neurodsp.timefrequency.compute_wavelet_transform(sig, fs, freqs, n_cycles=7, scaling=0.5, norm='amp')[source]

Compute the time-frequency representation of a signal using morlet wavelets.

sig1d array

Time series.


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.

n_cyclesfloat or 1d array

Length of the filter, as the number of cycles for each frequency. If 1d array, this defines n_cycles for each frequency.


Scaling factor.

norm{‘sss’, ‘amp’}, optional

Normalization method:

  • ‘sss’ - divide by the square root of the sum of squares

  • ‘amp’ - divide by the sum of amplitudes

mwt2d array

Time frequency representation of the input signal.


  • This computes the continuous wavelet transform at the specified frequencies and

    along all shifts.


Compute a Morlet wavelet time-frequency representation of a signal:

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