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klein panic
297
venv/lib/python3.11/site-packages/numpy/polynomial/_polybase.pyi
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297
venv/lib/python3.11/site-packages/numpy/polynomial/_polybase.pyi
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import abc
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import decimal
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import numbers
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import sys
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from collections.abc import Iterator, Mapping, Sequence
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from typing import (
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TYPE_CHECKING,
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Any,
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ClassVar,
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Final,
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Generic,
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Literal,
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SupportsIndex,
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TypeAlias,
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TypeGuard,
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TypeVar,
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overload,
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)
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import numpy as np
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import numpy.typing as npt
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from numpy._typing import (
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_FloatLike_co,
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_NumberLike_co,
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_ArrayLikeFloat_co,
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_ArrayLikeComplex_co,
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)
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from ._polytypes import (
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_AnyInt,
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_CoefLike_co,
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_Array2,
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_Tuple2,
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_Series,
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_CoefSeries,
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_SeriesLikeInt_co,
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_SeriesLikeCoef_co,
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_ArrayLikeCoefObject_co,
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_ArrayLikeCoef_co,
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)
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if sys.version_info >= (3, 11):
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from typing import LiteralString
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elif TYPE_CHECKING:
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from typing_extensions import LiteralString
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else:
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LiteralString: TypeAlias = str
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__all__: Final[Sequence[str]] = ("ABCPolyBase",)
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_NameCo = TypeVar("_NameCo", bound=None | LiteralString, covariant=True)
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_Self = TypeVar("_Self", bound="ABCPolyBase")
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_Other = TypeVar("_Other", bound="ABCPolyBase")
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_AnyOther: TypeAlias = ABCPolyBase | _CoefLike_co | _SeriesLikeCoef_co
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_Hundred: TypeAlias = Literal[100]
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class ABCPolyBase(Generic[_NameCo], metaclass=abc.ABCMeta):
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__hash__: ClassVar[None] # type: ignore[assignment]
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__array_ufunc__: ClassVar[None]
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maxpower: ClassVar[_Hundred]
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_superscript_mapping: ClassVar[Mapping[int, str]]
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_subscript_mapping: ClassVar[Mapping[int, str]]
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_use_unicode: ClassVar[bool]
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basis_name: _NameCo
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coef: _CoefSeries
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domain: _Array2[np.inexact[Any] | np.object_]
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window: _Array2[np.inexact[Any] | np.object_]
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_symbol: LiteralString
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@property
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def symbol(self, /) -> LiteralString: ...
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def __init__(
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self,
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/,
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coef: _SeriesLikeCoef_co,
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domain: None | _SeriesLikeCoef_co = ...,
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window: None | _SeriesLikeCoef_co = ...,
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symbol: str = ...,
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) -> None: ...
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@overload
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def __call__(self, /, arg: _Other) -> _Other: ...
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# TODO: Once `_ShapeType@ndarray` is covariant and bounded (see #26081),
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# additionally include 0-d arrays as input types with scalar return type.
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@overload
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def __call__(
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self,
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/,
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arg: _FloatLike_co | decimal.Decimal | numbers.Real | np.object_,
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) -> np.float64 | np.complex128: ...
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@overload
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def __call__(
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self,
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/,
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arg: _NumberLike_co | numbers.Complex,
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) -> np.complex128: ...
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@overload
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def __call__(self, /, arg: _ArrayLikeFloat_co) -> (
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npt.NDArray[np.float64]
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| npt.NDArray[np.complex128]
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| npt.NDArray[np.object_]
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): ...
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@overload
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def __call__(
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self,
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/,
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arg: _ArrayLikeComplex_co,
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) -> npt.NDArray[np.complex128] | npt.NDArray[np.object_]: ...
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@overload
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def __call__(
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self,
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/,
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arg: _ArrayLikeCoefObject_co,
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) -> npt.NDArray[np.object_]: ...
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def __str__(self, /) -> str: ...
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def __repr__(self, /) -> str: ...
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def __format__(self, fmt_str: str, /) -> str: ...
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def __eq__(self, x: object, /) -> bool: ...
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def __ne__(self, x: object, /) -> bool: ...
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def __neg__(self: _Self, /) -> _Self: ...
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def __pos__(self: _Self, /) -> _Self: ...
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def __add__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __sub__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __mul__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __truediv__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __floordiv__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __mod__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __divmod__(self: _Self, x: _AnyOther, /) -> _Tuple2[_Self]: ...
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def __pow__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __radd__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __rsub__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __rmul__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __rtruediv__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __rfloordiv__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __rmod__(self: _Self, x: _AnyOther, /) -> _Self: ...
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def __rdivmod__(self: _Self, x: _AnyOther, /) -> _Tuple2[_Self]: ...
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def __len__(self, /) -> int: ...
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def __iter__(self, /) -> Iterator[np.inexact[Any] | object]: ...
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def __getstate__(self, /) -> dict[str, Any]: ...
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def __setstate__(self, dict: dict[str, Any], /) -> None: ...
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def has_samecoef(self, /, other: ABCPolyBase) -> bool: ...
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def has_samedomain(self, /, other: ABCPolyBase) -> bool: ...
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def has_samewindow(self, /, other: ABCPolyBase) -> bool: ...
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@overload
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def has_sametype(self: _Self, /, other: ABCPolyBase) -> TypeGuard[_Self]: ...
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@overload
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def has_sametype(self, /, other: object) -> Literal[False]: ...
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def copy(self: _Self, /) -> _Self: ...
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def degree(self, /) -> int: ...
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def cutdeg(self: _Self, /) -> _Self: ...
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def trim(self: _Self, /, tol: _FloatLike_co = ...) -> _Self: ...
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def truncate(self: _Self, /, size: _AnyInt) -> _Self: ...
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@overload
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def convert(
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self,
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domain: None | _SeriesLikeCoef_co,
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kind: type[_Other],
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/,
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window: None | _SeriesLikeCoef_co = ...,
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) -> _Other: ...
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@overload
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def convert(
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self,
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/,
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domain: None | _SeriesLikeCoef_co = ...,
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*,
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kind: type[_Other],
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window: None | _SeriesLikeCoef_co = ...,
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) -> _Other: ...
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@overload
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def convert(
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self: _Self,
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/,
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domain: None | _SeriesLikeCoef_co = ...,
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kind: type[_Self] = ...,
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window: None | _SeriesLikeCoef_co = ...,
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) -> _Self: ...
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def mapparms(self, /) -> _Tuple2[Any]: ...
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def integ(
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self: _Self, /,
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m: SupportsIndex = ...,
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k: _CoefLike_co | _SeriesLikeCoef_co = ...,
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lbnd: None | _CoefLike_co = ...,
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) -> _Self: ...
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def deriv(self: _Self, /, m: SupportsIndex = ...) -> _Self: ...
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def roots(self, /) -> _CoefSeries: ...
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def linspace(
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self, /,
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n: SupportsIndex = ...,
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domain: None | _SeriesLikeCoef_co = ...,
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) -> _Tuple2[_Series[np.float64 | np.complex128]]: ...
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@overload
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@classmethod
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def fit(
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cls: type[_Self], /,
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x: _SeriesLikeCoef_co,
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y: _SeriesLikeCoef_co,
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deg: int | _SeriesLikeInt_co,
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domain: None | _SeriesLikeCoef_co = ...,
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rcond: _FloatLike_co = ...,
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full: Literal[False] = ...,
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w: None | _SeriesLikeCoef_co = ...,
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window: None | _SeriesLikeCoef_co = ...,
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symbol: str = ...,
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) -> _Self: ...
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@overload
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@classmethod
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def fit(
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cls: type[_Self], /,
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x: _SeriesLikeCoef_co,
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y: _SeriesLikeCoef_co,
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deg: int | _SeriesLikeInt_co,
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domain: None | _SeriesLikeCoef_co = ...,
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rcond: _FloatLike_co = ...,
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*,
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full: Literal[True],
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w: None | _SeriesLikeCoef_co = ...,
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window: None | _SeriesLikeCoef_co = ...,
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symbol: str = ...,
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) -> tuple[_Self, Sequence[np.inexact[Any] | np.int32]]: ...
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@overload
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@classmethod
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def fit(
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cls: type[_Self],
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x: _SeriesLikeCoef_co,
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y: _SeriesLikeCoef_co,
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deg: int | _SeriesLikeInt_co,
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domain: None | _SeriesLikeCoef_co,
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rcond: _FloatLike_co,
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full: Literal[True], /,
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w: None | _SeriesLikeCoef_co = ...,
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window: None | _SeriesLikeCoef_co = ...,
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symbol: str = ...,
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) -> tuple[_Self, Sequence[np.inexact[Any] | np.int32]]: ...
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@classmethod
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def fromroots(
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cls: type[_Self], /,
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roots: _ArrayLikeCoef_co,
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domain: None | _SeriesLikeCoef_co = ...,
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window: None | _SeriesLikeCoef_co = ...,
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symbol: str = ...,
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) -> _Self: ...
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@classmethod
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def identity(
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cls: type[_Self], /,
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domain: None | _SeriesLikeCoef_co = ...,
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window: None | _SeriesLikeCoef_co = ...,
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symbol: str = ...,
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) -> _Self: ...
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@classmethod
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def basis(
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cls: type[_Self], /,
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deg: _AnyInt,
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domain: None | _SeriesLikeCoef_co = ...,
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window: None | _SeriesLikeCoef_co = ...,
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symbol: str = ...,
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) -> _Self: ...
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@classmethod
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def cast(
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cls: type[_Self], /,
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series: ABCPolyBase,
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domain: None | _SeriesLikeCoef_co = ...,
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window: None | _SeriesLikeCoef_co = ...,
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) -> _Self: ...
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@classmethod
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def _str_term_unicode(cls, i: str, arg_str: str) -> str: ...
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@staticmethod
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def _str_term_ascii(i: str, arg_str: str) -> str: ...
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@staticmethod
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def _repr_latex_term(i: str, arg_str: str, needs_parens: bool) -> str: ...
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