Merge pull request #1 from p2p-ld/feat-instancecheck

Instance Checking

docs/design.md

@@ -17,6 +17,7 @@ relatively low. Its `Dtype[ArrayClass, "{shape_expression}"]` syntax is not well
 suited for modeling arrays intended to be general across implementations, and 
 makes it challenging to adapt to pydantic's schema generation system.
 
+(design_challenges)=
 ## Challenges
 
 The Python type annotation system is weird and not like the rest of Python! 

docs/index.md

@@ -57,6 +57,25 @@ model = MyModel(array=('data.zarr', '/nested/dataset'))
 model = MyModel(array="data.mp4")
 ```
 
+And use the `NDArray` type annotation like a regular type outside
+of pydantic -- eg. to validate an array anywhere, use `isinstance`:
+
+```python
+array_type = NDArray[Shape["1, 2, 3"], int]
+isinstance(np.zeros((1,2,3), dtype=int), array_type)
+# True
+isinstance(zarr.zeros((1,2,3), dtype=int), array_type)
+# True
+isinstance(np.zeros((4,5,6), dtype=int), array_type)
+# False
+isinstance(np.zeros((1,2,3), dtype=float), array_type)
+# False
+```
+
+```{note}
+`NDArray` can't do validation with static type checkers yet, see 
+{ref}`design_challenges` and {ref}`type_checkers`
+```
 
 ## Features:
 - **Types** - Annotations (based on [npytyping](https://github.com/ramonhagenaars/nptyping))

docs/todo.md

@@ -10,6 +10,21 @@ type system and is no longer actively maintained. We will be reimplementing a sy
 that extends its array specification syntax to include things like ranges and extensible
 dtypes with varying precision (and is much less finnicky to deal with).
 
+(type_checkers)=
+## Type Checker Integration
+
+The `.pyi` stubfile generation ({mod}`numpydantic.meta`) works for 
+keeping type checkers from complaining about various array formats
+not literally being `NDArray` objects, but it doesn't do the kind of 
+validation we would want to be able to use `NDArray` objects as full-fledged
+python types, including validation propagation through scopes and 
+IDE type checking for invalid literals.
+
+We want to hook into the type checking process to satisfy these type checkers:
+- mypy - has hooks, can be done with an extension
+- pyright - unclear if has hooks, might nee to monkeypatch
+- pycharm - unlikely this is possible, extensions need to be in Java and installed separately
+
 
 ## Validation
 

src/numpydantic/exceptions.py

@@ -3,9 +3,25 @@ Exceptions used within numpydantic
 """
 
 
-class DtypeError(TypeError):
+class InterfaceError(Exception):
+    """Parent mixin class for errors raised by :class:`.Interface` subclasses"""
+
+
+class DtypeError(TypeError, InterfaceError):
     """Exception raised for invalid dtypes"""
 
 
-class ShapeError(ValueError):
+class ShapeError(ValueError, InterfaceError):
     """Exception raise for invalid shapes"""
+
+
+class MatchError(ValueError, InterfaceError):
+    """Exception for errors raised during :class:`.Interface.match`-ing"""
+
+
+class NoMatchError(MatchError):
+    """No match was found by :class:`.Interface.match`"""
+
+
+class TooManyMatchesError(MatchError):
+    """Too many matches found by :class:`.Interface.match`"""

src/numpydantic/interface/interface.py

@@ -10,7 +10,12 @@ import numpy as np
 from nptyping.shape_expression import check_shape
 from pydantic import SerializationInfo
 
-from numpydantic.exceptions import DtypeError, ShapeError
+from numpydantic.exceptions import (
+    DtypeError,
+    NoMatchError,
+    ShapeError,
+    TooManyMatchesError,
+)
 from numpydantic.types import DtypeType, NDArrayType, ShapeType
 
 T = TypeVar("T", bound=NDArrayType)
@@ -32,6 +37,25 @@ class Interface(ABC, Generic[T]):
     def validate(self, array: Any) -> T:
         """
         Validate input, returning final array type
+
+        Calls the methods, in order:
+
+        * :meth:`.before_validation`
+        * :meth:`.validate_dtype`
+        * :meth:`.validate_shape`
+        * :meth:`.after_validation`
+
+        passing the ``array`` argument and returning it from each.
+
+        Implementing an interface subclass largely consists of overriding these methods
+        as needed.
+
+        Raises:
+            If validation fails, rather than eg. returning ``False``, exceptions will
+            be raised (to halt the rest of the pydantic validation process).
+            When using interfaces outside of pydantic, you must catch both
+            :class:`.DtypeError` and :class:`.ShapeError` (both of which are children
+            of :class:`.InterfaceError` )
         """
         array = self.before_validation(array)
         array = self.validate_dtype(array)
@@ -120,17 +144,38 @@ class Interface(ABC, Generic[T]):
         return array.tolist()
 
     @classmethod
-    def interfaces(cls) -> Tuple[Type["Interface"], ...]:
+    def interfaces(
+        cls, with_disabled: bool = False, sort: bool = True
+    ) -> Tuple[Type["Interface"], ...]:
         """
         Enabled interface subclasses
+
+        Args:
+            with_disabled (bool): If ``True`` , get every known interface.
+                If ``False`` (default), get only enabled interfaces.
+            sort (bool): If ``True`` (default), sort interfaces by priority.
+                If ``False`` , sorted by definition order. Used for recursion:
+                we only want to sort once at the top level.
         """
-        return tuple(
-            sorted(
-                [i for i in Interface.__subclasses__() if i.enabled()],
+        # get recursively
+        subclasses = []
+        for i in cls.__subclasses__():
+            if with_disabled:
+                subclasses.append(i)
+
+            if i.enabled():
+                subclasses.append(i)
+
+            subclasses.extend(i.interfaces(with_disabled=with_disabled, sort=False))
+
+        if sort:
+            subclasses = sorted(
+                subclasses,
                 key=attrgetter("priority"),
                 reverse=True,
             )
-        )
+
+        return tuple(subclasses)
 
     @classmethod
     def return_types(cls) -> Tuple[NDArrayType, ...]:
@@ -150,9 +195,21 @@ class Interface(ABC, Generic[T]):
         return tuple(in_types)
 
     @classmethod
-    def match(cls, array: Any) -> Type["Interface"]:
+    def match(cls, array: Any, fast: bool = False) -> Type["Interface"]:
         """
         Find the interface that should be used for this array based on its input type
+
+        First runs the ``check`` method for all interfaces returned by
+        :meth:`.Interface.interfaces` **except** for :class:`.NumpyInterface` ,
+        and if no match is found then try the numpy interface. This is because
+        :meth:`.NumpyInterface.check` can be expensive, as we could potentially
+        try to
+
+        Args:
+            fast (bool): if ``False`` , check all interfaces and raise exceptions for
+              having multiple matching interfaces (default). If ``True`` ,
+              check each interface (as ordered by its ``priority`` , decreasing),
+              and return on the first match.
         """
         # first try and find a non-numpy interface, since the numpy interface
         # will try and load the array into memory in its check method
@@ -160,17 +217,24 @@ class Interface(ABC, Generic[T]):
         non_np_interfaces = [i for i in interfaces if i.__name__ != "NumpyInterface"]
         np_interface = [i for i in interfaces if i.__name__ == "NumpyInterface"][0]
 
-        matches = [i for i in non_np_interfaces if i.check(array)]
+        if fast:
+            matches = []
+            for i in non_np_interfaces:
+                if i.check(array):
+                    return i
+        else:
+            matches = [i for i in non_np_interfaces if i.check(array)]
+
         if len(matches) > 1:
             msg = f"More than one interface matches input {array}:\n"
             msg += "\n".join([f"  - {i}" for i in matches])
-            raise ValueError(msg)
+            raise TooManyMatchesError(msg)
         elif len(matches) == 0:
             # now try the numpy interface
             if np_interface.check(array):
                 return np_interface
             else:
-                raise ValueError(f"No matching interfaces found for input {array}")
+                raise NoMatchError(f"No matching interfaces found for input {array}")
         else:
             return matches[0]
 
@@ -186,8 +250,8 @@ class Interface(ABC, Generic[T]):
         if len(matches) > 1:
             msg = f"More than one interface matches output {array}:\n"
             msg += "\n".join([f"  - {i}" for i in matches])
-            raise ValueError(msg)
+            raise TooManyMatchesError(msg)
         elif len(matches) == 0:
-            raise ValueError(f"No matching interfaces found for output {array}")
+            raise NoMatchError(f"No matching interfaces found for output {array}")
         else:
             return matches[0]

src/numpydantic/meta.py

@@ -24,7 +24,8 @@ def generate_ndarray_stub() -> str:
         # Create import statements, saving aliased name of type if needed
         if arr.__module__.startswith("numpydantic") or arr.__module__ == "typing":
             type_name = str(arr) if arr.__module__ == "typing" else arr.__name__
-            import_strings.append(f"from {arr.__module__} import {type_name}")
+            if arr.__module__ != "typing":
+                import_strings.append(f"from {arr.__module__} import {type_name}")
         else:
             # since other packages could use the same name for an imported object
             # (eg dask and zarr both use an Array class)
@@ -39,6 +40,7 @@ def generate_ndarray_stub() -> str:
         type_names.append(type_name)
 
     import_strings.extend(_BUILTIN_IMPORTS)
+    import_strings = list(dict.fromkeys(import_strings))
     import_string = "\n".join(import_strings)
 
     class_union = " | ".join(type_names)

src/numpydantic/ndarray.py

@@ -13,7 +13,7 @@ Extension of nptyping NDArray for pydantic that allows for JSON-Schema serializa
 
 """
 
-from typing import Any, Tuple
+from typing import TYPE_CHECKING, Any, Tuple
 
 import numpy as np
 from nptyping.error import InvalidArgumentsError
@@ -28,6 +28,8 @@ from pydantic import GetJsonSchemaHandler
 from pydantic_core import core_schema
 
 from numpydantic.dtype import DType
+from numpydantic.exceptions import InterfaceError
+from numpydantic.interface import Interface
 from numpydantic.maps import python_to_nptyping
 from numpydantic.schema import (
     _handler_type,
@@ -37,6 +39,9 @@ from numpydantic.schema import (
 )
 from numpydantic.types import DtypeType, ShapeType
 
+if TYPE_CHECKING:  # pragma: no cover
+    from nptyping.base_meta_classes import SubscriptableMeta
+
 
 class NDArrayMeta(_NDArrayMeta, implementation="NDArray"):
     """
@@ -44,6 +49,35 @@ class NDArrayMeta(_NDArrayMeta, implementation="NDArray"):
     completion of the transition away from nptyping
     """
 
+    if TYPE_CHECKING:  # pragma: no cover
+        __getitem__ = SubscriptableMeta.__getitem__
+
+    def __instancecheck__(self, instance: Any):
+        """
+        Extended type checking that determines whether
+
+        1) the ``type`` of the given instance is one of those in
+            :meth:`.Interface.input_types`
+
+        but also
+
+        2) it satisfies the constraints set on the :class:`.NDArray` annotation
+
+        Args:
+            instance (:class:`typing.Any`): Thing to check!
+
+        Returns:
+            bool: ``True`` if matches constraints, ``False`` otherwise.
+        """
+        shape, dtype = self.__args__
+        try:
+            interface_cls = Interface.match(instance, fast=True)
+            interface = interface_cls(shape, dtype)
+            _ = interface.validate(instance)
+            return True
+        except InterfaceError:
+            return False
+
     def _get_dtype(cls, dtype_candidate: Any) -> DType:
         """
         Override of base _get_dtype method to allow for compound tuple types

src/numpydantic/schema.py

@@ -225,7 +225,9 @@ def get_validate_interface(shape: ShapeType, dtype: DtypeType) -> Callable:
     :meth:`.Interface.validate` method
     """
 
-    def validate_interface(value: Any, info: "ValidationInfo") -> NDArrayType:
+    def validate_interface(
+        value: Any, info: Optional["ValidationInfo"] = None
+    ) -> NDArrayType:
         interface_cls = Interface.match(value)
         interface = interface_cls(shape, dtype)
         value = interface.validate(value)

tests/test_interface/test_interface.py

@@ -1,5 +1,3 @@
-import pdb
-
 import pytest
 
 import numpy as np
@@ -14,9 +12,12 @@ def interfaces():
     class Interface1(Interface):
         input_types = (list,)
         return_type = tuple
+        priority = 1000
+        checked = False
 
         @classmethod
         def check(cls, array):
+            cls.checked = True
             if isinstance(array, list):
                 return True
             return False
@@ -26,18 +27,34 @@ def interfaces():
             return True
 
     Interface2 = type("Interface2", Interface1.__bases__, dict(Interface1.__dict__))
+    Interface2.checked = False
+    Interface2.priority = 999
 
     class Interface3(Interface1):
+        priority = 998
+        checked = False
+
         @classmethod
         def enabled(cls) -> bool:
             return False
 
+    class Interface4(Interface3):
+        priority = 997
+        checked = False
+
+        @classmethod
+        def enabled(cls) -> bool:
+            return True
+
     class Interfaces:
         interface1 = Interface1
         interface2 = Interface2
         interface3 = Interface3
+        interface4 = Interface4
 
     yield Interfaces
+    # Interface.__subclasses__().remove(Interface1)
+    # Interface.__subclasses__().remove(Interface2)
     del Interface1
     del Interface2
     del Interface3
@@ -66,6 +83,20 @@ def test_interface_match_error(interfaces):
     assert "No matching interfaces" in str(e.value)
 
 
+def test_interface_match_fast(interfaces):
+    """
+    fast matching should return as soon as an interface is found
+    and not raise an error for duplicates
+    """
+    Interface.interfaces()[0].checked = False
+    Interface.interfaces()[1].checked = False
+    # this doesnt' raise an error
+    matched = Interface.match([1, 2, 3], fast=True)
+    assert matched == Interface.interfaces()[0]
+    assert Interface.interfaces()[0].checked
+    assert not Interface.interfaces()[1].checked
+
+
 def test_interface_enabled(interfaces):
     """
     An interface shouldn't be included if it's not enabled
@@ -101,3 +132,22 @@ def test_interfaces_sorting():
     ifaces = Interface.interfaces()
     priorities = [i.priority for i in ifaces]
     assert (np.diff(priorities) <= 0).all()
+
+
+def test_interface_with_disabled(interfaces):
+    """
+    Get all interfaces, even if not enabled
+    """
+    ifaces = Interface.interfaces(with_disabled=True)
+    assert interfaces.interface3 in ifaces
+
+
+def test_interface_recursive(interfaces):
+    """
+    Get all interfaces, including subclasses of subclasses
+    """
+    ifaces = Interface.interfaces()
+    assert issubclass(interfaces.interface4, interfaces.interface3)
+    assert issubclass(interfaces.interface3, interfaces.interface1)
+    assert issubclass(interfaces.interface1, Interface)
+    assert interfaces.interface4 in ifaces

tests/test_ndarray.py

@@ -223,3 +223,23 @@ def test_json_schema_ellipsis():
 
     schema = ConstrainedAnyShape.model_json_schema()
     _recursive_array(schema)
+
+
+def test_instancecheck():
+    """
+    NDArray should handle ``isinstance()`` s.t. valid arrays are ``True``
+    and invalid arrays are ``False``
+
+    We don't make this test exhaustive because correctness of validation
+    is tested elsewhere. We are just testing that the type checking works
+    """
+    array_type = NDArray[Shape["1, 2, 3"], int]
+
+    assert isinstance(np.zeros((1, 2, 3), dtype=int), array_type)
+    assert not isinstance(np.zeros((2, 2, 3), dtype=int), array_type)
+    assert not isinstance(np.zeros((1, 2, 3), dtype=float), array_type)
+
+    def my_function(array: NDArray[Shape["1, 2, 3"], int]):
+        return array
+
+    my_function(np.zeros((1, 2, 3), int))