ENH: Add large image sharding test

Author: thewtex

test/test_large_serialization.py

@@ -1,6 +1,12 @@
+from packaging import version
+
 from dask_image import imread
 from ngff_zarr import config, to_multiscales, to_ngff_image, to_ngff_zarr
 from zarr.storage import MemoryStore
+import zarr
+import pytest
+
+zarr_version = version.parse(zarr.__version__)
 
 
 def test_large_image_serialization(input_images):
@@ -24,3 +30,32 @@ def test_large_image_serialization(input_images):
     # verify_against_baseline(dataset_name, baseline_name, multiscales)
 
     config.memory_target = default_mem_target
+
+
+def test_large_image_serialization_with_sharding(input_images):
+    pytest.mark.skipif(
+        zarr_version < version.parse("3.0.0b1"), reason="zarr version < 3.0.0b1"
+    )
+    default_mem_target = config.memory_target
+    config.memory_target = int(1e6)
+
+    dataset_name = "lung_series"
+    data = imread.imread(input_images[dataset_name])
+    image = to_ngff_image(
+        data=data,
+        dims=("z", "y", "x"),
+        scale={"z": 2.5, "y": 1.40625, "x": 1.40625},
+        translation={"z": 332.5, "y": 360.0, "x": 0.0},
+        name="LIDC2",
+    )
+    multiscales = to_multiscales(image)
+    # baseline_name = "auto/memory_target_1e6.zarr"
+    # store_new_multiscales(dataset_name, baseline_name, multiscales)
+    test_store = MemoryStore()
+    chunks_per_shard = 1
+    to_ngff_zarr(
+        test_store, multiscales, version="0.5", chunks_per_shard=chunks_per_shard
+    )
+    # verify_against_baseline(dataset_name, baseline_name, multiscales)
+
+    config.memory_target = default_mem_target