# You can include LaTex labels...
pylab.xlabel("$n$")
pylab.ylabel("$x_n$")
# 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
# 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)
# Settings for the recurrence plot
EPS = 0.05 # Fixed threshold
METRIC = "euclidean" # ("manhattan","euclidean","supremum")
pylab.plot(time_series, "r")
pylab.xlabel("$n$")
pylab.ylabel("$x_n$")
# 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.savefig(f"output/{user}_rqa.png")
# Calculate some standard RQA measures
DET = rp.determinism(l_min=2)
LAM = rp.laminarity(v_min=2)
writer.writerow(
[user, len(time_series),
rp.recurrence_rate(), DET, LAM])
