V05 dev1

[will-moore]

Using schemas from ome/ngff#249
And corresponding data at https://minio-dev.openmicroscopy.org/idr/v0.5-dev1/6001240.zarr

Test at https://deploy-preview-35--ome-ngff-validator.netlify.app/?source=https://minio-dev.openmicroscopy.org/idr/v0.5-dev1/6001240.zarr

And using tensorstore to create v3 zarr data, https://deploy-preview-35--ome-ngff-validator.netlify.app/?source=https://minio-dev.openmicroscopy.org/idr/v0.5-dev1/6001240b.zarr
(needed fix in zarrita.js to work)

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[will-moore]

@joshmoore @d-v-b
The data above at https://minio-dev.openmicroscopy.org/idr/v0.5-dev1/6001240.zarr was generated from a local v0.4 NGFF sample with this code (NB: this isn't using ome-zarr-py, just plain zarr v3):
One issue is that it doesn't preserve the chunk sizes, since it reads the data as numpy rather than with Dask, so we lose chunks info.
Also this code doesn't handle labels, plates, bioformats2raw layout etc.

moved to repo

import numpy as np
import zarr
import os

path = "/Users/wmoore/Desktop/ZARR/data/6001240.zarr"
# remote fails - see below
# path = "https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0062A/6001240.zarr"

rootpath = "6001240_V0.5-dev1.zarr"
if os.path.exists(rootpath):
    import shutil
    shutil.rmtree(rootpath)

store_class = zarr.store.LocalStore
if path.startswith("http"):
    # TypeError: Can't instantiate abstract class RemoteStore with abstract methods get_partial_values, list, list_dir, list_prefix, set_partial_values
    store_class = zarr.store.RemoteStore
read_store = store_class(path, mode="r")
# Needs zarr_format=2 or we get ValueError("store mode does not support writing")
read_root = zarr.open_group(store=read_store, zarr_format=2)

# Create new Image...
write_store = zarr.store.LocalStore(rootpath, mode="w")
root = zarr.Group.create(write_store)
# copy NGFF attrs
for key in read_root.attrs.keys():
    root.attrs[key] = read_root.attrs[key]
# update version
root.attrs["multiscales"][0]["version"] = "0.5-dev"

# convert arrays
multiscales = read_root.attrs.get("multiscales")
for ds in multiscales[0]["datasets"]:
    ds_path = ds["path"]
    data = zarr.load(store=read_store, path=ds_path)
    print('array', ds_path, data.shape)
    a = root.create_array(str(ds_path), shape=data.shape, chunk_shape=[64] * data.ndim, dtype=data.dtype)
    # These 2 lines are equivalent to e.g. a[:,:] = data (for any number of dimensions)
    s = [np.s_[:]] * len(data.shape)
    a[tuple(s)] = data

see: ome/ome2024-ngff-challenge@88317e6

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This pull request has been mentioned on Image.sc Forum. There might be relevant details there:

https://forum.image.sc/t/ome2024-ngff-challenge/97363/9

[will-moore]

Updated the samples at https://minio-dev.openmicroscopy.org/idr/v0.5-dev1/6001240.zarr and https://minio-dev.openmicroscopy.org/idr/v0.5-dev1/6001240b.zarr with version 0.5-dev1 to corresponds to last commit above and fix of version in schemas at ome/ngff#249
cc @joshmoore