def decimate_and_recurrence(signal, decimate_q=4, dim=1, tau=1, metric='supremum', normalize=False, threshold=0.2): if len(signal): decimated_signal = decimate_filtfilt(x=signal, q=int(decimate_q)) rp = RecurrencePlot(decimated_signal, dim=dim, tau=tau, metric=metric, normalize=normalize, threshold=threshold) return rp.laminarity(),rp.determinism() else: return np.nan
metric=METRIC, normalize=False, threshold=EPS) # Show the recurrence plot pylab.matshow(rp.recurrence_matrix()) pylab.xlabel("$n$") pylab.ylabel("$n$") pylab.show() # Calculate and print the recurrence rate print("Recurrence rate:", rp.recurrence_rate()) # Calculate some standard RQA measures DET = rp.determinism(l_min=2) LAM = rp.laminarity(v_min=2) print("Determinism:", DET) print("Laminarity:", LAM) # Generate a recurrence plot object with fixed recurrence rate RR rp = RecurrencePlot(time_series, dim=DIM, tau=TAU, metric=METRIC, normalize=False, recurrence_rate=RR) # Calculate and print the recurrence rate again to check if it worked... RR = rp.recurrence_rate() print("Recurrence rate:", RR)
# Generate a recurrence plot object with fixed recurrence threshold EPS rp = RecurrencePlot(time_series, dim=DIM, tau=TAU, metric=METRIC, normalize=False, threshold=EPS) # Show the recurrence plot pylab.matshow(rp.recurrence_matrix()) pylab.xlabel("$n$") pylab.ylabel("$n$") pylab.show() # Calculate and print the recurrence rate print "Recurrence rate:", rp.recurrence_rate() # Calculate some standard RQA measures DET = rp.determinism(l_min=2) LAM = rp.laminarity(v_min=2) print "Determinism:", DET print "Laminarity:", LAM # Generate a recurrence plot object with fixed recurrence rate RR rp = RecurrencePlot(time_series, dim=DIM, tau=TAU, metric=METRIC, normalize=False, recurrence_rate=RR) # Calculate and print the recurrence rate again to check if it worked... RR = rp.recurrence_rate() print "Recurrence rate:", RR # Calculate some standard RQA measures DET = rp.determinism(l_min=2) LAM = rp.laminarity(v_min=2)