101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
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from collections.abc import Callable
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import functools
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from typing import Literal
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import numpy as np
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from numpy.typing import ArrayLike
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def window_hanning(x: ArrayLike) -> ArrayLike: ...
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def window_none(x: ArrayLike) -> ArrayLike: ...
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def detrend(
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x: ArrayLike,
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key: Literal["default", "constant", "mean", "linear", "none"]
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| Callable[[ArrayLike, int | None], ArrayLike]
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| None = ...,
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axis: int | None = ...,
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) -> ArrayLike: ...
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def detrend_mean(x: ArrayLike, axis: int | None = ...) -> ArrayLike: ...
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def detrend_none(x: ArrayLike, axis: int | None = ...) -> ArrayLike: ...
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def detrend_linear(y: ArrayLike) -> ArrayLike: ...
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def psd(
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x: ArrayLike,
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NFFT: int | None = ...,
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Fs: float | None = ...,
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detrend: Literal["none", "mean", "linear"]
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| Callable[[ArrayLike, int | None], ArrayLike]
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| None = ...,
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window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ...,
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noverlap: int | None = ...,
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pad_to: int | None = ...,
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sides: Literal["default", "onesided", "twosided"] | None = ...,
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scale_by_freq: bool | None = ...,
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) -> tuple[ArrayLike, ArrayLike]: ...
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def csd(
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x: ArrayLike,
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y: ArrayLike | None,
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NFFT: int | None = ...,
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Fs: float | None = ...,
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detrend: Literal["none", "mean", "linear"]
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| Callable[[ArrayLike, int | None], ArrayLike]
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| None = ...,
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window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ...,
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noverlap: int | None = ...,
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pad_to: int | None = ...,
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sides: Literal["default", "onesided", "twosided"] | None = ...,
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scale_by_freq: bool | None = ...,
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) -> tuple[ArrayLike, ArrayLike]: ...
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complex_spectrum = functools.partial(tuple[ArrayLike, ArrayLike])
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magnitude_spectrum = functools.partial(tuple[ArrayLike, ArrayLike])
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angle_spectrum = functools.partial(tuple[ArrayLike, ArrayLike])
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phase_spectrum = functools.partial(tuple[ArrayLike, ArrayLike])
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def specgram(
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x: ArrayLike,
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NFFT: int | None = ...,
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Fs: float | None = ...,
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detrend: Literal["none", "mean", "linear"] | Callable[[ArrayLike, int | None], ArrayLike] | None = ...,
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window: Callable[[ArrayLike], ArrayLike] | ArrayLike | None = ...,
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noverlap: int | None = ...,
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pad_to: int | None = ...,
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sides: Literal["default", "onesided", "twosided"] | None = ...,
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scale_by_freq: bool | None = ...,
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mode: Literal["psd", "complex", "magnitude", "angle", "phase"] | None = ...,
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) -> tuple[ArrayLike, ArrayLike, ArrayLike]: ...
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def cohere(
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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[[ArrayLike, int | None], ArrayLike] = ...,
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window: Callable[[ArrayLike], ArrayLike] | ArrayLike = ...,
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noverlap: int = ...,
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pad_to: int | None = ...,
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sides: Literal["default", "onesided", "twosided"] = ...,
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scale_by_freq: bool | None = ...,
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) -> tuple[ArrayLike, ArrayLike]: ...
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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__(
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self,
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dataset: ArrayLike,
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bw_method: Literal["scott", "silverman"]
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| float
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| Callable[[GaussianKDE], float]
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| None = ...,
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) -> None: ...
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def scotts_factor(self) -> float: ...
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def silverman_factor(self) -> float: ...
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def covariance_factor(self) -> float: ...
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def evaluate(self, points: ArrayLike) -> np.ndarray: ...
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def __call__(self, points: ArrayLike) -> np.ndarray: ...
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