def test_functional_erpac(self):
"""Test function test_functional_pac."""
# erpac simultation
n_epochs, n_times, sf, edges = 400, 1000, 512., 50
x, times = pac_signals_wavelet(f_pha=10,
f_amp=100,
n_epochs=n_epochs,
noise=.1,
n_times=n_times,
sf=sf)
times = times[edges:-edges]
# phase / amplitude extraction (single time)
p = EventRelatedPac(f_pha=[8, 12],
f_amp=(30, 200, 5, 5),
dcomplex='wavelet',
width=12)
kw = dict(n_jobs=1, edges=edges)
phases = p.filter(sf, x, ftype='phase', **kw)
amplitudes = p.filter(sf, x, ftype='amplitude', **kw)
n_amp = len(p.yvec)
# generate a normal distribution
gt = np.zeros((n_amp, n_times - 2 * edges))
b_amp = np.abs(p.yvec.reshape(-1, 1) - np.array([[80, 120]])).argmin(0)
gt[b_amp[0]:b_amp[1] + 1, :] = True
plt.figure(figsize=(16, 5))
plt.subplot(131)
p.pacplot(gt, times, p.yvec, title='Ground truth', cmap='magma')
for n_meth, meth in enumerate(['circular', 'gc']):
# compute erpac + p-values
erpac = p.fit(phases,
amplitudes,
method=meth,
mcp='bonferroni',
n_perm=30).squeeze()
pvalues = p.pvalues.squeeze()
# find everywhere erpac is significant + compare to ground truth
is_signi = pvalues < .05
erpac[~is_signi] = np.nan
# computes accuracy
acc = 100 * (is_signi == gt).sum() / (n_amp * n_times)
assert acc > 80.
# plot the result
title = f"Method={p.method}\nAccuracy={np.around(acc, 2)}%"
plt.subplot(1, 3, n_meth + 2)
p.pacplot(erpac, times, p.yvec, title=title)
plt.tight_layout()
plt.show()
# 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',
rmaxis=True,
vmin=vmin,
vmax=vmax)
plt.axvline(1., linestyle='--', color='k', linewidth=2)
p.show()
# 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()
###############################################################################
# As you can see from the image above, there is an increase of alpha <-> gamma
# (~90Hz) coupling that is occurring especially during the planning phase (i.e
# between [0, 1500]ms)
###############################################################################
# Align time-frequency map based on alpha phase peak
###############################################################################
# to confirm the previous result showing a potential alpha <-> gamma coupling
x1, tvec = pac_signals_wavelet(f_pha=10, f_amp=100, n_epochs=n_epochs, noise=2,
n_times=n_times, sf=sf)
x2 = np.random.rand(n_epochs, 1000)
x = np.concatenate((x1, x2), axis=1)
time = np.arange(x.shape[1]) / sf
p = EventRelatedPac(f_pha=[9, 11], f_amp='hres')
pha = p.filter(sf, x, ftype='phase', n_jobs=-1)
amp = p.filter(sf, x, ftype='amplitude', n_jobs=-1)
plt.figure(figsize=(14, 6))
for n_m, (method, nb) in enumerate(zip(['circular', 'gc'], ['A', 'B'])):
# to be fair with the comparison between ERPAC and gcERPAC, the smoothing
# parameter of the gcERPAC is turned off but results could look way better
# if for example with add a `smooth=20`
erpac = p.fit(pha, amp, method=method, n_jobs=-1).squeeze()
plt.subplot(1, 2, n_m + 1)
p.pacplot(erpac, time, p.yvec, xlabel='Time (second)', cmap=cfg["cmap"],
ylabel='Frequency for amplitude (Hz)', title=p.method,
vmin=0., rmaxis=True, fz_labels=20, fz_title=22, fz_cblabel=20)
plt.axvline(1., linestyle='--', color='w', linewidth=2)
if n_m == 1: plt.ylabel('')
ax = plt.gca()
ax.text(*tuple(cfg["nb_pos"]), nb, transform=ax.transAxes, **cfg["nb_cfg"])
plt.tight_layout()
plt.savefig(f"../figures/Fig5.png", dpi=300, bbox_inches='tight')
plt.show()
f_amp=100,
n_epochs=n_epochs,
noise=2,
n_times=n_times,
sf=sf)
x2 = np.random.rand(n_epochs, 1000)
x = np.concatenate((x1, x2), axis=1)
time = np.arange(x.shape[1]) / sf
p = EventRelatedPac(f_pha=[9, 11], f_amp=(60, 140, 5, 1))
pha = p.filter(sf, x, ftype='phase', n_jobs=1)
amp = p.filter(sf, x, ftype='amplitude', n_jobs=1)
plt.figure(figsize=(8, 6))
erpac = p.fit(pha, amp, method='circular', n_jobs=-1).squeeze()
p.pacplot(erpac,
time,
p.yvec,
xlabel='Time (second)',
cmap=cfg["cmap"],
ylabel='Frequency for amplitude (Hz)',
title='Event Related PAC',
vmin=0.,
rmaxis=True)
plt.axvline(1., linestyle='--', color='w', linewidth=2)
plt.tight_layout()
plt.savefig(f"{cfg['path']}/Fig5.png", dpi=300, bbox_inches='tight')
plt.show()
times = times[edges:-edges]
# phase / amplitude extraction (single time)
p = EventRelatedPac(f_pha=[8, 12], f_amp=(30, 200, 5, 5),
dcomplex='wavelet', width=12)
kw = dict(n_jobs=1, edges=edges)
phases = p.filter(sf, x, ftype='phase', **kw)
amplitudes = p.filter(sf, x, ftype='amplitude', **kw)
n_amp = len(p.yvec)
# generate a normal distribution
gt = np.zeros((n_amp, n_times - 2 * edges))
b_amp = np.abs(p.yvec.reshape(-1, 1) - np.array([[80, 120]])).argmin(0)
gt[b_amp[0]:b_amp[1] + 1, :] = True
plt.figure(figsize=(16, 5))
plt.subplot(131)
p.pacplot(gt, times, p.yvec, title='Ground truth', cmap='magma')
for n_meth, meth in enumerate(['circular', 'gc']):
# compute erpac + p-values
erpac = p.fit(phases, amplitudes, method=meth,
mcp='bonferroni', n_perm=30).squeeze()
pvalues = p.pvalues.squeeze()
# find everywhere erpac is significant + compare to ground truth
is_signi = pvalues < .05
erpac[~is_signi] = np.nan
# computes accuracy
acc = 100 * (is_signi == gt).sum() / (n_amp * n_times)
assert acc > 80.
# plot the result
title = f"Method={p.method}\nAccuracy={np.around(acc, 2)}%"
plt.subplot(1, 3, n_meth + 2)