safecheck 0.5.3

Utilities for typechecking, shapechecking and dispatch.

safecheck

Opinionated combination of typechecking libraries. Safecheck is a (very) minimal wrapper of the following libraries to provide a unified and simple-to-use interface:

• typechecking beartype
• shapechecking jaxtyping

Safecheck configures a unified typecheck decorator that invokes beartype.beartype if the function annotations do not contain any jaxtyping-related types. If the function contains jaxtyping-related types typecheck invokes jaxtyping.jaxtyped with beartype.beartype as a runtime type-checker. safecheck is highly-efficient, it adds no measurable overhead to the underlying type and shape checking logic.

One of the goals of safecheck is to abstract over the runtime-typechecker and -shapechecker such that the concrete implementation can be swapped without requiring changes to the codebase.

We re-export most of the functionality of beartype and jaxtyping, and it might be a good idea to disallow imports from beartype and jaxtyping if you are using safecheck, e.g. using ruff or Flake8.

To unify the jaxtyping.Array interface, we export jax.Array as JaxArray if Jax is available, torch.Tensor as TorchArray if PyTorch is available and numpy.typing.NDArray as NumpyArray if NumPy is available.

In addition to the unified typecheck, the library provides a typecheck_overload decorator.

API

decorators

typecheck(fn)

typechecks a function without jaxtyping annotations, otherwise additionally shapecheck the function.

typecheck_overload(fn)

ensures that an implementing function satisfied at least one of its defined overloads.

introspection

is_instance(obj, hint)

like isinstance(...), but better.

assert_instance(obj, hint)

like assert isinstance(...), but better.

is_subtype(subhint, superhint)

tests if a type is a subtype of another type.

validators

Validators enable runtime validation using typing.Annotated, but these annotations are not enforced by any static type checker and always require a runtime @typecheck.

Is

for example: Annotated[str, Is[lambda x: x > 0)]]

IsAttr

for example: Annotated[NumpyArray, IsAttr["ndim", IsEqual[1]]]

IsEqual

for example: Annotated[list, IsEqual[list(range(42))]]

IsSubclass

for example: Annotated[type, IsSubclass[str, bytes]]

IsInstance

for example: Annotated[object, IsInstance[str, bytes]]

union array types

Exported union array types from safecheck.

Shaped      # Any type at all (e.g. object or string)
Num         # Any integer, unsigned integer, floating, or complex
Real        # Any integer, unsigned integer or floating
Inexact     # Any floating or complex
Float       # Any floating point
Complex     # Any complex
Integer     # Any integer or unsigned integer
UInt        # Any unsigned integer
Int         # Any signed integer

concrete array types

Exported array types from safecheck.

Int8
Int16
Int32
Int64
Float16
Float32
Float64
Bool
UInt8
UInt16
UInt32
UInt64
Complex64
Complex128

Examples

Type-checking a simple function.

from safecheck import typecheck


@typecheck
def f(x: int) -> int:
    return x

# f(1) -> 1
# f("1") -> fails

Type-checking a simple method.

from safecheck import typecheck


class A:
    @typecheck
    def f(self, x: int) -> int:
        return x

# A().f(1) -> 1
# A().f("1") -> fails

Shape-checking a simple function.

from safecheck import typecheck, NumpyArray, Integer


@typecheck
def f(x: Integer[NumpyArray, "n"]) -> Integer[NumpyArray, "n"]:
    return x

# import numpy as np
# f(np.array([1, 2, 3, 4, 5])) -> array([1, 2, 3, 4, 5])
# f(np.array([1.0, 2.0, 3.0, 4.0, 5.0])) -> fails
# f(np.array([[1], [2], [3], [4], [5]])) -> fails

Shape-checking a simple method.

from safecheck import typecheck, NumpyArray, Integer


class A:
    @typecheck
    def f(self, x: Integer[NumpyArray, "n"]) -> Integer[NumpyArray, "n"]:
        return x

# import numpy as np
# A().f(np.array([1, 2, 3, 4, 5])) -> array([1, 2, 3, 4, 5])
# A().f(np.array([1.0, 2.0, 3.0, 4.0, 5.0])) -> fails
# A().f(np.array([[1], [2], [3], [4], [5]])) -> fails

Type-checking an overloaded function.

from typing_extensions import overload  # python < 3.11, otherwise ``from typing import overload``
from safecheck import typecheck_overload


@overload
def f(x: int) -> int:
    ...


@typecheck_overload
def f(x):
    return x

# f(1) -> 1
# f("1") -> fails

Type-checking an overloaded method.

from typing_extensions import overload  # python < 3.11, otherwise ``from typing import overload``
from safecheck import typecheck_overload


class A:
    @overload
    def f(self, x: int) -> int:
        ...

    @typecheck_overload
    def f(self, x):
        return x

# A().f(1) -> 1
# A().f("1") -> fails

Shape-checking an overloaded function.

from typing_extensions import overload  # python < 3.11, otherwise ``from typing import overload``
from safecheck import typecheck_overload, NumpyArray, Integer


@overload
def f(x: Integer[NumpyArray, "n"]) -> Integer[NumpyArray, "n"]:
    ...


@typecheck_overload
def f(x):
    return x

# import numpy as np
# f(np.array([1, 2, 3, 4, 5])) -> array([1, 2, 3, 4, 5])
# f(np.array([1.0, 2.0, 3.0, 4.0, 5.0])) -> fails
# f(np.array([[1], [2], [3], [4], [5]])) -> fails

Shape-checking an overloaded method.

from typing_extensions import overload  # python < 3.11, otherwise ``from typing import overload``
from safecheck import typecheck_overload, NumpyArray, Integer


class A:
    @overload
    def f(self, x: Integer[NumpyArray, "n"]) -> Integer[NumpyArray, "n"]:
        ...

    @typecheck_overload
    def f(self, x):
        return x

# import numpy as np
# A().f(np.array([1, 2, 3, 4, 5])) -> array([1, 2, 3, 4, 5])
# A().f(np.array([1.0, 2.0, 3.0, 4.0, 5.0])) -> fails
# A().f(np.array([[1], [2], [3], [4], [5]])) -> fails