BUG: tzyxc bin shrink crash

ngff_zarr/methods/_itk.py

@@ -149,6 +149,11 @@ def _downsample_itk_bin_shrink(
         # For consistency for now, do not utilize direction until there is standardized support for
         # direction cosines / orientation in OME-NGFF
         # block_0.attrs.pop("direction", None)
+        if "c" in previous_image.dims:
+            raise ValueError(
+                "Downsampling with ITK BinShrinkImageFilter does not support channel dimension 'c'. "
+                "Use ITK Gaussian downsampling instead."
+            )
         block_input = itk.image_from_array(np.ones_like(block_0))
         spacing = [previous_image.scale[d] for d in spatial_dims]
         block_input.SetSpacing(spacing)

ngff_zarr/methods/_itkwasm.py

@@ -146,7 +146,7 @@ def _downsample_itkwasm(
         ]
         assert all(
             block_output.size[dim] == computed_size[dim]
-            for dim in range(block_output.data.ndim)
+            for dim in range(len(block_output.size))
         )
         output_chunks = list(previous_image.data.chunks)
         output_chunks_start = 0
@@ -172,7 +172,7 @@ def _downsample_itkwasm(
         ]
         assert all(
             block_output.size[dim] == computed_size[dim]
-            for dim in range(block_output.data.ndim)
+            for dim in range(len(block_output.size))
         )
         for i in range(len(output_chunks)):
             output_chunks[i][-1] = block_output.data.shape[i]
@@ -181,7 +181,7 @@ def _downsample_itkwasm(
 
         non_spatial_dims = [d for d in dims if d not in _spatial_dims]
         if "c" in non_spatial_dims and previous_image.dims[-1] == "c":
-            non_spatial_dims.pop("c")
+            non_spatial_dims.remove("c")
 
         if output_chunks_start > 0:
             # We'll iterate over each index for the non-spatial dimensions, run the desired

ngff_zarr/methods/_support.py

@@ -111,15 +111,25 @@ def _dim_scale_factors(dims, scale_factor, previous_dim_factors):
         result_scale_factors = {
             d: int(scale_factor[d] / previous_dim_factors[d]) for d in scale_factor
         }
+        # if a dim is not in the scale_factors, add it with a scale factor of 1
+        for d in dims:
+            if d not in result_scale_factors:
+                result_scale_factors[d] = 1
+
     return result_scale_factors
 
+
 def _update_previous_dim_factors(scale_factor, spatial_dims, previous_dim_factors):
+    previous_dim_factors = copy.copy(previous_dim_factors)
     if isinstance(scale_factor, int):
-        previous_dim_factors = { d : scale_factor for d in spatial_dims }
+        for d in spatial_dims:
+            previous_dim_factors[d] = scale_factor
     else:
-        previous_dim_factors = scale_factor
+        for d in scale_factor:
+            previous_dim_factors[d] = scale_factor[d]
     return previous_dim_factors
 
+
 def _align_chunks(previous_image, default_chunks, dim_factors):
     block_0_shape = [c[0] for c in previous_image.data.chunks]
 

pyproject.toml

@@ -32,7 +32,7 @@ dependencies = [
   "dask[array]",
   "importlib_resources",
   "itkwasm >= 1.0b183",
-  "itkwasm-downsample >= 1.2.0",
+  "itkwasm-downsample >= 1.7.1",
   "numpy",
   "platformdirs",
   "psutil; sys_platform != \"emscripten\"",

test/test_to_ngff_zarr_itk.py

@@ -1,6 +1,6 @@
 import pytest
 
-from ngff_zarr import Methods, to_multiscales
+from ngff_zarr import Methods, to_multiscales, to_ngff_image
 
 from ._data import verify_against_baseline
 import platform
@@ -20,6 +20,41 @@ def test_bin_shrink_isotropic_scale_factors(input_images):
     verify_against_baseline(dataset_name, baseline_name, multiscales)
 
 
+def test_bin_shrink_tzyxc():
+    import dask.array as da
+
+    test_array = da.ones((96, 64, 64, 64, 2), chunks=(1, 64, 64, 64, 1), dtype="uint8")
+    img = to_ngff_image(
+        test_array,
+        dims=list("tzyxc"),
+        scale={
+            "t": 100_000.0,
+            "z": 0.98,
+            "y": 0.98,
+            "x": 0.98,
+            "c": 1.0,
+        },
+        axes_units={
+            "t": "millisecond",
+            "z": "micrometer",
+            "y": "micrometer",
+            "x": "micrometer",
+        },
+        name="000x_000y_000z",
+    )
+
+    # expect a ValueError
+    try:
+        to_multiscales(
+            img,
+            scale_factors=[{"z": 2, "y": 2, "x": 2}, {"z": 4, "y": 4, "x": 4}],
+            method=Methods.ITK_BIN_SHRINK,
+        )
+        assert False, "Expected ValueError for non-spatial dimensions"
+    except ValueError:
+        pass
+
+
 @pytest.mark.skipif(
     platform.system() == "Linux" and platform.machine() == "aarch64",
     reason="Skipping on Linux ARM systems",

test/test_to_ngff_zarr_itkwasm.py

@@ -107,6 +107,39 @@ def test_bin_shrink_tczyx():
     assert multiscales.images[1].data.shape[2] == 16
 
 
+def test_bin_shrink_tzyxc():
+    import dask.array as da
+
+    test_array = da.ones(
+        (96, 256, 256, 256, 2), chunks=(1, 128, 128, 128, 1), dtype="uint16"
+    )
+    img = to_ngff_image(
+        test_array,
+        dims=list("tzyxc"),
+        scale={
+            "t": 100_000.0,
+            "z": 0.98,
+            "y": 0.98,
+            "x": 0.98,
+            "c": 1.0,
+        },
+        axes_units={
+            "t": "millisecond",
+            "z": "micrometer",
+            "y": "micrometer",
+            "x": "micrometer",
+        },
+        name="000x_000y_000z",
+    )
+
+    multiscales = to_multiscales(
+        img,
+        scale_factors=[{"z": 2, "y": 2, "x": 2}, {"z": 4, "y": 4, "x": 4}],
+        method=Methods.ITKWASM_BIN_SHRINK,
+    )
+    assert len(multiscales.images) == 3
+
+
 def test_bin_shrink_isotropic_scale_factors(input_images):
     dataset_name = "cthead1"
     image = input_images[dataset_name]