def save_tfr_channel_averages(experiment, tfr_name, blmode, blstart, blend,
tmin, tmax, fmin, fmax):
"""
"""
column_names = []
row_descs = []
csv_data = []
channel_groups = experiment.channel_groups
# accumulate csv contents
for subject in experiment.subjects.values():
tfr = subject.tfr.get(tfr_name)
if not tfr:
continue
ch_names = tfr.ch_names
info = tfr.info
for key, mne_tfr in tfr.content.items():
# crop and correct to baseline
mne_tfr = mne_tfr.copy().crop(tmin=tmin,
tmax=tmax,
fmin=fmin,
fmax=fmax)
times = mne_tfr.times
column_names = format_floats(times)
freqs = format_floats(mne_tfr.freqs)
data = mne.baseline.rescale(mne_tfr.data,
times,
baseline=(blstart, blend),
mode=blmode)
data_labels, averaged_data = average_to_channel_groups(
data, info, ch_names, channel_groups)
if not data_labels:
raise Exception('No channel groups matching the data found')
for ix in range(averaged_data.shape[0]):
for iy in range(averaged_data.shape[1]):
ch_type, area = data_labels[ix]
csv_data.append(averaged_data[ix, iy].tolist())
row_desc = (subject.name, key, ch_type, area, freqs[iy])
row_descs.append(row_desc)
folder = filemanager.create_timestamped_folder(experiment)
fname = tfr_name + '_all_subjects_channel_averages_tfr.csv'
path = os.path.join(folder, fname)
filemanager.save_csv(path, csv_data, column_names, row_descs)
logging.getLogger('ui_logger').info('Saved the csv file to ' + path)
def save_channel_averages(experiment, selected_name):
"""
"""
column_names = []
row_descs = []
csv_data = []
# accumulate csv contents
for subject in experiment.subjects.values():
evoked = subject.evoked.get(selected_name)
if not evoked:
continue
for key, mne_evoked in evoked.content.items():
data_labels, averaged_data = create_averages(
experiment, mne_evoked)
if not data_labels:
raise Exception('No channel groups matching the data found')
csv_data.extend(averaged_data.tolist())
column_names = format_floats(mne_evoked.times)
for ch_type, area in data_labels:
row_desc = (subject.name, key, ch_type, area)
row_descs.append(row_desc)
folder = filemanager.create_timestamped_folder(experiment)
fname = selected_name + '_all_subjects_channel_averages_evoked.csv'
path = os.path.join(folder, fname)
filemanager.save_csv(path, csv_data, column_names, row_descs)
logging.getLogger('ui_logger').info('Saved the csv file to ' + path)
def save_all_channels(experiment, selected_name):
"""
"""
column_names = []
row_descs = []
csv_data = []
# accumulate csv contents
for subject in experiment.subjects.values():
evoked = subject.evoked.get(selected_name)
if not evoked:
continue
for key, mne_evoked in evoked.content.items():
column_names = format_floats(mne_evoked.times)
for ch_idx, ch_name in enumerate(mne_evoked.info['ch_names']):
if ch_name in mne_evoked.info['bads']:
continue
csv_data.append(mne_evoked.data[ch_idx].tolist())
row_desc = (subject.name, key, ch_name)
row_descs.append(row_desc)
folder = filemanager.create_timestamped_folder(experiment)
fname = selected_name + '_all_subjects_all_channels_evoked.csv'
path = os.path.join(folder, fname)
filemanager.save_csv(path, csv_data, column_names, row_descs)
logging.getLogger('ui_logger').info('Saved the csv file to ' + path)
def save_tse_all_channels(experiment, tfr_name, blmode, blstart,
blend, tmin, tmax, fmin, fmax):
""" Saves all channels of a tse to a csv file.
"""
column_names = []
row_descs = []
csv_data = []
# accumulate csv contents
for subject in experiment.subjects.values():
tfr = subject.tfr.get(tfr_name)
if not tfr:
continue
tses = _compute_tse(tfr, fmin, fmax)
for key, tse in tses.items():
times, tse = _crop_and_correct_to_baseline(
tse, blmode, blstart, blend, tmin, tmax, tfr.times)
csv_data.extend(tse.tolist())
for ch_name in tfr.ch_names:
row_desc = (subject.name, key, ch_name)
row_descs.append(row_desc)
column_names = format_floats(times)
folder = filemanager.create_timestamped_folder(experiment)
fname = tfr_name + '_all_subjects_all_channels_tse.csv'
path = os.path.join(folder, fname)
filemanager.save_csv(path, csv_data, column_names, row_descs)
logging.getLogger('ui_logger').info('Saved the csv file to ' + path)
def on_pick(ax, info_idx, names_idx):
""" When a subplot representing a specific channel is clicked on the
main topography plot, show a new figure containing FOOOF fit plot
for every condition """
fig = ax.figure
fig.delaxes(ax)
for idx, (report_key, report) in enumerate(reports.items()):
report_ax = fig.add_subplot(1, len(reports), idx + 1)
fooof = report.get_fooof(names_idx)
# Use plot function from fooof
fooof.plot(
ax=report_ax,
plot_peaks='dot',
add_legend=False,
)
# Add information about the fit to the axis title
text = ("Condition: " + str(report_key) + "\n" + "R squred: " +
format_float(fooof.r_squared_) + "\n" + "Peaks: \n")
for peak_params in fooof.peak_params_:
text = text + '{0} ({1}, {2})\n'.format(
*format_floats(peak_params))
report_ax.set_title(text)
fig.tight_layout()
def save_tfr_all_channels(experiment, tfr_name,
blmode, blstart, blend,
tmin, tmax, fmin, fmax):
""" Saves all channels of tfr item to a csv file.
"""
column_names = []
row_descs = []
csv_data = []
# accumulate csv contents
for subject in experiment.subjects.values():
tfr = subject.tfr.get(tfr_name)
if not tfr:
continue
ch_names = tfr.ch_names
for key, mne_tfr in tfr.content.items():
# crop and correct to baseline
mne_tfr = mne_tfr.copy().crop(tmin=tmin,
tmax=tmax,
fmin=fmin,
fmax=fmax)
times = mne_tfr.times
column_names = format_floats(times)
freqs = format_floats(mne_tfr.freqs)
data = mne.baseline.rescale(mne_tfr.data, times, baseline=(blstart, blend),
mode=blmode)
for ix in range(data.shape[0]):
for iy in range(data.shape[1]):
csv_data.append(data[ix, iy].tolist())
row_desc = (subject.name, key, ch_names[ix], freqs[iy])
row_descs.append(row_desc)
folder = filemanager.create_timestamped_folder(experiment)
fname = tfr_name + '_all_subjects_all_channels_tfr.csv'
path = os.path.join(folder, fname)
filemanager.save_csv(path, csv_data, column_names, row_descs)
logging.getLogger('ui_logger').info('Saved the csv file to ' + path)
def save_channel_averages(experiment, selected_name, log_transformed=False):
column_names = []
row_descs = []
csv_data = []
channel_groups = experiment.channel_groups
# accumulate csv contents
for subject in experiment.subjects.values():
spectrum = subject.spectrum.get(selected_name)
if not spectrum:
continue
ch_names = spectrum.ch_names
freqs = spectrum.freqs
info = subject.get_raw().info
for key, psd in spectrum.content.items():
data_labels, averaged_data = average_to_channel_groups(
psd, info, ch_names, channel_groups)
if not data_labels:
raise Exception('No channel groups matching the data found.')
if log_transformed:
csv_data.extend(10 * np.log10(averaged_data.tolist()))
else:
csv_data.extend(averaged_data.tolist())
column_names = format_floats(freqs)
for ch_type, area in data_labels:
row_desc = (subject.name, key, ch_type, area)
row_descs.append(row_desc)
folder = filemanager.create_timestamped_folder(experiment)
fname = selected_name + '_all_subjects_channel_averages_spectrum.csv'
path = os.path.join(folder, fname)
filemanager.save_csv(path, csv_data, column_names, row_descs)
logging.getLogger('ui_logger').info('Saved the csv file to ' + path)
def save_tse_channel_averages(experiment, tfr_name, blmode, blstart, blend,
tmin, tmax, fmin, fmax):
"""
"""
column_names = []
row_descs = []
csv_data = []
channel_groups = experiment.channel_groups
# accumulate csv contents
for subject in experiment.subjects.values():
tfr = subject.tfr.get(tfr_name)
if not tfr:
continue
times, tses = _compute_tse(tfr, fmin, fmax, tmin, tmax, blmode,
blstart, blend)
column_names = format_floats(times)
for key, tse in tses.items():
data_labels, averaged_data = average_to_channel_groups(
tse, tfr.info, tfr.ch_names, channel_groups)
if not data_labels:
raise Exception('No channel groups matching the data found.')
csv_data.extend(averaged_data.tolist())
for ch_type, area in data_labels:
row_desc = (subject.name, key, ch_type, area)
row_descs.append(row_desc)
folder = filemanager.create_timestamped_folder(experiment)
fname = tfr_name + '_all_subjects_channel_averages_tfr.csv'
path = os.path.join(folder, fname)
filemanager.save_csv(path, csv_data, column_names, row_descs)
logging.getLogger('ui_logger').info('Saved the csv file to ' + path)
def tfr_info(experiment, data, window):
""" Fills info element
"""
try:
selected_name = data['outputs']['tfr'][0]
tfr = experiment.active_subject.tfr[selected_name]
params = tfr.params
message = ""
if 'decim' in params:
message += 'Decimated by factor: {0}\n'.format(params['decim'])
if 'evoked_subtracted' in params:
message += 'Evoked subtracted: {0}\n'.format(
params['evoked_subtracted'])
if 'conditions' in params:
message += 'Conditions: ' + ', '.join(params['conditions']) + '\n'
if 'n_cycles' in params:
message += 'Cycles: ' + ', '.join(format_floats(
params['n_cycles'])) + '\n'
if hasattr(tfr, 'times'):
message += 'Times: {0}s - {1}s\n'.format(
format_float(tfr.times[0]), format_float(tfr.times[-1]))
if hasattr(tfr, 'freqs'):
message += 'Freqs: {0} - {1}\n'.format(format_float(tfr.freqs[0]),
format_float(tfr.freqs[-1]))
if 'groups' in params:
for key, names in params['groups'].items():
message += '\nGroup ' + str(key) + ': \n'
for name in names:
message += name + '\n'
except Exception as exc:
message = ""
return message
def save_all_channels(experiment, selected_name):
column_names = []
row_descs = []
csv_data = []
for subject in experiment.subjects.values():
spectrum = subject.spectrum.get(selected_name)
if not spectrum:
continue
for key, psd in spectrum.content.items():
csv_data.extend(psd.tolist())
column_names = format_floats(spectrum.freqs)
for ch_name in spectrum.ch_names:
row_desc = (subject.name, key, ch_name)
row_descs.append(row_desc)
folder = filemanager.create_timestamped_folder(experiment)
fname = selected_name + '_all_subjects_all_channels_spectrum.csv'
path = os.path.join(folder, fname)
filemanager.save_csv(path, csv_data, column_names, row_descs)
logging.getLogger('ui_logger').info('Saved the csv file to ' + path)
