AIM-PIbd-32-Kurbanova-A-A/aimenv/Lib/site-packages/numpy/polynomial/_polybase.pyi
2024-10-02 22:15:59 +04:00

298 lines
8.6 KiB
Python

import abc
import decimal
import numbers
import sys
from collections.abc import Iterator, Mapping, Sequence
from typing import (
TYPE_CHECKING,
Any,
ClassVar,
Final,
Generic,
Literal,
SupportsIndex,
TypeAlias,
TypeGuard,
TypeVar,
overload,
)
import numpy as np
import numpy.typing as npt
from numpy._typing import (
_FloatLike_co,
_NumberLike_co,
_ArrayLikeFloat_co,
_ArrayLikeComplex_co,
)
from ._polytypes import (
_AnyInt,
_CoefLike_co,
_Array2,
_Tuple2,
_Series,
_CoefSeries,
_SeriesLikeInt_co,
_SeriesLikeCoef_co,
_ArrayLikeCoefObject_co,
_ArrayLikeCoef_co,
)
if sys.version_info >= (3, 11):
from typing import LiteralString
elif TYPE_CHECKING:
from typing_extensions import LiteralString
else:
LiteralString: TypeAlias = str
__all__: Final[Sequence[str]] = ("ABCPolyBase",)
_NameCo = TypeVar("_NameCo", bound=None | LiteralString, covariant=True)
_Self = TypeVar("_Self", bound="ABCPolyBase")
_Other = TypeVar("_Other", bound="ABCPolyBase")
_AnyOther: TypeAlias = ABCPolyBase | _CoefLike_co | _SeriesLikeCoef_co
_Hundred: TypeAlias = Literal[100]
class ABCPolyBase(Generic[_NameCo], metaclass=abc.ABCMeta):
__hash__: ClassVar[None] # type: ignore[assignment]
__array_ufunc__: ClassVar[None]
maxpower: ClassVar[_Hundred]
_superscript_mapping: ClassVar[Mapping[int, str]]
_subscript_mapping: ClassVar[Mapping[int, str]]
_use_unicode: ClassVar[bool]
basis_name: _NameCo
coef: _CoefSeries
domain: _Array2[np.inexact[Any] | np.object_]
window: _Array2[np.inexact[Any] | np.object_]
_symbol: LiteralString
@property
def symbol(self, /) -> LiteralString: ...
def __init__(
self,
/,
coef: _SeriesLikeCoef_co,
domain: None | _SeriesLikeCoef_co = ...,
window: None | _SeriesLikeCoef_co = ...,
symbol: str = ...,
) -> None: ...
@overload
def __call__(self, /, arg: _Other) -> _Other: ...
# TODO: Once `_ShapeType@ndarray` is covariant and bounded (see #26081),
# additionally include 0-d arrays as input types with scalar return type.
@overload
def __call__(
self,
/,
arg: _FloatLike_co | decimal.Decimal | numbers.Real | np.object_,
) -> np.float64 | np.complex128: ...
@overload
def __call__(
self,
/,
arg: _NumberLike_co | numbers.Complex,
) -> np.complex128: ...
@overload
def __call__(self, /, arg: _ArrayLikeFloat_co) -> (
npt.NDArray[np.float64]
| npt.NDArray[np.complex128]
| npt.NDArray[np.object_]
): ...
@overload
def __call__(
self,
/,
arg: _ArrayLikeComplex_co,
) -> npt.NDArray[np.complex128] | npt.NDArray[np.object_]: ...
@overload
def __call__(
self,
/,
arg: _ArrayLikeCoefObject_co,
) -> npt.NDArray[np.object_]: ...
def __str__(self, /) -> str: ...
def __repr__(self, /) -> str: ...
def __format__(self, fmt_str: str, /) -> str: ...
def __eq__(self, x: object, /) -> bool: ...
def __ne__(self, x: object, /) -> bool: ...
def __neg__(self: _Self, /) -> _Self: ...
def __pos__(self: _Self, /) -> _Self: ...
def __add__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __sub__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __mul__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __truediv__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __floordiv__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __mod__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __divmod__(self: _Self, x: _AnyOther, /) -> _Tuple2[_Self]: ...
def __pow__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __radd__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __rsub__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __rmul__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __rtruediv__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __rfloordiv__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __rmod__(self: _Self, x: _AnyOther, /) -> _Self: ...
def __rdivmod__(self: _Self, x: _AnyOther, /) -> _Tuple2[_Self]: ...
def __len__(self, /) -> int: ...
def __iter__(self, /) -> Iterator[np.inexact[Any] | object]: ...
def __getstate__(self, /) -> dict[str, Any]: ...
def __setstate__(self, dict: dict[str, Any], /) -> None: ...
def has_samecoef(self, /, other: ABCPolyBase) -> bool: ...
def has_samedomain(self, /, other: ABCPolyBase) -> bool: ...
def has_samewindow(self, /, other: ABCPolyBase) -> bool: ...
@overload
def has_sametype(self: _Self, /, other: ABCPolyBase) -> TypeGuard[_Self]: ...
@overload
def has_sametype(self, /, other: object) -> Literal[False]: ...
def copy(self: _Self, /) -> _Self: ...
def degree(self, /) -> int: ...
def cutdeg(self: _Self, /) -> _Self: ...
def trim(self: _Self, /, tol: _FloatLike_co = ...) -> _Self: ...
def truncate(self: _Self, /, size: _AnyInt) -> _Self: ...
@overload
def convert(
self,
domain: None | _SeriesLikeCoef_co,
kind: type[_Other],
/,
window: None | _SeriesLikeCoef_co = ...,
) -> _Other: ...
@overload
def convert(
self,
/,
domain: None | _SeriesLikeCoef_co = ...,
*,
kind: type[_Other],
window: None | _SeriesLikeCoef_co = ...,
) -> _Other: ...
@overload
def convert(
self: _Self,
/,
domain: None | _SeriesLikeCoef_co = ...,
kind: type[_Self] = ...,
window: None | _SeriesLikeCoef_co = ...,
) -> _Self: ...
def mapparms(self, /) -> _Tuple2[Any]: ...
def integ(
self: _Self, /,
m: SupportsIndex = ...,
k: _CoefLike_co | _SeriesLikeCoef_co = ...,
lbnd: None | _CoefLike_co = ...,
) -> _Self: ...
def deriv(self: _Self, /, m: SupportsIndex = ...) -> _Self: ...
def roots(self, /) -> _CoefSeries: ...
def linspace(
self, /,
n: SupportsIndex = ...,
domain: None | _SeriesLikeCoef_co = ...,
) -> _Tuple2[_Series[np.float64 | np.complex128]]: ...
@overload
@classmethod
def fit(
cls: type[_Self], /,
x: _SeriesLikeCoef_co,
y: _SeriesLikeCoef_co,
deg: int | _SeriesLikeInt_co,
domain: None | _SeriesLikeCoef_co = ...,
rcond: _FloatLike_co = ...,
full: Literal[False] = ...,
w: None | _SeriesLikeCoef_co = ...,
window: None | _SeriesLikeCoef_co = ...,
symbol: str = ...,
) -> _Self: ...
@overload
@classmethod
def fit(
cls: type[_Self], /,
x: _SeriesLikeCoef_co,
y: _SeriesLikeCoef_co,
deg: int | _SeriesLikeInt_co,
domain: None | _SeriesLikeCoef_co = ...,
rcond: _FloatLike_co = ...,
*,
full: Literal[True],
w: None | _SeriesLikeCoef_co = ...,
window: None | _SeriesLikeCoef_co = ...,
symbol: str = ...,
) -> tuple[_Self, Sequence[np.inexact[Any] | np.int32]]: ...
@overload
@classmethod
def fit(
cls: type[_Self],
x: _SeriesLikeCoef_co,
y: _SeriesLikeCoef_co,
deg: int | _SeriesLikeInt_co,
domain: None | _SeriesLikeCoef_co,
rcond: _FloatLike_co,
full: Literal[True], /,
w: None | _SeriesLikeCoef_co = ...,
window: None | _SeriesLikeCoef_co = ...,
symbol: str = ...,
) -> tuple[_Self, Sequence[np.inexact[Any] | np.int32]]: ...
@classmethod
def fromroots(
cls: type[_Self], /,
roots: _ArrayLikeCoef_co,
domain: None | _SeriesLikeCoef_co = ...,
window: None | _SeriesLikeCoef_co = ...,
symbol: str = ...,
) -> _Self: ...
@classmethod
def identity(
cls: type[_Self], /,
domain: None | _SeriesLikeCoef_co = ...,
window: None | _SeriesLikeCoef_co = ...,
symbol: str = ...,
) -> _Self: ...
@classmethod
def basis(
cls: type[_Self], /,
deg: _AnyInt,
domain: None | _SeriesLikeCoef_co = ...,
window: None | _SeriesLikeCoef_co = ...,
symbol: str = ...,
) -> _Self: ...
@classmethod
def cast(
cls: type[_Self], /,
series: ABCPolyBase,
domain: None | _SeriesLikeCoef_co = ...,
window: None | _SeriesLikeCoef_co = ...,
) -> _Self: ...
@classmethod
def _str_term_unicode(cls, i: str, arg_str: str) -> str: ...
@staticmethod
def _str_term_ascii(i: str, arg_str: str) -> str: ...
@staticmethod
def _repr_latex_term(i: str, arg_str: str, needs_parens: bool) -> str: ...