def test_filterfit(self):
"""Test function filterfit."""
p = EventRelatedPac()
x_pha = np.random.rand(100, 1000)
x_amp = np.random.rand(100, 1000)
p.filterfit(256, x_pha, x_amp=x_amp, method='circular')
p.filterfit(256, x_pha, x_amp=x_amp, method='gc')
x = np.concatenate((x1, x2), axis=1)
time = np.arange(x.shape[1]) / sf
###############################################################################
# Define an ERPAC object and extract the phase and the amplitude
###############################################################################
# use :class:`tensorpac.EventRelatedPac.filter` method to extract phases and
# amplitudes
# define an ERPAC object
p = EventRelatedPac(f_pha=[9, 11], f_amp=(60, 140, 5, 1))
# method for correcting p-values for multiple comparisons
mcp = 'bonferroni'
# extract phases and amplitudes
erpac = p.filterfit(sf, x, method='circular', mcp=mcp).squeeze()
# get the p-values and squeeze unused dimensions
pvalues = p.pvalues.squeeze()
# set to nan everywhere it's not significant
erpac[pvalues > .05] = np.nan
vmin, vmax = np.nanmin(erpac), np.nanmax(erpac)
p.pacplot(erpac,
time,
p.yvec,
xlabel='Time (second)',
cmap='Spectral_r',
ylabel='Amplitude frequency',
title=p.method,
cblabel='ERPAC',
x2 = np.random.rand(n_epochs, 1000)
# now, concatenate the two signals across the time axis
x = np.concatenate((x1, x2), axis=1)
time = np.arange(x.shape[1]) / sf
###############################################################################
# Define an ERPAC object and extract the phase and the amplitude
###############################################################################
# use :class:`tensorpac.EventRelatedPac.filter` method to extract phases and
# amplitudes
# define an ERPAC object
p = EventRelatedPac(f_pha=[9, 11], f_amp=(60, 140, 5, 1))
# extract phases and amplitudes
erpac = p.filterfit(sf, x, method='gc', n_perm=20,).squeeze()
pvalues = p.pvalues.squeeze()
erpac[pvalues > .05] = np.nan
vmin, vmax = np.nanmin(erpac), np.nanmax(erpac)
p.pacplot(erpac, time, p.yvec, xlabel='Time (second)',
cmap='Spectral_r', ylabel='Amplitude frequency', title=p.method,
cblabel='ERPAC', rmaxis=True, vmin=vmin, vmax=vmax)
plt.axvline(1., linestyle='--', color='k', linewidth=2)
p.show()
# [8, 12]Hz. This range of frequencies is then gonig to be used to see if there
# is indeed an alpha <-> gamma coupling (Aru et al. 2015
# :cite:`aru2015untangling`)
###############################################################################
# Compute and plot the Event-Related PAC
###############################################################################
# To go one step further we can use the Event-Related PAC (ERPAC) in order to
# isolate the gamma range that is coupled with the alpha phase such as when, in
# time, this coupling occurs. Here, we compute the ERPAC using the
# Gaussian-Copula mutual information (Ince et al. 2017
# :cite:`ince2017statistical`), between the alpha [8, 12]Hz and several gamma
# amplitudes, at each time point.
rp_obj = EventRelatedPac(f_pha=[8, 12], f_amp=(30, 160, 30, 2))
erpac = rp_obj.filterfit(sf, data, method='gc', smooth=100)
###############################################################################
plt.figure(figsize=(8, 6))
rp_obj.pacplot(erpac.squeeze(),
times,
rp_obj.yvec,
xlabel='Time',
ylabel='Amplitude frequency (Hz)',
title='Event-Related PAC occurring for alpha phase',
fz_labels=15,
fz_title=18)
add_motor_condition(135, color='white')
plt.show()