def test_properties(self):
"""Test Pac properties."""
p = Pac()
# Idpac :
p.idpac
p.idpac = (2, 1, 1)
# Dcomplex :
p.dcomplex
p.dcomplex = 'wavelet'
# Cycle :
p.cycle
p.cycle = (12, 24)
# Width :
p.width
p.width = 12
def test_properties(self):
"""Test Pac properties."""
p = Pac()
# Idpac :
p.idpac
p.idpac = (2, 1, 1)
# Filt :
p.filt
p.filt = 'butter'
# Dcomplex :
p.dcomplex
p.dcomplex = 'wavelet'
# Cycle :
p.cycle
p.cycle = (12, 24)
# Filtorder :
p.filtorder
p.filtorder = 6
# Width :
p.width
p.width = 12
noise=1.,
n_epochs=n_epochs,
n_times=n_times)
# First, let's use the MVL, without any further correction by surrogates :
p = Pac(idpac=(4, 0, 0),
f_pha=(5, 14, 2, .3),
f_amp=(80, 120, 2, 1),
verbose=False)
plt.figure(figsize=(18, 9))
# Define several cycle options for the fir1 (eegfilt like) filter :
p.filt = 'fir1'
print('Filtering with fir1 filter')
for i, k in enumerate([(1, 3), (2, 4), (3, 6)]):
p.cycle = k
xpac = p.filterfit(1024, data)
plt.subplot(2, 3, i + 1)
p.comodulogram(xpac.mean(-1), title='Fir1 - cycle ' + str(k))
# Define several wavelet width :
p.dcomplex = 'wavelet'
print('Filtering with wavelets')
for i, k in enumerate([7, 9, 12]):
p.width = k
xpac = p.filterfit(1024, data)
plt.subplot(2, 3, i + 4)
p.comodulogram(xpac.mean(-1), title='Wavelet - width ' + str(k))
plt.show()
