Your IP : 216.73.216.74
# ruff: noqa: ANN401
from collections.abc import Callable, Iterable, Sequence
from typing import (
Any,
Concatenate,
ParamSpec,
Protocol,
SupportsIndex,
SupportsInt,
TypeAlias,
TypeVar,
overload,
type_check_only,
)
from typing import Literal as L
from _typeshed import Incomplete
from typing_extensions import TypeIs, deprecated
import numpy as np
from numpy import (
_OrderKACF,
bool_,
complex128,
complexfloating,
datetime64,
float64,
floating,
generic,
integer,
intp,
object_,
timedelta64,
vectorize,
)
from numpy._core.multiarray import bincount
from numpy._globals import _NoValueType
from numpy._typing import (
ArrayLike,
DTypeLike,
NDArray,
_ArrayLike,
_ArrayLikeBool_co,
_ArrayLikeComplex_co,
_ArrayLikeDT64_co,
_ArrayLikeFloat_co,
_ArrayLikeInt_co,
_ArrayLikeNumber_co,
_ArrayLikeObject_co,
_ArrayLikeTD64_co,
_ComplexLike_co,
_DTypeLike,
_FloatLike_co,
_NestedSequence,
_NumberLike_co,
_ScalarLike_co,
_ShapeLike,
)
__all__ = [
"select",
"piecewise",
"trim_zeros",
"copy",
"iterable",
"percentile",
"diff",
"gradient",
"angle",
"unwrap",
"sort_complex",
"flip",
"rot90",
"extract",
"place",
"vectorize",
"asarray_chkfinite",
"average",
"bincount",
"digitize",
"cov",
"corrcoef",
"median",
"sinc",
"hamming",
"hanning",
"bartlett",
"blackman",
"kaiser",
"trapezoid",
"trapz",
"i0",
"meshgrid",
"delete",
"insert",
"append",
"interp",
"quantile",
]
_T = TypeVar("_T")
_T_co = TypeVar("_T_co", covariant=True)
# The `{}ss` suffix refers to the Python 3.12 syntax: `**P`
_Pss = ParamSpec("_Pss")
_ScalarT = TypeVar("_ScalarT", bound=generic)
_ScalarT1 = TypeVar("_ScalarT1", bound=generic)
_ScalarT2 = TypeVar("_ScalarT2", bound=generic)
_ArrayT = TypeVar("_ArrayT", bound=np.ndarray)
_2Tuple: TypeAlias = tuple[_T, _T]
_MeshgridIdx: TypeAlias = L['ij', 'xy']
@type_check_only
class _TrimZerosSequence(Protocol[_T_co]):
def __len__(self, /) -> int: ...
@overload
def __getitem__(self, key: int, /) -> object: ...
@overload
def __getitem__(self, key: slice, /) -> _T_co: ...
###
@overload
def rot90(
m: _ArrayLike[_ScalarT],
k: int = ...,
axes: tuple[int, int] = ...,
) -> NDArray[_ScalarT]: ...
@overload
def rot90(
m: ArrayLike,
k: int = ...,
axes: tuple[int, int] = ...,
) -> NDArray[Any]: ...
@overload
def flip(m: _ScalarT, axis: None = ...) -> _ScalarT: ...
@overload
def flip(m: _ScalarLike_co, axis: None = ...) -> Any: ...
@overload
def flip(m: _ArrayLike[_ScalarT], axis: _ShapeLike | None = ...) -> NDArray[_ScalarT]: ...
@overload
def flip(m: ArrayLike, axis: _ShapeLike | None = ...) -> NDArray[Any]: ...
def iterable(y: object) -> TypeIs[Iterable[Any]]: ...
@overload
def average(
a: _ArrayLikeFloat_co,
axis: None = None,
weights: _ArrayLikeFloat_co | None = None,
returned: L[False] = False,
*,
keepdims: L[False] | _NoValueType = ...,
) -> floating: ...
@overload
def average(
a: _ArrayLikeFloat_co,
axis: None = None,
weights: _ArrayLikeFloat_co | None = None,
*,
returned: L[True],
keepdims: L[False] | _NoValueType = ...,
) -> _2Tuple[floating]: ...
@overload
def average(
a: _ArrayLikeComplex_co,
axis: None = None,
weights: _ArrayLikeComplex_co | None = None,
returned: L[False] = False,
*,
keepdims: L[False] | _NoValueType = ...,
) -> complexfloating: ...
@overload
def average(
a: _ArrayLikeComplex_co,
axis: None = None,
weights: _ArrayLikeComplex_co | None = None,
*,
returned: L[True],
keepdims: L[False] | _NoValueType = ...,
) -> _2Tuple[complexfloating]: ...
@overload
def average(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: _ShapeLike | None = None,
weights: object | None = None,
*,
returned: L[True],
keepdims: bool | bool_ | _NoValueType = ...,
) -> _2Tuple[Incomplete]: ...
@overload
def average(
a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
axis: _ShapeLike | None = None,
weights: object | None = None,
returned: bool | bool_ = False,
*,
keepdims: bool | bool_ | _NoValueType = ...,
) -> Incomplete: ...
@overload
def asarray_chkfinite(
a: _ArrayLike[_ScalarT],
dtype: None = ...,
order: _OrderKACF = ...,
) -> NDArray[_ScalarT]: ...
@overload
def asarray_chkfinite(
a: object,
dtype: None = ...,
order: _OrderKACF = ...,
) -> NDArray[Any]: ...
@overload
def asarray_chkfinite(
a: Any,
dtype: _DTypeLike[_ScalarT],
order: _OrderKACF = ...,
) -> NDArray[_ScalarT]: ...
@overload
def asarray_chkfinite(
a: Any,
dtype: DTypeLike,
order: _OrderKACF = ...,
) -> NDArray[Any]: ...
@overload
def piecewise(
x: _ArrayLike[_ScalarT],
condlist: _ArrayLike[bool_] | Sequence[_ArrayLikeBool_co],
funclist: Sequence[
Callable[Concatenate[NDArray[_ScalarT], _Pss], NDArray[_ScalarT | Any]]
| _ScalarT | object
],
/,
*args: _Pss.args,
**kw: _Pss.kwargs,
) -> NDArray[_ScalarT]: ...
@overload
def piecewise(
x: ArrayLike,
condlist: _ArrayLike[bool_] | Sequence[_ArrayLikeBool_co],
funclist: Sequence[
Callable[Concatenate[NDArray[Any], _Pss], NDArray[Any]]
| object
],
/,
*args: _Pss.args,
**kw: _Pss.kwargs,
) -> NDArray[Any]: ...
def select(
condlist: Sequence[ArrayLike],
choicelist: Sequence[ArrayLike],
default: ArrayLike = ...,
) -> NDArray[Any]: ...
@overload
def copy(
a: _ArrayT,
order: _OrderKACF,
subok: L[True],
) -> _ArrayT: ...
@overload
def copy(
a: _ArrayT,
order: _OrderKACF = ...,
*,
subok: L[True],
) -> _ArrayT: ...
@overload
def copy(
a: _ArrayLike[_ScalarT],
order: _OrderKACF = ...,
subok: L[False] = ...,
) -> NDArray[_ScalarT]: ...
@overload
def copy(
a: ArrayLike,
order: _OrderKACF = ...,
subok: L[False] = ...,
) -> NDArray[Any]: ...
def gradient(
f: ArrayLike,
*varargs: ArrayLike,
axis: _ShapeLike | None = ...,
edge_order: L[1, 2] = ...,
) -> Any: ...
@overload
def diff(
a: _T,
n: L[0],
axis: SupportsIndex = ...,
prepend: ArrayLike = ...,
append: ArrayLike = ...,
) -> _T: ...
@overload
def diff(
a: ArrayLike,
n: int = ...,
axis: SupportsIndex = ...,
prepend: ArrayLike = ...,
append: ArrayLike = ...,
) -> NDArray[Any]: ...
@overload # float scalar
def interp(
x: _FloatLike_co,
xp: _ArrayLikeFloat_co,
fp: _ArrayLikeFloat_co,
left: _FloatLike_co | None = None,
right: _FloatLike_co | None = None,
period: _FloatLike_co | None = None,
) -> float64: ...
@overload # float array
def interp(
x: NDArray[floating | integer | np.bool] | _NestedSequence[_FloatLike_co],
xp: _ArrayLikeFloat_co,
fp: _ArrayLikeFloat_co,
left: _FloatLike_co | None = None,
right: _FloatLike_co | None = None,
period: _FloatLike_co | None = None,
) -> NDArray[float64]: ...
@overload # float scalar or array
def interp(
x: _ArrayLikeFloat_co,
xp: _ArrayLikeFloat_co,
fp: _ArrayLikeFloat_co,
left: _FloatLike_co | None = None,
right: _FloatLike_co | None = None,
period: _FloatLike_co | None = None,
) -> NDArray[float64] | float64: ...
@overload # complex scalar
def interp(
x: _FloatLike_co,
xp: _ArrayLikeFloat_co,
fp: _ArrayLike[complexfloating],
left: _NumberLike_co | None = None,
right: _NumberLike_co | None = None,
period: _FloatLike_co | None = None,
) -> complex128: ...
@overload # complex or float scalar
def interp(
x: _FloatLike_co,
xp: _ArrayLikeFloat_co,
fp: Sequence[complex | complexfloating],
left: _NumberLike_co | None = None,
right: _NumberLike_co | None = None,
period: _FloatLike_co | None = None,
) -> complex128 | float64: ...
@overload # complex array
def interp(
x: NDArray[floating | integer | np.bool] | _NestedSequence[_FloatLike_co],
xp: _ArrayLikeFloat_co,
fp: _ArrayLike[complexfloating],
left: _NumberLike_co | None = None,
right: _NumberLike_co | None = None,
period: _FloatLike_co | None = None,
) -> NDArray[complex128]: ...
@overload # complex or float array
def interp(
x: NDArray[floating | integer | np.bool] | _NestedSequence[_FloatLike_co],
xp: _ArrayLikeFloat_co,
fp: Sequence[complex | complexfloating],
left: _NumberLike_co | None = None,
right: _NumberLike_co | None = None,
period: _FloatLike_co | None = None,
) -> NDArray[complex128 | float64]: ...
@overload # complex scalar or array
def interp(
x: _ArrayLikeFloat_co,
xp: _ArrayLikeFloat_co,
fp: _ArrayLike[complexfloating],
left: _NumberLike_co | None = None,
right: _NumberLike_co | None = None,
period: _FloatLike_co | None = None,
) -> NDArray[complex128] | complex128: ...
@overload # complex or float scalar or array
def interp(
x: _ArrayLikeFloat_co,
xp: _ArrayLikeFloat_co,
fp: _ArrayLikeNumber_co,
left: _NumberLike_co | None = None,
right: _NumberLike_co | None = None,
period: _FloatLike_co | None = None,
) -> NDArray[complex128 | float64] | complex128 | float64: ...
@overload
def angle(z: _ComplexLike_co, deg: bool = ...) -> floating: ...
@overload
def angle(z: object_, deg: bool = ...) -> Any: ...
@overload
def angle(z: _ArrayLikeComplex_co, deg: bool = ...) -> NDArray[floating]: ...
@overload
def angle(z: _ArrayLikeObject_co, deg: bool = ...) -> NDArray[object_]: ...
@overload
def unwrap(
p: _ArrayLikeFloat_co,
discont: float | None = ...,
axis: int = ...,
*,
period: float = ...,
) -> NDArray[floating]: ...
@overload
def unwrap(
p: _ArrayLikeObject_co,
discont: float | None = ...,
axis: int = ...,
*,
period: float = ...,
) -> NDArray[object_]: ...
def sort_complex(a: ArrayLike) -> NDArray[complexfloating]: ...
def trim_zeros(
filt: _TrimZerosSequence[_T],
trim: L["f", "b", "fb", "bf"] = ...,
) -> _T: ...
@overload
def extract(condition: ArrayLike, arr: _ArrayLike[_ScalarT]) -> NDArray[_ScalarT]: ...
@overload
def extract(condition: ArrayLike, arr: ArrayLike) -> NDArray[Any]: ...
def place(arr: NDArray[Any], mask: ArrayLike, vals: Any) -> None: ...
@overload
def cov(
m: _ArrayLikeFloat_co,
y: _ArrayLikeFloat_co | None = ...,
rowvar: bool = ...,
bias: bool = ...,
ddof: SupportsIndex | SupportsInt | None = ...,
fweights: ArrayLike | None = ...,
aweights: ArrayLike | None = ...,
*,
dtype: None = ...,
) -> NDArray[floating]: ...
@overload
def cov(
m: _ArrayLikeComplex_co,
y: _ArrayLikeComplex_co | None = ...,
rowvar: bool = ...,
bias: bool = ...,
ddof: SupportsIndex | SupportsInt | None = ...,
fweights: ArrayLike | None = ...,
aweights: ArrayLike | None = ...,
*,
dtype: None = ...,
) -> NDArray[complexfloating]: ...
@overload
def cov(
m: _ArrayLikeComplex_co,
y: _ArrayLikeComplex_co | None = ...,
rowvar: bool = ...,
bias: bool = ...,
ddof: SupportsIndex | SupportsInt | None = ...,
fweights: ArrayLike | None = ...,
aweights: ArrayLike | None = ...,
*,
dtype: _DTypeLike[_ScalarT],
) -> NDArray[_ScalarT]: ...
@overload
def cov(
m: _ArrayLikeComplex_co,
y: _ArrayLikeComplex_co | None = ...,
rowvar: bool = ...,
bias: bool = ...,
ddof: SupportsIndex | SupportsInt | None = ...,
fweights: ArrayLike | None = ...,
aweights: ArrayLike | None = ...,
*,
dtype: DTypeLike,
) -> NDArray[Any]: ...
# NOTE `bias` and `ddof` are deprecated and ignored
@overload
def corrcoef(
m: _ArrayLikeFloat_co,
y: _ArrayLikeFloat_co | None = None,
rowvar: bool = True,
bias: _NoValueType = ...,
ddof: _NoValueType = ...,
*,
dtype: None = None,
) -> NDArray[floating]: ...
@overload
def corrcoef(
m: _ArrayLikeComplex_co,
y: _ArrayLikeComplex_co | None = None,
rowvar: bool = True,
bias: _NoValueType = ...,
ddof: _NoValueType = ...,
*,
dtype: None = None,
) -> NDArray[complexfloating]: ...
@overload
def corrcoef(
m: _ArrayLikeComplex_co,
y: _ArrayLikeComplex_co | None = None,
rowvar: bool = True,
bias: _NoValueType = ...,
ddof: _NoValueType = ...,
*,
dtype: _DTypeLike[_ScalarT],
) -> NDArray[_ScalarT]: ...
@overload
def corrcoef(
m: _ArrayLikeComplex_co,
y: _ArrayLikeComplex_co | None = None,
rowvar: bool = True,
bias: _NoValueType = ...,
ddof: _NoValueType = ...,
*,
dtype: DTypeLike | None = None,
) -> NDArray[Any]: ...
def blackman(M: _FloatLike_co) -> NDArray[floating]: ...
def bartlett(M: _FloatLike_co) -> NDArray[floating]: ...
def hanning(M: _FloatLike_co) -> NDArray[floating]: ...
def hamming(M: _FloatLike_co) -> NDArray[floating]: ...
def i0(x: _ArrayLikeFloat_co) -> NDArray[floating]: ...
def kaiser(
M: _FloatLike_co,
beta: _FloatLike_co,
) -> NDArray[floating]: ...
@overload
def sinc(x: _FloatLike_co) -> floating: ...
@overload
def sinc(x: _ComplexLike_co) -> complexfloating: ...
@overload
def sinc(x: _ArrayLikeFloat_co) -> NDArray[floating]: ...
@overload
def sinc(x: _ArrayLikeComplex_co) -> NDArray[complexfloating]: ...
@overload
def median(
a: _ArrayLikeFloat_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
keepdims: L[False] = ...,
) -> floating: ...
@overload
def median(
a: _ArrayLikeComplex_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
keepdims: L[False] = ...,
) -> complexfloating: ...
@overload
def median(
a: _ArrayLikeTD64_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
keepdims: L[False] = ...,
) -> timedelta64: ...
@overload
def median(
a: _ArrayLikeObject_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
keepdims: L[False] = ...,
) -> Any: ...
@overload
def median(
a: _ArrayLikeFloat_co | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
axis: _ShapeLike | None = ...,
out: None = ...,
overwrite_input: bool = ...,
keepdims: bool = ...,
) -> Any: ...
@overload
def median(
a: _ArrayLikeFloat_co | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
axis: _ShapeLike | None,
out: _ArrayT,
overwrite_input: bool = ...,
keepdims: bool = ...,
) -> _ArrayT: ...
@overload
def median(
a: _ArrayLikeFloat_co | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
axis: _ShapeLike | None = ...,
*,
out: _ArrayT,
overwrite_input: bool = ...,
keepdims: bool = ...,
) -> _ArrayT: ...
_MethodKind = L[
"inverted_cdf",
"averaged_inverted_cdf",
"closest_observation",
"interpolated_inverted_cdf",
"hazen",
"weibull",
"linear",
"median_unbiased",
"normal_unbiased",
"lower",
"higher",
"midpoint",
"nearest",
]
@overload
def percentile(
a: _ArrayLikeFloat_co,
q: _FloatLike_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: L[False] = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> floating: ...
@overload
def percentile(
a: _ArrayLikeComplex_co,
q: _FloatLike_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: L[False] = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> complexfloating: ...
@overload
def percentile(
a: _ArrayLikeTD64_co,
q: _FloatLike_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: L[False] = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> timedelta64: ...
@overload
def percentile(
a: _ArrayLikeDT64_co,
q: _FloatLike_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: L[False] = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> datetime64: ...
@overload
def percentile(
a: _ArrayLikeObject_co,
q: _FloatLike_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: L[False] = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> Any: ...
@overload
def percentile(
a: _ArrayLikeFloat_co,
q: _ArrayLikeFloat_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: L[False] = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> NDArray[floating]: ...
@overload
def percentile(
a: _ArrayLikeComplex_co,
q: _ArrayLikeFloat_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: L[False] = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> NDArray[complexfloating]: ...
@overload
def percentile(
a: _ArrayLikeTD64_co,
q: _ArrayLikeFloat_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: L[False] = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> NDArray[timedelta64]: ...
@overload
def percentile(
a: _ArrayLikeDT64_co,
q: _ArrayLikeFloat_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: L[False] = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> NDArray[datetime64]: ...
@overload
def percentile(
a: _ArrayLikeObject_co,
q: _ArrayLikeFloat_co,
axis: None = ...,
out: None = ...,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: L[False] = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> NDArray[object_]: ...
@overload
def percentile(
a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeDT64_co | _ArrayLikeObject_co,
q: _ArrayLikeFloat_co,
axis: _ShapeLike | None = ...,
out: None = ...,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: bool = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> Any: ...
@overload
def percentile(
a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeDT64_co | _ArrayLikeObject_co,
q: _ArrayLikeFloat_co,
axis: _ShapeLike | None,
out: _ArrayT,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: bool = ...,
*,
weights: _ArrayLikeFloat_co | None = ...,
) -> _ArrayT: ...
@overload
def percentile(
a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeDT64_co | _ArrayLikeObject_co,
q: _ArrayLikeFloat_co,
axis: _ShapeLike | None = ...,
*,
out: _ArrayT,
overwrite_input: bool = ...,
method: _MethodKind = ...,
keepdims: bool = ...,
weights: _ArrayLikeFloat_co | None = ...,
) -> _ArrayT: ...
# NOTE: Not an alias, but they do have identical signatures
# (that we can reuse)
quantile = percentile
_ScalarT_fm = TypeVar(
"_ScalarT_fm",
bound=floating | complexfloating | timedelta64,
)
class _SupportsRMulFloat(Protocol[_T_co]):
def __rmul__(self, other: float, /) -> _T_co: ...
@overload
def trapezoid( # type: ignore[overload-overlap]
y: Sequence[_FloatLike_co],
x: Sequence[_FloatLike_co] | None = ...,
dx: float = ...,
axis: SupportsIndex = ...,
) -> float64: ...
@overload
def trapezoid(
y: Sequence[_ComplexLike_co],
x: Sequence[_ComplexLike_co] | None = ...,
dx: float = ...,
axis: SupportsIndex = ...,
) -> complex128: ...
@overload
def trapezoid(
y: _ArrayLike[bool_ | integer],
x: _ArrayLike[bool_ | integer] | None = ...,
dx: float = ...,
axis: SupportsIndex = ...,
) -> float64 | NDArray[float64]: ...
@overload
def trapezoid( # type: ignore[overload-overlap]
y: _ArrayLikeObject_co,
x: _ArrayLikeFloat_co | _ArrayLikeObject_co | None = ...,
dx: float = ...,
axis: SupportsIndex = ...,
) -> float | NDArray[object_]: ...
@overload
def trapezoid(
y: _ArrayLike[_ScalarT_fm],
x: _ArrayLike[_ScalarT_fm] | _ArrayLikeInt_co | None = ...,
dx: float = ...,
axis: SupportsIndex = ...,
) -> _ScalarT_fm | NDArray[_ScalarT_fm]: ...
@overload
def trapezoid(
y: Sequence[_SupportsRMulFloat[_T]],
x: Sequence[_SupportsRMulFloat[_T] | _T] | None = ...,
dx: float = ...,
axis: SupportsIndex = ...,
) -> _T: ...
@overload
def trapezoid(
y: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
x: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co | None = ...,
dx: float = ...,
axis: SupportsIndex = ...,
) -> (
floating | complexfloating | timedelta64
| NDArray[floating | complexfloating | timedelta64 | object_]
): ...
@deprecated("Use 'trapezoid' instead")
def trapz(y: ArrayLike, x: ArrayLike | None = None, dx: float = 1.0, axis: int = -1) -> generic | NDArray[generic]: ...
@overload
def meshgrid(
*,
copy: bool = ...,
sparse: bool = ...,
indexing: _MeshgridIdx = ...,
) -> tuple[()]: ...
@overload
def meshgrid(
x1: _ArrayLike[_ScalarT],
/,
*,
copy: bool = ...,
sparse: bool = ...,
indexing: _MeshgridIdx = ...,
) -> tuple[NDArray[_ScalarT]]: ...
@overload
def meshgrid(
x1: ArrayLike,
/,
*,
copy: bool = ...,
sparse: bool = ...,
indexing: _MeshgridIdx = ...,
) -> tuple[NDArray[Any]]: ...
@overload
def meshgrid(
x1: _ArrayLike[_ScalarT1],
x2: _ArrayLike[_ScalarT2],
/,
*,
copy: bool = ...,
sparse: bool = ...,
indexing: _MeshgridIdx = ...,
) -> tuple[NDArray[_ScalarT1], NDArray[_ScalarT2]]: ...
@overload
def meshgrid(
x1: ArrayLike,
x2: _ArrayLike[_ScalarT],
/,
*,
copy: bool = ...,
sparse: bool = ...,
indexing: _MeshgridIdx = ...,
) -> tuple[NDArray[Any], NDArray[_ScalarT]]: ...
@overload
def meshgrid(
x1: _ArrayLike[_ScalarT],
x2: ArrayLike,
/,
*,
copy: bool = ...,
sparse: bool = ...,
indexing: _MeshgridIdx = ...,
) -> tuple[NDArray[_ScalarT], NDArray[Any]]: ...
@overload
def meshgrid(
x1: ArrayLike,
x2: ArrayLike,
/,
*,
copy: bool = ...,
sparse: bool = ...,
indexing: _MeshgridIdx = ...,
) -> tuple[NDArray[Any], NDArray[Any]]: ...
@overload
def meshgrid(
x1: ArrayLike,
x2: ArrayLike,
x3: ArrayLike,
/,
*,
copy: bool = ...,
sparse: bool = ...,
indexing: _MeshgridIdx = ...,
) -> tuple[NDArray[Any], NDArray[Any], NDArray[Any]]: ...
@overload
def meshgrid(
x1: ArrayLike,
x2: ArrayLike,
x3: ArrayLike,
x4: ArrayLike,
/,
*,
copy: bool = ...,
sparse: bool = ...,
indexing: _MeshgridIdx = ...,
) -> tuple[NDArray[Any], NDArray[Any], NDArray[Any], NDArray[Any]]: ...
@overload
def meshgrid(
*xi: ArrayLike,
copy: bool = ...,
sparse: bool = ...,
indexing: _MeshgridIdx = ...,
) -> tuple[NDArray[Any], ...]: ...
@overload
def delete(
arr: _ArrayLike[_ScalarT],
obj: slice | _ArrayLikeInt_co,
axis: SupportsIndex | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def delete(
arr: ArrayLike,
obj: slice | _ArrayLikeInt_co,
axis: SupportsIndex | None = ...,
) -> NDArray[Any]: ...
@overload
def insert(
arr: _ArrayLike[_ScalarT],
obj: slice | _ArrayLikeInt_co,
values: ArrayLike,
axis: SupportsIndex | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def insert(
arr: ArrayLike,
obj: slice | _ArrayLikeInt_co,
values: ArrayLike,
axis: SupportsIndex | None = ...,
) -> NDArray[Any]: ...
def append(
arr: ArrayLike,
values: ArrayLike,
axis: SupportsIndex | None = ...,
) -> NDArray[Any]: ...
@overload
def digitize(
x: _FloatLike_co,
bins: _ArrayLikeFloat_co,
right: bool = ...,
) -> intp: ...
@overload
def digitize(
x: _ArrayLikeFloat_co,
bins: _ArrayLikeFloat_co,
right: bool = ...,
) -> NDArray[intp]: ...