Add conversion of ImageArray to dask (#262)

* add conversion to dask

* add testing

* expand testing

* add documentation

* rename `ImageArray.dask` to `ImageArray.dask_array`

* add explicit dask[array] requirement, matches ndtiff

iohub/ngff/nodes.py

@@ -340,6 +340,13 @@ def numpy(self):
         `self.numpy()` is equivalent to `self[:]`."""
         return self[:]
 
+    def dask_array(self):
+        """Return as a dask array"""
+        import dask.array as da
+
+        # Note: Designed to work with zarr DirectoryStore
+        return da.from_zarr(self.store.path, component=self.path)
+
     def downscale(self):
         raise NotImplementedError
 

setup.cfg

@@ -43,6 +43,7 @@ install_requires =
     pillow>=9.4.0
     blosc2
     xarray>=2024.1.1
+    dask[array]
 
 [options.extras_require]
 dev =

tests/ngff/test_ngff.py

@@ -270,6 +270,27 @@ def test_create_zeros(ch_shape_dtype, arr_name):
         assert dataset[arr_name].dtype == dtype
 
 
+@given(
+    channels_and_random_5d=_channels_and_random_5d(),
+    arr_name=short_alpha_numeric,
+)
+@settings(
+    max_examples=16,
+    suppress_health_check=[HealthCheck.data_too_large],
+)
+def test_ome_zarr_to_dask(channels_and_random_5d, arr_name):
+    """Test `iohub.ngff.Position.data` to dask"""
+    channel_names, random_5d = channels_and_random_5d
+    with _temp_ome_zarr(random_5d, channel_names, "0") as dataset:
+        assert_array_almost_equal(
+            dataset.data.dask_array().compute(), random_5d
+        )
+    with _temp_ome_zarr(random_5d, channel_names, arr_name) as dataset:
+        assert_array_almost_equal(
+            dataset[arr_name].dask_array().compute(), random_5d
+        )
+
+
 @given(
     channels_and_random_5d=_channels_and_random_5d(),
     arr_name=short_alpha_numeric,