mne_denoise.dss.variants.narrowband_scan#
- mne_denoise.dss.variants.narrowband_scan(data: ndarray, sfreq: float, *, freq_range: tuple[float, float] = (1, 40), freq_step: float = 1.0, bandwidth: float = 2.0, n_components: int = 1, callback: Callable[[ProgressEvent], object] | None = None, verbose: bool | str | int | None = None, **dss_kws) tuple[DSS, ndarray, ndarray][source]#
Scan candidate frequencies with narrowband DSS.
- Parameters:
- datandarray, shape (n_channels, n_times) or (n_channels, n_times, n_epochs)
Channel-first input data.
- sfreqfloat
Sampling frequency in Hz.
- freq_rangetuple of float, default=(1, 40)
Candidate frequency range in Hz; it is clipped to the implementation’s valid range.
- freq_stepfloat, default=1.0
Candidate spacing in Hz.
- bandwidthfloat, default=2.0
Bandpass width in Hz.
- n_componentsint, default=1
Components fitted at each candidate.
- callbackcallable or None, default=None
Synchronous callback after each attempted candidate.
- verbosebool, str, int, or None, default=None
Logging level.
- **dss_kws
Additional keyword arguments for
DSS.
- Returns:
- best_dssDSS
Fitted DSS at the highest-scoring candidate.
- frequenciesndarray, shape (n_freqs,)
Candidate frequencies.
- scoresndarray, shape (n_freqs,)
Leading DSS eigenvalue for each candidate. Failed candidates have score zero and the scan continues.