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)
