def addNecessaryChanlsForEvoked(oEvoked: mne.Evoked):
temp = oEvoked.times.reshape(1, -1)
realTime_data = temp
# realTime_data = oEpoch.times.reshape(1,1,-1)
info_realTime = create_info(['realTime'], oEvoked.info['sfreq'], ['stim'])
realTime_raw = mne.EvokedArray(realTime_data, info_realTime)
oEvoked.add_channels([realTime_raw], force_update_info=True)
return oEvoked
def test_plot_mne_evoked():
"Test plotting evoked from the mne sample dataset"
data_path = mne.datasets.sample.data_path()
evoked_path = os.path.join(data_path, 'MEG', 'sample',
'sample_audvis-ave.fif')
evoked = Evoked(evoked_path, setno="Left Auditory")
p = plot.Array(evoked)
p.close()
def test_logging():
"""Test logging (to file)
"""
with open(fname_log, 'r') as old_log_file:
old_lines = clean_lines(old_log_file.readlines())
with open(fname_log_2, 'r') as old_log_file_2:
old_lines_2 = clean_lines(old_log_file_2.readlines())
if op.isfile(test_name):
os.remove(test_name)
# test it one way (printing default off)
set_log_file(test_name)
set_log_level('WARNING')
# should NOT print
evoked = Evoked(fname_evoked, condition=1)
with open(test_name) as fid:
assert_true(fid.readlines() == [])
# should NOT print
evoked = Evoked(fname_evoked, condition=1, verbose=False)
with open(test_name) as fid:
assert_true(fid.readlines() == [])
# should NOT print
evoked = Evoked(fname_evoked, condition=1, verbose='WARNING')
with open(test_name) as fid:
assert_true(fid.readlines() == [])
# SHOULD print
evoked = Evoked(fname_evoked, condition=1, verbose=True)
with open(test_name, 'r') as new_log_file:
new_lines = clean_lines(new_log_file.readlines())
assert_equal(new_lines, old_lines)
set_log_file(None) # Need to do this to close the old file
os.remove(test_name)
# now go the other way (printing default on)
set_log_file(test_name)
set_log_level('INFO')
# should NOT print
evoked = Evoked(fname_evoked, condition=1, verbose='WARNING')
with open(test_name) as fid:
assert_true(fid.readlines() == [])
# should NOT print
evoked = Evoked(fname_evoked, condition=1, verbose=False)
with open(test_name) as fid:
assert_true(fid.readlines() == [])
# SHOULD print
evoked = Evoked(fname_evoked, condition=1)
with open(test_name, 'r') as new_log_file:
new_lines = clean_lines(new_log_file.readlines())
with open(fname_log, 'r') as old_log_file:
assert_equal(new_lines, old_lines)
# check to make sure appending works (and as default, raises a warning)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
set_log_file(test_name, overwrite=False)
assert len(w) == 0
set_log_file(test_name)
assert len(w) == 1
evoked = Evoked(fname_evoked, condition=1)
with open(test_name, 'r') as new_log_file:
new_lines = clean_lines(new_log_file.readlines())
assert_equal(new_lines, old_lines_2)
# make sure overwriting works
set_log_file(test_name, overwrite=True)
# this line needs to be called to actually do some logging
evoked = Evoked(fname_evoked, condition=1)
del evoked
with open(test_name, 'r') as new_log_file:
new_lines = clean_lines(new_log_file.readlines())
assert_equal(new_lines, old_lines)
def time_label(t):
return "time=%0.2f ms" % (1e3 * t)
for subject in subjects:
print(subject)
meg_dir = '/Users/ea84/Dropbox/shepard_sourceloc/%s/' % (subject)
os.environ[
"SUBJECTS_DIR"] = '/Volumes/Server/MORPHLAB/Users/Ellie/Shepard/mri/'
evoked_fname = meg_dir + subject + '_shepard-evoked-ave.fif'
inv_fname = meg_dir + subject + '_shepard-inv.fif'
stc_fname = meg_dir + subject + '_shepard_evoked'
evoked = Evoked(evoked_fname)
inverse_operator = read_inverse_operator(inv_fname)
stc_evoked = apply_inverse(evoked,
inverse_operator,
lambda2,
method='dSPM')
stc_evoked.save(stc_fname)
brain = Brain(subject, 'split', 'partially_inflated', size=(800, 400))
for hemi in ['lh', 'rh']:
stc = read_stc(stc_fname + '-%s.stc' % hemi)
data = stc['data']
times = np.arange(data.shape[1]) * stc['tstep'] + stc['tmin']
brain.add_data(data,
colormap='RdBu',
vertices=stc['vertices'],
raw_input(
'NOTE: Save as subj_rejfile.pickled. \nPress enter when you are done rejecting epochs in the GUI...'
)
rejfile = eelbrain.load.unpickle(ica_rej_fname)
# create a mask to reject the bad epochs
rejs = rejfile['accept'].x
# mask epochs and info
epochs = epochs[rejs]
trial_info = trial_info[rejs]
# if evoked is made, load
# else make evoked to check for auditory response
if op.isfile(evoked_fname):
evoked = Evoked(evoked_fname)
else:
evoked = epochs.average()
evoked.save(evoked_fname)
# check for M100
evoked.plot()
# set metadata: allows you to specify more complex info about events,
# can use pandas-style queries to access subsets of data
epochs.metadata = trial_info
epochs.save(epochs_fname)
# SANITY CHECK!!:
assert (len(epochs.events) == len(trial_info))
#-------------------------------------------------------------------------------