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 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()
elif j > len(p_names) - 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs,
rejection if reject else None)
print(x.shape)
raw_after = add_data_simple(raw_after, name, x)
xx = np.concatenate([
raw_before['Opened'],
raw_before['Baseline'],
raw_after['Baseline'],
raw_before['Left'],
raw_before['Right'],
raw_after['Left'],
raw_after['Right'],
])
#xx[:, channels.index('C3')] = xx[:, channels.index('C3')]
rejection, spatial, topography, unmixing_matrix, bandpass, _ = ICADialog.get_rejection(
xx, channels, fs, mode='csp', states=None)
from mne.viz import plot_topomap
print(spatial)
fig, axes = plt.subplots(ncols=rejection.topographies.shape[1])
if not isinstance(axes, type(axes1)):
axes = [axes]
for ax, top in zip(axes, rejection.topographies.T):
plot_topomap(top,
ch_names_to_2d_pos(channels),
axes=ax,
show=False)
fig.savefig('csp_S{}_D{}.png'.format(subj, day + 1))
fig.show()
fs, channels, p_names = get_info(f, settings['drop_channels'])
rejection, alpha, ica = load_rejections(f, reject_alpha=False)
raw_before = OrderedDict()
raw_after = OrderedDict()
for j, name in enumerate(p_names):
if j < 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None)
raw_before = add_data_simple(raw_before, name, x)
elif j > len(p_names) - 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None)
print(x.shape)
raw_after = add_data_simple(raw_after, name, x)
xx = np.concatenate([raw_before['Opened'], raw_before['Baseline'], raw_after['Baseline'], raw_before['Left'], raw_before['Right'], raw_after['Left'], raw_after['Right'], ])
#xx[:, channels.index('C3')] = xx[:, channels.index('C3')]
rejection, spatial, topography, unmixing_matrix, bandpass, _ = ICADialog.get_rejection(xx, channels, fs, mode='csp', states=None)
from mne.viz import plot_topomap
print(spatial)
fig, axes = plt.subplots(ncols=rejection.topographies.shape[1])
if not isinstance(axes, type(axes1)) :
axes = [axes]
for ax, top in zip(axes, rejection.topographies.T):
plot_topomap(top, ch_names_to_2d_pos(channels), axes=ax, show=False)
fig.savefig('csp_S{}_D{}.png'.format(subj, day+1))
fig.show()
def __init__(self,
current_protocol,
protocols,
signals,
n_signals=1,
parent=None,
n_channels=32,
max_protocol_n_samples=None,
experiment=None,
freq=500,
plot_raw_flag=True,
plot_signals_flag=True,
plot_source_space_flag=False,
show_subject_window=True,
channels_labels=None,
subject_backend_expyriment=False):
super(MainWindow, self).__init__(parent)
# Which windows to draw:
self.plot_source_space_flag = plot_source_space_flag
self.show_subject_window = show_subject_window
# status info
self.status = PlayerLineInfo([p.name for p in protocols],
[[p.duration for p in protocols]])
self.source_freq = freq
self.experiment = experiment
self.signals = signals
# player panel
self.player_panel = PlayerButtonsWidget(parent=self)
self.player_panel.restart.clicked.connect(self.restart_experiment)
for signal in signals:
self.player_panel.start.clicked.connect(signal.reset_statistic_acc)
self.player_panel.start.clicked.connect(self.update_first_status)
self._first_time_start_press = True
# timer label
self.timer_label = QtGui.QLabel('tf')
# signals viewer
self.signals_viewer = DerivedSignalViewer(
freq, [signal.name for signal in signals])
# raw data viewer
self.raw_viewer = RawSignalViewer(freq, channels_labels)
self.n_channels = n_channels
self.n_samples = 2000
self.plot_raw_checkbox = QtGui.QCheckBox('plot raw')
self.plot_raw_checkbox.setChecked(plot_raw_flag)
self.plot_signals_checkbox = QtGui.QCheckBox('plot signals')
self.plot_signals_checkbox.setChecked(plot_signals_flag)
self.autoscale_raw_chekbox = QtGui.QCheckBox('autoscale')
self.autoscale_raw_chekbox.setChecked(True)
# topomaper
pos = ch_names_to_2d_pos(channels_labels)
#self.topomaper = TopomapWidget(pos)
# dc_blocker
self.dc_blocker = DCBlocker()
# main window layout
layout = pg.LayoutWidget(self)
layout.addWidget(self.signals_viewer, 0, 0, 1, 3)
layout.addWidget(self.plot_raw_checkbox, 1, 0, 1, 1)
layout.addWidget(self.plot_signals_checkbox, 1, 2, 1, 1)
layout.addWidget(self.autoscale_raw_chekbox, 1, 1, 1, 1)
layout.addWidget(self.raw_viewer, 2, 0, 1, 3)
layout.addWidget(self.player_panel, 3, 0, 1, 1)
layout.addWidget(self.timer_label, 3, 1, 1, 1)
#layout.addWidget(self.topomaper, 3, 2, 1, 1)
layout.addWidget(self.status, 4, 0, 1, 3)
layout.layout.setRowStretch(0, 2)
layout.layout.setRowStretch(2, 2)
self.setCentralWidget(layout)
# main window settings
self.resize(800, 600)
self.show()
# subject window
if show_subject_window:
if not subject_backend_expyriment:
self.subject_window = SubjectWindow(self, current_protocol)
self.subject_window.show()
else:
self.subject_window = ExpyrimentSubjectWindow(
self, current_protocol)
self._subject_window_want_to_close = False
else:
self.subject_window = None
self._subject_window_want_to_close = None
# Source space window
if plot_source_space_flag:
source_space_protocol = SourceSpaceRecontructor(signals)
self.source_space_window = SourceSpaceWindow(
self, source_space_protocol)
self.source_space_window.show()
# time counter
self.time_counter = 0
self.time_counter1 = 0
self.t0 = time.time()
self.t = self.t0
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:
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)
#plt.plot(fft_filter(f['protocol6/raw_data'][:, ch], fs, band=(3, 35)))
#plt.plot(fft_filter(np.dot(f['protocol6/raw_data'], rejections)[:, ch], fs, band=(3, 35)))
#plt.show()
#from scipy.signal import welch
#plt.plot(*welch(f['protocol1/raw_data'][:60*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol1/raw_data'][60*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][:30*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][30*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.legend(['Close', 'Open', 'Left', 'Right'])
#plt.show()
# 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))
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(
4, n_tops * len(subj),
n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1 +
top_ica.shape[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
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
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
color = cm[''.join(
[i for i in name.split()[1] if not i.isdigit()])]
ax1.plot(time, fft_filter(x, fs, (2, 45)), c=color, alpha=0.4)
ax1.plot(time, alpha[name], c=color)
t += len(x)
ax.plot([j_p], [pow.mean() / norm],
'o',
c=color,
markersize=10)
ax.errorbar([j_p], [pow.mean() / norm],
yerr=pow.std() / norm,
c=color,
ecolor=color)
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
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:
labels_, fs_ = get_lsl_info_from_xml(f['stream_info.xml'][0])
print(labels_)
channels = [label for label in labels_ if label not in ['A1', 'A2', 'AUX']]
print(labels_)
pz_index = channels.index('Pz')
raw = raw - np.dot(raw[:, [pz_index]], np.ones((1, raw.shape[1])))
del channels[pz_index]
raw = raw[:, np.arange(raw.shape[1]) != pz_index]
signal = DerivedSignal(ind=0, name='Signal', bandpass_low=9, bandpass_high=14,
spatial_filter=np.array([0]), n_channels=raw.shape[1])
w = SignalsSSDManager([signal], raw, ch_names_to_2d_pos(channels), channels, None, None, [], sampling_freq=fs_ )
w.exec_()
rejections = signal.rejections.get_list()
new_rejections[experiment] = rejections
with open(new_rejections_file, 'wb') as pkl:
pickle.dump(new_rejections, pkl)
del a
else:
print('file exist')
with open(new_rejections_file, 'rb') as handle:
new_rejections = pickle.load(handle)
print(new_rejections)
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)
#plt.plot(fft_filter(f['protocol6/raw_data'][:, ch], fs, band=(3, 35)))
#plt.plot(fft_filter(np.dot(f['protocol6/raw_data'], rejections)[:, ch], fs, band=(3, 35)))
#plt.show()
#from scipy.signal import welch
#plt.plot(*welch(f['protocol1/raw_data'][:60*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol1/raw_data'][60*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][:30*500//2, channels.index('C3')], fs, nperseg=1000))
#plt.plot(*welch(f['protocol2/raw_data'][30*500//2:, channels.index('C3')], fs, nperseg=1000))
#plt.legend(['Close', 'Open', 'Left', 'Right'])
#plt.show()
# 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))
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(4, n_tops * len(subj),
n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1 + top_ica.shape[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
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
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
color = cm[''.join([i for i in name.split()[1] if not i.isdigit()])]
ax1.plot(time, fft_filter(x, fs, (2, 45)), c=color, alpha=0.4)
ax1.plot(time, alpha[name], c=color)
t += len(x)
ax.plot([j_p], [pow.mean() / norm], 'o', c=color, markersize=10)
ax.errorbar([j_p], [pow.mean() / norm], yerr=pow.std() / norm, c=color, ecolor=color)
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
fs, channels, p_names = get_info(f, settings['drop_channels'])
rejection, alpha, ica = load_rejections(f, reject_alpha=False)
raw_before = OrderedDict()
raw_after = OrderedDict()
for j, name in enumerate(p_names):
if j < 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None)
raw_before = add_data_simple(raw_before, name, x)
elif j > len(p_names) - 3:
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None)
print(x.shape)
raw_after = add_data_simple(raw_after, name, x)
xx = np.concatenate([raw_before['Opened'], raw_before['Baseline'], raw_after['Baseline'], raw_before['Left'], raw_before['Right'], raw_after['Left'], raw_after['Right'], ])
#xx[:, channels.index('C3')] = xx[:, channels.index('C3')]
rejection, spatial, topography, unmixing_matrix, bandpass, _ = ICADialog.get_rejection(xx, channels, fs, mode='csp', states=None)
from mne.viz import plot_topomap
print(spatial)
fig, axes = plt.subplots(ncols=rejection.topographies.shape[1])
if not isinstance(axes, type(axes1)) :
axes = [axes]
for ax, top in zip(axes, rejection.topographies.T):
plot_topomap(top, ch_names_to_2d_pos(channels), axes=ax, show=False)
fig.savefig('csp_S{}_D{}.png'.format(subj, day+1))
fig.show()
with h5py.File('{}\\{}\\{}'.format(settings['dir'], experiment, 'experiment_data.h5')) as f:
fs, channels, p_names = get_info(f, settings['drop_channels'])
rejection, alpha, ica = None, None, None#load_rejections(f, reject_alpha=True)
odict = OrderedDict()
for j, name in enumerate(p_names):
x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None)
odict = add_data_simple(odict, name, x)
raw[names[j_experiment]] = odict
for j, key in enumerate(state_plot):
f, Pxx = welch(raw[names[j_experiment]][key], fs, nperseg=2048, axis=0)
#axes[j].semilogy(f, Pxx, alpha=1, c=cm[j_experiment*2+3])
ax = axes[j, j_experiment]
a, b = plot_topomap(np.log10(Pxx[np.argmin(np.abs(f-peak)), :]), ch_names_to_2d_pos(channels), cmap='Reds',
axes=ax, show=False, vmax=-10.5, vmin=-13)
if j_experiment == 0:
ax.set_ylabel(key)
if j == len(state_plot)-1:
ax.set_xlabel(names[j_experiment])
#axes[j].set_xlim(0, 250)
#axes[j].set_ylim(1e-19, 5e-10)
#x_plot = np.abs(hilbert(fft_filter(raw_before[key][:, channels.index(ch)], fs)))
#leg.append('P={:.3f}, D={:.3f}s'.format(Pxx[(f > 9) & (f < 14)].mean(), sum((x_plot > 5)) / fs/2))
#axes[j].legend(leg)
fig2.colorbar(a)
plt.show()
