MAINT: move things in numeric.pyi to the top-level __init__.pyi

numpy-stubs/__init__.pyi

@@ -7,6 +7,7 @@ from typing import (
     Any,
     ByteString,
     Container,
+    Callable,
     Dict,
     IO,
     Iterable,
@@ -485,6 +486,90 @@ def zeros(
 def ones(
     shape: _ShapeLike, dtype: _DtypeLike = ..., order: Optional[str] = ...
 ) -> ndarray: ...
+def zeros_like(
+    a: _ArrayLike,
+    dtype: Optional[dtype] = ...,
+    order: str = ...,
+    subok: bool = ...,
+    shape: Optional[Union[int, Sequence[int]]] = ...,
+) -> ndarray: ...
+def ones_like(
+    a: _ArrayLike,
+    dtype: Optional[dtype] = ...,
+    order: str = ...,
+    subok: bool = ...,
+    shape: Optional[_ShapeLike] = ...,
+) -> ndarray[int]: ...
+def full(
+    shape: _ShapeLike, fill_value: Any, dtype: Optional[dtype] = ..., order: str = ...
+) -> ndarray: ...
+def full_like(
+    a: _ArrayLike,
+    fill_value: Any,
+    dtype: Optional[dtype] = ...,
+    order: str = ...,
+    subok: bool = ...,
+    shape: Optional[_ShapeLike] = ...,
+) -> ndarray: ...
+def count_nonzero(
+    a: _ArrayLike, axis: Optional[Union[int, Tuple[int], Tuple[int, int]]] = ...
+) -> Union[int, ndarray]: ...
+def isfortran(a: ndarray) -> bool: ...
+def argwhere(a: _ArrayLike) -> ndarray: ...
+def flatnonzero(a: _ArrayLike) -> ndarray: ...
+def correlate(a: _ArrayLike, v: _ArrayLike, mode: str = ...) -> ndarray: ...
+def convolve(a: _ArrayLike, v: _ArrayLike, mode: str = ...) -> ndarray: ...
+def outer(a: _ArrayLike, b: _ArrayLike, out: ndarray = ...) -> ndarray: ...
+def tensordot(
+    a: _ArrayLike,
+    b: _ArrayLike,
+    axes: Union[
+        int, Tuple[int, int], Tuple[Tuple[int, int], ...], Tuple[List[int, int], ...]
+    ] = ...,
+) -> ndarray: ...
+def roll(
+    a: _ArrayLike,
+    shift: Union[int, Tuple[int, ...]],
+    axis: Optional[Union[int, Tuple[int, ...]]] = ...,
+) -> ndarray: ...
+def rollaxis(a: _ArrayLike, axis: int, start: int = ...) -> ndarray: ...
+def moveaxis(
+    a: ndarray,
+    source: Union[int, Sequence[int]],
+    destination: Union[int, Sequence[int]],
+) -> ndarray: ...
+def cross(
+    a: _ArrayLike,
+    b: _ArrayLike,
+    axisa: int = ...,
+    axisb: int = ...,
+    axisc: int = ...,
+    axis: Optional[int] = ...,
+) -> ndarray: ...
+def indices(
+    dimensions: Sequence[int], dtype: dtype = ..., sparse: bool = ...
+) -> Union[ndarray, Tuple[ndarray, ...]]: ...
+def fromfunction(function: Callable, shape: Tuple[int, int], **kwargs) -> Any: ...
+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: Optional[dtype] = ...) -> 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: ...
 
 # TODO(shoyer): remove when the full numpy namespace is defined
 def __getattr__(name: str) -> Any: ...