File size: 7,767 Bytes
2216aae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 |
import abc
import decimal
from collections.abc import Iterator, Sequence
from typing import (
Any,
ClassVar,
Generic,
Literal,
Self,
SupportsIndex,
TypeAlias,
overload,
)
from typing_extensions import TypeIs, TypeVar
import numpy as np
import numpy.typing as npt
from numpy._typing import (
_ArrayLikeComplex_co,
_ArrayLikeFloat_co,
_FloatLike_co,
_NumberLike_co,
)
from ._polytypes import (
_AnyInt,
_Array2,
_ArrayLikeCoef_co,
_ArrayLikeCoefObject_co,
_CoefLike_co,
_CoefSeries,
_Series,
_SeriesLikeCoef_co,
_SeriesLikeInt_co,
_Tuple2,
)
__all__ = ["ABCPolyBase"]
_NameT_co = TypeVar("_NameT_co", bound=str | None, default=str | None, covariant=True)
_PolyT = TypeVar("_PolyT", bound=ABCPolyBase)
_AnyOther: TypeAlias = ABCPolyBase | _CoefLike_co | _SeriesLikeCoef_co
class ABCPolyBase(Generic[_NameT_co], abc.ABC):
__hash__: ClassVar[None] = None # type: ignore[assignment] # pyright: ignore[reportIncompatibleMethodOverride]
__array_ufunc__: ClassVar[None] = None
maxpower: ClassVar[Literal[100]] = 100
_superscript_mapping: ClassVar[dict[int, str]] = ...
_subscript_mapping: ClassVar[dict[int, str]] = ...
_use_unicode: ClassVar[bool] = ...
_symbol: str
@property
def symbol(self, /) -> str: ...
@property
@abc.abstractmethod
def domain(self) -> _Array2[np.float64 | Any]: ...
@property
@abc.abstractmethod
def window(self) -> _Array2[np.float64 | Any]: ...
@property
@abc.abstractmethod
def basis_name(self) -> _NameT_co: ...
coef: _CoefSeries
def __init__(
self,
/,
coef: _SeriesLikeCoef_co,
domain: _SeriesLikeCoef_co | None = None,
window: _SeriesLikeCoef_co | None = None,
symbol: str = "x",
) -> None: ...
#
@overload
def __call__(self, /, arg: _PolyT) -> _PolyT: ...
@overload
def __call__(self, /, arg: _FloatLike_co | decimal.Decimal) -> np.float64 | Any: ...
@overload
def __call__(self, /, arg: _NumberLike_co) -> np.complex128 | Any: ...
@overload
def __call__(self, /, arg: _ArrayLikeFloat_co) -> npt.NDArray[np.float64 | Any]: ...
@overload
def __call__(self, /, arg: _ArrayLikeComplex_co) -> npt.NDArray[np.complex128 | Any]: ...
@overload
def __call__(self, /, arg: _ArrayLikeCoefObject_co) -> npt.NDArray[np.object_]: ...
# unary ops
def __neg__(self, /) -> Self: ...
def __pos__(self, /) -> Self: ...
# binary ops
def __add__(self, x: _AnyOther, /) -> Self: ...
def __sub__(self, x: _AnyOther, /) -> Self: ...
def __mul__(self, x: _AnyOther, /) -> Self: ...
def __pow__(self, x: _AnyOther, /) -> Self: ...
def __truediv__(self, x: _AnyOther, /) -> Self: ...
def __floordiv__(self, x: _AnyOther, /) -> Self: ...
def __mod__(self, x: _AnyOther, /) -> Self: ...
def __divmod__(self, x: _AnyOther, /) -> _Tuple2[Self]: ...
# reflected binary ops
def __radd__(self, x: _AnyOther, /) -> Self: ...
def __rsub__(self, x: _AnyOther, /) -> Self: ...
def __rmul__(self, x: _AnyOther, /) -> Self: ...
def __rtruediv__(self, x: _AnyOther, /) -> Self: ...
def __rfloordiv__(self, x: _AnyOther, /) -> Self: ...
def __rmod__(self, x: _AnyOther, /) -> Self: ...
def __rdivmod__(self, x: _AnyOther, /) -> _Tuple2[Self]: ...
# iterable and sized
def __len__(self, /) -> int: ...
def __iter__(self, /) -> Iterator[np.float64 | Any]: ...
# pickling
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: ...
def has_sametype(self, /, other: object) -> TypeIs[Self]: ...
#
def copy(self, /) -> Self: ...
def degree(self, /) -> int: ...
def cutdeg(self, /, deg: int) -> Self: ...
def trim(self, /, tol: _FloatLike_co = 0) -> Self: ...
def truncate(self, /, size: _AnyInt) -> Self: ...
#
@overload
def convert(
self,
/,
domain: _SeriesLikeCoef_co | None,
kind: type[_PolyT],
window: _SeriesLikeCoef_co | None = None,
) -> _PolyT: ...
@overload
def convert(
self,
/,
domain: _SeriesLikeCoef_co | None = None,
*,
kind: type[_PolyT],
window: _SeriesLikeCoef_co | None = None,
) -> _PolyT: ...
@overload
def convert(
self,
/,
domain: _SeriesLikeCoef_co | None = None,
kind: None = None,
window: _SeriesLikeCoef_co | None = None,
) -> Self: ...
#
def mapparms(self, /) -> _Tuple2[Any]: ...
def integ(
self,
/,
m: SupportsIndex = 1,
k: _CoefLike_co | _SeriesLikeCoef_co = [],
lbnd: _CoefLike_co | None = None,
) -> Self: ...
def deriv(self, /, m: SupportsIndex = 1) -> Self: ...
def roots(self, /) -> _CoefSeries: ...
def linspace(
self,
/,
n: SupportsIndex = 100,
domain: _SeriesLikeCoef_co | None = None,
) -> _Tuple2[_Series[np.float64 | np.complex128]]: ...
#
@overload
@classmethod
def fit(
cls,
x: _SeriesLikeCoef_co,
y: _SeriesLikeCoef_co,
deg: int | _SeriesLikeInt_co,
domain: _SeriesLikeCoef_co | None = None,
rcond: _FloatLike_co | None = None,
full: Literal[False] = False,
w: _SeriesLikeCoef_co | None = None,
window: _SeriesLikeCoef_co | None = None,
symbol: str = "x",
) -> Self: ...
@overload
@classmethod
def fit(
cls,
x: _SeriesLikeCoef_co,
y: _SeriesLikeCoef_co,
deg: int | _SeriesLikeInt_co,
domain: _SeriesLikeCoef_co | None = None,
rcond: _FloatLike_co | None = None,
*,
full: Literal[True],
w: _SeriesLikeCoef_co | None = None,
window: _SeriesLikeCoef_co | None = None,
symbol: str = "x",
) -> tuple[Self, Sequence[np.inexact | np.int32]]: ...
@overload
@classmethod
def fit(
cls,
x: _SeriesLikeCoef_co,
y: _SeriesLikeCoef_co,
deg: int | _SeriesLikeInt_co,
domain: _SeriesLikeCoef_co | None,
rcond: _FloatLike_co,
full: Literal[True],
/,
w: _SeriesLikeCoef_co | None = None,
window: _SeriesLikeCoef_co | None = None,
symbol: str = "x",
) -> tuple[Self, Sequence[np.inexact | np.int32]]: ...
#
@classmethod
def fromroots(
cls,
roots: _ArrayLikeCoef_co,
domain: _SeriesLikeCoef_co | None = [],
window: _SeriesLikeCoef_co | None = None,
symbol: str = "x",
) -> Self: ...
@classmethod
def identity(
cls,
domain: _SeriesLikeCoef_co | None = None,
window: _SeriesLikeCoef_co | None = None,
symbol: str = "x",
) -> Self: ...
@classmethod
def basis(
cls,
deg: _AnyInt,
domain: _SeriesLikeCoef_co | None = None,
window: _SeriesLikeCoef_co | None = None,
symbol: str = "x",
) -> Self: ...
@classmethod
def cast(
cls,
series: ABCPolyBase,
domain: _SeriesLikeCoef_co | None = None,
window: _SeriesLikeCoef_co | None = None,
) -> Self: ...
@classmethod
def _str_term_unicode(cls, /, i: str, arg_str: str) -> str: ...
@classmethod
def _str_term_ascii(cls, /, i: str, arg_str: str) -> str: ...
@classmethod
def _repr_latex_term(cls, /, i: str, arg_str: str, needs_parens: bool) -> str: ...
|