neurodsp.spectral.compute_scv¶

neurodsp.spectral.
compute_scv
(sig, fs, window='hann', nperseg=None, noverlap=0, outlier_pct=None)[source]¶ Compute the spectral coefficient of variation (SCV) at each frequency.
 Parameters
 sig1d array
Time series of measurement values.
 fsfloat
Sampling rate, in Hz.
 windowstr or tuple or array_like, optional, default: ‘hann’
Desired window to use. See scipy.signal.get_window for a list of available windows. If array_like, the array will be used as the window and its length must be nperseg.
 npersegint, optional
Length of each segment, in number of samples. If None, and window is str or tuple, is set to 1 second of data. If None, and window is array_like, is set to the length of the window.
 noverlapint, optional, default: 0
Number of points to overlap between segments.
 outlier_pctfloat, optional
Percentage of the windows with the lowest and highest total log power to discard. Must be between 0 and 100.
 Returns
 freqs1d array
Frequencies at which the measure was calculated.
 scv1d array
Spectral coefficient of variation.
Notes
White noise should have a SCV of 1 at all frequencies.
Examples
Compute the spectral coefficient of variation of a simulated time series:
>>> from neurodsp.sim import sim_combined >>> sig = sim_combined(n_seconds=10, fs=500, ... components={'sim_powerlaw': {}, 'sim_oscillation' : {'freq': 10}}) >>> freqs, scv = compute_scv(sig, fs=500)