# 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()