bycycle.burst.detect_bursts_amp

bycycle.burst.detect_bursts_amp(df_features, burst_fraction_threshold=1, min_n_cycles=3)

Detect bursts based on amplitude thresholding.

Parameters:

df_featurespandas.DataFrame

Waveform features for individual cycles from compute_burst_features().

burst_fraction_thresholdint or float, optional, default: 1

Minimum fraction of a cycle to be identified as a burst.

min_n_cyclesint, optional, default: 3

The minimum number of cycles of consecutive cycles required to be considered a burst.

Returns:

df_featurespandas.DataFrame

Dataframe updated, with a additional column to indicate if the cycle is part of a burst.

Examples

Apply thresholding for dual amplitude burst detection:

>>> from bycycle.features import compute_burst_features, compute_shape_features
>>> from neurodsp.sim import sim_bursty_oscillation
>>> fs = 500
>>> sig = sim_bursty_oscillation(10, fs, freq=10)
>>> df_shapes = compute_shape_features(sig, fs, f_range=(8, 12))
>>> df_burst = compute_burst_features(df_shapes, sig, burst_method='amp',
...                                   burst_kwargs={'fs': fs, 'f_range': (8, 12)})
>>> df_burst = detect_bursts_amp(df_burst)