bycycle.cyclepoints.find_extrema

bycycle.cyclepoints.find_extrema(sig, fs, f_range, boundary=0, first_extrema='peak', filter_kwargs=None, pass_type='bandpass', pad=True)

Identify peaks and troughs in a time series.

Parameters:

sig1d array

Time series.

fsfloat

Sampling rate, in Hz.

f_rangetuple of (float, float)

Frequency range, in Hz, to narrowband filter the signal, used to find zero-crossings.

boundaryint, optional, default: 0

Number of samples from edge of the signal to ignore.

first_extrema: {‘peak’, ‘trough’, None}

If ‘peak’, then force the output to begin with a peak and end in a trough. If ‘trough’, then force the output to begin with a trough and end in peak. If None, force nothing.

filter_kwargsdict, optional, default: None

Keyword arguments to filter_signal(), such as ‘n_cycles’ or ‘n_seconds’ to control filter length.

pass_typestr, optional, default: ‘bandpass’

Which kind of filter pass_type is consistent with the frequency definition provided.

padbool, optional, default: True

Whether to pad sig with zeros to prevent missed cyclepoints at the edges.

Returns:

peaks1d array

Indices at which oscillatory peaks occur in the input sig.

troughs1d array

Indices at which oscillatory troughs occur in the input sig.

Notes

This function assures that there are the same number of peaks and troughs if the first extrema is forced to be either peak or trough.

Examples

Find the locations of peaks and burst in a signal:

>>> from neurodsp.sim import sim_bursty_oscillation
>>> fs = 500
>>> sig = sim_bursty_oscillation(10, fs, freq=10)
>>> peaks, troughs = find_extrema(sig, fs, f_range=(8, 12))

Examples using bycycle.cyclepoints.find_extrema

2. Cycle-by-cycle algorithm

2. Cycle-by-cycle algorithm