# 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)
print("Determinism:", DET)
print("Laminarity:", LAM)
# Generate a recurrence network at fixed recurrence rate
rn = RecurrenceNetwork(time_series,
dim=DIM,
tau=TAU,
metric=METRIC,
normalize=False,
recurrence_rate=RR)
# Calculate average path length, transitivity and assortativity
L = rn.average_path_length()
T = rn.transitivity()
C = rn.global_clustering()
R = rn.assortativity()
print("Average path length:", L)
print("Transitivity:", T)
print("Global clustering:", C)
print("Assortativity:", R)
# 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)
print "Determinism:", DET
print "Laminarity:", LAM
# Generate a recurrence network at fixed recurrence rate
rn = RecurrenceNetwork(time_series, dim=DIM, tau=TAU, metric=METRIC,
normalize=False, recurrence_rate=RR)
# Calculate average path length, transitivity and assortativity
L = rn.average_path_length()
T = rn.transitivity()
C = rn.global_clustering()
R = rn.assortativity()
print "Average path length:", L
print "Transitivity:", T
print "Global clustering:", C
print "Assortativity:", R
for j in xrange(t_steps):
# Get time series section for current window
time_series = data[j * delta:j * delta + T_embedded]
local_step_sequence[j] = j * delta + T_embedded / 2
# Prepare recurrence network from original data
rec_net = RecurrenceNetwork(time_series.flatten(),
dim=DIM,
tau=TAU,
metric=METRIC,
normalize=False,
silence_level=2,
recurrence_rate=RR)
# Calculations for original recurrence network
local_result["Average path length"][j] = rec_net.average_path_length()
local_result["Transitivity"][j] = rec_net.transitivity()
#local_result["Assortativity"][j] = rec_net.assortativity()
#local_result["Diameter"][j] = rec_net.diameter()
# Calculate RQA measures
#local_result["Determinism"][j] = rec_net.determinism()
#local_result["Laminarity"][j] = rec_net.laminarity()
#local_result["Mean diagonal line length"][j] = rec_net.average_diaglength()
#local_result["Trapping time"][j] = rec_net.trapping_time()
#local_result["Diagonal line entropy"][j] = rec_net.diag_entropy()
#local_result["Autocorrelation"][j] = autocorrelation(time_series, lag=1)
#local_result["Mean"][j] = time_series.mean()
#local_result["Standard deviation"][j] = time_series.std()
# Run analysis for each realization separately
for i in xrange(n_realizations):
# Loop over moving windows
for j in xrange(t_steps):
# Get time series section for current window
time_series = values[i,j * delta:j * delta + T_embedded]
step_sequence[j] = j * delta + T_embedded / 2
# Prepare recurrence network from original data
rec_net = RecurrenceNetwork(time_series.copy(), dim=DIM, tau=TAU,
metric=METRIC, normalize=False,
silence_level=2, recurrence_rate=RR)
# Calculations for original recurrence network
results["Transitivity"][i,j] = rec_net.transitivity()
results["Average path length"][i,j] = rec_net.average_path_length()
#results["Assortativity"][i,j] = rec_net.assortativity()
#results["Diameter"][i,j] = rec_net.diameter()
# Calculate RQA measures
#local_result["Determinism"][j] = rec_net.determinism()
#local_result["Laminarity"][j] = rec_net.laminarity()
#local_result["Mean diagonal line length"][j] = rec_net.average_diaglength()
#local_result["Trapping time"][j] = rec_net.trapping_time()
#local_result["Diagonal line entropy"][j] = rec_net.diag_entropy()
#local_result["Autocorrelation"][j] = autocorrelation(time_series,
# lag=1)
#local_result["Mean"][j] = time_series.mean()
#local_result["Standard deviation"][j] = time_series.std()
