bycycle.plts.plot_burst_detect_param¶

bycycle.plts.plot_burst_detect_param(df_features, sig, fs, burst_param, thresh, xlim=None, interp=True, ax=None, **kwargs)[source]¶

Plot a burst detection parameter and threshold.

Parameters:

df_featurespandas.DataFrame: Dataframe output of compute_features().
sig1d array: Time series to plot.
fsfloat: Sampling rate, in Hz.
burst_paramstr: Column name of the parameter of interest in df.
threshfloat: The burst parameter threshold. Parameter values greater than thresh are considered bursts.
xlimtuple of (float, float), optional, default: None: Start and stop times for plot.
interpbool, optional, default: True: Interpolates points if true.
axmatplotlib.Axes, optional: Figure axes upon which to plot.
**kwargs: Keyword arguments to pass into plot_time_series.

Notes

Default keyword arguments include:

figsize: tuple of (float, float), default: (15, 3)
xlabel: str, default: ‘Time (s)’
ylabel: str, default: ‘Voltage (uV)
color: str, default: ‘r’.
- Note: color here is the fill color, rather than line color.

Examples

Plot the monotonicity of a bursting signal:

>>> from bycycle.features import compute_features
>>> from neurodsp.sim import sim_bursty_oscillation
>>> fs = 500
>>> sig = sim_bursty_oscillation(10, fs, freq=10)
>>> threshold_kwargs = {'amp_fraction_threshold': 0., 'amp_consistency_threshold': .5,
...                     'period_consistency_threshold': .5, 'monotonicity_threshold': .8}
>>> df_features = compute_features(sig, fs, f_range=(8, 12),
...                                            threshold_kwargs=threshold_kwargs)
>>> plot_burst_detect_param(df_features, sig, fs, 'monotonicity', .8)