Merge pull request #14 from janelia-cellmap/feature/from-array

Feature/from array

.github/workflows/test.yml

@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.9', '3.10', '3.11']
+        python-version: ['3.10', '3.11', '3.12']
     steps:
     - uses: actions/checkout@v4
     - name: Set up Python

pyproject.toml

@@ -7,7 +7,7 @@ name = "cellmap-schemas"
 dynamic = ["version"]
 description = 'Schemas for data used by the Cellmap project team at Janelia Research Campus.'
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 license = "MIT"
 keywords = ["cellmap", "ngff", "n5", "zarr"]
 authors = [
@@ -45,7 +45,7 @@ dependencies = [
 ]
 
 [[tool.hatch.envs.test.matrix]]
-python = ["3.9", "3.10", "3.11"]
+python = ["3.10", "3.11", "3.12"]
 
 [tool.hatch.envs.test.scripts]
 run-coverage = "pytest --cov-config=pyproject.toml --cov=pkg --cov=tests"

src/cellmap_schemas/__about__.py

@@ -1,4 +1,4 @@
 # SPDX-FileCopyrightText: 2022-present Howard Hughes Medical Institute
 #
 # SPDX-License-Identifier: MIT
-__version__ = "0.7.0"
+__version__ = "0.7.1"

src/cellmap_schemas/annotation.py

@@ -14,13 +14,14 @@
 from typing import (
     Any,
     Generic,
-    List,
     Literal,
     Mapping,
     Optional,
     TypeVar,
-    Union,
 )
+import numpy as np
+import numpy.typing as npt
+from typing_extensions import Self
 from pydantic_zarr.v2 import GroupSpec, ArraySpec
 from pydantic import BaseModel, model_validator, field_serializer
 import zarr
@@ -111,7 +112,7 @@ class Label(BaseModel, extra="forbid"):
 
 
 class LabelList(BaseModel, extra="forbid"):
-    labels: List[Label]
+    labels: list[Label]
     annotation_type: AnnotationType = "semantic"
 
 
@@ -191,7 +192,7 @@ class SemanticSegmentation(BaseModel, extra="forbid"):
     """
 
     type: Literal["semantic_segmentation"] = "semantic_segmentation"
-    encoding: dict[Union[Possibility, Literal["present"]], int]
+    encoding: dict[Literal[Possibility, Literal["present"]], int]
 
 
 class InstanceSegmentation(BaseModel, extra="forbid"):
@@ -221,7 +222,7 @@ class InstanceSegmentation(BaseModel, extra="forbid"):
     encoding: dict[Possibility, int]
 
 
-AnnotationType = Union[SemanticSegmentation, InstanceSegmentation]
+AnnotationType = SemanticSegmentation | InstanceSegmentation
 
 TName = TypeVar("TName", bound=str)
 
@@ -247,16 +248,55 @@ class AnnotationArrayAttrs(BaseModel, Generic[TName]):
 
     class_name: TName
     # a mapping from values to frequencies
-    complement_counts: Optional[dict[Possibility, int]]
+    complement_counts: dict[Possibility, int] | None
     # a mapping from class names to values
     # this is array metadata because labels might disappear during downsampling
     annotation_type: AnnotationType
 
     @model_validator(mode="after")
     def check_encoding(self: "AnnotationArrayAttrs"):
-        assert set(self.annotation_type.encoding.keys()).issuperset((self.complement_counts.keys()))
+        if (
+            isinstance(self.annotation_type, SemanticSegmentation)
+            and self.complement_counts is not None
+        ):
+            assert set(self.annotation_type.encoding.keys()).issuperset(
+                (self.complement_counts.keys())
+            )
         return self
 
+    @classmethod
+    def from_array(
+        cls,
+        array: np.ndarray,
+        class_name: TName,
+        annotation_type: AnnotationType,
+        complement_counts: None | dict[Possibility, int] | Literal["auto"],
+    ) -> Self:
+        if complement_counts == "auto":
+            num_unknown = (array == annotation_type.encoding["unknown"]).sum()
+            num_absent = (array == annotation_type.encoding["absent"]).sum()
+            num_present = array.size - (num_unknown + num_absent)
+
+            if isinstance(annotation_type, SemanticSegmentation):
+                complement_counts_parsed = {
+                    "unknown": num_unknown,
+                    "absent": num_absent,
+                    "present": num_present,
+                }
+            elif isinstance(annotation_type, InstanceSegmentation):
+                complement_counts_parsed = {
+                    "unknown": num_unknown,
+                    "absent": num_absent,
+                }
+        else:
+            complement_counts_parsed = complement_counts
+
+        return cls(
+            class_name=class_name,
+            annotation_type=annotation_type,
+            complement_counts=complement_counts_parsed,
+        )
+
 
 class AnnotationGroupAttrs(BaseModel, Generic[TName]):
     """
@@ -327,11 +367,15 @@ class CropGroupAttrs(BaseModel, Generic[TName], validate_assignment=True):
     class_names: list[TName]
 
     @field_serializer("start_date")
-    def ser_end_date(value: date):
+    def ser_end_date(value: date) -> None | str:
+        if value is None:
+            return None
         return serialize_date(value)
 
     @field_serializer("end_date")
-    def ser_start_date(value: date):
+    def ser_start_date(value: date) -> None | str:
+        if value is None:
+            return None
         return serialize_date(value)
 
 
@@ -352,6 +396,21 @@ class AnnotationArray(ArraySpec):
 
     attributes: CellmapWrapper[AnnotationWrapper[AnnotationArrayAttrs]]
 
+    @classmethod
+    def from_array_infer_attrs(
+        cls,
+        array: npt.NDArray[Any],
+        class_name: TName,
+        annotation_type: AnnotationType,
+        complement_counts: None | dict[Possibility, int] | Literal["auto"],
+        **kwargs,
+    ) -> Self:
+        annotation_attrs = AnnotationArrayAttrs.from_array(
+            array, class_name, annotation_type, complement_counts
+        )
+        annotation_attrs_wrapped = wrap_attributes(annotation_attrs)
+        return super().from_array(array, attributes=annotation_attrs_wrapped.model_dump(), **kwargs)
+
 
 class AnnotationGroup(GroupSpec):
     """