pyhctsa.operations.criticality.rad¶

pyhctsa.operations.criticality.rad(x, tau=1, centre=True)¶

Compute the Rescaled Auto-Density (RAD) feature of a time series.

The RAD is a metric for inferring the distance to criticality in a system, designed to be robust to uncertainty in noise strength. It is calibrated using experiments on the Hopf bifurcation with variable and unknown measurement noise.

This method was devised and implemented by Brendan Harris (@brendanjohnharris, GitHub, 2023).

References

Parameters:¶

x : array-like¶

The input time series (1D array).

tau : int or str, optional¶

The embedding and differencing delay, in units of the time step (default: 1). If a string, must be “tau”, in which case the delay is set to the first crossing of the autocorrelation function.

centre : bool, optional¶

Whether to center the time series at zero and take absolute values before analysis (default: True).

Returns:¶

The RAD feature value, quantifying proximity to criticality.

Return type:¶

float