def test_read_evoked():
"""Test reading evokeds."""
for data_type in tconf.task_types:
all_annots = list()
for run_index in tconf.run_inds[:2]:
annots = hcp.read_annot(
data_type=data_type, run_index=run_index, **hcp_params)
all_annots.append(annots)
evokeds = hcp.read_evokeds(data_type=data_type,
kind='average', **hcp_params)
n_average = sum(ee.kind == 'average' for ee in evokeds)
assert_equal(n_average, len(evokeds))
n_chans = 248
if data_type == 'task_motor':
n_chans += 4
n_chans -= len(set(sum(
[an['channels']['all'] for an in all_annots], [])))
assert_equal(n_chans, len(evokeds[0].ch_names))
assert_true(
_check_bounds(evokeds[0].times,
tconf.epochs_bounds[data_type])
)
def official_ERF():
hcp_evokeds = hcp.read_evokeds(onset='stim', **hcp_params)
for ev in hcp_evokeds:
if not ev.comment == 'Wrkmem_LM-TIM-face_BT-diff_MODE-mag':
continue
# Once more we add and interpolate missing channels
evoked_hcp = preproc.interpolate_missing(ev, **hcp_params)
return evoked_hcp
def test_map_ch_coords_to_mne():
"""Test mapping of channel coords to MNE."""
data_type = 'task_working_memory'
hcp_evokeds = hcp.read_evokeds(onset='stim', data_type=data_type,
**hcp_params)
for evoked in hcp_evokeds:
if evoked.comment == 'Wrkmem_LM-TIM-face_BT-diff_MODE-mag':
break
old_coord = evoked.info['chs'][0]['loc']
map_ch_coords_to_mne(evoked)
new_coord = evoked.info['chs'][0]['loc']
assert_true((old_coord != new_coord).any())
def test_map_ch_coords_to_mne():
"""Test mapping of channel coords to MNE."""
data_type = 'task_working_memory'
hcp_evokeds = hcp.read_evokeds(onset='stim',
data_type=data_type,
**hcp_params)
for evoked in hcp_evokeds:
if evoked.comment == 'Wrkmem_LM-TIM-face_BT-diff_MODE-mag':
break
old_coord = evoked.info['chs'][0]['loc']
map_ch_coords_to_mne(evoked)
new_coord = evoked.info['chs'][0]['loc']
assert_true((old_coord != new_coord).any())
def test_interpolate_missing():
"""Test interpolation of missing channels."""
data_type = 'task_working_memory'
raw = hcp.read_raw(data_type='task_working_memory', run_index=0,
**hcp_params)
raw.load_data()
n_chan = len(raw.ch_names)
raw.drop_channels(['A1'])
assert_equal(len(raw.ch_names), n_chan - 1)
raw = interpolate_missing(raw, data_type=data_type, **hcp_params)
assert_equal(len(raw.ch_names), n_chan)
evoked = hcp.read_evokeds(data_type=data_type, **hcp_params)[0]
assert_equal(len(evoked.ch_names), 243)
evoked_int = interpolate_missing(evoked, data_type=data_type, **hcp_params)
assert_equal(len(evoked_int.ch_names), 248)
def test_interpolate_missing():
"""Test interpolation of missing channels."""
data_type = 'task_working_memory'
raw = hcp.read_raw(data_type='task_working_memory',
run_index=0,
**hcp_params)
raw.load_data()
n_chan = len(raw.ch_names)
raw.drop_channels(['A1'])
assert_equal(len(raw.ch_names), n_chan - 1)
raw = interpolate_missing(raw, data_type=data_type, **hcp_params)
assert_equal(len(raw.ch_names), n_chan)
evoked = hcp.read_evokeds(data_type=data_type, **hcp_params)[0]
assert_equal(len(evoked.ch_names), 243)
evoked_int = interpolate_missing(evoked, data_type=data_type, **hcp_params)
assert_equal(len(evoked_int.ch_names), 248)
subject = '100307' # our test subject
task = 'task_working_memory'
meg_sub_fol = op.join(MEG_DIR, subject)
fwd_fname = op.join(meg_sub_fol, '{}-fwd.fif'.format(subject))
inv_fname = op.join(meg_sub_fol, '{}-inv.fif'.format(subject))
noise_cov_fname = op.join(meg_sub_fol, '{}-noise-cov.fif'.format(subject, task))
stc_fname = op.join(meg_sub_fol, '{}-{}.stc'.format(subject, task))
n_jobs = 4
run_index = 0
##############################################################################
# We're reading the evoked data.
# These are the same as in :ref:`tut_plot_evoked`
hcp_evokeds = hcp.read_evokeds(onset='stim', subject=subject,
data_type=task, HCP_DIR=HCP_DIR)
for evoked in hcp_evokeds:
if not evoked.comment == 'Wrkmem_LM-TIM-face_BT-diff_MODE-mag':
continue
##############################################################################
# We'll now use a convenience function to get our forward and source models
# instead of computing them by hand.
# src_outputs = hcp.anatomy.compute_forward_stack(
# subject=subject, subjects_dir=subjects_dir,
# HCP_DIR=HCP_DIR, recordings_path=recordings_path,
# # speed up computations here. Setting `add_dist` to True may improve the
# # accuracy.
# src_params=dict(add_dist=False),
# info_from=dict(data_type=task, run_index=run_index))
##############################################################################
# we assume our data is inside a designated folder under $HOME
storage_dir = op.expanduser('~/mne-hcp-data')
hcp_path = op.join(storage_dir, 'HCP')
recordings_path = op.join(storage_dir, 'hcp-meg')
subjects_dir = op.join(storage_dir, 'hcp-subjects')
subject = '105923' # our test subject
data_type = 'task_working_memory'
run_index = 0
##############################################################################
# We're reading the evoked data.
# These are the same as in :ref:`tut_plot_evoked`
hcp_evokeds = hcp.read_evokeds(onset='stim', subject=subject,
data_type=data_type, hcp_path=hcp_path)
for evoked in hcp_evokeds:
if not evoked.comment == 'Wrkmem_LM-TIM-face_BT-diff_MODE-mag':
continue
##############################################################################
# We'll now use a convenience function to get our forward and source models
# instead of computing them by hand.
src_outputs = hcp.anatomy.compute_forward_stack(
subject=subject, subjects_dir=subjects_dir,
hcp_path=hcp_path, recordings_path=recordings_path,
# speed up computations here. Setting `add_dist` to True may improve the
# accuracy.
src_params=dict(add_dist=False),
info_from=dict(data_type=data_type, run_index=run_index))
##############################################################################
# we assume our data is inside a designated folder under $HOME
storage_dir = op.expanduser('~/mne-hcp-data')
hcp_path = op.join(storage_dir, 'HCP')
recordings_path = op.join(storage_dir, 'hcp-meg')
subjects_dir = op.join(storage_dir, 'hcp-subjects')
subject = '105923' # our test subject
data_type = 'task_working_memory'
run_index = 0
##############################################################################
# We're reading the evoked data.
# These are the same as in :ref:`tut_plot_evoked`
hcp_evokeds = hcp.read_evokeds(onset='stim', subject=subject,
data_type=data_type, hcp_path=hcp_path)
for evoked in hcp_evokeds:
if not evoked.comment == 'Wrkmem_LM-TIM-face_BT-diff_MODE-mag':
continue
##############################################################################
# We'll now use a convenience function to get our forward and source models
# instead of computing them by hand.
src_outputs = hcp.anatomy.compute_forward_stack(
subject=subject, subjects_dir=subjects_dir,
hcp_path=hcp_path, recordings_path=recordings_path,
# speed up computations here. Setting `add_dist` to True may improve the
# accuracy.
src_params=dict(add_dist=False),
info_from=dict(data_type=data_type, run_index=run_index))
mne.set_log_level('WARNING')
##############################################################################
# we assume our data is inside a designated folder under $HOME
# and set the IO params
storage_dir = op.expanduser('~')
hcp_params = dict(hcp_path=op.join(storage_dir, 'mne-hcp-data', 'HCP'),
subject='105923',
data_type='task_working_memory')
##############################################################################
# we take the some evoked and create an interpolated copy
evoked = hcp.read_evokeds(**hcp_params)[0]
# The HCP pipelines don't interpolate missing channels
print('%i channels out of 248 expected' % len(evoked.ch_names))
evoked_interpolated = preproc.interpolate_missing(evoked, **hcp_params)
##############################################################################
# Let's visualize what has changed!
# we calculate the difference ...
bads = set(evoked_interpolated.ch_names) - set(evoked.ch_names)
print(bads)
# ... and mark the respective channels as bad ...
evoked_interpolated.info['bads'] += list(bads)
evoked.apply_baseline()
evoked = preproc.interpolate_missing(evoked, **hcp_params)
evokeds_from_epochs_hcp.append(evoked)
##############################################################################
# Finally we can read the actual official ERF file
#
# These are obtained from the 'eravg' pipelines.
# We read the matlab file, MNE-HCP is doing some conversions, and then we
# search our condition of interest. Here we're looking at the image as onset.
# and we want the average, not the standard deviation.
evoked_hcp = None
hcp_evokeds = hcp.read_evokeds(onset='stim', **hcp_params)
for ev in hcp_evokeds:
if not ev.comment == 'Wrkmem_LM-TIM-face_BT-diff_MODE-mag':
continue
# Once more we add and interpolate missing channels
evoked_hcp = preproc.interpolate_missing(ev, **hcp_params)
##############################################################################
# Time to compare the outputs
#
evoked = mne.combine_evoked(evokeds, weights='equal')
evoked_from_epochs_hcp = mne.combine_evoked(
evoked.apply_baseline()
evoked = preproc.interpolate_missing(evoked, **hcp_params)
evokeds_from_epochs_hcp.append(evoked)
##############################################################################
# Finally we can read the actual official ERF file
#
# These are obtained from the 'eravg' pipelines.
# We read the matlab file, MNE-HCP is doing some conversions, and then we
# search our condition of interest. Here we're looking at the image as onset.
# and we want the average, not the standard deviation.
evoked_hcp = None
hcp_evokeds = hcp.read_evokeds(onset='stim', **hcp_params)
for ev in hcp_evokeds:
if not ev.comment == 'Wrkmem_LM-TIM-face_BT-diff_MODE-mag':
continue
# Once more we add and interpolate missing channels
evoked_hcp = preproc.interpolate_missing(ev, **hcp_params)
##############################################################################
# Time to compare the outputs
#
evoked = mne.combine_evoked(evokeds, weights='equal')
evoked_from_epochs_hcp = mne.combine_evoked(