def test_polar_plot(self):
"""Test the polar plot."""
matplotlib.use('agg')
p = PreferredPhase(f_pha=[5, 7], f_amp=(60, 200, 10, 1))
x_pha = np.random.rand(100, 1000)
x_amp = np.random.rand(100, 1000)
ampbin, pp, vecbin = p.filterfit(256, x_pha, x_amp=x_amp)
pp = np.squeeze(pp).T
ampbin = np.squeeze(ampbin).mean(-1)
p.polar(ampbin.T, vecbin, p.yvec, cmap='RdBu_r', interp=.1,
cblabel='Amplitude bins')
matplotlib.pyplot.close('all')
def test_fit(self):
"""Test function fit."""
data = np.random.rand(100, 1000)
p = PreferredPhase()
pha = p.filter(256, data, 'phase')
amp = p.filter(256, data, 'amplitude')
p.fit(pha, amp)
sf = 1024.
n_epochs = 100
n_times = 2000
pp = np.pi / 2
data, time = pac_signals_wavelet(f_pha=6,
f_amp=100,
n_epochs=n_epochs,
sf=sf,
noise=1,
n_times=n_times,
pp=pp)
###############################################################################
# Extract phases, amplitudes and compute the preferred phase
###############################################################################
p = PreferredPhase(f_pha=[5, 7], f_amp=(60, 200, 10, 1))
# Extract the phase and the amplitude :
pha = p.filter(sf, data, ftype='phase', n_jobs=1)
amp = p.filter(sf, data, ftype='amplitude', n_jobs=1)
# Now, compute the PP :
ampbin, pp, vecbin = p.fit(pha, amp, n_bins=72)
###############################################################################
# Plot the preferred phase
###############################################################################
# Here, we first plot the preferred phase across trials according to
# amplitudes. Then, the distribution of 100hz amplitudes is first plotted
# according to the 72 phase bins and also using a polar representation
n_epochs=n_epochs,
sf=sf,
noise=3,
n_times=n_times,
pp=pp)
# compute the binned amplitude
b_obj = BinAmplitude(data,
sf,
f_pha=[5, 7],
f_amp=[90, 110],
n_jobs=1,
n_bins=18)
# compute the preferred phase
p = PreferredPhase(f_pha=[5, 7], f_amp='hres')
pha = p.filter(sf, data, ftype='phase', n_jobs=1)
amp = p.filter(sf, data, ftype='amplitude', n_jobs=1)
ampbin, pp, vecbin = p.fit(pha, amp, n_bins=72)
pp = np.squeeze(pp).T
ampbin = np.squeeze(ampbin).mean(-1)
plt.figure(figsize=(18, 8))
plt.subplot(121)
ax = b_obj.plot(color='#34495e', alpha=.5, unit='deg')
plt.ylim(40, 140)
plt.title("Binned 100hz amplitude according to the 6hz phase")
# plt.autoscale(tight=True)
ax = plt.gca()
ax.text(*tuple(cfg["nb_pos"]), 'A', transform=ax.transAxes, **cfg["nb_cfg"])
# As you can see, the amplitude is modulated inside the resting and even more # inside the planning phase. On the other hand, during the execution, the # distribution stays flat. Note also that the amplitude is maximum arround # -150° (or 210°) during the planning phase ############################################################################### # Identify the preferred phase ############################################################################### # The preferred phase is given by the phase bin at which the amplitude is # maximum. Said differently, the gamma amplitude is binned according to the # alpha phase. The preferred-phase is computed as the maximum of this # histogram. To identify this preferred-phase, we propose here a polar plotting # method to visualize retrieve the previous result using the histogram method # define the preferred phase object pp_obj = PreferredPhase(f_pha=[8, 12]) # only extract the alpha phase pp_pha = pp_obj.filter(sf, data, ftype='phase') pp_pha_rest = pp_pha[..., time_rest] pp_pha_prep = pp_pha[..., time_prep] pp_pha_exec = pp_pha[..., time_exec] # compute the preferred phase (reuse the amplitude computed above) ampbin_rest, _, vecbin = pp_obj.fit(pp_pha_rest, amp_rest, n_bins=72) ampbin_prep, _, vecbin = pp_obj.fit(pp_pha_prep, amp_prep, n_bins=72) ampbin_exec, _, vecbin = pp_obj.fit(pp_pha_exec, amp_exec, n_bins=72) # mean binned amplitude across trials ampbin_rest = np.squeeze(ampbin_rest).mean(-1).T ampbin_prep = np.squeeze(ampbin_prep).mean(-1).T ampbin_exec = np.squeeze(ampbin_exec).mean(-1).T ###############################################################################
def test_filterfit(self):
"""Test function filterfit."""
p = PreferredPhase()
x_pha = np.random.rand(100, 1000)
x_amp = np.random.rand(100, 1000)
p.filterfit(256, x_pha, x_amp=x_amp)
def test_filter(self):
"""Test function filter."""
data = np.random.rand(7, 1000)
p = PreferredPhase()
p.filter(256, data, 'phase')
p.filter(256, data, 'amplitude')