bycycle.objs.Bycycle

class bycycle.objs.Bycycle(center_extrema='peak', burst_method='cycles', burst_kwargs=None, thresholds=None, find_extrema_kwargs=None, return_samples=True)

Compute bycycle features from a signal.

Attributes:

df_featurespandas.DataFrame

A dataframe containing shape and burst features for each cycle.

sig1d array

Voltage time series.

fsfloat

Sampling rate, in Hz.

f_rangetuple of (float, float)

Frequency range for narrowband signal of interest (Hz).

center_extrema{‘peak’, ‘trough’}

The center extrema in the cycle.

• ‘peak’ : cycles are defined trough-to-trough

• ‘trough’ : cycles are defined peak-to-peak

burst_method{‘cycles’, ‘amp’}

Method for detecting bursts.

• ‘cycles’: detect bursts based on the consistency of consecutive periods & amplitudes

• ‘amp’: detect bursts using an amplitude threshold

burst_kwargsdict, optional, default: None

Additional keyword arguments defined in compute_burst_fraction() for dual amplitude threshold burst detection (i.e. when burst_method=’amp’).

threshold_kwargsdict, optional, default: None

Feature thresholds for cycles to be considered bursts, matching keyword arguments for:

• detect_bursts_cycles() for consistency burst detection (i.e. when burst_method=’cycles’)

• detect_bursts_amp() for amplitude threshold burst detection (i.e. when burst_method=’amp’).

find_extrema_kwargsdict, optional, default: None

Keyword arguments for function to find peaks an troughs (find_extrema()) to change filter parameters or boundary. By default, the filter length is set to three cycles of the low cutoff frequency (f_range[0]).

return_samplesbool, optional, default: True

Returns samples indices of cyclepoints used for determining features if True.

__init__(center_extrema='peak', burst_method='cycles', burst_kwargs=None, thresholds=None, find_extrema_kwargs=None, return_samples=True)

Initialize object settings.

Methods

__init__([center_extrema, burst_method, ...])	Initialize object settings.
fit(sig, fs, f_range)	Run the bycycle algorithm on a signal.
load(df_features, sig, fs, f_range)	Load external results.
plot([xlim, figsize, plot_only_results, interp])	Plot burst detection results.
recompute_edges([reduction])	Recomputes features for cycles on the edge of bursts.
reduce_thresholds(reduction)	Adjust thresholds by a given amount.

fit(sig, fs, f_range)

Run the bycycle algorithm on a signal.

Parameters:

sig1d array

Time series.

fsfloat

Sampling rate, in Hz.

f_rangetuple of (float, float)

Frequency range for narrowband signal of interest (Hz).

load(df_features, sig, fs, f_range)

Load external results.

plot(xlim=None, figsize=(15, 3), plot_only_results=False, interp=True)

Plot burst detection results.

Parameters:

xlimtuple of (float, float), optional, default: None

Start and stop times for plot.

figsizetuple of (float, float), optional, default: (15, 3)

Size of each plot.

plot_only_resultbool, optional, default: False

Plot only the signal and bursts, excluding burst parameter plots.

interpbool, optional, default: True

If True, interpolates between given values. Otherwise, plots in a step-wise fashion.

recompute_edges(reduction=None)

Recomputes features for cycles on the edge of bursts.

Parameters:

reductionfloat, optional, default: None

Reduces all float thresholds by given amount.

reduce_thresholds(reduction)

Adjust thresholds by a given amount.

Parameters:

reductionfloat, optional, default: None

Reduces all float thresholds by given amount.

Returns:

reduced_thresholdsdict

Copy of thresholds with reduction applied.