79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
from typing import Callable, Literal, Sequence
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import numpy as np
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from ._typing import *
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def window_hanning(x): ...
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def window_none(x): ...
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def detrend(
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x: Sequence,
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key: Literal["default", "constant", "mean", "linear", "none"] | Callable = ...,
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axis: int = ...,
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) -> Sequence: ...
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def detrend_mean(x: Sequence, axis: int = ...) -> Sequence: ...
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def detrend_none(x, axis: int = ...): ...
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def detrend_linear(y: Sequence) -> Sequence: ...
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def stride_windows(x: Sequence, n: int, noverlap: int = ..., axis: int = ...): ...
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def psd(
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x: Sequence,
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NFFT: int = ...,
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Fs: float = ...,
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detrend: Literal["none", "mean", "linear"] | Callable = ...,
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window: Callable | np.ndarray = ...,
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noverlap: int = 0,
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pad_to: int = ...,
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sides: Literal["default", "onesided", "twosided"] = ...,
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scale_by_freq: bool = ...,
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) -> tuple[np.ndarray, np.ndarray]: ...
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def csd(
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x: ArrayLike,
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y: ArrayLike,
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NFFT: int = ...,
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Fs: float = ...,
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detrend: Literal["none", "mean", "linear"] | Callable = ...,
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window: Callable | np.ndarray = ...,
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noverlap: int = 0,
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pad_to: int = ...,
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sides: Literal["default", "onesided", "twosided"] = ...,
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scale_by_freq: bool = ...,
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) -> tuple[np.ndarray, np.ndarray]: ...
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complex_spectrum = ...
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magnitude_spectrum = ...
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angle_spectrum = ...
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phase_spectrum = ...
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def specgram(
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x: ArrayLike,
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NFFT: int = ...,
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Fs: float = ...,
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detrend: Literal["none", "mean", "linear"] | Callable = ...,
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window: Callable | np.ndarray = ...,
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noverlap: int = 0,
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pad_to: int = ...,
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sides: Literal["default", "onesided", "twosided"] = ...,
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scale_by_freq: bool = ...,
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mode: str = ...,
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) -> tuple[ArrayLike, ArrayLike, ArrayLike]: ...
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def cohere(
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x,
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y,
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NFFT: int = ...,
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Fs: float = ...,
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detrend: Literal["none", "mean", "linear"] | Callable = ...,
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window: Callable | np.ndarray = ...,
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noverlap: int = 0,
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pad_to: int = ...,
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sides: Literal["default", "onesided", "twosided"] = ...,
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scale_by_freq: bool = ...,
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) -> tuple[np.ndarray, np.ndarray]: ...
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class GaussianKDE:
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def __init__(self, dataset: ArrayLike, bw_method: str | Scalar | Callable = ...) -> None: ...
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def scotts_factor(self): ...
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def silverman_factor(self): ...
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covariance_factor = ...
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def evaluate(self, points) -> np.ndarray: ...
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