neurodsp.timefrequency.compute_wavelet_transform

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.

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.

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.

scalingfloat

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

Returns:
mwtarray

Time frequency representation of the input signal.

Notes

This computes the continuous wavelet transform at specified frequencies across time.

Examples

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])

Examples using neurodsp.timefrequency.compute_wavelet_transform

Morlet Wavelet Analysis

Morlet Wavelet Analysis