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from typing import (
Any,
Callable,
Literal,
)
import numpy as np
from pandas._typing import (
WindowingRankType,
npt,
)
def roll_sum(
values: np.ndarray, # const float64_t[:]
start: np.ndarray, # np.ndarray[np.int64]
end: np.ndarray, # np.ndarray[np.int64]
minp: int, # int64_t
) -> np.ndarray: ... # np.ndarray[float]
def roll_mean(
values: np.ndarray, # const float64_t[:]
start: np.ndarray, # np.ndarray[np.int64]
end: np.ndarray, # np.ndarray[np.int64]
minp: int, # int64_t
) -> np.ndarray: ... # np.ndarray[float]
def roll_var(
values: np.ndarray, # const float64_t[:]
start: np.ndarray, # np.ndarray[np.int64]
end: np.ndarray, # np.ndarray[np.int64]
minp: int, # int64_t
ddof: int = ...,
) -> np.ndarray: ... # np.ndarray[float]
def roll_skew(
values: np.ndarray, # np.ndarray[np.float64]
start: np.ndarray, # np.ndarray[np.int64]
end: np.ndarray, # np.ndarray[np.int64]
minp: int, # int64_t
) -> np.ndarray: ... # np.ndarray[float]
def roll_kurt(
values: np.ndarray, # np.ndarray[np.float64]
start: np.ndarray, # np.ndarray[np.int64]
end: np.ndarray, # np.ndarray[np.int64]
minp: int, # int64_t
) -> np.ndarray: ... # np.ndarray[float]
def roll_median_c(
values: np.ndarray, # np.ndarray[np.float64]
start: np.ndarray, # np.ndarray[np.int64]
end: np.ndarray, # np.ndarray[np.int64]
minp: int, # int64_t
) -> np.ndarray: ... # np.ndarray[float]
def roll_max(
values: np.ndarray, # np.ndarray[np.float64]
start: np.ndarray, # np.ndarray[np.int64]
end: np.ndarray, # np.ndarray[np.int64]
minp: int, # int64_t
) -> np.ndarray: ... # np.ndarray[float]
def roll_min(
values: np.ndarray, # np.ndarray[np.float64]
start: np.ndarray, # np.ndarray[np.int64]
end: np.ndarray, # np.ndarray[np.int64]
minp: int, # int64_t
) -> np.ndarray: ... # np.ndarray[float]
def roll_quantile(
values: np.ndarray, # const float64_t[:]
start: np.ndarray, # np.ndarray[np.int64]
end: np.ndarray, # np.ndarray[np.int64]
minp: int, # int64_t
quantile: float, # float64_t
interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"],
) -> np.ndarray: ... # np.ndarray[float]
def roll_rank(
values: np.ndarray,
start: np.ndarray,
end: np.ndarray,
minp: int,
percentile: bool,
method: WindowingRankType,
ascending: bool,
) -> np.ndarray: ... # np.ndarray[float]
def roll_apply(
obj: object,
start: np.ndarray, # np.ndarray[np.int64]
end: np.ndarray, # np.ndarray[np.int64]
minp: int, # int64_t
function: Callable[..., Any],
raw: bool,
args: tuple[Any, ...],
kwargs: dict[str, Any],
) -> npt.NDArray[np.float64]: ...
def roll_weighted_sum(
values: np.ndarray, # const float64_t[:]
weights: np.ndarray, # const float64_t[:]
minp: int,
) -> np.ndarray: ... # np.ndarray[np.float64]
def roll_weighted_mean(
values: np.ndarray, # const float64_t[:]
weights: np.ndarray, # const float64_t[:]
minp: int,
) -> np.ndarray: ... # np.ndarray[np.float64]
def roll_weighted_var(
values: np.ndarray, # const float64_t[:]
weights: np.ndarray, # const float64_t[:]
minp: int, # int64_t
ddof: int, # unsigned int
) -> np.ndarray: ... # np.ndarray[np.float64]
def ewm(
vals: np.ndarray, # const float64_t[:]
start: np.ndarray, # const int64_t[:]
end: np.ndarray, # const int64_t[:]
minp: int,
com: float, # float64_t
adjust: bool,
ignore_na: bool,
deltas: np.ndarray | None = None, # const float64_t[:]
normalize: bool = True,
) -> np.ndarray: ... # np.ndarray[np.float64]
def ewmcov(
input_x: np.ndarray, # const float64_t[:]
start: np.ndarray, # const int64_t[:]
end: np.ndarray, # const int64_t[:]
minp: int,
input_y: np.ndarray, # const float64_t[:]
com: float, # float64_t
adjust: bool,
ignore_na: bool,
bias: bool,
) -> np.ndarray: ... # np.ndarray[np.float64]
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