Merge branch 'master' into fix-issue-10244

1 files changed, 189 insertions, 0 deletions

diff --git a/numpy/core/numeric.pyi b/numpy/core/numeric.pyi
new file mode 100644
index 000000000..d91cb31c2
--- /dev/null
+++ b/numpy/core/numeric.pyi
@@ -0,0 +1,189 @@
+import sys
+from typing import (
+    Any,
+    Optional,
+    Union,
+    Sequence,
+    Tuple,
+    Callable,
+    List,
+    overload,
+    TypeVar,
+    Iterable,
+)
+
+from numpy import ndarray, generic, dtype, bool_, signedinteger, _OrderKACF, _OrderCF
+from numpy.typing import ArrayLike, DTypeLike, _ShapeLike
+
+if sys.version_info >= (3, 8):
+    from typing import Literal
+else:
+    from typing_extensions import Literal
+
+_T = TypeVar("_T")
+_ArrayType = TypeVar("_ArrayType", bound=ndarray)
+
+_CorrelateMode = Literal["valid", "same", "full"]
+
+@overload
+def zeros_like(
+    a: _ArrayType,
+    dtype: None = ...,
+    order: _OrderKACF = ...,
+    subok: Literal[True] = ...,
+    shape: None = ...,
+) -> _ArrayType: ...
+@overload
+def zeros_like(
+    a: ArrayLike,
+    dtype: DTypeLike = ...,
+    order: _OrderKACF = ...,
+    subok: bool = ...,
+    shape: Optional[_ShapeLike] = ...,
+) -> ndarray: ...
+def ones(
+    shape: _ShapeLike,
+    dtype: DTypeLike = ...,
+    order: _OrderCF = ...,
+    *,
+    like: ArrayLike = ...,
+) -> ndarray: ...
+@overload
+def ones_like(
+    a: _ArrayType,
+    dtype: None = ...,
+    order: _OrderKACF = ...,
+    subok: Literal[True] = ...,
+    shape: None = ...,
+) -> _ArrayType: ...
+@overload
+def ones_like(
+    a: ArrayLike,
+    dtype: DTypeLike = ...,
+    order: _OrderKACF = ...,
+    subok: bool = ...,
+    shape: Optional[_ShapeLike] = ...,
+) -> ndarray: ...
+@overload
+def empty_like(
+    a: _ArrayType,
+    dtype: None = ...,
+    order: _OrderKACF = ...,
+    subok: Literal[True] = ...,
+    shape: None = ...,
+) -> _ArrayType: ...
+@overload
+def empty_like(
+    a: ArrayLike,
+    dtype: DTypeLike = ...,
+    order: _OrderKACF = ...,
+    subok: bool = ...,
+    shape: Optional[_ShapeLike] = ...,
+) -> ndarray: ...
+def full(
+    shape: _ShapeLike,
+    fill_value: Any,
+    dtype: DTypeLike = ...,
+    order: _OrderCF = ...,
+    *,
+    like: ArrayLike = ...,
+) -> ndarray: ...
+@overload
+def full_like(
+    a: _ArrayType,
+    fill_value: Any,
+    dtype: None = ...,
+    order: _OrderKACF = ...,
+    subok: Literal[True] = ...,
+    shape: None = ...,
+) -> _ArrayType: ...
+@overload
+def full_like(
+    a: ArrayLike,
+    fill_value: Any,
+    dtype: DTypeLike = ...,
+    order: _OrderKACF = ...,
+    subok: bool = ...,
+    shape: Optional[_ShapeLike] = ...,
+) -> ndarray: ...
+@overload
+def count_nonzero(
+    a: ArrayLike, axis: None = ..., *, keepdims: Literal[False] = ...
+) -> int: ...
+@overload
+def count_nonzero(
+    a: ArrayLike, axis: _ShapeLike = ..., *, keepdims: bool = ...
+) -> Union[signedinteger[Any], ndarray]: ...  # TODO: np.intp
+def isfortran(a: Union[ndarray, generic]) -> bool: ...
+def argwhere(a: ArrayLike) -> ndarray: ...
+def flatnonzero(a: ArrayLike) -> ndarray: ...
+def correlate(a: ArrayLike, v: ArrayLike, mode: _CorrelateMode = ...) -> ndarray: ...
+def convolve(a: ArrayLike, v: ArrayLike, mode: _CorrelateMode = ...) -> ndarray: ...
+@overload
+def outer(a: ArrayLike, b: ArrayLike, out: None = ...) -> ndarray: ...
+@overload
+def outer(a: ArrayLike, b: ArrayLike, out: _ArrayType = ...) -> _ArrayType: ...
+def tensordot(
+    a: ArrayLike,
+    b: ArrayLike,
+    axes: Union[int, Tuple[_ShapeLike, _ShapeLike]] = ...,
+) -> ndarray: ...
+def roll(
+    a: ArrayLike,
+    shift: _ShapeLike,
+    axis: Optional[_ShapeLike] = ...,
+) -> ndarray: ...
+def rollaxis(a: ndarray, axis: int, start: int = ...) -> ndarray: ...
+def moveaxis(
+    a: ndarray,
+    source: _ShapeLike,
+    destination: _ShapeLike,
+) -> ndarray: ...
+def cross(
+    a: ArrayLike,
+    b: ArrayLike,
+    axisa: int = ...,
+    axisb: int = ...,
+    axisc: int = ...,
+    axis: Optional[int] = ...,
+) -> ndarray: ...
+@overload
+def indices(
+    dimensions: Sequence[int],
+    dtype: DTypeLike = ...,
+    sparse: Literal[False] = ...,
+) -> ndarray: ...
+@overload
+def indices(
+    dimensions: Sequence[int],
+    dtype: DTypeLike = ...,
+    sparse: Literal[True] = ...,
+) -> Tuple[ndarray, ...]: ...
+def fromfunction(
+    function: Callable[..., _T],
+    shape: Sequence[int],
+    *,
+    dtype: DTypeLike = ...,
+    like: ArrayLike = ...,
+    **kwargs: Any,
+) -> _T: ...
+def isscalar(element: Any) -> bool: ...
+def binary_repr(num: int, width: Optional[int] = ...) -> str: ...
+def base_repr(number: int, base: int = ..., padding: int = ...) -> str: ...
+def identity(n: int, dtype: DTypeLike = ..., *, like: ArrayLike = ...) -> ndarray: ...
+def allclose(
+    a: ArrayLike,
+    b: ArrayLike,
+    rtol: float = ...,
+    atol: float = ...,
+    equal_nan: bool = ...,
+) -> bool: ...
+def isclose(
+    a: ArrayLike,
+    b: ArrayLike,
+    rtol: float = ...,
+    atol: float = ...,
+    equal_nan: bool = ...,
+) -> Union[bool_, ndarray]: ...
+def array_equal(a1: ArrayLike, a2: ArrayLike) -> bool: ...
+def array_equiv(a1: ArrayLike, a2: ArrayLike) -> bool: ...