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_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_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 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_content(self):
# if exists, delete first
self.delete_content()
for key, psd in self._content.items():
row_descs = [(ch_name,) for ch_name in self._ch_names]
column_names = self._freqs.tolist()
data = psd.tolist()
path = os.path.join(self._spectrum_directory,
self._name + '_' + str(key) + '.csv')
filemanager.save_csv(path, data, column_names, row_descs)
def test_save_and_load_csv():
with tempfile.TemporaryDirectory() as dirpath:
filepath = os.path.join(dirpath, 'data.csv')
data = np.array([[1,2,3], [4,5,6]])
column_names = ['A', 'B', 'C']
row_descs = [('X', 'Kissa'), ('Y', 'Koira')]
save_csv(filepath, data, column_names, row_descs)
loaded_column_names, loaded_row_descs, loaded_data = load_csv(filepath)
assert(loaded_column_names == column_names)
assert(loaded_row_descs[0] == row_descs[0])
assert(loaded_row_descs[1] == row_descs[1])
assert(np.array_equal(loaded_data, data))
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 save_content(self):
"""Saves spectral data and info structure to the spectrum directory.
"""
try:
# save info
info_path = os.path.join(self._directory, self._name + '-info.fif')
mne.io.meas_info.write_info(info_path, self._info)
self._params['info_set'] = True
# save data
for key, psd in self._content.items():
row_descs = [(ch_name, ) for ch_name in self.ch_names]
column_names = self._freqs.tolist()
data = psd.tolist()
path = os.path.join(self._directory,
self._name + '_' + str(key) + '.csv')
filemanager.save_csv(path, data, column_names, row_descs)
except Exception as exc:
raise Exception("Writing spectrums failed. Please check that the "
"entire experiment folder has write permissions.")
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)
def save_all_channels(experiment, selected_name):
""" Saves peak params and aperiodic params to a csv file for every
subject and channel and condition """
row_descs = []
csv_data = []
column_names = ['CF', 'Amp', 'BW',
'Aperiodic offset', 'Aperiodic exponent']
for subject in experiment.subjects.values():
fooof_item = subject.fooof_report.get(selected_name)
if not fooof_item:
continue
for key, report in fooof_item.content.items():
for ch_idx, ch_name in enumerate(fooof_item.params['ch_names']):
ch_report = report.get_fooof(ch_idx)
for peak in ch_report.peak_params_:
csv_data.append([format_float(peak[0]),
format_float(peak[1]),
format_float(peak[2]),
'', ''])
row_descs.append((subject.name, key, ch_name))
aparams = ch_report.aperiodic_params_
csv_data.append(['', '', '',
format_float(aparams[0]),
format_float(aparams[1])])
row_descs.append((subject.name, key, ch_name))
# Save the resulting csv into a output folder 'meggie way'
folder = create_timestamped_folder(experiment)
fname = selected_name + '_all_subject_all_channels_fooof.csv'
path = os.path.join(folder, fname)
save_csv(path, csv_data, column_names, row_descs)
logging.getLogger('ui_logger').info('Saved the csv file to ' + path)