def test_conjunction_analysis(self):
"""Test the conjunction analysis."""
y, gt = sim_mi_cc(x, snr=1.)
dt = DatasetEphy(x, y, roi, times=time)
wf = WfMi(mi_type='cc', inference='rfx')
mi, pv = wf.fit(dt, **kw_mi)
cj_ss, cj = wf.conjunction_analysis(dt)
assert cj_ss.shape == (n_subjects, n_times, n_roi)
assert cj.shape == (n_times, n_roi)
###############################################################################
# Perform the conjunction analysis
# --------------------------------
#
# Now we have the values of MI we can compute the conjunction analysis. The
# following method returns two DataArray :
#
# - conj_ss : DataArray of shape (n_subjects, n_times, n_roi) that contains the
# significant MI of each subject
# - conj : DataArray of shape (n_times, n_roi) that contains the number of
# subjects that have a significant effect at each time point and for
# each roi
# perform the conjunction analysis
conj_ss, conj = wf.conjunction_analysis()
###############################################################################
# Plot where there's significant effect for each subject
# ------------------------------------------------------
#
# printing the results
print(conj_ss)
fig = plt.figure(figsize=(10, 8))
q = 0
for n_s in range(n_subjects):
color = ephy[f'subject_{n_s}']['color']
for n_r, roi in enumerate(conj.roi.data):
q += 1