def get_info(f, drop_channels):
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
print('fs: {}\nall labels {}: {}'.format(fs, len(labels), labels))
channels = [label for label in labels if label not in drop_channels]
print('selected channels {}: {}'.format(len(channels), channels))
n_protocols = len([k for k in f.keys() if ('protocol' in k and k != 'protocol0')])
protocol_names = [f['protocol{}'.format(j+1)].attrs['name'] for j in range(n_protocols)]
print('protocol_names:', protocol_names)
return fs, channels, protocol_names
def get_info(f, drop_channels):
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
print('fs: {}\nall labels {}: {}'.format(fs, len(labels), labels))
channels = [label for label in labels if label not in drop_channels]
print('selected channels {}: {}'.format(len(channels), channels))
n_protocols = len(
[k for k in f.keys() if ('protocol' in k and k != 'protocol0')])
protocol_names = [
f['protocol{}'.format(j + 1)].attrs['name'] for j in range(n_protocols)
]
print('protocol_names:', protocol_names)
return fs, channels, protocol_names
def stream_file_in_a_thread(file_path, reference, stream_name):
file_name, file_extension = os.path.splitext(file_path)
if file_extension == '.fif':
raw = mne.io.read_raw_fif(file_path, verbose='ERROR')
start, stop = raw.time_as_index([0, 60]) # read the first 15s of data
source_buffer = raw.get_data(start=start, stop=stop)
elif file_extension == '.vhdr':
raw = read_raw_brainvision(vhdr_fname=file_path, verbose='ERROR')
source_buffer = raw.get_data()
else:
source_buffer = load_h5py_all_samples(file_path=file_path).T
try:
if file_extension in ('.fif', '.vhdr'):
labels = raw.info['ch_names']
fs = raw.info['sfreq']
else:
try:
labels, fs = load_channels_and_fs(file_path)
except ValueError:
xml_str = load_xml_str_from_hdf5_dataset(
file_path, 'stream_info.xml')
labels, fs = get_lsl_info_from_xml(xml_str)
exclude = [
ex.upper()
for ex in ChannelsSelector.parse_channels_string(reference)
]
labels = [label for label in labels if label.upper() not in exclude]
print('Using {} channels and fs={}.\n[{}]'.format(
len(labels), fs, labels))
except (FileNotFoundError, DatasetNotFound):
print(
'Channels labels and fs not found. Using default 32 channels and fs=500Hz.'
)
labels = None
fs = None
thread = Process(target=run_eeg_sim,
args=(),
kwargs={
'chunk_size': 0,
'source_buffer': source_buffer,
'name': stream_name,
'labels': labels,
'freq': fs
})
thread.start()
time.sleep(2)
return thread
def stream_file_in_a_thread(file_path, reference, stream_name):
file_name, file_extension = os.path.splitext(file_path)
if file_extension == '.fif':
raw = mne.io.read_raw_fif(file_path, verbose='ERROR')
start, stop = raw.time_as_index([0, 60]) # read the first 15s of data
source_buffer = raw.get_data(start=start, stop=stop)
elif file_extension == '.vhdr':
raw = read_raw_brainvision(vhdr_fname=file_path, verbose='ERROR')
source_buffer = raw.get_data()
else:
source_buffer = load_h5py_all_samples(file_path=file_path).T
try:
if file_extension in ('.fif', '.vhdr'):
labels = raw.info['ch_names']
fs = raw.info['sfreq']
else:
try:
labels, fs = load_channels_and_fs(file_path)
except ValueError:
xml_str = load_xml_str_from_hdf5_dataset(file_path, 'stream_info.xml')
labels, fs = get_lsl_info_from_xml(xml_str)
exclude = [ex.upper() for ex in ChannelsSelector.parse_channels_string(reference)]
labels = [label for label in labels if label.upper() not in exclude]
print('Using {} channels and fs={}.\n[{}]'.format(len(labels), fs, labels))
except (FileNotFoundError, DatasetNotFound):
print('Channels labels and fs not found. Using default 32 channels and fs=500Hz.')
labels = None
fs = None
thread = Process(target=run_eeg_sim, args=(),
kwargs={'chunk_size': 0, 'source_buffer': source_buffer,
'name': stream_name, 'labels': labels, 'freq': fs})
thread.start()
time.sleep(2)
return thread
# return vals, vecs and topographics (in descending order)
reversed_slice = slice(-1, None, -1)
topo = inv(vecs[:,reversed_slice]).T
return vals[reversed_slice], vecs[:, reversed_slice], topo
if __name__ == '__main__':
dir_ = 'D:\\vnd_spbu\\pilot\\mu5days'
experiment = 'pilot5days_Rakhmankulov_Day3_03-01_12-51-41'
with h5py.File('{}\\{}\\{}'.format(dir_, experiment, 'experiment_data.h5')) as f:
ica = f['protocol1/signals_stats/left/rejections/rejection1'][:]
x_filters = dc_blocker(np.dot(f['protocol1/raw_data'][:], ica))
x_rotation = dc_blocker(np.dot(f['protocol2/raw_data'][:], ica))
x_dict = {
'closed': x_filters[:x_filters.shape[0] // 2],
'opened': x_filters[x_filters.shape[0] // 2:],
'rotate': x_rotation
}
drop_channels = ['AUX', 'A1', 'A2']
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
print('fs: {}\nall labels {}: {}'.format(fs, len(labels), labels))
channels = [label for label in labels if label not in drop_channels]
scores, unmixing_matrix, topographies = csp3(x_dict, 250, (9, 14), lambda_=0.5, regularization_coef=0.01)
app = QtWidgets.QApplication([])
selector = ICADialog(np.vstack((x_filters, x_rotation)), channels, fs, unmixing_matrix=unmixing_matrix, mode='csp', scores=scores)
selector.exec_()
else:
with h5py.File('{}\\{}\\{}'.format(pilot_dir, experiment,
'experiment_data.h5')) as f:
rejections = [
f['protocol1/signals_stats/left/rejections/rejection{}'.
format(j + 1)][:] for j in range(2)
]
rejection = rejections[0]
if reject_alpha:
rejection = np.dot(rejection, rejections[1])
# load data
with h5py.File('{}\\{}\\{}'.format(pilot_dir, experiment,
'experiment_data.h5')) as f:
labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0])
print('fs: {}\nall labels {}: {}'.format(fs, len(labels), labels))
channels = [
label for label in labels if label not in ['A1', 'A2', 'AUX']
]
pz_index = channels.index('Pz')
print('selected channels {}: {}'.format(
len(channels) - use_pz, channels))
data = []
for j in PROTOCOLS['ALL']:
raw = f['protocol{}/raw_data'.format(j)][:]
raw = raw[:raw.shape[0] - raw.shape[0] % fs]
if use_pz:
raw = raw[:, np.arange(raw.shape[1]) != pz_index]
data.append(np.dot(raw, rejection)[:, channels.index(channel)])
assert [
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
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