Merge pull request #44 from BioImageTools/pre-commit

Fix pre-commit

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.15
+    rev: v0.23
     hooks:
       - id: validate-pyproject
 

docs/tutorial.py

@@ -25,14 +25,17 @@
 
 # ## Updating models
 #
-# All the fields in the models can be updated in place. When you do this, any validation on the individual field you are updating will take place.
+# All the fields in the models can be updated in place. When you do this, any
+# validation on the individual field you are updating will take place.
 #
 # For example, there is no name for the first multiscales entry, so lets add it
 
 multiscales_meta[0].name = "The first multiscales entry"
 pprint(multiscales_meta)
 
-# One constraint in the OME-zarr spec is that the coordiante transforms have to be a scale, or a scale then tranlsation (strictly in that order). So if we try and make a transformation just a translation, it will raise an error.
+# One constraint in the OME-zarr spec is that the coordiante transforms have to be a
+# scale, or a scale then tranlsation (strictly in that order). So if we try and make a
+# transformation just a translation, it will raise an error.
 
 multiscales_meta[0].datasets[0].coordinateTransformations = VectorTranslation(
     type="translation", translation=[1, 2, 3]
@@ -44,11 +47,13 @@
 
 # ## Accessing data
 #
-# Although these models do not handle reading or writing data, they do expose the zarr arrays.
+# Although these models do not handle reading or writing data, they do expose the zarr
+# arrays.
 
 zarr_arr = ome_zarr_image.group[multiscales_meta[0].datasets[0].path]
 pprint(zarr_arr)
 
 # ## Not using validation
 #
-# If you want to create models that are not validated against the OME-zarr specifciation, you can use the ``model_construct`` method on the models.
+# If you want to create models that are not validated against the OME-zarr
+# specifciation, you can use the ``model_construct`` method on the models.

pyproject.toml

@@ -45,7 +45,7 @@ dev = [
 
 test = ["pytest"]
 
-pydantic=["pydantic", "pydantic-zarr"]
+pydantic = ["pydantic", "pydantic-zarr"]
 
 [tool.uv]
 default-groups = ["docs", "dev", "pydantic", "test"]
@@ -77,6 +77,10 @@ select = [
 ignore = [
     "D401", # First line should be in imperative mood (remove to opt in)
     "D200", # One line docstring should fit on one line.
+    "D205", # 1 blank line required between summary line and description
+    "D400", # First line should end with a period
+    "D100", # Missing docstring in public module
+    "D104", # Missing docstring in public package
 ]
 
 [tool.ruff.lint.per-file-ignores]

src/ome_zarr_models/__init__.py

@@ -1,5 +1,3 @@
-""""""
-
 from importlib.metadata import PackageNotFoundError, version
 
 try:

src/ome_zarr_models/base.py

@@ -7,4 +7,8 @@ class Base(pydantic.BaseModel):
     """
 
     class Config:
+        """
+        Pydantic config.
+        """
+
         validate_assignment = True

src/ome_zarr_models/v04/coordinate_transformations.py

@@ -1,12 +1,14 @@
 from __future__ import annotations
 
-from collections.abc import Iterable, Sequence
-from typing import Literal
+from typing import TYPE_CHECKING, Literal
 
 from typing_extensions import Self
 
 from ome_zarr_models.base import Base
 
+if TYPE_CHECKING:
+    from collections.abc import Iterable, Sequence
+
 __all__ = [
     "Identity",
     "VectorScale",
@@ -51,6 +53,9 @@ def build(cls, data: Iterable[float]) -> Self:
 
     @property
     def ndim(self) -> int:
+        """
+        Number of dimensions.
+        """
         return len(self.scale)
 
 
@@ -90,6 +95,9 @@ def build(cls, data: Iterable[float]) -> Self:
 
     @property
     def ndim(self) -> int:
+        """
+        Number of dimensions.
+        """
         return len(self.translation)
 
 
@@ -123,8 +131,8 @@ def _build_transforms(
     scale: Sequence[float], translation: Sequence[float] | None
 ) -> tuple[VectorScale] | tuple[VectorScale, VectorTranslation]:
     """
-    Create a `VectorScale` and optionally a `VectorTranslation` from a scale and a translation
-    parameter.
+    Create a `VectorScale` and optionally a `VectorTranslation` from a scale and a
+    translation parameter.
     """
     vec_scale = VectorScale.build(scale)
     if translation is None:

src/ome_zarr_models/v04/labels.py

@@ -2,14 +2,16 @@
 
 import warnings
 from collections import Counter
-from collections.abc import Hashable, Iterable
-from typing import Annotated, Literal
+from typing import TYPE_CHECKING, Annotated, Literal
 
 from pydantic import AfterValidator, Field, model_validator
 
 from ome_zarr_models.base import Base
 from ome_zarr_models.v04.multiscales import MultiscaleGroupAttrs
 
+if TYPE_CHECKING:
+    from collections.abc import Hashable, Iterable
+
 __all__ = ["ConInt", "RGBA", "Color", "Source", "Property", "ImageLabel", "GroupAttrs"]
 
 ConInt = Annotated[int, Field(strict=True, ge=0, le=255)]
@@ -28,27 +30,40 @@ def _duplicates(values: Iterable[Hashable]) -> dict[Hashable, int]:
 
 class Color(Base):
     """
-    A label value and RGBA as defined in https://ngff.openmicroscopy.org/0.4/#label-md
+    A label value and RGBA.
+
+    References
+    ----------
+    https://ngff.openmicroscopy.org/0.4/#label-md
     """
 
     label_value: int = Field(..., serialization_alias="label-value")
     rgba: RGBA | None
 
 
 class Source(Base):
+    """
+    Source data for the labels.
+    """
+
     # TODO: add validation that this path resolves to something
     image: str | None = "../../"
 
 
 class Property(Base):
+    """
+    A single property.
+    """
+
     label_value: int = Field(..., serialization_alias="label-value")
 
 
 def _parse_colors(colors: list[Color] | None) -> list[Color] | None:
     if colors is None:
         msg = (
             "The field `colors` is `None`. Version 0.4 of"
-            "the OME-NGFF spec states that `colors` should be a list of label descriptors."
+            "the OME-NGFF spec states that `colors` should be a list of "
+            "label descriptors."
         )
         warnings.warn(msg, stacklevel=1)
     else:
@@ -67,7 +82,8 @@ def _parse_version(version: Literal["0.4"] | None) -> Literal["0.4"] | None:
     if version is None:
         _ = (
             "The `version` attribute is `None`. Version 0.4 of "
-            "the OME-NGFF spec states that `version` should either be unset or the string 0.4"
+            "the OME-NGFF spec states that `version` should either be unset or "
+            "the string 0.4"
         )
     return version
 
@@ -106,7 +122,7 @@ class ImageLabel(Base):
     source: Source | None = None
 
     @model_validator(mode="after")
-    def parse_model(self) -> ImageLabel:
+    def _parse_model(self) -> ImageLabel:
         return _parse_imagelabel(self)
 
 

src/ome_zarr_models/v04/multiscales.py

@@ -1,10 +1,8 @@
 from __future__ import annotations
 
 from collections import Counter
-from collections.abc import Sequence
-from typing import Annotated, Any, get_args
+from typing import TYPE_CHECKING, Annotated, Any, get_args
 
-import zarr
 from pydantic import AfterValidator, Field, model_validator
 from pydantic_zarr.v2 import ArraySpec, GroupSpec
 
@@ -15,9 +13,16 @@
     ScaleTransform,
     TranslationTransform,
     VectorTransform,
+    _build_transforms,
     _ndim,
 )
-from ome_zarr_models.v04.omero import Omero
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable, Sequence
+
+    import zarr
+
+    from ome_zarr_models.v04.omero import Omero
 
 __all__ = ["VALID_NDIM", "Dataset", "Multiscale", "MultiscaleGroup"]
 
@@ -82,7 +87,10 @@ def _ensure_axis_length(axes: Sequence[Axis]) -> Sequence[Axis]:
     Ensures that there are between 2 and 5 axes (inclusive)
     """
     if (len_axes := len(axes)) not in VALID_NDIM:
-        msg = f"Incorrect number of axes provided ({len_axes}). Only 2, 3, 4, or 5 axes are allowed."
+        msg = (
+            f"Incorrect number of axes provided ({len_axes}). "
+            "Only 2, 3, 4, or 5 axes are allowed."
+        )
         raise ValueError(msg)
     return axes
 
@@ -93,7 +101,10 @@ def _ensure_axis_names(axes: Sequence[Axis]) -> Sequence[Axis]:
     """
     name_dupes = duplicates(a.name for a in axes)
     if len(name_dupes) > 0:
-        msg = f"Axis names must be unique. Axis names {tuple(name_dupes.keys())} are repeated."
+        msg = (
+            f"Axis names must be unique. Axis names {tuple(name_dupes.keys())} are "
+            "repeated."
+        )
         raise ValueError(msg)
     return axes
 
@@ -112,7 +123,10 @@ def _ensure_axis_types(axes: Sequence[Axis]) -> Sequence[Axis]:
     type_census = Counter(axis_types)
     num_spaces = type_census["space"]
     if num_spaces not in [2, 3]:
-        msg = f"Invalid number of space axes: {num_spaces}. Only 2 or 3 space axes are allowed."
+        msg = (
+            f"Invalid number of space axes: {num_spaces}. "
+            "Only 2 or 3 space axes are allowed."
+        )
         raise ValueError(msg)
 
     if not all(a == "space" for a in axis_types[-num_spaces:]):
@@ -124,12 +138,18 @@ def _ensure_axis_types(axes: Sequence[Axis]) -> Sequence[Axis]:
         raise ValueError(msg)
 
     if (num_channels := type_census["channel"]) > 1:
-        msg = f"Invalid number of channel axes: {num_channels}. Only 1 channel axis is allowed."
+        msg = (
+            f"Invalid number of channel axes: {num_channels}. "
+            "Only 1 channel axis is allowed."
+        )
         raise ValueError(msg)
 
     custom_axes = set(axis_types) - set(get_args(AxisType))
     if (num_custom := len(custom_axes)) > 1:
-        msg = f"Invalid number of custom axes: {num_custom}. Only 1 custom axis is allowed."
+        msg = (
+            f"Invalid number of custom axes: {num_custom}. "
+            "Only 1 custom axis is allowed."
+        )
         raise ValueError(msg)
     return axes
 
@@ -151,28 +171,27 @@ class Dataset(Base):
     ]
 
     @classmethod
-    def build(
-        cls, 
-        *, 
-        path: str, 
-        scale: Iterable[float], 
-        translation: Iterable[float] ):
+    def build(cls, *, path: str, scale: Iterable[float], translation: Iterable[float]):
         """
         Construct a `Dataset` from a path, a scale, and a translation.
         """
         return cls(
-            path=path, 
-            coordinateTransformations=_build_transforms(scale=scale, translation=translation))
+            path=path,
+            coordinateTransformations=_build_transforms(
+                scale=scale, translation=translation
+            ),
+        )
 
 
 def _ensure_top_transforms_dimensionality(data: Multiscale) -> Multiscale:
     """
-    Ensure that the dimensionality of the top-level coordinateTransformations, if present,
-    is consistent with the rest of the model.
+    Ensure that the dimensionality of the top-level coordinateTransformations,
+    if present, is consistent with the rest of the model.
     """
     ctx = data.coordinateTransformations
     if ctx is not None:
-        # check that the dimensionality of the coordinateTransformations is internally consistent
+        # check that the dimensionality of the coordinateTransformations is
+        # internally consistent
         _ = _ensure_transform_dimensionality(ctx)
 
     return data
@@ -213,7 +232,8 @@ def _ensure_axes_dataset_transforms(data) -> Multiscale:
             if self_ndim != tx.ndim:
                 msg = (
                     f"The length of axes does not match the dimensionality of "
-                    f"the {tx.type} transform in datasets[{ds_idx}].coordinateTransformations. "
+                    f"the {tx.type} transform in "
+                    f"datasets[{ds_idx}].coordinateTransformations. "
                     f"Got {self_ndim} axes, but the {tx.type} transform has "
                     f"dimensionality {tx.ndim}"
                 )
@@ -246,8 +266,9 @@ class Multiscale(Base):
     @property
     def ndim(self) -> int:
         """
-        Report the dimensionality of the data described by this metadata, which is determined
-        by the length of the axes attribute.
+        Dimensionality of the data described by this metadata.
+
+        Determined by the length of the axes attribute.
         """
         return len(self.axes)
 
@@ -278,11 +299,17 @@ class MultiscaleGroupAttrs(Base):
 
 
 class MultiscaleGroup(GroupSpec[MultiscaleGroupAttrs, ArraySpec | GroupSpec]):
+    """
+    A multiscale group.
+    """
+
     @classmethod
     def from_zarr(cls, node: zarr.Group) -> MultiscaleGroup:
         """
-        Create an instance of `Group` from a `node`, a `zarr.Group`. This method discovers Zarr arrays in the hierarchy rooted at `node` by inspecting the OME-NGFF
-        multiscales metadata.
+        Create an instance of `Group` from a `node`, a `zarr.Group`.
+
+        This method discovers Zarr arrays in the hierarchy rooted at `node` by
+        inspecting the OME-NGFF multiscales metadata.
 
         Parameters
         ----------
@@ -296,14 +323,16 @@ def from_zarr(cls, node: zarr.Group) -> MultiscaleGroup:
         """
         # on unlistable storage backends, the members of this group will be {}
         raise NotImplementedError
+        """
         guess = GroupSpec.from_zarr(node, depth=0)
 
         try:
             multi_meta_maybe = guess.attributes["multiscales"]
         except KeyError as e:
             store_path = get_path(node.store)
             msg = (
-                "Failed to find mandatory `multiscales` key in the attributes of the Zarr group at "
+                "Failed to find mandatory `multiscales` key in the attributes of the "
+                "Zarr group at "
                 f"{node.store}://{store_path}://{node.path}."
             )
             raise KeyError(msg) from e
@@ -335,3 +364,4 @@ def from_zarr(cls, node: zarr.Group) -> MultiscaleGroup:
             update={"members": members_normalized.members}
         )
         return cls(**guess_inferred_members.model_dump())
+        """