Merge pull request #5 from p2p-ld/dtype-str

Better string dtype handling

.github/workflows/tests.yml

@@ -38,7 +38,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
 
       - name: Install dependencies
         run: pip install -e ".[tests]"

docs/changelog.md

@@ -2,6 +2,17 @@
 
 ## 1.*
 
+### 1.3.0 - 24-08-05 - Better string dtype handling
+
+API Changes:
+- Split apart the validation methods into smaller chunks to better support
+  overrides by interfaces. Customize getting and raising errors for dtype and shape,
+  as well as separation of concerns between getting, validating, and raising.
+
+Bugfix:
+- [#4](https://github.com/p2p-ld/numpydantic/issues/4) - Support dtype checking
+  for strings in zarr and numpy arrays
+
 ### 1.2.3 - 24-07-31 - Vendor `nptyping`
 
 `nptyping` vendored into `numpydantic.vendor.nptyping` - 

docs/interfaces.md

@@ -41,19 +41,42 @@ when cast to an `ndarray`, we only try as a last resort.
 
 ## Validation
 
-Validation is a chain of lifecycle methods, with a single argument passed and returned
+Validation is a chain of lifecycle methods, each of which can be overridden
-to and from each:
+for interfaces to implement custom behavior that matches the array format.
 
-{meth}`.Interface.validate` calls in order:
+{meth}`.Interface.validate` calls the following methods, in order:
+
+An initial hook for modifying the input data before validation, eg.
+if it needs to be coerced or wrapped in some proxy class. This method
+should accept all and only the types specified in that interface's
+{attr}`~.Interface.input_types`.
 
 - {meth}`.Interface.before_validation`
-- {meth}`.Interface.validate_dtype`
-- {meth}`.Interface.validate_shape`
-- {meth}`.Interface.after_validation`
 
-The `before` and `after` methods provide hooks for coercion, loading, etc. such that
+A cluster of methods for validating dtype.
-`validate` can accept one of the types in the interface's 
+Separating these methods allow for array formats that store dtype information
-{attr}`~.Interface.input_types` and return the {attr}`~.Interface.return_type` .
+in a nonstandard attribute, require additional coercion, or for implementing
+custom exception handlers or rescuers.
+Check the method signatures and return types
+when overriding and the docstrings for details.
+
+- {meth}`.Interface.get_dtype`
+- {meth}`.Interface.validate_dtype`
+- {meth}`.Interface.raise_for_dtype`
+
+A halftime hook for modifying the array or bailing early between validation phases.
+
+- {meth}`.Interface.after_validate_dtype`
+
+A cluster of methods for validating shape, similar to the dtype cluster.
+
+- {meth}`.Interface.get_shape`
+- {meth}`.Interface.validate_shape`
+- {meth}`.Interface.raise_for_shape`
+
+A final hook for modifying the array before passing it to be assigned to the field.
+This method should return an object matching the interface's {attr}`~.Interface.return_type`.
+- {meth}`.Interface.after_validation`
 
 ## Diagram
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "numpydantic"
-version = "1.2.3"
+version = "1.3.0"
 description = "Type and shape validation and serialization for numpy arrays in pydantic models"
 authors = [
     {name = "sneakers-the-rat", email = "sneakers-the-rat@protonmail.com"},

src/numpydantic/interface/interface.py

@@ -40,12 +40,29 @@ class Interface(ABC, Generic[T]):
 
         Calls the methods, in order:
 
-        * :meth:`.before_validation`
+        * array = :meth:`.before_validation` (array)
-        * :meth:`.validate_dtype`
+        * dtype = :meth:`.get_dtype` (array) - get the dtype from the array,
-        * :meth:`.validate_shape`
+            override if eg. the dtype is not contained in ``array.dtype``
-        * :meth:`.after_validation`
+        * valid = :meth:`.validate_dtype` (dtype) - check that the dtype matches
+            the one in the NDArray specification. Override if special
+            validation logic is needed for a given format
+        * :meth:`.raise_for_dtype` (valid, dtype) - after checking dtype validity,
+            raise an exception if it was invalid. Override to implement custom
+            exceptions or error conditions, or make validation errors conditional.
+        * array = :meth:`.after_validate_dtype` (array) - hook for additional
+            validation or array modification mid-validation
+        * shape = :meth:`.get_shape` (array) - get the shape from the array,
+            override if eg. the shape is not contained in ``array.shape``
+        * valid = :meth:`.validate_shape` (shape) - check that the shape matches
+            the one in the NDArray specification. Override if special validation
+            logic is needed.
+        * :meth:`.raise_for_shape` (valid, shape) - after checking shape validity,
+            raise an exception if it was invalid. You know the deal bc it's the same
+            as raise for dtype.
+        * :meth:`.after_validation` - hook after validation for modifying the array
+            that is set as the model field value
 
-        passing the ``array`` argument and returning it from each.
+        Follow the method signatures and return types to override.
 
         Implementing an interface subclass largely consists of overriding these methods
         as needed.
@@ -58,8 +75,16 @@ class Interface(ABC, Generic[T]):
             of :class:`.InterfaceError` )
         """
         array = self.before_validation(array)
-        array = self.validate_dtype(array)
+
-        array = self.validate_shape(array)
+        dtype = self.get_dtype(array)
+        dtype_valid = self.validate_dtype(dtype)
+        self.raise_for_dtype(dtype_valid, dtype)
+        array = self.after_validate_dtype(array)
+
+        shape = self.get_shape(array)
+        shape_valid = self.validate_shape(shape)
+        self.raise_for_shape(shape_valid, shape)
+
         array = self.after_validation(array)
         return array
 
@@ -72,40 +97,76 @@ class Interface(ABC, Generic[T]):
         """
         return array
 
-    def validate_dtype(self, array: NDArrayType) -> NDArrayType:
+    def get_dtype(self, array: NDArrayType) -> DtypeType:
         """
-        Validate the dtype of the given array, returning it unmutated.
+        Get the dtype from the input array
+        """
+        return array.dtype
 
+    def validate_dtype(self, dtype: DtypeType) -> bool:
+        """
+        Validate the dtype of the given array, returning
+        ``True`` if valid, ``False`` if not.
+
+
+        """
+        if self.dtype is Any:
+            return True
+
+        if isinstance(self.dtype, tuple):
+            valid = dtype in self.dtype
+        elif self.dtype is np.str_:
+            valid = getattr(dtype, "type", None) is np.str_ or dtype is np.str_
+        else:
+            valid = dtype == self.dtype
+        return valid
+
+    def raise_for_dtype(self, valid: bool, dtype: DtypeType) -> None:
+        """
+        After validating, raise an exception if invalid
         Raises:
             :class:`~numpydantic.exceptions.DtypeError`
         """
-        if self.dtype is Any:
-            return array
-
-        if isinstance(self.dtype, tuple):
-            valid = array.dtype in self.dtype
-        else:
-            valid = array.dtype == self.dtype
-
         if not valid:
-            raise DtypeError(f"Invalid dtype! expected {self.dtype}, got {array.dtype}")
+            raise DtypeError(f"Invalid dtype! expected {self.dtype}, got {dtype}")
+
+    def after_validate_dtype(self, array: NDArrayType) -> NDArrayType:
+        """
+        Hook to modify array after validating dtype.
+        Default is a no-op.
+        """
         return array
 
-    def validate_shape(self, array: NDArrayType) -> NDArrayType:
+    def get_shape(self, array: NDArrayType) -> Tuple[int, ...]:
         """
-        Validate the shape of the given array, returning it unmutated
+        Get the shape from the array as a tuple of integers
+        """
+        return array.shape
+
+    def validate_shape(self, shape: Tuple[int, ...]) -> bool:
+        """
+        Validate the shape of the given array against the shape
+        specifier, returning ``True`` if valid, ``False`` if not.
+
+
+        """
+        if self.shape is Any:
+            return True
+
+        return check_shape(shape, self.shape)
+
+    def raise_for_shape(self, valid: bool, shape: Tuple[int, ...]) -> None:
+        """
+        Raise a ShapeError if the shape is invalid.
 
         Raises:
             :class:`~numpydantic.exceptions.ShapeError`
         """
-        if self.shape is Any:
+        if not valid:
-            return array
-        if not check_shape(array.shape, self.shape):
             raise ShapeError(
                 f"Invalid shape! expected shape {self.shape.prepared_args}, "
-                f"got shape {array.shape}"
+                f"got shape {shape}"
             )
-        return array
 
     def after_validation(self, array: NDArrayType) -> T:
         """

src/numpydantic/interface/zarr.py

@@ -7,18 +7,22 @@ from dataclasses import dataclass
 from pathlib import Path
 from typing import Any, Optional, Sequence, Union
 
+import numpy as np
 from pydantic import SerializationInfo
 
 from numpydantic.interface.interface import Interface
+from numpydantic.types import DtypeType
 
 try:
     import zarr
+    from numcodecs import VLenUTF8
     from zarr.core import Array as ZarrArray
     from zarr.storage import StoreLike
 except ImportError:  # pragma: no cover
     ZarrArray = None
     StoreLike = None
     storage = None
+    VLenUTF8 = None
 
 
 @dataclass
@@ -113,6 +117,19 @@ class ZarrInterface(Interface):
         """
         return self._get_array(array)
 
+    def get_dtype(self, array: ZarrArray) -> DtypeType:
+        """
+        Override base dtype getter to handle zarr's string-as-object encoding.
+        """
+        if (
+            getattr(array.dtype, "type", None) is np.object_
+            and array.filters
+            and any([isinstance(f, VLenUTF8) for f in array.filters])
+        ):
+            return np.str_
+        else:
+            return array.dtype
+
     @classmethod
     def to_json(
         cls,

tests/conftest.py

@@ -67,6 +67,7 @@ RGB_UNION: TypeAlias = Union[
 NUMBER: TypeAlias = NDArray[Shape["*, *, *"], Number]
 INTEGER: TypeAlias = NDArray[Shape["*, *, *"], Integer]
 FLOAT: TypeAlias = NDArray[Shape["*, *, *"], Float]
+STRING: TypeAlias = NDArray[Shape["*, *, *"], str]
 
 
 @pytest.fixture(
@@ -121,10 +122,15 @@ def shape_cases(request) -> ValidationCase:
         ValidationCase(annotation=INTEGER, dtype=np.uint8, passes=True),
         ValidationCase(annotation=INTEGER, dtype=float, passes=False),
         ValidationCase(annotation=INTEGER, dtype=np.float32, passes=False),
+        ValidationCase(annotation=INTEGER, dtype=str, passes=False),
         ValidationCase(annotation=FLOAT, dtype=float, passes=True),
         ValidationCase(annotation=FLOAT, dtype=np.float32, passes=True),
         ValidationCase(annotation=FLOAT, dtype=int, passes=False),
         ValidationCase(annotation=FLOAT, dtype=np.uint8, passes=False),
+        ValidationCase(annotation=FLOAT, dtype=str, passes=False),
+        ValidationCase(annotation=STRING, dtype=str, passes=True),
+        ValidationCase(annotation=STRING, dtype=int, passes=False),
+        ValidationCase(annotation=STRING, dtype=float, passes=False),
     ],
     ids=[
         "float",
@@ -139,10 +145,15 @@ def shape_cases(request) -> ValidationCase:
         "integer-uint8",
         "integer-float",
         "integer-float32",
+        "integer-str",
         "float-float",
         "float-float32",
         "float-int",
         "float-uint8",
+        "float-str",
+        "str-str",
+        "str-int",
+        "str-float",
     ],
 )
 def dtype_cases(request) -> ValidationCase: