def test_visualise_multivariate_te():
"""Visualise output of multivariate TE estimation."""
data = Data()
data.generate_mute_data(100, 5)
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'max_lag_sources': 5,
'min_lag_sources': 4,
'n_perm_max_stat': 25,
'n_perm_min_stat': 25,
'n_perm_omnibus': 50,
'n_perm_max_seq': 50,
}
network_analysis = MultivariateTE()
results = network_analysis.analyse_network(settings,
data,
targets=[0, 1, 2])
# generate graph plots
visualise_graph.plot_selected_vars(results, target=1, sign_sources=False)
plt.show()
visualise_graph.plot_network(results, fdr=False)
plt.show()
visualise_graph.plot_network(results, fdr=True)
plt.show()
visualise_graph.plot_selected_vars(results, target=1, sign_sources=True)
plt.show()
def test_visualise_multivariate_te():
"""Visualise output of multivariate TE estimation."""
data = Data()
data.generate_mute_data(100, 5)
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'max_lag_sources': 5,
'min_lag_sources': 4,
'n_perm_max_stat': 25,
'n_perm_min_stat': 25,
'n_perm_omnibus': 50,
'n_perm_max_seq': 50,
}
network_analysis = MultivariateTE()
results = network_analysis.analyse_network(settings, data,
targets=[0, 1, 2])
# generate graph plots
visualise_graph.plot_selected_vars(results, target=1, sign_sources=False)
plt.show()
visualise_graph.plot_network(results, fdr=False)
plt.show()
visualise_graph.plot_network(results, fdr=True)
plt.show()
visualise_graph.plot_selected_vars(results, target=1, sign_sources=True)
plt.show()
def test_plot_network():
"""Test results class for multivariate TE network inference."""
covariance = 0.4
n = 10000
delay = 1
normalisation = False
source = np.random.normal(0, 1, size=n)
target_1 = (covariance * source +
(1 - covariance) * np.random.normal(0, 1, size=n))
target_2 = (covariance * source +
(1 - covariance) * np.random.normal(0, 1, size=n))
source = source[delay:]
target_1 = target_1[:-delay]
target_2 = target_2[:-delay]
# Discretise data for speed
settings_dis = {'discretise_method': 'equal', 'n_discrete_bins': 5}
est = JidtDiscreteCMI(settings_dis)
source_dis, target_1_dis = est._discretise_vars(var1=source, var2=target_1)
source_dis, target_2_dis = est._discretise_vars(var1=source, var2=target_2)
data = Data(np.vstack((source_dis, target_1_dis, target_2_dis)),
dim_order='ps',
normalise=normalisation)
settings = {
'cmi_estimator': 'JidtDiscreteCMI',
'discretise_method': 'none',
'n_discrete_bins': 5, # alphabet size of the variables analysed
'n_perm_max_stat': 21,
'n_perm_omnibus': 30,
'n_perm_max_seq': 30,
'min_lag_sources': 1,
'max_lag_sources': 2,
'max_lag_target': 1,
'alpha_fdr': 0.5
}
nw = MultivariateTE()
# Analyse a single target and the whole network
res_single = nw.analyse_single_target(settings=settings,
data=data,
target=1)
res_network = nw.analyse_network(settings=settings, data=data)
graph, fig = plot_network(res_single, 'max_te_lag', fdr=False)
plt.close(fig)
graph, fig = plot_network(res_network, 'max_te_lag', fdr=False)
plt.close(fig)
for sign_sources in [True, False]:
graph, fig = plot_selected_vars(res_network,
target=1,
sign_sources=True,
fdr=False)
plt.close(fig)
def test_visualise_multivariate_te():
"""Visualise output of multivariate TE estimation."""
dat = Data()
dat.generate_mute_data(100, 5)
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'max_lag_sources': 5,
'min_lag_sources': 4,
'n_perm_max_stat': 25,
'n_perm_min_stat': 25,
'n_perm_omnibus': 50,
'n_perm_max_seq': 50,
}
network_analysis = MultivariateTE()
res = network_analysis.analyse_network(settings, dat, targets=[0, 1, 2])
vis.plot_network(res)
def test_visualise_multivariate_te():
"""Visualise output of multivariate TE estimation."""
dat = Data()
dat.generate_mute_data(100, 5)
max_lag = 5
min_lag = 4
analysis_opts = {
'cmi_calc_name': 'jidt_kraskov',
'n_perm_max_stat': 25,
'n_perm_min_stat': 25,
'n_perm_omnibus': 50,
'n_perm_max_seq': 50,
}
network_analysis = Multivariate_te(max_lag, min_lag, analysis_opts)
res = network_analysis.analyse_network(dat, targets=[0, 1, 2])
vis.plot_network(res)
# Import classes
from idtxl.multivariate_te import MultivariateTE
from idtxl.data import Data
from idtxl.visualise_graph import plot_network
import matplotlib.pyplot as plt
# 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 = MultivariateTE()
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()
"""Plot graph output from multivariate TE estimation.
author: patricia
"""
from idtxl.data import Data
from idtxl.multivariate_te import MultivariateTE
from idtxl import visualise_graph
# Generate some example output
data = Data()
data.generate_mute_data(n_replications=2, n_samples=500)
print('Demo data with {0} procs, {1} samples, {2} reps.'.format(
data.n_processes, data.n_samples, data.n_replications))
settings = {
'cmi_estimator': 'JidtKraskovCMI',
'max_lag_sources': 3,
'max_lag_target': 3,
'min_lag_sources': 1
}
mte = MultivariateTE()
res_single = mte.analyse_single_target(settings=settings, data=data, target=3)
res_full = mte.analyse_network(settings=settings, data=data)
# generate graph plots
g_single = visualise_graph.plot_selected_vars(res_single, mte)
g_full = visualise_graph.plot_network(res_full)
# Import classes
from idtxl.bivariate_te import BivariateTE
from idtxl.data import Data
from idtxl.visualise_graph import plot_network
import matplotlib.pyplot as plt
# 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 = BivariateTE()
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()
"""Plot graph output from multivariate TE estimation.
author: patricia
"""
from idtxl.data import Data
from idtxl.multivariate_te import Multivariate_te
from idtxl import visualise_graph
# Generate some example output
data = Data()
data.generate_mute_data(n_replications=2, n_samples=500)
print('Demo data with {0} procs, {1} samples, {2} reps.'.format(
data.n_processes, data.n_samples, data.n_replications))
opts = {'cmi_calc_name': 'jidt_kraskov'}
mte = Multivariate_te(max_lag_sources=3, max_lag_target=3,
min_lag_sources=1, options=opts)
res_single = mte.analyse_single_target(data=data, target=3)
res_full = mte.analyse_network(data=data)
# generate graph plots
g_single = visualise_graph.plot_selected_vars(res_single, mte)
g_full = visualise_graph.plot_network(res_full)
import pickle
from idtxl.visualise_graph import plot_network
import matplotlib.pyplot as plt
with open('target_results/network_center_full.p', 'rb') as pkl_file:
center_res = pickle.load(pkl_file)
# d) Plot inferred network to console and via matplotlib
center_res.print_edge_list(weights='max_te_lag', fdr=False)
plot_network(results=center_res, weights='max_te_lag', fdr=False)
# with open('network_preborder_full.p', 'rb') as pkl_file:
# pre_border_res = pickle.load(pkl_file)
# pre_border_res.print_edge_list(weights='max_te_lag', fdr=False)
# plot_network(results=pre_border_res, weights='max_te_lag', fdr=False)
plt.show()
def run(self):
network_analysis = MultivariateTE()
results = network_analysis.analyse_network(settings=self.settings, data=self.data)
#results.print_edge_list(weights='max_te_lag', fdr=False)
return plot_network(results=results, weights='max_te_lag', fdr=False)
'min_lag_sources': 1,
'add_conditionals': [(0, 1), (0, 2), (1, 1), (1, 2)]
}
# c) Run analysis
center_res = network_analysis.analyse_network(settings=settings,
data=data_center,
targets=[6],
sources=[2, 3, 4, 5])
pre_border_res = network_analysis.analyse_network(settings=settings,
data=data_pre_border,
targets=[6],
sources=[2, 3, 4, 5])
# d) Plot inferred network to console and via matplotlib
plot_network(res=center_res, n_nodes=data_center.n_processes)
# print_res_to_console(data=data_pre_border, res=pre_border_res)
# plot_network(res=pre_border_res, n_nodes=data_pre_border.n_processes)
pickle.dump(center_res, open('network_center_b2m1.p', 'wb'))
pickle.dump(pre_border_res, open('network_preborder_b2m1.p', 'wb'))
# pickle.dump(res1, open('results_all_trials.p', 'wb'))
# results = pickle.load(open('results.p', 'rb'))
# compare
# center_res = pickle.load(open('results_all_trials_center_smallsample.p', 'rb'))
# pre_border_res = pickle.load(open('results_all_trials_pre_border_2rep.p', 'rb'))
#
# comparison_settings = {
# 'stats_type': 'independent',