Your IP : 216.73.216.74
from collections.abc import Callable, Sequence
from typing import (
Any,
TypeAlias,
TypeVar,
overload,
)
from typing import (
Literal as L,
)
import numpy as np
from numpy import (
_OrderCF,
complex128,
complexfloating,
datetime64,
float64,
floating,
generic,
int_,
intp,
object_,
signedinteger,
timedelta64,
)
from numpy._typing import (
ArrayLike,
DTypeLike,
NDArray,
_ArrayLike,
_ArrayLikeComplex_co,
_ArrayLikeFloat_co,
_ArrayLikeInt_co,
_ArrayLikeObject_co,
_DTypeLike,
_SupportsArray,
_SupportsArrayFunc,
)
__all__ = [
"diag",
"diagflat",
"eye",
"fliplr",
"flipud",
"tri",
"triu",
"tril",
"vander",
"histogram2d",
"mask_indices",
"tril_indices",
"tril_indices_from",
"triu_indices",
"triu_indices_from",
]
###
_T = TypeVar("_T")
_ScalarT = TypeVar("_ScalarT", bound=generic)
_ComplexFloatingT = TypeVar("_ComplexFloatingT", bound=np.complexfloating)
_InexactT = TypeVar("_InexactT", bound=np.inexact)
_NumberCoT = TypeVar("_NumberCoT", bound=_Number_co)
# The returned arrays dtype must be compatible with `np.equal`
_MaskFunc: TypeAlias = Callable[[NDArray[int_], _T], NDArray[_Number_co | timedelta64 | datetime64 | object_]]
_Int_co: TypeAlias = np.integer | np.bool
_Float_co: TypeAlias = np.floating | _Int_co
_Number_co: TypeAlias = np.number | np.bool
_ArrayLike1D: TypeAlias = _SupportsArray[np.dtype[_ScalarT]] | Sequence[_ScalarT]
_ArrayLike1DInt_co: TypeAlias = _SupportsArray[np.dtype[_Int_co]] | Sequence[int | _Int_co]
_ArrayLike1DFloat_co: TypeAlias = _SupportsArray[np.dtype[_Float_co]] | Sequence[float | _Float_co]
_ArrayLike2DFloat_co: TypeAlias = _SupportsArray[np.dtype[_Float_co]] | Sequence[_ArrayLike1DFloat_co]
_ArrayLike1DNumber_co: TypeAlias = _SupportsArray[np.dtype[_Number_co]] | Sequence[complex | _Number_co]
###
@overload
def fliplr(m: _ArrayLike[_ScalarT]) -> NDArray[_ScalarT]: ...
@overload
def fliplr(m: ArrayLike) -> NDArray[Any]: ...
@overload
def flipud(m: _ArrayLike[_ScalarT]) -> NDArray[_ScalarT]: ...
@overload
def flipud(m: ArrayLike) -> NDArray[Any]: ...
@overload
def eye(
N: int,
M: int | None = ...,
k: int = ...,
dtype: None = ...,
order: _OrderCF = ...,
*,
device: L["cpu"] | None = ...,
like: _SupportsArrayFunc | None = ...,
) -> NDArray[float64]: ...
@overload
def eye(
N: int,
M: int | None,
k: int,
dtype: _DTypeLike[_ScalarT],
order: _OrderCF = ...,
*,
device: L["cpu"] | None = ...,
like: _SupportsArrayFunc | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def eye(
N: int,
M: int | None = ...,
k: int = ...,
*,
dtype: _DTypeLike[_ScalarT],
order: _OrderCF = ...,
device: L["cpu"] | None = ...,
like: _SupportsArrayFunc | None = ...,
) -> NDArray[_ScalarT]: ...
@overload
def eye(
N: int,
M: int | None = ...,
k: int = ...,
dtype: DTypeLike = ...,
order: _OrderCF = ...,
*,
device: L["cpu"] | None = ...,
like: _SupportsArrayFunc | None = ...,
) -> NDArray[Any]: ...
@overload
def diag(v: _ArrayLike[_ScalarT], k: int = ...) -> NDArray[_ScalarT]: ...
@overload
def diag(v: ArrayLike, k: int = ...) -> NDArray[Any]: ...
@overload
def diagflat(v: _ArrayLike[_ScalarT], k: int = ...) -> NDArray[_ScalarT]: ...
@overload
def diagflat(v: ArrayLike, k: int = ...) -> NDArray[Any]: ...
@overload
def tri(
N: int,
M: int | None = ...,
k: int = ...,
dtype: None = ...,
*,
like: _SupportsArrayFunc | None = ...
) -> NDArray[float64]: ...
@overload
def tri(
N: int,
M: int | None,
k: int,
dtype: _DTypeLike[_ScalarT],
*,
like: _SupportsArrayFunc | None = ...
) -> NDArray[_ScalarT]: ...
@overload
def tri(
N: int,
M: int | None = ...,
k: int = ...,
*,
dtype: _DTypeLike[_ScalarT],
like: _SupportsArrayFunc | None = ...
) -> NDArray[_ScalarT]: ...
@overload
def tri(
N: int,
M: int | None = ...,
k: int = ...,
dtype: DTypeLike = ...,
*,
like: _SupportsArrayFunc | None = ...
) -> NDArray[Any]: ...
@overload
def tril(m: _ArrayLike[_ScalarT], k: int = 0) -> NDArray[_ScalarT]: ...
@overload
def tril(m: ArrayLike, k: int = 0) -> NDArray[Any]: ...
@overload
def triu(m: _ArrayLike[_ScalarT], k: int = 0) -> NDArray[_ScalarT]: ...
@overload
def triu(m: ArrayLike, k: int = 0) -> NDArray[Any]: ...
@overload
def vander( # type: ignore[misc]
x: _ArrayLikeInt_co,
N: int | None = ...,
increasing: bool = ...,
) -> NDArray[signedinteger]: ...
@overload
def vander( # type: ignore[misc]
x: _ArrayLikeFloat_co,
N: int | None = ...,
increasing: bool = ...,
) -> NDArray[floating]: ...
@overload
def vander(
x: _ArrayLikeComplex_co,
N: int | None = ...,
increasing: bool = ...,
) -> NDArray[complexfloating]: ...
@overload
def vander(
x: _ArrayLikeObject_co,
N: int | None = ...,
increasing: bool = ...,
) -> NDArray[object_]: ...
@overload
def histogram2d(
x: _ArrayLike1D[_ComplexFloatingT],
y: _ArrayLike1D[_ComplexFloatingT | _Float_co],
bins: int | Sequence[int] = ...,
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[_ComplexFloatingT],
NDArray[_ComplexFloatingT],
]: ...
@overload
def histogram2d(
x: _ArrayLike1D[_ComplexFloatingT | _Float_co],
y: _ArrayLike1D[_ComplexFloatingT],
bins: int | Sequence[int] = ...,
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[_ComplexFloatingT],
NDArray[_ComplexFloatingT],
]: ...
@overload
def histogram2d(
x: _ArrayLike1D[_InexactT],
y: _ArrayLike1D[_InexactT | _Int_co],
bins: int | Sequence[int] = ...,
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[_InexactT],
NDArray[_InexactT],
]: ...
@overload
def histogram2d(
x: _ArrayLike1D[_InexactT | _Int_co],
y: _ArrayLike1D[_InexactT],
bins: int | Sequence[int] = ...,
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[_InexactT],
NDArray[_InexactT],
]: ...
@overload
def histogram2d(
x: _ArrayLike1DInt_co | Sequence[float],
y: _ArrayLike1DInt_co | Sequence[float],
bins: int | Sequence[int] = ...,
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[float64],
NDArray[float64],
]: ...
@overload
def histogram2d(
x: Sequence[complex],
y: Sequence[complex],
bins: int | Sequence[int] = ...,
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[complex128 | float64],
NDArray[complex128 | float64],
]: ...
@overload
def histogram2d(
x: _ArrayLike1DNumber_co,
y: _ArrayLike1DNumber_co,
bins: _ArrayLike1D[_NumberCoT] | Sequence[_ArrayLike1D[_NumberCoT]],
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[_NumberCoT],
NDArray[_NumberCoT],
]: ...
@overload
def histogram2d(
x: _ArrayLike1D[_InexactT],
y: _ArrayLike1D[_InexactT],
bins: Sequence[_ArrayLike1D[_NumberCoT] | int],
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[_NumberCoT | _InexactT],
NDArray[_NumberCoT | _InexactT],
]: ...
@overload
def histogram2d(
x: _ArrayLike1DInt_co | Sequence[float],
y: _ArrayLike1DInt_co | Sequence[float],
bins: Sequence[_ArrayLike1D[_NumberCoT] | int],
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[_NumberCoT | float64],
NDArray[_NumberCoT | float64],
]: ...
@overload
def histogram2d(
x: Sequence[complex],
y: Sequence[complex],
bins: Sequence[_ArrayLike1D[_NumberCoT] | int],
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[_NumberCoT | complex128 | float64],
NDArray[_NumberCoT | complex128 | float64],
]: ...
@overload
def histogram2d(
x: _ArrayLike1DNumber_co,
y: _ArrayLike1DNumber_co,
bins: Sequence[Sequence[bool]],
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[np.bool],
NDArray[np.bool],
]: ...
@overload
def histogram2d(
x: _ArrayLike1DNumber_co,
y: _ArrayLike1DNumber_co,
bins: Sequence[Sequence[int]],
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[np.int_ | np.bool],
NDArray[np.int_ | np.bool],
]: ...
@overload
def histogram2d(
x: _ArrayLike1DNumber_co,
y: _ArrayLike1DNumber_co,
bins: Sequence[Sequence[float]],
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[np.float64 | np.int_ | np.bool],
NDArray[np.float64 | np.int_ | np.bool],
]: ...
@overload
def histogram2d(
x: _ArrayLike1DNumber_co,
y: _ArrayLike1DNumber_co,
bins: Sequence[Sequence[complex]],
range: _ArrayLike2DFloat_co | None = ...,
density: bool | None = ...,
weights: _ArrayLike1DFloat_co | None = ...,
) -> tuple[
NDArray[float64],
NDArray[np.complex128 | np.float64 | np.int_ | np.bool],
NDArray[np.complex128 | np.float64 | np.int_ | np.bool],
]: ...
# NOTE: we're assuming/demanding here the `mask_func` returns
# an ndarray of shape `(n, n)`; otherwise there is the possibility
# of the output tuple having more or less than 2 elements
@overload
def mask_indices(
n: int,
mask_func: _MaskFunc[int],
k: int = ...,
) -> tuple[NDArray[intp], NDArray[intp]]: ...
@overload
def mask_indices(
n: int,
mask_func: _MaskFunc[_T],
k: _T,
) -> tuple[NDArray[intp], NDArray[intp]]: ...
def tril_indices(
n: int,
k: int = ...,
m: int | None = ...,
) -> tuple[NDArray[int_], NDArray[int_]]: ...
def tril_indices_from(
arr: NDArray[Any],
k: int = ...,
) -> tuple[NDArray[int_], NDArray[int_]]: ...
def triu_indices(
n: int,
k: int = ...,
m: int | None = ...,
) -> tuple[NDArray[int_], NDArray[int_]]: ...
def triu_indices_from(
arr: NDArray[Any],
k: int = ...,
) -> tuple[NDArray[int_], NDArray[int_]]: ...