# # Calculate significance levels for network measures # print "Calculating significance levels based on", N_ENSEMBLE, "surrogates..." # Initialize progress bar progress = progressbar.ProgressBar().start() # Create a copy of data for generating surrogates from surrogate_data = data.copy() if SHUFFLE_EMBEDDED: # Get embedding of full time series surrogate_embedding = rec_net.embed_time_series( surrogate_data, DIM, TAU) # Prepare stuff local_surrogate_result = {} for measure in symbols.keys(): local_surrogate_result[measure] = np.empty(N_ENSEMBLE) for j in xrange(N_ENSEMBLE): if SHUFFLE_EMBEDDED: # Shuffle embedded time series along time axis, that is, whole # embedded state vectors are shuffled around. permuted_indices = np.random.permutation( surrogate_embedding.shape[0]) # Use the first T state vectors from the shuffled and embedded