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74 lines
3.2 KiB
74 lines
3.2 KiB
1 year ago
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"""
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This type stub file was generated by pyright.
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"""
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import numpy as np
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from collections.abc import Callable
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from typing import Literal
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from numpy.typing import ArrayLike
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def window_hanning(x: ArrayLike) -> ArrayLike:
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...
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def window_none(x: ArrayLike) -> ArrayLike:
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...
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def detrend(x: ArrayLike, key: Literal["default", "constant", "mean", "linear", "none"] | Callable[[ArrayLike, int | None], ArrayLike] | None = ..., axis: int | None = ...) -> ArrayLike:
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...
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def detrend_mean(x: ArrayLike, axis: int | None = ...) -> ArrayLike:
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...
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def detrend_none(x: ArrayLike, axis: int | None = ...) -> ArrayLike:
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...
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def detrend_linear(y: ArrayLike) -> ArrayLike:
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...
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def psd(x: ArrayLike, NFFT: int | None = ..., Fs: float | None = ..., detrend: Literal["none", "mean", "linear"] | Callable[[ArrayLike, int | None], ArrayLike] | None = ..., window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ..., noverlap: int | None = ..., pad_to: int | None = ..., sides: Literal["default", "onesided", "twosided"] | None = ..., scale_by_freq: bool | None = ...) -> tuple[ArrayLike, ArrayLike]:
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...
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def csd(x: ArrayLike, y: ArrayLike | None, NFFT: int | None = ..., Fs: float | None = ..., detrend: Literal["none", "mean", "linear"] | Callable[[ArrayLike, int | None], ArrayLike] | None = ..., window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ..., noverlap: int | None = ..., pad_to: int | None = ..., sides: Literal["default", "onesided", "twosided"] | None = ..., scale_by_freq: bool | None = ...) -> tuple[ArrayLike, ArrayLike]:
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...
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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(x: ArrayLike, NFFT: int | None = ..., Fs: float | None = ..., detrend: Literal["none", "mean", "linear"] | Callable[[ArrayLike, int | None], ArrayLike] | None = ..., window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ..., noverlap: int | None = ..., pad_to: int | None = ..., sides: Literal["default", "onesided", "twosided"] | None = ..., scale_by_freq: bool | None = ..., mode: Literal["psd", "complex", "magnitude", "angle", "phase"] | None = ...) -> tuple[ArrayLike, ArrayLike, ArrayLike]:
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...
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def cohere(x: ArrayLike, y: ArrayLike, NFFT: int = ..., Fs: float = ..., detrend: Literal["none", "mean", "linear"] | Callable[[ArrayLike, int | None], ArrayLike] = ..., window: Callable[[ArrayLike], ArrayLike] | ArrayLike = ..., noverlap: int = ..., pad_to: int | None = ..., sides: Literal["default", "onesided", "twosided"] = ..., scale_by_freq: bool | None = ...) -> tuple[ArrayLike, ArrayLike]:
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...
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class GaussianKDE:
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dataset: ArrayLike
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dim: int
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num_dp: int
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factor: float
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data_covariance: ArrayLike
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data_inv_cov: ArrayLike
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covariance: ArrayLike
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inv_cov: ArrayLike
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norm_factor: float
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def __init__(self, dataset: ArrayLike, bw_method: Literal["scott", "silverman"] | float | Callable[[GaussianKDE], float] | None = ...) -> None:
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...
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def scotts_factor(self) -> float:
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...
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def silverman_factor(self) -> float:
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...
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def covariance_factor(self) -> float:
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...
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def evaluate(self, points: ArrayLike) -> np.ndarray:
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...
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def __call__(self, points: ArrayLike) -> np.ndarray:
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...
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