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