demo_bivariate_mi.py

# Import classes
import matplotlib.pyplot as plt

from idtxl.bivariate_mi import BivariateMI
from idtxl.data import Data
from idtxl.visualise_graph import plot_network

# a) Generate test data
data = Data()
data.generate_mute_data(n_samples=1000, n_replications=5)

# b) Initialise analysis object and define settings
network_analysis = BivariateMI()
settings = {
    "cmi_estimator": "JidtGaussianCMI",
    "max_lag_sources": 5,
    "min_lag_sources": 1,
}

# c) Run analysis
results = network_analysis.analyse_network(settings=settings, data=data)

# d) Plot inferred network to console and via matplotlib
results.print_edge_list(weights="max_te_lag", fdr=False)
plot_network(results=results, weights="max_te_lag", fdr=False)
plt.show()