Finish off labels for v05

src/ome_zarr_models/_v05/image_label.py

@@ -1,11 +1,23 @@
+from pydantic import Field
 from pydantic_zarr.v2 import ArraySpec, GroupSpec
 
 from ome_zarr_models._v05.base import BaseGroupv05, BaseOMEAttrs
-from ome_zarr_models.v04.image_label import ImageLabelAttrs
+from ome_zarr_models._v05.image_label_types import Label
+from ome_zarr_models._v05.multiscales import Multiscale
+from ome_zarr_models.base import BaseAttrs
 
 __all__ = ["ImageLabel", "ImageLabelAttrs"]
 
 
+class ImageLabelAttrs(BaseAttrs):
+    """
+    Attributes for an image label object.
+    """
+
+    image_label: Label = Field(..., alias="image-label")
+    multiscales: list[Multiscale]
+
+
 class OMEImageLabelAttrs(BaseOMEAttrs):
     ome: ImageLabelAttrs
 

src/ome_zarr_models/_v05/image_label_types.py

@@ -0,0 +1,26 @@
+from typing import Literal
+
+from ome_zarr_models.v04.image_label_types import (
+    RGBA,
+    Color,
+    LabelBase,
+    Property,
+    Source,
+    Uint8,
+)
+
+__all__ = [
+    "RGBA",
+    "Color",
+    "Property",
+    "Source",
+    "Uint8",
+]
+
+
+class Label(LabelBase):
+    """
+    Metadata for a single image-label.
+    """
+
+    version: Literal["0.5"] | None = None

src/ome_zarr_models/v04/image_label.py

@@ -4,7 +4,7 @@
 
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Literal, Self
+from typing import TYPE_CHECKING, Self
 
 import zarr
 from pydantic import Field
@@ -13,9 +13,7 @@
 from ome_zarr_models.base import BaseAttrs
 from ome_zarr_models.v04.base import BaseGroupv04
 from ome_zarr_models.v04.image import Image
-from ome_zarr_models.v04.image_label_types import (
-    Label as _Label,
-)
+from ome_zarr_models.v04.image_label_types import Label
 from ome_zarr_models.v04.multiscales import Multiscale
 
 if TYPE_CHECKING:
@@ -27,10 +25,6 @@
 ]
 
 
-class Label(_Label):
-    version: Literal["0.5"]
-
-
 class ImageLabelAttrs(BaseAttrs):
     """
     Attributes for an image label object.

src/ome_zarr_models/v04/image_label_types.py

@@ -5,7 +5,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Annotated, Literal
+from typing import Annotated, Literal, Self, TypeVar
 
 from pydantic import AfterValidator, Field, model_validator
 
@@ -71,24 +71,31 @@ def _parse_colors(colors: tuple[Color] | None) -> tuple[Color] | None:
     return colors
 
 
-class Label(BaseAttrs):
-    """
-    Metadata for a single image-label.
-    """
-
+class LabelBase(BaseAttrs):
     # TODO: validate
     # "All the values under the label-value (of colors) key MUST be unique."
     colors: Annotated[tuple[Color, ...] | None, AfterValidator(_parse_colors)] = None
     properties: tuple[Property, ...] | None = None
     source: Source | None = None
-    version: Literal["0.4"] | None
+    version: str | None = None
 
     @model_validator(mode="after")
-    def _parse_model(self) -> Label:
+    def _parse_model(self) -> Self:
         return _parse_imagelabel(self)
 
 
-def _parse_imagelabel(model: Label) -> Label:
+class Label(LabelBase):
+    """
+    Metadata for a single image-label.
+    """
+
+    version: Literal["0.4"] | None = None
+
+
+_T = TypeVar("_T", bound=LabelBase)
+
+
+def _parse_imagelabel(model: _T) -> _T:
     """
     Check that label_values are consistent across properties and colors
     """

tests/v05/data/image_label_example.json

@@ -28,6 +28,61 @@
       "source": {
         "image": "../../"
       }
-    }
+    },
+    "multiscales": [
+      {
+        "name": "example",
+        "axes": [
+          { "name": "t", "type": "time", "unit": "millisecond" },
+          { "name": "c", "type": "channel" },
+          { "name": "z", "type": "space", "unit": "micrometer" },
+          { "name": "y", "type": "space", "unit": "micrometer" },
+          { "name": "x", "type": "space", "unit": "micrometer" }
+        ],
+        "datasets": [
+          {
+            "path": "0",
+            "coordinateTransformations": [
+              {
+                "type": "scale",
+                "scale": [1.0, 1.0, 0.5, 0.5, 0.5]
+              }
+            ]
+          },
+          {
+            "path": "1",
+            "coordinateTransformations": [
+              {
+                "type": "scale",
+                "scale": [1.0, 1.0, 1.0, 1.0, 1.0]
+              }
+            ]
+          },
+          {
+            "path": "2",
+            "coordinateTransformations": [
+              {
+                "type": "scale",
+                "scale": [1.0, 1.0, 2.0, 2.0, 2.0]
+              }
+            ]
+          }
+        ],
+        "coordinateTransformations": [
+          {
+            "type": "scale",
+            "scale": [0.1, 1.0, 1.0, 1.0, 1.0]
+          }
+        ],
+        "type": "gaussian",
+        "metadata": {
+          "description": "the fields in metadata depend on the downscaling implementation. Here, the parameters passed to the skimage function are given",
+          "method": "skimage.transform.pyramid_gaussian",
+          "version": "0.16.1",
+          "args": "[true]",
+          "kwargs": { "multichannel": true }
+        }
+      }
+    ]
   }
 }

tests/v05/test_image_label.py

@@ -1,8 +1,83 @@
-from ome_zarr_models._v05.image_label import ImageLabel
+from ome_zarr_models._v05.axes import Axis
+from ome_zarr_models._v05.coordinate_transformations import VectorScale
+from ome_zarr_models._v05.image_label import ImageLabel, ImageLabelAttrs
+from ome_zarr_models._v05.image_label_types import Color, Label, Source
+from ome_zarr_models._v05.multiscales import Dataset, Multiscale
 from tests.v05.conftest import json_to_zarr_group
 
 
 def test_image_label() -> None:
     zarr_group = json_to_zarr_group(json_fname="image_label_example.json")
     ome_group = ImageLabel.from_zarr(zarr_group)
-    assert ome_group.ome_attributes == None
+    assert ome_group.ome_attributes == ImageLabelAttrs(
+        image_label=Label(
+            colors=(
+                Color(label_value=0, rgba=(0, 0, 128, 128)),
+                Color(label_value=1, rgba=(0, 128, 0, 128)),
+            ),
+            properties=[
+                {
+                    "label_value": 0,
+                    "area (pixels)": 1200,
+                    "class": "intercellular space",
+                },
+                {
+                    "label_value": 1,
+                    "area (pixels)": 1650,
+                    "class": "cell",
+                    "cell type": "neuron",
+                },
+            ],
+            source=Source(image="../../"),
+            version=None,
+        ),
+        multiscales=[
+            Multiscale(
+                axes=[
+                    Axis(name="t", type="time", unit="millisecond"),
+                    Axis(name="c", type="channel", unit=None),
+                    Axis(name="z", type="space", unit="micrometer"),
+                    Axis(name="y", type="space", unit="micrometer"),
+                    Axis(name="x", type="space", unit="micrometer"),
+                ],
+                datasets=(
+                    Dataset(
+                        path="0",
+                        coordinateTransformations=(
+                            VectorScale(type="scale", scale=[1.0, 1.0, 0.5, 0.5, 0.5]),
+                        ),
+                    ),
+                    Dataset(
+                        path="1",
+                        coordinateTransformations=(
+                            VectorScale(type="scale", scale=[1.0, 1.0, 1.0, 1.0, 1.0]),
+                        ),
+                    ),
+                    Dataset(
+                        path="2",
+                        coordinateTransformations=(
+                            VectorScale(type="scale", scale=[1.0, 1.0, 2.0, 2.0, 2.0]),
+                        ),
+                    ),
+                ),
+                version=None,
+                coordinateTransformations=(
+                    VectorScale(type="scale", scale=[0.1, 1.0, 1.0, 1.0, 1.0]),
+                ),
+                metadata={
+                    "description": (
+                        "the fields in metadata depend on the downscaling "
+                        "implementation. Here, the parameters passed to the skimage "
+                        "function are given"
+                    ),
+                    "method": "skimage.transform.pyramid_gaussian",
+                    "version": "0.16.1",
+                    "args": "[true]",
+                    "kwargs": {"multichannel": True},
+                },
+                name="example",
+                type="gaussian",
+            )
+        ],
+        version="0.5",
+    )