add array shape ranges! take over shape specification and checking from nptyping

src/numpydantic/__init__.py

@@ -8,8 +8,7 @@ apply_patches()
 
 from numpydantic.ndarray import NDArray
 from numpydantic.meta import update_ndarray_stub
-
+from numpydantic.shape import Shape
-from nptyping import Shape
 
 update_ndarray_stub()
 

src/numpydantic/interface/interface.py

@@ -7,7 +7,6 @@ from operator import attrgetter
 from typing import Any, Generic, Optional, Tuple, Type, TypeVar, Union
 
 import numpy as np
-from nptyping.shape_expression import check_shape
 from pydantic import SerializationInfo
 
 from numpydantic.exceptions import (
@@ -16,6 +15,7 @@ from numpydantic.exceptions import (
     ShapeError,
     TooManyMatchesError,
 )
+from numpydantic.shape import check_shape
 from numpydantic.types import DtypeType, NDArrayType, ShapeType
 
 T = TypeVar("T", bound=NDArrayType)

src/numpydantic/ndarray.py

@@ -42,6 +42,8 @@ from numpydantic.types import DtypeType, ShapeType
 if TYPE_CHECKING:  # pragma: no cover
     from nptyping.base_meta_classes import SubscriptableMeta
 
+    from numpydantic import Shape
+
 
 class NDArrayMeta(_NDArrayMeta, implementation="NDArray"):
     """
@@ -78,6 +80,28 @@ class NDArrayMeta(_NDArrayMeta, implementation="NDArray"):
         except InterfaceError:
             return False
 
+    def _get_shape(cls, dtype_candidate: Any) -> "Shape":
+        """
+        Override of base method to use our local definition of shape
+        """
+        from numpydantic.shape import Shape
+
+        if dtype_candidate is Any or dtype_candidate is Shape:
+            shape = Any
+        elif issubclass(dtype_candidate, Shape):
+            shape = dtype_candidate
+        elif cls._is_literal_like(dtype_candidate):
+            shape_expression = dtype_candidate.__args__[0]
+            shape = Shape[shape_expression]
+        else:
+            raise InvalidArgumentsError(
+                f"Unexpected argument '{dtype_candidate}', expecting"
+                " Shape[<ShapeExpression>]"
+                " or Literal[<ShapeExpression>]"
+                " or typing.Any."
+            )
+        return shape
+
     def _get_dtype(cls, dtype_candidate: Any) -> DType:
         """
         Override of base _get_dtype method to allow for compound tuple types

src/numpydantic/schema.py

@@ -5,11 +5,10 @@ Helper functions for use with :class:`~numpydantic.NDArray` - see the note in
 
 import hashlib
 import json
-from typing import Any, Callable, Optional, Union
+from typing import TYPE_CHECKING, Any, Callable, Optional, Union
 
 import nptyping.structure
 import numpy as np
-from nptyping import Shape
 from pydantic import SerializationInfo
 from pydantic_core import CoreSchema, core_schema
 from pydantic_core.core_schema import ListSchema, ValidationInfo
@@ -19,6 +18,9 @@ from numpydantic.interface import Interface
 from numpydantic.maps import np_to_python
 from numpydantic.types import DtypeType, NDArrayType, ShapeType
 
+if TYPE_CHECKING:
+    from numpydantic import Shape
+
 _handler_type = Callable[[Any], core_schema.CoreSchema]
 _UNSUPPORTED_TYPES = (complex,)
 
@@ -88,7 +90,7 @@ def _lol_dtype(dtype: DtypeType, _handler: _handler_type) -> CoreSchema:
     return array_type
 
 
-def list_of_lists_schema(shape: Shape, array_type: CoreSchema) -> ListSchema:
+def list_of_lists_schema(shape: "Shape", array_type: CoreSchema) -> ListSchema:
     """
     Make a pydantic JSON schema for an array as a list of lists.
 
@@ -98,13 +100,15 @@ def list_of_lists_schema(shape: Shape, array_type: CoreSchema) -> ListSchema:
     This function is typically called from :func:`.make_json_schema`
 
     Args:
-        shape (:class:`.Shape` ): Shape determines the depth and max/min elements
+        shape (:class:`~numpydantic.Shape`): Shape determines the depth and max/min
-            for each layer of list schema
+            elements for each layer of list schema
         array_type ( :class:`pydantic_core.CoreSchema` ): The pre-rendered pydantic
             core schema to use in the innermost list entry
     """
+    from numpydantic.shape import _is_range
 
-    shape_parts = shape.__args__[0].split(",")
+    shape_parts = [part.strip() for part in shape.__args__[0].split(",")]
+    # labels, if present
     split_parts = [
         p.split(" ")[1] if len(p.split(" ")) == 2 else None for p in shape_parts
     ]
@@ -128,19 +132,29 @@ def list_of_lists_schema(shape: Shape, array_type: CoreSchema) -> ListSchema:
             list_schema = core_schema.list_schema(inner_schema, metadata=metadata)
         elif arg == "...":
             list_schema = _unbounded_shape(inner_schema, metadata=metadata)
+        else:
+            if _is_range(arg):
+                arg_min, arg_max = arg.split("-")
+                arg_min = None if arg_min == "*" else int(arg_min)
+                arg_max = None if arg_max == "*" else int(arg_max)
+
             else:
                 try:
                     arg = int(arg)
+                    arg_min = arg
+                    arg_max = arg
                 except ValueError as e:
+
                     raise ValueError(
-                    "Array shapes must be integers, wildcards, or ellipses. "
+                        "Array shapes must be integers, wildcards, ellipses, or "
-                    "Shape variables (for declaring that one dimension must be the "
+                        "ranges. Shape variables (for declaring that one dimension "
-                    "same size as another) are not supported because it is "
+                        "must be the same size as another) are not supported because "
-                    "impossible to express dynamic minItems/maxItems in JSON Schema. "
+                        "it is impossible to express dynamic minItems/maxItems in "
+                        "JSON Schema. "
                         "See: https://github.com/orgs/json-schema-org/discussions/730"
                     ) from e
             list_schema = core_schema.list_schema(
-                inner_schema, min_length=arg, max_length=arg, metadata=metadata
+                inner_schema, min_length=arg_min, max_length=arg_max, metadata=metadata
             )
     return list_schema
 

src/numpydantic/shape.py

@@ -0,0 +1,211 @@
+"""
+Declaration and validation functions for array shapes.
+
+Mostly a mildly modified version of nptyping's
+:func:`npytping.shape_expression.check_shape`
+and its internals to allow for extended syntax, including ranges of shapes.
+
+Modifications from nptyping:
+
+- **"..."** - In nptyping, ``'...'`` means "any number of dimensions with the same shape
+  as the last dimension. ie ``Shape[2, ...]`` means "any number of 2-length
+  dimensions. Here ``'...'`` always means "any number of any-shape dimensions"
+- **Ranges** - (inclusive) shape ranges are allowed. eg. to specify an array
+  where the first dimension can be 2, 3, or 4 length:
+
+     Shape["2-4, ..."]
+
+  To specify a range with an unbounded min or max, use wildcards, eg. for
+  an array with the first dimension at least length 2, and the second dimension
+  at most length 5 (both inclusive):
+
+      Shape["2-*, *-5"]
+
+"""
+
+import re
+import string
+from abc import ABC
+from functools import lru_cache
+from typing import Any, Dict, List, Union
+
+from nptyping.base_meta_classes import ContainerMeta
+from nptyping.error import InvalidShapeError
+from nptyping.nptyping_type import NPTypingType
+from nptyping.shape_expression import (
+    get_dimensions,
+    normalize_shape_expression,
+    remove_labels,
+)
+from nptyping.typing_ import ShapeExpression, ShapeTuple
+
+
+class ShapeMeta(ContainerMeta, implementation="Shape"):
+    """
+    Metaclass that is coupled to nptyping.Shape.
+
+    Overridden from nptyping to use local shape validation function
+    """
+
+    def _validate_expression(cls, item: str) -> None:
+        validate_shape_expression(item)
+
+    def _normalize_expression(cls, item: str) -> str:
+        return normalize_shape_expression(item)
+
+    def _get_additional_values(cls, item: Any) -> Dict[str, Any]:
+        dim_strings = get_dimensions(item)
+        dim_string_without_labels = remove_labels(dim_strings)
+        return {"prepared_args": dim_string_without_labels}
+
+
+class Shape(NPTypingType, ABC, metaclass=ShapeMeta):
+    """
+    A container for shape expressions that describe the shape of an multi
+    dimensional array.
+
+    Simple example:
+
+    >>> Shape['2, 2']
+    Shape['2, 2']
+
+    A Shape can be compared to a typing.Literal. You can use Literals in
+    NDArray as well.
+
+    >>> from typing import Literal
+
+    >>> Shape['2, 2'] == Literal['2, 2']
+    True
+
+    """
+
+    __args__ = ("*, ...",)
+    prepared_args = ("*", "...")
+
+
+def validate_shape_expression(shape_expression: Union[ShapeExpression, Any]) -> None:
+    """
+    CHANGES FROM NPTYPING: Allow ranges
+    """
+    shape_expression_no_quotes = shape_expression.replace("'", "").replace('"', "")
+    if shape_expression is not Any and not re.match(
+        _REGEX_SHAPE_EXPRESSION, shape_expression_no_quotes
+    ):
+        raise InvalidShapeError(
+            f"'{shape_expression}' is not a valid shape expression."
+        )
+
+
+@lru_cache
+def check_shape(shape: ShapeTuple, target: "Shape") -> bool:
+    """
+    Check whether the given shape corresponds to the given shape_expression.
+    :param shape: the shape in question.
+    :param target: the shape expression to which shape is tested.
+    :return: True if the given shape corresponds to shape_expression.
+    """
+    target_shape = _handle_ellipsis(shape, target.prepared_args)
+    return _check_dimensions_against_shape(shape, target_shape)
+
+
+def _check_dimensions_against_shape(shape: ShapeTuple, target: List[str]) -> bool:
+    # Walk through the shape and test them against the given target,
+    # taking into consideration variables, wildcards, etc.
+
+    if len(shape) != len(target):
+        return False
+    shape_as_strings = (str(dim) for dim in shape)
+    variables: Dict[str, str] = {}
+    for dim, target_dim in zip(shape_as_strings, target):
+        if _is_wildcard(target_dim) or _is_assignable_var(dim, target_dim, variables):
+            continue
+        if _is_range(target_dim) and _check_range(dim, target_dim):
+            continue
+        if dim != target_dim:
+            return False
+    return True
+
+
+def _handle_ellipsis(shape: ShapeTuple, target: List[str]) -> List[str]:
+    # Let the ellipsis allows for any number of dimensions by replacing the
+    # ellipsis with the dimension size repeated the number of times that
+    # corresponds to the shape of the instance.
+    if target[-1] == "...":
+        dim_to_repeat = "*"
+        target = target[0:-1]
+        if len(shape) > len(target):
+            difference = len(shape) - len(target)
+            target += difference * [dim_to_repeat]
+    return target
+
+
+def _is_range(target_dim: str) -> bool:
+    """Whether the dimension is a range (literally whether it includes a hyphen)"""
+    return "-" in target_dim and len(target_dim.split("-")) == 2
+
+
+def _check_range(dim: str, target_dim: str) -> bool:
+    """check whether the given dimension is within the target_dim range"""
+    dim = int(dim)
+
+    range_min, range_max = target_dim.split("-")
+    if _is_wildcard(range_min):
+        return dim <= int(range_max)
+    elif _is_wildcard(range_max):
+        return dim >= int(range_min)
+    else:
+        return int(range_min) <= dim <= int(range_max)
+
+
+def _is_wildcard(dim: str) -> bool:
+    """
+    CHANGES FROM NPTYPING: added '*-*' range, which is a wildcard
+    """
+    # Return whether dim is a wildcard (i.e. the character that takes any
+    # dimension size).
+    return dim == "*" or dim == "*-*"
+
+
+# CHANGES FROM NPTYPING: Allow ranges
+_REGEX_SEPARATOR = r"(\s*,\s*)"
+_REGEX_DIMENSION_SIZE = r"(\s*[0-9]+\s*)"
+_REGEX_DIMENSION_RANGE = r"(\s*[0-9\*]+-[0-9\*]+\s*)"
+_REGEX_VARIABLE = r"(\s*\b[A-Z]\w*\s*)"
+_REGEX_LABEL = r"(\s*\b[a-z]\w*\s*)"
+_REGEX_LABELS = rf"({_REGEX_LABEL}({_REGEX_SEPARATOR}{_REGEX_LABEL})*)"
+_REGEX_WILDCARD = r"(\s*\*\s*)"
+_REGEX_DIMENSION_BREAKDOWN = rf"(\s*\[{_REGEX_LABELS}\]\s*)"
+_REGEX_DIMENSION = (
+    rf"({_REGEX_DIMENSION_SIZE}"
+    rf"|{_REGEX_DIMENSION_RANGE}"
+    rf"|{_REGEX_VARIABLE}"
+    rf"|{_REGEX_WILDCARD}"
+    rf"|{_REGEX_DIMENSION_BREAKDOWN})"
+)
+_REGEX_DIMENSION_WITH_LABEL = rf"({_REGEX_DIMENSION}(\s+{_REGEX_LABEL})*)"
+_REGEX_DIMENSIONS = (
+    rf"{_REGEX_DIMENSION_WITH_LABEL}({_REGEX_SEPARATOR}{_REGEX_DIMENSION_WITH_LABEL})*"
+)
+_REGEX_DIMENSIONS_ELLIPSIS = rf"({_REGEX_DIMENSIONS}{_REGEX_SEPARATOR}\.\.\.\s*)"
+_REGEX_SHAPE_EXPRESSION = rf"^({_REGEX_DIMENSIONS}|{_REGEX_DIMENSIONS_ELLIPSIS})$"
+
+# --------------------------------------------------
+# Below - unchanged from nptyping
+# --------------------------------------------------
+
+
+def _is_assignable_var(dim: str, target_dim: str, variables: Dict[str, str]) -> bool:
+    # Return whether target_dim is a variable and can be assigned with dim.
+    return _is_variable(target_dim) and _can_assign_variable(dim, target_dim, variables)
+
+
+def _is_variable(dim: str) -> bool:
+    # Return whether dim is a variable.
+    return dim[0] in string.ascii_uppercase
+
+
+def _can_assign_variable(dim: str, target_dim: str, variables: Dict[str, str]) -> bool:
+    # Check and assign a variable.
+    assignable = variables.get(target_dim) in (None, dim)
+    variables[target_dim] = dim
+    return assignable

tests/test_ndarray.py

@@ -7,9 +7,9 @@ import json
 
 import numpy as np
 from pydantic import BaseModel, ValidationError, Field
-from nptyping import Shape, Number
+from nptyping import Number
 
-from numpydantic import NDArray
+from numpydantic import NDArray, Shape
 from numpydantic.exceptions import ShapeError, DtypeError
 from numpydantic import dtype
 

tests/test_shape.py

@@ -0,0 +1,80 @@
+import pdb
+
+import pytest
+
+from typing import Any
+
+from pydantic import BaseModel, ValidationError
+import numpy as np
+
+from numpydantic import NDArray, Shape
+
+
+@pytest.mark.parametrize(
+    "shape,valid",
+    [
+        ((2, 6), True),
+        ((2, 7), True),
+        ((3, 6), True),
+        ((3, 7), True),
+        ((4, 6), True),
+        ((4, 7), True),
+        ((1, 6), False),
+        ((5, 6), False),
+        ((2, 5), False),
+        ((2, 8), False),
+    ],
+)
+def test_shape_range(shape, valid):
+    """Specify a dimension with a range of possible sizes"""
+
+    class MyModel(BaseModel):
+        array: NDArray[Shape["2-4, 6-7"], Any]
+
+    if valid:
+        _ = MyModel(array=np.zeros(shape, dtype=np.uint8))
+    else:
+        with pytest.raises(ValidationError):
+            _ = MyModel(array=np.zeros(shape, dtype=np.uint8))
+
+
+@pytest.mark.parametrize(
+    "shape,valid",
+    [
+        ((2, 5), True),
+        ((10, 5), True),
+        ((2, 2), True),
+        ((1, 5), False),
+        ((2, 6), False),
+    ],
+)
+def test_shape_wildcard(shape, valid):
+    """Specify an open-ended minimum or maximum size for a given dimension"""
+
+    class MyModel(BaseModel):
+        array: NDArray[Shape["2-*, *-5"], Any]
+
+    if valid:
+        _ = MyModel(array=np.zeros(shape, dtype=np.uint8))
+    else:
+        with pytest.raises(ValidationError):
+            _ = MyModel(array=np.zeros(shape, dtype=np.uint8))
+
+
+def test_range_shape_schema():
+    """
+    Range shapes should correctly generate JSON Schema
+    """
+
+    class MyModel(BaseModel):
+        array_range: NDArray[Shape["2-4"], Any]
+        array_range_min: NDArray[Shape["2-*"], Any]
+        array_range_max: NDArray[Shape["*-4"], Any]
+
+    schema = MyModel.model_json_schema()
+    assert schema["properties"]["array_range"]["minItems"] == 2
+    assert schema["properties"]["array_range"]["maxItems"] == 4
+    assert schema["properties"]["array_range_min"]["minItems"] == 2
+    assert "maxItems" not in schema["properties"]["array_range_min"]
+    assert schema["properties"]["array_range_max"]["maxItems"] == 4
+    assert "minItems" not in schema["properties"]["array_range_max"]