neurodsp.spectral.compute_spectrum_wavelet

neurodsp.spectral.compute_spectrum_wavelet(sig, fs, freqs, avg_type='mean', **kwargs)

Compute the power spectral density using wavelets.

Parameters:

sigarray

Time series.

fsfloat

Sampling rate, in Hz.

freqs1d array or list of float

If array, frequency values to estimate with morlet wavelets. If list, define the frequency range, as [freq_start, freq_stop, freq_step]. The freq_step is optional, and defaults to 1. Range is inclusive of freq_stop value.

avg_type{‘mean’, ‘median’}, optional

Method to average across the windows.

**kwargs

Optional inputs for using wavelets.

Returns:

freqs1d array

Frequencies at which the measure was calculated.

spectrumarray

Power spectral density.

Examples

Compute the power spectrum of a simulated time series using wavelets:

>>> from neurodsp.sim import sim_combined
>>> sig = sim_combined(n_seconds=10, fs=500,
...                    components={'sim_powerlaw': {}, 'sim_oscillation' : {'freq': 10}})
>>> freqs, spectrum = compute_spectrum_wavelet(sig, fs=500, freqs=[1, 30])