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__pycache__	[ DIR ]	drwxr-xr-x
include	[ DIR ]	drwxr-xr-x
lib	[ DIR ]	drwxr-xr-x
tests	[ DIR ]	drwxr-xr-x
__init__.py	5.24 KB	-rw-r--r--
__init__.pyi	126 B	-rw-r--r--
_add_newdocs.py	187.41 KB	-rw-r--r--
_add_newdocs_scalars.py	8.59 KB	-rw-r--r--
_asarray.py	4.08 KB	-rw-r--r--
_asarray.pyi	1.89 KB	-rw-r--r--
_dtype.py	9.61 KB	-rw-r--r--
_dtype_ctypes.py	3.59 KB	-rw-r--r--
_exceptions.py	5.99 KB	-rw-r--r--
_internal.py	26.73 KB	-rw-r--r--
_internal.pyi	1.34 KB	-rw-r--r--
_methods.py	10.54 KB	-rw-r--r--
_multiarray_tests.cpython-310-...	122.6 KB	-rw-r--r--
_multiarray_umath.cpython-310-...	3.63 MB	-rw-r--r--
_operand_flag_tests.cpython-31...	14.2 KB	-rw-r--r--
_rational_tests.cpython-310-x8...	43.46 KB	-rw-r--r--
_simd.cpython-310-x86_64-linux...	1.91 MB	-rw-r--r--
_string_helpers.py	2.79 KB	-rw-r--r--
_struct_ufunc_tests.cpython-31...	14.38 KB	-rw-r--r--
_type_aliases.py	7.1 KB	-rw-r--r--
_type_aliases.pyi	520 B	-rw-r--r--
_ufunc_config.py	13.07 KB	-rw-r--r--
_ufunc_config.pyi	1.22 KB	-rw-r--r--
_umath_tests.cpython-310-x86_6...	34.8 KB	-rw-r--r--
arrayprint.py	60.18 KB	-rw-r--r--
arrayprint.pyi	4.56 KB	-rw-r--r--
cversions.py	347 B	-rw-r--r--
defchararray.py	68.1 KB	-rw-r--r--
einsumfunc.py	50.24 KB	-rw-r--r--
einsumfunc.pyi	3.62 KB	-rw-r--r--
fromnumeric.py	119.9 KB	-rw-r--r--
fromnumeric.pyi	7.83 KB	-rw-r--r--
function_base.py	18.57 KB	-rw-r--r--
function_base.pyi	1.44 KB	-rw-r--r--
generate_numpy_api.py	6.94 KB	-rw-r--r--
getlimits.py	19.31 KB	-rw-r--r--
machar.py	10.56 KB	-rw-r--r--
memmap.py	11.41 KB	-rw-r--r--
multiarray.py	54.01 KB	-rw-r--r--
numeric.py	74.93 KB	-rw-r--r--
numeric.pyi	4.76 KB	-rw-r--r--
numerictypes.py	16.91 KB	-rw-r--r--
numerictypes.pyi	2.85 KB	-rw-r--r--
overrides.py	7.94 KB	-rw-r--r--
records.py	36.58 KB	-rw-r--r--
setup.py	44.62 KB	-rw-r--r--
setup_common.py	19.31 KB	-rw-r--r--
shape_base.py	28.32 KB	-rw-r--r--
shape_base.pyi	1.04 KB	-rw-r--r--
umath.py	1.99 KB	-rw-r--r--
umath_tests.py	389 B	-rw-r--r--

Code Editor : numeric.pyi

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 = ...,
) -> Any: ...  # TODO: np.intp or ndarray[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 = ...,
) -> Any: ...

def array_equal(a1: ArrayLike, a2: ArrayLike, equal_nan: bool = ...) -> bool: ...

def array_equiv(a1: ArrayLike, a2: ArrayLike) -> bool: ...

${this.title}

Code Editor : numeric.pyi