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from collections.abc import Callable

import functools

from typing import Literal

import numpy as np

from numpy.typing import ArrayLike

def window_hanning(x: ArrayLike) -> ArrayLike: ...

def window_none(x: ArrayLike) -> ArrayLike: ...

def detrend(

x: ArrayLike,

key: Literal["default", "constant", "mean", "linear", "none"]

| Callable[[ArrayLike, int | None], ArrayLike]

| None = ...,

axis: int | None = ...,

) -> ArrayLike: ...

def detrend_mean(x: ArrayLike, axis: int | None = ...) -> ArrayLike: ...

def detrend_none(x: ArrayLike, axis: int | None = ...) -> ArrayLike: ...

def detrend_linear(y: ArrayLike) -> ArrayLike: ...

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]: ...

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]: ...

complex_spectrum = functools.partial(tuple[ArrayLike, ArrayLike])

magnitude_spectrum = functools.partial(tuple[ArrayLike, ArrayLike])

angle_spectrum = functools.partial(tuple[ArrayLike, ArrayLike])

phase_spectrum = functools.partial(tuple[ArrayLike, ArrayLike])

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]: ...

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]: ...

class GaussianKDE:

dataset: ArrayLike

dim: int

num_dp: int

factor: float

data_covariance: ArrayLike

data_inv_cov: ArrayLike

covariance: ArrayLike

inv_cov: ArrayLike

norm_factor: float

def __init__(

self,

dataset: ArrayLike,

bw_method: Literal["scott", "silverman"]

| float

| Callable[[GaussianKDE], float]

| None = ...,

) -> None: ...

def scotts_factor(self) -> float: ...

def silverman_factor(self) -> float: ...

def covariance_factor(self) -> float: ...

def evaluate(self, points: ArrayLike) -> np.ndarray: ...

def __call__(self, points: ArrayLike) -> np.ndarray: ...

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