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)
