def test_erpac():
"""Test the ERPAC."""
data = np.random.rand(2, 1024)
p = Pac(idpac=(4, 0, 0))
pha = p.filter(1024, data, axis=1, ftype='phase')
amp = p.filter(1024, data, axis=1, ftype='amplitude')
p.erpac(pha, amp, traxis=1)
npts=npts, dpha=10, damp=10, sf=sf)
# Generate noise and concatenate the coupling and the noise :
x2 = np.random.rand(ntrials, 700)
x = np.concatenate((x1, x2), axis=1) # Shape : (ntrials, npts)
time = np.arange(x.shape[1]) / sf
# Define a PAC object :
p = Pac(fpha=[9, 11], famp=(60, 140, 5, 1), dcomplex='wavelet', width=12)
# Extract the phase and the amplitude :
pha = p.filter(sf, x, axis=1, ftype='phase') # Shape (npha, ntrials, npts)
amp = p.filter(sf, x, axis=1, ftype='amplitude') # Shape (namp, ntrials, npts)
# Compute the ERPAC and use the traxis to specify that the trial axis is the
# first one :
erpac, pval = p.erpac(pha, amp, traxis=1)
# Remove unused dimensions :
erpac, pval = np.squeeze(erpac), np.squeeze(pval)
# Plot without p-values :
p.pacplot(erpac, time, p.yvec, xlabel='Time (second)', cmap='Spectral_r',
ylabel='Amplitude frequency', title=str(p), cblabel='ERPAC',
vmin=0., rmaxis=True)
# Plot with every non-significant values masked in gray :
# p.pacplot(erpac, time, p.yvec, xlabel='Time (second)', cmap='Spectral_r',
# ylabel='Amplitude frequency', title='ERPAC example', vmin=0.,
# vmax=1., pvalues=pval, bad='lightgray', plotas='contour',
# cblabel='ERPAC')