(
    axes=axes,
    paths=['s0', 's1'],
    arrays=arrays,
    scales=[ [1.25, 1.25], [2.5, 2.5] ],
    translations=[ [0.0, 0.0], [1.0, 1.0] ],
    chunks=(64, 64),
    compressor=None)

store = zarr.DirectoryStore('min_example2.zarr', dimension_separator='/')
stored_group = group_model.to_zarr(store, path="")
# no data (chunks) has been written to these arrays, you must do that separately.
stored_group['s0'] = arrays[0]
stored_group['s1'] = arrays[1]

• We have full control over metadata - e.g. Axis types and downsampling by different factors in various dimensions etc.
• No help with actually downsampling arrays - lib just helps with metadata creation & validation
• But flexible in how we write the data to arrays. E.g. could do a plane at a time etc.

Others

https://github.com/janelia-cellmap/pydantic-zarr - Not OME-Zarr

https://github.com/CBI-PITT/stack_to_multiscale_ngff - Python based command like tool - E.g TIFFs to OME-Zarr

python ~/stack_to_multiscale_ngff/stack_to_multiscale_ngff/builder.py '/path/to/tiff/stack/channel1' 
'/path/to/tiff/stack/channel2' '/path/to/tiff/stack/channel3' '/path/to/output/multiscale.omehans' --scale 1 1 0.280 0.114 
0.114 --origionalChunkSize 1 1 1 1024 1024 --finalChunkSize 1 1 64 64 64 --fileType tif

https://github.com/bioio-devs/bioio - uses https://github.com/bioio-devs/bioio-ome-zarr which uses ome-zarr-py.