def test_fit(self):
"""Test function fit."""
data = np.random.rand(100, 1000)
p = EventRelatedPac()
pha = p.filter(256, data, 'phase')
amp = p.filter(256, data, 'amplitude')
p.fit(pha, amp, method='circular')
p.fit(pha, amp, method='gc')
p.fit(pha, amp, method='gc', n_perm=2)
p.fit(pha, amp, method='gc', smooth=5)
p.surrogates, p.pvalues
def test_fit(self):
"""Test function fit."""
data = np.random.rand(100, 1000)
p = EventRelatedPac()
pha = p.filter(256, data, 'phase')
amp = p.filter(256, data, 'amplitude')
p.fit(pha, amp, method='circular')
p.fit(pha, amp, method='gc')
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()
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')
# Second signal : one second of random noise
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))
# 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,
# an alpha <-> gamma coupling. This peak is essentially comprised between
# [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()
def test_filter(self):
"""Test function filter."""
data = np.random.rand(7, 1000)
p = EventRelatedPac()
p.filter(256, data, 'phase')
p.filter(256, data, 'amplitude')
# Second signal : one second of random noise
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, 3))
# extract phases and amplitudes
pha = p.filter(sf, x, ftype='phase')
amp = p.filter(sf, x, ftype='amplitude')
###############################################################################
# Compute the ERPAC using the two implemented methods and plot it
###############################################################################
# implemented ERPAC methods
methods = ['circular', 'gc']
plt.figure(figsize=(16, 8))
for n_m, m in enumerate(methods):
# compute the erpac
plt.rc('font', family=cfg["font"])
###############################################################################
n_epochs = 300
n_times = 1000
sf = 1000.
###############################################################################
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('')
n_epochs = 300
n_times = 1000
sf = 1000.
###############################################################################
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=(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)
from tensorpac import EventRelatedPac
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-poster')
sns.set_style("white")
plt.rc('font', family=cfg["font"])
# 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