def te(mdf1): import numpy as np from idtxl.multivariate_te import MultivariateTE from idtxl.data import Data n_procs = 1 settings = { 'cmi_estimator': 'JidtDiscreteCMI', 'n_perm_max_stat': 21, 'max_lag_target': 5, 'max_lag_sources': 5, 'min_lag_sources': 4 } settings['cmi_estimator'] = 'JidtDiscreteCMI' binary_trains = [] for spiketrain in mdf1.spiketrains: x = conv.BinnedSpikeTrain(spiketrain, binsize=5 * pq.ms, t_start=0 * pq.s) binary_trains.append(x.to_array()) print(binary_trains) dat = Data(np.array(binary_trains), dim_order='spr') dat.n_procs = n_procs settings = { 'cmi_estimator': 'JidtKraskov', 'max_lag_sources': 3, 'max_lag_target': 3, 'min_lag_sources': 1 } print(dat) mte = MultivariateTE() res_full = mte.analyse_network(settings=settings, data=dat) # generate graph plots g_single = visualise_graph.plot_selected_vars(res_single, mte) g_full = visualise_graph.plot_network(res_full)