mne_denoise.asr.fit_rms_distribution#

mne_denoise.asr.fit_rms_distribution(values: ndarray, *, min_clean_fraction: float = 0.25, max_dropout_fraction: float = 0.1, fit_quantiles: tuple[float, float] = (0.022, 0.6), beta_grid: ndarray | None = None, return_info: Literal[False] = False) → tuple[float, float][source]#

mne_denoise.asr.fit_rms_distribution(values: ndarray, *, min_clean_fraction: float = 0.25, max_dropout_fraction: float = 0.1, fit_quantiles: tuple[float, float] = (0.022, 0.6), beta_grid: ndarray | None = None, return_info: Literal[True]) → tuple[float, float, dict[str, Any]]

Fit robust location and scale to RMS statistics.

Parameters:

valuesndarray, shape (n_windows,): RMS or amplitude values for one component or channel.
min_clean_fractionfloat, default=0.25: Minimum fraction treated as clean.
max_dropout_fractionfloat, default=0.1: Maximum low-tail fraction ignored as dropouts.
fit_quantilestuple of float, default=(0.022, 0.6): Quantile interval searched for the clean distribution.
beta_gridndarray or None, default=None: Generalized-Gaussian shape grid.
return_infobool, default=False: If True, return fitting diagnostics.

Returns: