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:
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]
except (FileNotFoundError, DatasetNotFound):
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
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)
# 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
