def test_meg_field_interpolation_helmet():
"""Test interpolation of MEG field onto helmet
"""
evoked = read_evoked(evoked_fname, setno='Left Auditory')
info = evoked.info
surf = get_meg_helmet_surf(info)
# let's reduce the number of channels by a bunch to speed it up
info['bads'] = info['ch_names'][:200]
# bad ch_type
assert_raises(ValueError, make_surface_mapping, info, surf, 'foo')
# bad mode
assert_raises(ValueError, make_surface_mapping, info, surf, 'meg',
mode='foo')
# no picks
evoked_eeg = pick_types_evoked(evoked, meg=False, eeg=True)
assert_raises(RuntimeError, make_surface_mapping, evoked_eeg.info,
surf, 'meg')
# bad surface def
nn = surf['nn']
del surf['nn']
assert_raises(KeyError, make_surface_mapping, info, surf, 'meg')
surf['nn'] = nn
cf = surf['coord_frame']
del surf['coord_frame']
assert_raises(KeyError, make_surface_mapping, info, surf, 'meg')
surf['coord_frame'] = cf
# now do it
data = make_surface_mapping(info, surf, 'meg', mode='fast')
assert_array_equal(data.shape, (304, 106)) # data onto surf
def test_helmet():
"""Test loading helmet surfaces
"""
base_dir = op.join(op.dirname(__file__), '..', 'fiff')
fname_raw = op.join(base_dir, 'tests', 'data', 'test_raw.fif')
fname_kit_raw = op.join(base_dir, 'kit', 'tests', 'data', 'test_bin.fif')
fname_bti_raw = op.join(base_dir, 'bti', 'tests', 'data',
'exported4D_linux.fif')
fname_ctf_raw = op.join(base_dir, 'tests', 'data', 'test_ctf_raw.fif')
fname_trans = op.join(base_dir, 'tests', 'data',
'sample-audvis-raw-trans.txt')
trans = _get_mri_head_t_from_trans_file(fname_trans)
for fname in [fname_raw, fname_kit_raw, fname_bti_raw, fname_ctf_raw]:
helmet = get_meg_helmet_surf(read_info(fname), trans)
assert_equal(len(helmet['rr']), 304) # they all have 304 verts
assert_equal(len(helmet['rr']), len(helmet['nn']))
print(__doc__)
import numpy as np
import mne
data_path = mne.datasets.sample.data_path()
subjects_dir = data_path + '/subjects'
evoked_fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
trans_fname = data_path + '/MEG/sample/sample_audvis_raw-trans.fif'
setno = 'Left Auditory'
trans = mne.read_trans(trans_fname)
evoked = mne.fiff.read_evoked(evoked_fname, setno=setno,
baseline=(-0.2, 0.0))
# let's do this in MRI coordinates so they're easy to plot
helmet_surf = mne.get_meg_helmet_surf(evoked.info, trans)
head_surf = mne.get_head_surface('sample', subjects_dir=subjects_dir)
helmet_map = mne.make_surface_mapping(evoked.info, helmet_surf, 'meg', trans,
n_jobs=-1)
head_map = mne.make_surface_mapping(evoked.info, head_surf, 'eeg', trans,
n_jobs=-1)
# let's look at the N100
evoked.crop(0.09, 0.10)
evoked_eeg = mne.fiff.pick_types_evoked(evoked, meg=False, eeg=True)
evoked_meg = mne.fiff.pick_types_evoked(evoked, meg=True, eeg=False)
helmet_data = np.dot(helmet_map, evoked_meg.data[:, 0])
head_data = np.dot(head_map, evoked_eeg.data[:, 0])
# Plot them
