powers['{}. Closed'.format(j+1)] = pow[:len(pow)//2]
powers['{}. Opened'.format(j+1)] = pow[len(pow)//2:]
elif name == 'Rotate':
powers['{}. Right'.format(j+1)] = pow[:len(pow)//2]
powers['{}. Left'.format(j+1)] = pow[len(pow)//2:]
else:
powers['{}. {}'.format(j+1, name)] = pow
# plot rejections
for j_t in range(top_ica.shape[1]):
ax = fg.add_subplot(5, top_ica.shape[1]*len(subj), top_ica.shape[1]*len(subj)*3 + top_ica.shape[1]*j_s + j_t + 1)
ax.set_xlabel('ICA{}'.format(j_t+1))
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
channels = [label for label in labels if label not in drop_channels]
pos = ch_names_to_2d_pos(channels)
plot_topomap(data=top_ica[:, j_t], pos=pos, axes=ax, show=False)
for j_t in range(top_alpha.shape[1]):
ax = fg.add_subplot(5, top_alpha.shape[1]*len(subj), top_alpha.shape[1]*len(subj)*4 + top_alpha.shape[1]*j_s + j_t + 1)
ax.set_xlabel('CSP{}'.format(j_t+1))
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
channels = [label for label in labels if label not in drop_channels]
pos = ch_names_to_2d_pos(channels)
plot_topomap(data=top_alpha[:, j_t], pos=pos, axes=ax, show=False)
# plot powers
norm = powers['{}. Baseline'.format(p_names.index('Baseline') + 1)].mean()
#norm = np.mean(pow_theta)
print('norm', norm)
ax = fg.add_subplot(2, len(subj), j_s + 1)
elif len(lengths_buffer) > 0:
lengths.append(np.mean(lengths_buffer))
lengths_buffer = []
print(lengths)
return np.array(lengths)
with h5py.File('{}\\{}\\{}'.format(dir_, experiment, 'experiment_data.h5')) as f:
fs, channels, p_names = get_info(f, settings['drop_channels'])
if reject:
rejections = load_rejections(f, reject_alpha=True)[0]
else:
rejections = None
spatial = f['protocol15/signals_stats/left/spatial_filter'][:]
plot_topomap(spatial, ch_names_to_2d_pos(channels), axes=plt.gca(), show=False)
plt.savefig('alphaS{}_Day{}_spatial_filter'.format(subj, day+1))
mu_band = f['protocol15/signals_stats/left/bandpass'][:]
#mu_band = (12, 13)
max_gap = 1 / min(mu_band) * 2
min_sate_duration = max_gap * 2
raw = OrderedDict()
signal = OrderedDict()
for j, name in enumerate(p_names):
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejections)
raw = add_data(raw, name, x, j)
signal = add_data(signal, name, f['protocol{}/signals_data'.format(j + 1)][:], j)
del raw[list(raw.keys())[-1]]
# make csp:
if run_ica:
sharey=False)
for j, data in enumerate([first, last]):
raw_data = np.concatenate([
np.concatenate(
[raw[key] for key in data if 'Close' in key]),
np.concatenate([raw[key] for key in data if 'Open' in key])
])
rej, spatial, top = ICADialog.get_rejection(raw_data,
channels,
fs,
mode='csp',
states=None)[:3]
tops.append(top)
spats.append(spatial)
plot_topomap(top,
ch_names_to_2d_pos(channels),
axes=axes[j, 0],
show=False)
plot_topomap(spatial,
ch_names_to_2d_pos(channels),
axes=axes[j, 1],
show=False)
axes[j, 0].set_xlabel(
'Topography ({})'.format('before' if j == 0 else 'after'))
axes[j, 1].set_xlabel('Spatial filter ({})'.format(
'before' if j == 0 else 'after'))
#plt.show()
# plot raw data
ch_plot = ['C3', 'P3', 'ICA'] #, 'Pz', 'Fp1']
fig1, axes = plt.subplots(len(ch_plot),
def update_figure(self,
data,
pos=None,
names=None,
show_names=None,
show_colorbar=True,
central_text=None,
right_bottom_text=None,
show_not_found_symbol=False,
montage=None):
if montage is None:
if pos is None:
pos = ch_names_to_2d_pos(names)
else:
pos = montage.get_pos('EEG')
names = montage.get_names('EEG')
data = np.array(data)
self.axes.clear()
if self.colorbar:
self.colorbar.remove()
if show_names is None:
show_names = [
'O1', 'O2', 'CZ', 'T3', 'T4', 'T7', 'T8', 'FP1', 'FP2'
]
show_names = [name.upper() for name in show_names]
mask = np.array([name.upper() in show_names
for name in names]) if names else None
v_min, v_max = None, None
if (data == data[0]).all():
data[0] += 0.1
data[1] -= 0.1
v_min, v_max = -1, 1
a, b = plot_topomap(data,
pos,
axes=self.axes,
show=False,
contours=0,
names=names,
show_names=True,
mask=mask,
mask_params=dict(marker='o',
markerfacecolor='w',
markeredgecolor='w',
linewidth=0,
markersize=3),
vmin=v_min,
vmax=v_max)
if central_text is not None:
self.axes.text(0,
0,
central_text,
horizontalalignment='center',
verticalalignment='center')
if right_bottom_text is not None:
self.axes.text(-0.65,
0.65,
right_bottom_text,
horizontalalignment='left',
verticalalignment='top')
if show_not_found_symbol:
self.axes.text(0,
0,
'/',
horizontalalignment='center',
verticalalignment='center')
self.axes.text(0,
0,
'O',
size=10,
horizontalalignment='center',
verticalalignment='center')
if show_colorbar:
self.colorbar = self.fig.colorbar(a,
orientation='horizontal',
ax=self.axes)
self.colorbar.ax.tick_params(labelsize=6)
self.colorbar.ax.set_xticklabels(
self.colorbar.ax.get_xticklabels(), rotation=90)
self.draw()
def plot_results(pilot_dir,
subj,
channel,
alpha_band=(9, 14),
theta_band=(3, 6),
drop_channels=None,
dc=False,
reject_alpha=True,
normalize_by='opened'):
drop_channels = drop_channels or []
cm = get_colors()
fg = plt.figure(figsize=(30, 6))
for j_s, experiment in enumerate(subj):
with h5py.File('{}\\{}\\{}'.format(pilot_dir, experiment,
'experiment_data.h5')) as f:
rejections, top_alpha, top_ica = load_rejections(
f, reject_alpha=reject_alpha)
fs, channels, p_names = get_info(f, drop_channels)
ch = channels.index(channel)
# collect powers
powers = OrderedDict()
raw = OrderedDict()
alpha = OrderedDict()
pow_theta = []
for j, name in enumerate(p_names):
pow, alpha_x, x = get_protocol_power(f,
j,
fs,
rejections,
ch,
alpha_band,
dc=dc)
if 'FB' in name:
pow_theta.append(
get_protocol_power(f,
j,
fs,
rejections,
ch,
theta_band,
dc=dc)[0].mean())
powers = add_data(powers, name, pow, j)
raw = add_data(raw, name, x, j)
alpha = add_data(alpha, name, alpha_x, j)
# plot rejections
n_tops = top_ica.shape[1] #+ top_alpha.shape[1]
for j_t in range(top_ica.shape[1]):
ax = fg.add_subplot(
4, n_tops * len(subj),
n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1)
ax.set_xlabel('ICA{}'.format(j_t + 1) if j_t < top_alpha else
'CSP{}'.format(-top_alpha + j_t + 1))
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
channels = [
label for label in labels if label not in drop_channels
]
pos = ch_names_to_2d_pos(channels)
plot_topomap(data=top_ica[:, j_t],
pos=pos,
axes=ax,
show=False)
# plot powers
if normalize_by == 'opened':
norm = powers['1. Opened'].mean()
elif normalize_by == 'beta':
norm = np.mean(pow_theta)
else:
print('WARNING: norm = 1')
print('norm', norm)
ax1 = fg.add_subplot(3, len(subj), j_s + 1)
ax = fg.add_subplot(3, len(subj), j_s + len(subj) + 1)
t = 0
#print(powers.keys())
if j_s == 0:
print(powers['4. FB'].mean() / powers['3. Baseline'].mean())
for j_p, ((name, pow),
(name, x)) in enumerate(zip(powers.items(),
raw.items())):
if name == '2228. FB':
from scipy.signal import periodogram
fff = plt.figure()
fff.gca().plot(*periodogram(x, fs, nfft=fs * 3),
c=cm[name.split()[1]])
plt.xlim(0, 80)
plt.ylim(0, 3e-11)
plt.show()
#print(name)
time = np.arange(t, t + len(x)) / fs
ax1.plot(time, x, c=cm[name.split()[1]], alpha=0.4)
ax1.plot(time, alpha[name], c=cm[name.split()[1]])
t += len(x)
ax.plot([j_p], [pow.mean() / norm],
'o',
c=cm[name.split()[1]],
markersize=10)
c = cm[name.split()[1]]
ax.errorbar([j_p], [pow.mean() / norm],
yerr=pow.std() / norm,
c=c,
ecolor=c)
fb_x = np.hstack([[j] * len(pows)
for j, (key, pows) in enumerate(powers.items())
if 'FB' in key])
fb_y = np.hstack(
[pows for key, pows in powers.items() if 'FB' in key]) / norm
sns.regplot(x=fb_x,
y=fb_y,
ax=ax,
color=cm['FB'],
scatter=False,
truncate=True)
ax1.set_xlim(0, t / fs)
ax1.set_ylim(-40, 40)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=70)
ax.set_xticks(range(len(powers)))
ax.set_xticklabels(powers.keys())
ax.set_ylim(0, 3)
ax.set_xlim(-1, len(powers))
ax1.set_title('Day {}'.format(j_s + 1))
return fg