An error with the ome2024-ngff-challenge tool #59
Description
I am trying to update metadata such as license, organism, and modality in an OME-Zarr filesets converted with bioformats2raw using the ome2024-ngff-challenge tool. Now I am facing an issue where an error occurs when applying it to the following data.
(ngff-challenge) kyoda@kyoudakacstudio ngff-challenge % ome2024-ngff-challenge resave --cc-by Fig3a_FIB-SEM_synapse.zarr test5.zarr
Traceback (most recent call last):
File "/Users/kyoda/ngff-challenge/bin/ome2024-ngff-challenge", line 8, in <module>
sys.exit(dispatch())
^^^^^^^^^^
File "/Users/kyoda/ngff-challenge/lib/python3.11/site-packages/ome2024_ngff_challenge/__init__.py", line 36, in dispatch
return ns.func(ns)
^^^^^^^^^^^
File "/Users/kyoda/ngff-challenge/lib/python3.11/site-packages/ome2024_ngff_challenge/resave.py", line 433, in main
convert_image(
File "/Users/kyoda/ngff-challenge/lib/python3.11/site-packages/ome2024_ngff_challenge/resave.py", line 272, in convert_image
convert_array(
File "/Users/kyoda/ngff-challenge/lib/python3.11/site-packages/ome2024_ngff_challenge/resave.py", line 108, in convert_array
write = ts.open(write_config).result()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Error opening "zarr3" driver: Error writing local file "test5.zarr/0/zarr.json": Cannot create using specified "metadata" and schema: Incompatible chunk size constraints for dimension 3: read size of 1024, write size of 1538 [tensorstore_spec='{\"context\":{\"cache_pool\":{},\"data_copy_concurrency\":{},\"file_io_concurrency\":{},\"file_io_sync\":true},\"create\":true,\"driver\":\"zarr3\",\"dtype\":\"uint8\",\"kvstore\":{\"driver\":\"file\",\"path\":\"test5.zarr/0/\"},\"metadata\":{\"chunk_grid\":{\"configuration\":{\"chunk_shape\":[1,1,1,1538,2048]},\"name\":\"regular\"},\"chunk_key_encoding\":{\"name\":\"default\"},\"codecs\":[{\"configuration\":{\"chunk_shape\":[1,1,1,1024,1024],\"codecs\":[{\"configuration\":{\"endian\":\"little\"},\"name\":\"bytes\"},{\"configuration\":{\"clevel\":5,\"cname\":\"zstd\"},\"name\":\"blosc\"}],\"index_codecs\":[{\"configuration\":{\"endian\":\"little\"},\"name\":\"bytes\"},{\"name\":\"crc32c\"}],\"index_location\":\"end\"},\"name\":\"sharding_indexed\"}],\"data_type\":\"uint8\",\"dimension_names\":[\"t\",\"c\",\"z\",\"y\",\"x\"],\"node_type\":\"array\",\"shape\":[1,1,1,1538,2048]},\"transform\":{\"input_exclusive_max\":[[1],[1],[1],[1538],[2048]],\"input_inclusive_min\":[0,0,0,0,0],\"input_labels\":[\"t\",\"c\",\"z\",\"y\",\"x\"]}}'] [source locations='tensorstore/driver/zarr3/metadata.cc:901\ntensorstore/driver/zarr3/driver.cc:571\ntensorstore/driver/zarr3/driver.cc:571\ntensorstore/internal/cache/kvs_backed_cache.h:208\ntensorstore/driver/driver.cc:112']
I apologize if this is a basic question, but I would appreciate it if anyone could provide a solution. For reference, Zarr filesets with updated metadata were successfully generated for the following data without any issues.