ruff/crates/ty_python_semantic/resources/mdtest/libraries/numpy.md

numpy

[environment]
python-version = "3.14"

numpy's dtype

numpy functions often accept a dtype parameter. For example, one of np.array's overloads accepts a dtype parameter of type DTypeLike | None. Here, we build up something that resembles numpy's internals in order to model the type DTypeLike. Many details have been left out.

mini_numpy.py:

from typing import TypeVar, Generic, Any, Protocol, TypeAlias, runtime_checkable, final
import builtins

_ItemT_co = TypeVar("_ItemT_co", default=Any, covariant=True)


class generic(Generic[_ItemT_co]):
    @property
    def dtype(self) -> _DTypeT_co:
        raise NotImplementedError


_BoolItemT_co = TypeVar("_BoolItemT_co", bound=builtins.bool, default=builtins.bool, covariant=True)


class bool(generic[_BoolItemT_co], Generic[_BoolItemT_co]): ...


@final
class object_(generic): ...


_ScalarT = TypeVar("_ScalarT", bound=generic)
_ScalarT_co = TypeVar("_ScalarT_co", bound=generic, default=Any, covariant=True)


@final
class dtype(Generic[_ScalarT_co]): ...


_DTypeT_co = TypeVar("_DTypeT_co", bound=dtype, default=dtype, covariant=True)


@runtime_checkable
class _SupportsDType(Protocol[_DTypeT_co]):
    @property
    def dtype(self) -> _DTypeT_co: ...


_DTypeLike: TypeAlias = type[_ScalarT] | dtype[_ScalarT] | _SupportsDType[dtype[_ScalarT]]

DTypeLike: TypeAlias = _DTypeLike[Any] | str | None

Now we can make sure that a function which accepts DTypeLike | None works as expected:

import mini_numpy as np


def accepts_dtype(dtype: np.DTypeLike | None) -> None: ...


accepts_dtype(dtype=np.bool)
accepts_dtype(dtype=np.dtype[np.bool])
accepts_dtype(dtype=object)
accepts_dtype(dtype=np.object_)
accepts_dtype(dtype="U")