add support for the get_transform analog to set_transform

iohub/ngff/nodes.py

@@ -1010,6 +1010,43 @@ def get_axis_index(self, axis_name: str) -> int:
         """
         return self.axis_names.index(axis_name.lower())
 
+    def get_transforms(
+        self,
+        image: str | Literal["*"],
+    ) -> tuple[TransformationMeta, TransformationMeta]:
+        """Get the total coordinate scale and translation metadata
+        for one image array or the whole FOV.
+
+        Parameters
+        ----------
+        image : str | Literal["*"]
+            Name of one image array (e.g. "0") to transform,
+            or "*" for the whole FOV
+        """
+        transforms: list[TransformationMeta] = self.metadata.multiscales[0].coordinate_transformations
+        if image != "*" and image in self:
+            for i, dataset_meta in enumerate(self.metadata.multiscales[0].datasets):
+                if dataset_meta.path == image:
+                    transforms.extend(
+                        self.metadata.multiscales[0]
+                        .datasets[i]
+                        .coordinate_transformations
+                    )
+        elif image != "*":
+            raise ValueError(f"Key {image} not recognized.")
+
+        full_scale = np.array([1]*len(self.axes), dtype=float)
+        full_translation = np.array([0]*len(self.axes), dtype=float)
+        for transform in transforms:
+            if transform.type == "scale":
+                full_scale *= np.array(transform.scale)
+            elif transform.type == "translation":
+                full_translation += full_scale * np.array(transform.translation)
+
+        return TransformationMeta(
+            type="scale", scale=tuple(full_scale)
+        ), TransformationMeta(type="translation", translation=tuple(full_translation))
+
     def set_transform(
         self,
         image: str | Literal["*"],

tests/ngff/test_ngff.py

@@ -485,6 +485,57 @@ def test_set_transform_image(ch_shape_dtype, arr_name):
         ]
 
 
+@pytest.mark.parametrize(
+    "transforms",
+    [
+        (
+            [TransformationMeta(type="identity")],
+            TransformationMeta(type="scale", scale=(1.0, 1.0, 1.0, 1.0, 1.0)),
+            TransformationMeta(type="translation", translation=(0.0, 0.0, 0.0, 0.0, 0.0)),
+        ),
+        (
+            [TransformationMeta(type="scale", scale=(1.0, 2.0, 3.0, 4.0, 5.0))],
+            TransformationMeta(type="scale", scale=(1.0, 2.0, 3.0, 4.0, 5.0)),
+            TransformationMeta(type="translation", translation=(0.0, 0.0, 0.0, 0.0, 0.0)),
+        ),
+        (
+            [TransformationMeta(type="translation", translation=(1.0, 2.0, 3.0, 4.0, 5.0))],
+            TransformationMeta(type="scale", scale=(1.0, 1.0, 1.0, 1.0, 1.0)),
+            TransformationMeta(type="translation", translation=(1.0, 2.0, 3.0, 4.0, 5.0)),
+        ),
+        (
+            [
+                TransformationMeta(type="scale", scale=(2.0, 2.0, 2.0, 2.0, 2.0)),
+                TransformationMeta(type="translation", translation=(1.0, 1.0, 1.0, 1.0, 1.0)),
+            ],
+            TransformationMeta(type="scale", scale=(2.0, 2.0, 2.0, 2.0, 2.0)),
+            TransformationMeta(type="translation", translation=(2.0, 2.0, 2.0, 2.0, 2.0)),
+        ),
+    ],
+)
+@given(
+    ch_shape_dtype=_channels_and_random_5d_shape_and_dtype(),
+    arr_name=short_alpha_numeric,
+)
+def test_get_transform_image(transforms, ch_shape_dtype, arr_name):
+    """Test `iohub.ngff.Position.set_transform()`"""
+    transform, expected_scale, expected_translate = transforms
+    channel_names, shape, dtype = ch_shape_dtype
+    with TemporaryDirectory() as temp_dir:
+        store_path = os.path.join(temp_dir, "ome.zarr")
+        with open_ome_zarr(
+            store_path, layout="fov", mode="w-", channel_names=channel_names
+        ) as dataset:
+            dataset.create_zeros(name=arr_name, shape=shape, dtype=dtype)
+            assert dataset.metadata.multiscales[0].datasets[
+                0
+            ].coordinate_transformations == [TransformationMeta(type="identity")]
+            dataset.set_transform(image=arr_name, transform=transform)
+            scale, translate = dataset.get_transforms(image=arr_name)
+            assert scale == expected_scale
+            assert translate == expected_translate
+
+
 @given(
     ch_shape_dtype=_channels_and_random_5d_shape_and_dtype(),
     arr_name=short_alpha_numeric,