def test_make_eeg_average_ref_proj():
"""Test EEG average reference projection."""
raw = read_raw_fif(raw_fname, preload=True)
eeg = pick_types(raw.info, meg=False, eeg=True)
# No average EEG reference
assert not np.all(raw._data[eeg].mean(axis=0) < 1e-19)
# Apply average EEG reference
car = make_eeg_average_ref_proj(raw.info)
reref = raw.copy()
reref.add_proj(car)
reref.apply_proj()
assert_array_almost_equal(reref._data[eeg].mean(axis=0), 0, decimal=19)
# Error when custom reference has already been applied
with raw.info._unlock():
raw.info['custom_ref_applied'] = True
pytest.raises(RuntimeError, make_eeg_average_ref_proj, raw.info)
# test that an average EEG ref is not added when doing proj
raw.set_eeg_reference(projection=True)
assert _has_eeg_average_ref_proj(raw.info['projs'])
raw.del_proj(idx=-1)
assert not _has_eeg_average_ref_proj(raw.info['projs'])
raw.apply_proj()
assert not _has_eeg_average_ref_proj(raw.info['projs'])
def test_make_eeg_average_ref_proj():
"""Test EEG average reference projection."""
raw = read_raw_fif(raw_fname, preload=True)
eeg = mne.pick_types(raw.info, meg=False, eeg=True)
# No average EEG reference
assert not np.all(raw._data[eeg].mean(axis=0) < 1e-19)
# Apply average EEG reference
car = make_eeg_average_ref_proj(raw.info)
reref = raw.copy()
reref.add_proj(car)
reref.apply_proj()
assert_array_almost_equal(reref._data[eeg].mean(axis=0), 0, decimal=19)
# Error when custom reference has already been applied
raw.info['custom_ref_applied'] = True
pytest.raises(RuntimeError, make_eeg_average_ref_proj, raw.info)
# test that an average EEG ref is not added when doing proj
raw.set_eeg_reference(projection=True)
assert _has_eeg_average_ref_proj(raw.info['projs'])
raw.del_proj(idx=-1)
assert not _has_eeg_average_ref_proj(raw.info['projs'])
raw.apply_proj()
assert not _has_eeg_average_ref_proj(raw.info['projs'])
def test_sensitivity_maps():
"""Test sensitivity map computation."""
fwd = read_forward_solution(fwd_fname)
fwd = convert_forward_solution(fwd, surf_ori=True)
projs = read_proj(eog_fname)
projs.extend(read_proj(ecg_fname))
decim = 6
for ch_type in ['eeg', 'grad', 'mag']:
w = read_source_estimate(sensmap_fname % (ch_type, 'lh')).data
stc = sensitivity_map(fwd,
projs=None,
ch_type=ch_type,
mode='free',
exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
assert stc.subject == 'sample'
# let's just make sure the others run
if ch_type == 'grad':
# fixed (2)
w = read_source_estimate(sensmap_fname % (ch_type, '2-lh')).data
stc = sensitivity_map(fwd,
projs=None,
mode='fixed',
ch_type=ch_type,
exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
if ch_type == 'mag':
# ratio (3)
w = read_source_estimate(sensmap_fname % (ch_type, '3-lh')).data
stc = sensitivity_map(fwd,
projs=None,
mode='ratio',
ch_type=ch_type,
exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
if ch_type == 'eeg':
# radiality (4), angle (5), remaining (6), and dampening (7)
modes = ['radiality', 'angle', 'remaining', 'dampening']
ends = ['4-lh', '5-lh', '6-lh', '7-lh']
for mode, end in zip(modes, ends):
w = read_source_estimate(sensmap_fname % (ch_type, end)).data
stc = sensitivity_map(fwd,
projs=projs,
mode=mode,
ch_type=ch_type,
exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
# test corner case for EEG
stc = sensitivity_map(fwd,
projs=[make_eeg_average_ref_proj(fwd['info'])],
ch_type='eeg',
exclude='bads')
# test corner case for projs being passed but no valid ones (#3135)
pytest.raises(ValueError, sensitivity_map, fwd, projs=None, mode='angle')
pytest.raises(RuntimeError, sensitivity_map, fwd, projs=[], mode='angle')
# test volume source space
fname = op.join(sample_path, 'sample_audvis_trunc-meg-vol-7-fwd.fif')
fwd = read_forward_solution(fname)
sensitivity_map(fwd)
def test_sensitivity_maps():
"""Test sensitivity map computation"""
fwd = mne.read_forward_solution(fwd_fname, surf_ori=True)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
proj_eog = read_proj(eog_fname)
decim = 6
for ch_type in ['eeg', 'grad', 'mag']:
w = read_source_estimate(sensmap_fname % (ch_type, 'lh')).data
stc = sensitivity_map(fwd,
projs=None,
ch_type=ch_type,
mode='free',
exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
assert_true(stc.subject == 'sample')
# let's just make sure the others run
if ch_type == 'grad':
# fixed (2)
w = read_source_estimate(sensmap_fname % (ch_type, '2-lh')).data
stc = sensitivity_map(fwd,
projs=None,
mode='fixed',
ch_type=ch_type,
exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
if ch_type == 'mag':
# ratio (3)
w = read_source_estimate(sensmap_fname % (ch_type, '3-lh')).data
stc = sensitivity_map(fwd,
projs=None,
mode='ratio',
ch_type=ch_type,
exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
if ch_type == 'eeg':
# radiality (4), angle (5), remaining (6), and dampening (7)
modes = ['radiality', 'angle', 'remaining', 'dampening']
ends = ['4-lh', '5-lh', '6-lh', '7-lh']
for mode, end in zip(modes, ends):
w = read_source_estimate(sensmap_fname % (ch_type, end)).data
stc = sensitivity_map(fwd,
projs=proj_eog,
mode=mode,
ch_type=ch_type,
exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
# test corner case for EEG
stc = sensitivity_map(fwd,
projs=[make_eeg_average_ref_proj(fwd['info'])],
ch_type='eeg',
exclude='bads')
# test volume source space
fname = op.join(sample_path, 'sample_audvis_trunc-meg-vol-7-fwd.fif')
fwd = mne.read_forward_solution(fname)
sensitivity_map(fwd)
def test_sensitivity_maps():
"""Test sensitivity map computation."""
fwd = mne.read_forward_solution(fwd_fname)
fwd = mne.convert_forward_solution(fwd, surf_ori=True)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
projs = read_proj(eog_fname)
projs.extend(read_proj(ecg_fname))
decim = 6
for ch_type in ['eeg', 'grad', 'mag']:
w = read_source_estimate(sensmap_fname % (ch_type, 'lh')).data
stc = sensitivity_map(fwd, projs=None, ch_type=ch_type,
mode='free', exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
assert_true(stc.subject == 'sample')
# let's just make sure the others run
if ch_type == 'grad':
# fixed (2)
w = read_source_estimate(sensmap_fname % (ch_type, '2-lh')).data
stc = sensitivity_map(fwd, projs=None, mode='fixed',
ch_type=ch_type, exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
if ch_type == 'mag':
# ratio (3)
w = read_source_estimate(sensmap_fname % (ch_type, '3-lh')).data
stc = sensitivity_map(fwd, projs=None, mode='ratio',
ch_type=ch_type, exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
if ch_type == 'eeg':
# radiality (4), angle (5), remaining (6), and dampening (7)
modes = ['radiality', 'angle', 'remaining', 'dampening']
ends = ['4-lh', '5-lh', '6-lh', '7-lh']
for mode, end in zip(modes, ends):
w = read_source_estimate(sensmap_fname % (ch_type, end)).data
stc = sensitivity_map(fwd, projs=projs, mode=mode,
ch_type=ch_type, exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
# test corner case for EEG
stc = sensitivity_map(fwd, projs=[make_eeg_average_ref_proj(fwd['info'])],
ch_type='eeg', exclude='bads')
# test corner case for projs being passed but no valid ones (#3135)
assert_raises(ValueError, sensitivity_map, fwd, projs=None, mode='angle')
assert_raises(RuntimeError, sensitivity_map, fwd, projs=[], mode='angle')
# test volume source space
fname = op.join(sample_path, 'sample_audvis_trunc-meg-vol-7-fwd.fif')
fwd = mne.read_forward_solution(fname)
sensitivity_map(fwd)
def test_make_eeg_average_ref_proj():
"""Test EEG average reference projection"""
raw = Raw(raw_fname, add_eeg_ref=False, preload=True)
eeg = mne.pick_types(raw.info, meg=False, eeg=True)
# No average EEG reference
assert_true(not np.all(raw._data[eeg].mean(axis=0) < 1e-19))
# Apply average EEG reference
car = make_eeg_average_ref_proj(raw.info)
reref = raw.copy()
reref.add_proj(car)
reref.apply_proj()
assert_array_almost_equal(reref._data[eeg].mean(axis=0), 0, decimal=19)
# Error when custom reference has already been applied
raw.info['custom_ref_applied'] = True
assert_raises(RuntimeError, make_eeg_average_ref_proj, raw.info)
def test_make_eeg_average_ref_proj():
"""Test EEG average reference projection"""
raw = Raw(raw_fname, add_eeg_ref=False, preload=True)
eeg = mne.pick_types(raw.info, meg=False, eeg=True)
# No average EEG reference
assert_true(not np.all(raw._data[eeg].mean(axis=0) < 1e-19))
# Apply average EEG reference
car = make_eeg_average_ref_proj(raw.info)
reref = raw.copy()
reref.add_proj(car)
reref.apply_proj()
assert_array_almost_equal(reref._data[eeg].mean(axis=0), 0, decimal=19)
# Error when custom reference has already been applied
raw.info['custom_ref_applied'] = True
assert_raises(RuntimeError, make_eeg_average_ref_proj, raw.info)
def test_sensitivity_maps():
"""Test sensitivity map computation."""
fwd = mne.read_forward_solution(fwd_fname, surf_ori=True)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
projs = read_proj(eog_fname)
projs.extend(read_proj(ecg_fname))
decim = 6
for ch_type in ["eeg", "grad", "mag"]:
w = read_source_estimate(sensmap_fname % (ch_type, "lh")).data
stc = sensitivity_map(fwd, projs=None, ch_type=ch_type, mode="free", exclude="bads")
assert_array_almost_equal(stc.data, w, decim)
assert_true(stc.subject == "sample")
# let's just make sure the others run
if ch_type == "grad":
# fixed (2)
w = read_source_estimate(sensmap_fname % (ch_type, "2-lh")).data
stc = sensitivity_map(fwd, projs=None, mode="fixed", ch_type=ch_type, exclude="bads")
assert_array_almost_equal(stc.data, w, decim)
if ch_type == "mag":
# ratio (3)
w = read_source_estimate(sensmap_fname % (ch_type, "3-lh")).data
stc = sensitivity_map(fwd, projs=None, mode="ratio", ch_type=ch_type, exclude="bads")
assert_array_almost_equal(stc.data, w, decim)
if ch_type == "eeg":
# radiality (4), angle (5), remaining (6), and dampening (7)
modes = ["radiality", "angle", "remaining", "dampening"]
ends = ["4-lh", "5-lh", "6-lh", "7-lh"]
for mode, end in zip(modes, ends):
w = read_source_estimate(sensmap_fname % (ch_type, end)).data
stc = sensitivity_map(fwd, projs=projs, mode=mode, ch_type=ch_type, exclude="bads")
assert_array_almost_equal(stc.data, w, decim)
# test corner case for EEG
stc = sensitivity_map(fwd, projs=[make_eeg_average_ref_proj(fwd["info"])], ch_type="eeg", exclude="bads")
# test corner case for projs being passed but no valid ones (#3135)
assert_raises(ValueError, sensitivity_map, fwd, projs=None, mode="angle")
assert_raises(RuntimeError, sensitivity_map, fwd, projs=[], mode="angle")
# test volume source space
fname = op.join(sample_path, "sample_audvis_trunc-meg-vol-7-fwd.fif")
fwd = mne.read_forward_solution(fname)
sensitivity_map(fwd)
def test_sensitivity_maps():
"""Test sensitivity map computation"""
fwd = mne.read_forward_solution(fwd_fname, surf_ori=True)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
proj_eog = read_proj(eog_fname)
decim = 6
for ch_type in ['eeg', 'grad', 'mag']:
w = read_source_estimate(sensmap_fname % (ch_type, 'lh')).data
stc = sensitivity_map(fwd, projs=None, ch_type=ch_type,
mode='free', exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
assert_true(stc.subject == 'sample')
# let's just make sure the others run
if ch_type == 'grad':
# fixed (2)
w = read_source_estimate(sensmap_fname % (ch_type, '2-lh')).data
stc = sensitivity_map(fwd, projs=None, mode='fixed',
ch_type=ch_type, exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
if ch_type == 'mag':
# ratio (3)
w = read_source_estimate(sensmap_fname % (ch_type, '3-lh')).data
stc = sensitivity_map(fwd, projs=None, mode='ratio',
ch_type=ch_type, exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
if ch_type == 'eeg':
# radiality (4), angle (5), remaining (6), and dampening (7)
modes = ['radiality', 'angle', 'remaining', 'dampening']
ends = ['4-lh', '5-lh', '6-lh', '7-lh']
for mode, end in zip(modes, ends):
w = read_source_estimate(sensmap_fname % (ch_type, end)).data
stc = sensitivity_map(fwd, projs=proj_eog, mode=mode,
ch_type=ch_type, exclude='bads')
assert_array_almost_equal(stc.data, w, decim)
# test corner case for EEG
stc = sensitivity_map(fwd, projs=[make_eeg_average_ref_proj(fwd['info'])],
ch_type='eeg', exclude='bads')
def test_dipole_fitting():
"""Test dipole fitting"""
amp = 10e-9
tempdir = _TempDir()
rng = np.random.RandomState(0)
fname_dtemp = op.join(tempdir, 'test.dip')
fname_sim = op.join(tempdir, 'test-ave.fif')
fwd = convert_forward_solution(read_forward_solution(fname_fwd),
surf_ori=False, force_fixed=True)
evoked = read_evokeds(fname_evo)[0]
cov = read_cov(fname_cov)
n_per_hemi = 5
vertices = [np.sort(rng.permutation(s['vertno'])[:n_per_hemi])
for s in fwd['src']]
nv = sum(len(v) for v in vertices)
stc = SourceEstimate(amp * np.eye(nv), vertices, 0, 0.001)
with warnings.catch_warnings(record=True): # semi-def cov
evoked = generate_evoked(fwd, stc, evoked, cov, snr=20,
random_state=rng)
# For speed, let's use a subset of channels (strange but works)
picks = np.sort(np.concatenate([
pick_types(evoked.info, meg=True, eeg=False)[::2],
pick_types(evoked.info, meg=False, eeg=True)[::2]]))
evoked.pick_channels([evoked.ch_names[p] for p in picks])
evoked.add_proj(make_eeg_average_ref_proj(evoked.info))
write_evokeds(fname_sim, evoked)
# Run MNE-C version
run_subprocess([
'mne_dipole_fit', '--meas', fname_sim, '--meg', '--eeg',
'--noise', fname_cov, '--dip', fname_dtemp,
'--mri', fname_fwd, '--reg', '0', '--tmin', '0',
])
dip_c = read_dipole(fname_dtemp)
# Run mne-python version
sphere = make_sphere_model(head_radius=0.1)
dip, residuals = fit_dipole(evoked, fname_cov, sphere, fname_fwd)
# Sanity check: do our residuals have less power than orig data?
data_rms = np.sqrt(np.sum(evoked.data ** 2, axis=0))
resi_rms = np.sqrt(np.sum(residuals ** 2, axis=0))
assert_true((data_rms > resi_rms).all())
# Compare to original points
transform_surface_to(fwd['src'][0], 'head', fwd['mri_head_t'])
transform_surface_to(fwd['src'][1], 'head', fwd['mri_head_t'])
src_rr = np.concatenate([s['rr'][v] for s, v in zip(fwd['src'], vertices)],
axis=0)
src_nn = np.concatenate([s['nn'][v] for s, v in zip(fwd['src'], vertices)],
axis=0)
# MNE-C skips the last "time" point :(
dip.crop(dip_c.times[0], dip_c.times[-1])
src_rr, src_nn = src_rr[:-1], src_nn[:-1]
# check that we did at least as well
corrs, dists, gc_dists, amp_errs, gofs = [], [], [], [], []
for d in (dip_c, dip):
new = d.pos
diffs = new - src_rr
corrs += [np.corrcoef(src_rr.ravel(), new.ravel())[0, 1]]
dists += [np.sqrt(np.mean(np.sum(diffs * diffs, axis=1)))]
gc_dists += [180 / np.pi * np.mean(np.arccos(np.sum(src_nn * d.ori,
axis=1)))]
amp_errs += [np.sqrt(np.mean((amp - d.amplitude) ** 2))]
gofs += [np.mean(d.gof)]
assert_true(dists[0] >= dists[1], 'dists: %s' % dists)
assert_true(corrs[0] <= corrs[1], 'corrs: %s' % corrs)
assert_true(gc_dists[0] >= gc_dists[1], 'gc-dists (ori): %s' % gc_dists)
assert_true(amp_errs[0] >= amp_errs[1], 'amplitude errors: %s' % amp_errs)
def test_dipole_fitting():
"""Test dipole fitting."""
amp = 100e-9
tempdir = _TempDir()
rng = np.random.RandomState(0)
fname_dtemp = op.join(tempdir, 'test.dip')
fname_sim = op.join(tempdir, 'test-ave.fif')
fwd = convert_forward_solution(read_forward_solution(fname_fwd),
surf_ori=False,
force_fixed=True,
use_cps=True)
evoked = read_evokeds(fname_evo)[0]
cov = read_cov(fname_cov)
n_per_hemi = 5
vertices = [
np.sort(rng.permutation(s['vertno'])[:n_per_hemi]) for s in fwd['src']
]
nv = sum(len(v) for v in vertices)
stc = SourceEstimate(amp * np.eye(nv), vertices, 0, 0.001)
evoked = simulate_evoked(fwd,
stc,
evoked.info,
cov,
nave=evoked.nave,
random_state=rng)
# For speed, let's use a subset of channels (strange but works)
picks = np.sort(
np.concatenate([
pick_types(evoked.info, meg=True, eeg=False)[::2],
pick_types(evoked.info, meg=False, eeg=True)[::2]
]))
evoked.pick_channels([evoked.ch_names[p] for p in picks])
evoked.add_proj(make_eeg_average_ref_proj(evoked.info))
write_evokeds(fname_sim, evoked)
# Run MNE-C version
run_subprocess([
'mne_dipole_fit',
'--meas',
fname_sim,
'--meg',
'--eeg',
'--noise',
fname_cov,
'--dip',
fname_dtemp,
'--mri',
fname_fwd,
'--reg',
'0',
'--tmin',
'0',
])
dip_c = read_dipole(fname_dtemp)
# Run mne-python version
sphere = make_sphere_model(head_radius=0.1)
with pytest.warns(RuntimeWarning, match='projection'):
dip, residuals = fit_dipole(evoked, cov, sphere, fname_fwd)
# Sanity check: do our residuals have less power than orig data?
data_rms = np.sqrt(np.sum(evoked.data**2, axis=0))
resi_rms = np.sqrt(np.sum(residuals**2, axis=0))
assert (data_rms > resi_rms * 0.95).all(), \
'%s (factor: %s)' % ((data_rms / resi_rms).min(), 0.95)
# Compare to original points
transform_surface_to(fwd['src'][0], 'head', fwd['mri_head_t'])
transform_surface_to(fwd['src'][1], 'head', fwd['mri_head_t'])
assert_equal(fwd['src'][0]['coord_frame'], FIFF.FIFFV_COORD_HEAD)
src_rr = np.concatenate([s['rr'][v] for s, v in zip(fwd['src'], vertices)],
axis=0)
src_nn = np.concatenate([s['nn'][v] for s, v in zip(fwd['src'], vertices)],
axis=0)
# MNE-C skips the last "time" point :(
out = dip.crop(dip_c.times[0], dip_c.times[-1])
assert (dip is out)
src_rr, src_nn = src_rr[:-1], src_nn[:-1]
# check that we did about as well
corrs, dists, gc_dists, amp_errs, gofs = [], [], [], [], []
for d in (dip_c, dip):
new = d.pos
diffs = new - src_rr
corrs += [np.corrcoef(src_rr.ravel(), new.ravel())[0, 1]]
dists += [np.sqrt(np.mean(np.sum(diffs * diffs, axis=1)))]
gc_dists += [
180 / np.pi * np.mean(np.arccos(np.sum(src_nn * d.ori, axis=1)))
]
amp_errs += [np.sqrt(np.mean((amp - d.amplitude)**2))]
gofs += [np.mean(d.gof)]
if os.getenv('TRAVIS', 'false').lower() == 'true' and \
'OPENBLAS_NUM_THREADS' in os.environ:
# XXX possibly some OpenBLAS numerical differences make
# things slightly worse for us
factor = 0.7
else:
factor = 0.8
assert dists[0] / factor >= dists[1], 'dists: %s' % dists
assert corrs[0] * factor <= corrs[1], 'corrs: %s' % corrs
assert gc_dists[0] / factor >= gc_dists[1] * 0.8, \
'gc-dists (ori): %s' % gc_dists
assert amp_errs[0] / factor >= amp_errs[1],\
'amplitude errors: %s' % amp_errs
# This one is weird because our cov/sim/picking is weird
assert gofs[0] * factor <= gofs[1] * 2, 'gof: %s' % gofs
def test_dipole_fitting():
"""Test dipole fitting."""
amp = 10e-9
tempdir = _TempDir()
rng = np.random.RandomState(0)
fname_dtemp = op.join(tempdir, 'test.dip')
fname_sim = op.join(tempdir, 'test-ave.fif')
fwd = convert_forward_solution(read_forward_solution(fname_fwd),
surf_ori=False, force_fixed=True)
evoked = read_evokeds(fname_evo)[0]
cov = read_cov(fname_cov)
n_per_hemi = 5
vertices = [np.sort(rng.permutation(s['vertno'])[:n_per_hemi])
for s in fwd['src']]
nv = sum(len(v) for v in vertices)
stc = SourceEstimate(amp * np.eye(nv), vertices, 0, 0.001)
evoked = simulate_evoked(fwd, stc, evoked.info, cov, snr=20,
random_state=rng)
# For speed, let's use a subset of channels (strange but works)
picks = np.sort(np.concatenate([
pick_types(evoked.info, meg=True, eeg=False)[::2],
pick_types(evoked.info, meg=False, eeg=True)[::2]]))
evoked.pick_channels([evoked.ch_names[p] for p in picks])
evoked.add_proj(make_eeg_average_ref_proj(evoked.info))
write_evokeds(fname_sim, evoked)
# Run MNE-C version
run_subprocess([
'mne_dipole_fit', '--meas', fname_sim, '--meg', '--eeg',
'--noise', fname_cov, '--dip', fname_dtemp,
'--mri', fname_fwd, '--reg', '0', '--tmin', '0',
])
dip_c = read_dipole(fname_dtemp)
# Run mne-python version
sphere = make_sphere_model(head_radius=0.1)
dip, residuals = fit_dipole(evoked, fname_cov, sphere, fname_fwd)
# Sanity check: do our residuals have less power than orig data?
data_rms = np.sqrt(np.sum(evoked.data ** 2, axis=0))
resi_rms = np.sqrt(np.sum(residuals ** 2, axis=0))
factor = 1.
# XXX weird, inexplicable differenc for 3.5 build we'll assume is due to
# Anaconda bug for now...
if os.getenv('TRAVIS', 'false') == 'true' and \
sys.version[:3] in ('3.5', '2.7'):
factor = 0.8
assert_true((data_rms > factor * resi_rms).all(),
msg='%s (factor: %s)' % ((data_rms / resi_rms).min(), factor))
# Compare to original points
transform_surface_to(fwd['src'][0], 'head', fwd['mri_head_t'])
transform_surface_to(fwd['src'][1], 'head', fwd['mri_head_t'])
src_rr = np.concatenate([s['rr'][v] for s, v in zip(fwd['src'], vertices)],
axis=0)
src_nn = np.concatenate([s['nn'][v] for s, v in zip(fwd['src'], vertices)],
axis=0)
# MNE-C skips the last "time" point :(
dip.crop(dip_c.times[0], dip_c.times[-1])
src_rr, src_nn = src_rr[:-1], src_nn[:-1]
# check that we did at least as well
corrs, dists, gc_dists, amp_errs, gofs = [], [], [], [], []
for d in (dip_c, dip):
new = d.pos
diffs = new - src_rr
corrs += [np.corrcoef(src_rr.ravel(), new.ravel())[0, 1]]
dists += [np.sqrt(np.mean(np.sum(diffs * diffs, axis=1)))]
gc_dists += [180 / np.pi * np.mean(np.arccos(np.sum(src_nn * d.ori,
axis=1)))]
amp_errs += [np.sqrt(np.mean((amp - d.amplitude) ** 2))]
gofs += [np.mean(d.gof)]
assert_true(dists[0] >= dists[1] * factor, 'dists: %s' % dists)
assert_true(corrs[0] <= corrs[1] / factor, 'corrs: %s' % corrs)
assert_true(gc_dists[0] >= gc_dists[1] * factor,
'gc-dists (ori): %s' % gc_dists)
assert_true(amp_errs[0] >= amp_errs[1] * factor,
'amplitude errors: %s' % amp_errs)
assert_true(gofs[0] <= gofs[1] / factor, 'gof: %s' % gofs)
def test_dipole_fitting():
"""Test dipole fitting"""
amp = 10e-9
tempdir = _TempDir()
rng = np.random.RandomState(0)
fname_dtemp = op.join(tempdir, "test.dip")
fname_sim = op.join(tempdir, "test-ave.fif")
fwd = convert_forward_solution(read_forward_solution(fname_fwd), surf_ori=False, force_fixed=True)
evoked = read_evokeds(fname_evo)[0]
cov = read_cov(fname_cov)
n_per_hemi = 5
vertices = [np.sort(rng.permutation(s["vertno"])[:n_per_hemi]) for s in fwd["src"]]
nv = sum(len(v) for v in vertices)
stc = SourceEstimate(amp * np.eye(nv), vertices, 0, 0.001)
evoked = simulate_evoked(fwd, stc, evoked.info, cov, snr=20, random_state=rng)
# For speed, let's use a subset of channels (strange but works)
picks = np.sort(
np.concatenate(
[pick_types(evoked.info, meg=True, eeg=False)[::2], pick_types(evoked.info, meg=False, eeg=True)[::2]]
)
)
evoked.pick_channels([evoked.ch_names[p] for p in picks])
evoked.add_proj(make_eeg_average_ref_proj(evoked.info))
write_evokeds(fname_sim, evoked)
# Run MNE-C version
run_subprocess(
[
"mne_dipole_fit",
"--meas",
fname_sim,
"--meg",
"--eeg",
"--noise",
fname_cov,
"--dip",
fname_dtemp,
"--mri",
fname_fwd,
"--reg",
"0",
"--tmin",
"0",
]
)
dip_c = read_dipole(fname_dtemp)
# Run mne-python version
sphere = make_sphere_model(head_radius=0.1)
dip, residuals = fit_dipole(evoked, fname_cov, sphere, fname_fwd)
# Sanity check: do our residuals have less power than orig data?
data_rms = np.sqrt(np.sum(evoked.data ** 2, axis=0))
resi_rms = np.sqrt(np.sum(residuals ** 2, axis=0))
factor = 1.0
# XXX weird, inexplicable differenc for 3.5 build we'll assume is due to
# Anaconda bug for now...
if os.getenv("TRAVIS", "false") == "true" and sys.version[:3] in ("3.5", "2.7"):
factor = 0.8
assert_true((data_rms > factor * resi_rms).all(), msg="%s (factor: %s)" % ((data_rms / resi_rms).min(), factor))
# Compare to original points
transform_surface_to(fwd["src"][0], "head", fwd["mri_head_t"])
transform_surface_to(fwd["src"][1], "head", fwd["mri_head_t"])
src_rr = np.concatenate([s["rr"][v] for s, v in zip(fwd["src"], vertices)], axis=0)
src_nn = np.concatenate([s["nn"][v] for s, v in zip(fwd["src"], vertices)], axis=0)
# MNE-C skips the last "time" point :(
dip.crop(dip_c.times[0], dip_c.times[-1])
src_rr, src_nn = src_rr[:-1], src_nn[:-1]
# check that we did at least as well
corrs, dists, gc_dists, amp_errs, gofs = [], [], [], [], []
for d in (dip_c, dip):
new = d.pos
diffs = new - src_rr
corrs += [np.corrcoef(src_rr.ravel(), new.ravel())[0, 1]]
dists += [np.sqrt(np.mean(np.sum(diffs * diffs, axis=1)))]
gc_dists += [180 / np.pi * np.mean(np.arccos(np.sum(src_nn * d.ori, axis=1)))]
amp_errs += [np.sqrt(np.mean((amp - d.amplitude) ** 2))]
gofs += [np.mean(d.gof)]
assert_true(dists[0] >= dists[1] * factor, "dists: %s" % dists)
assert_true(corrs[0] <= corrs[1] / factor, "corrs: %s" % corrs)
assert_true(gc_dists[0] >= gc_dists[1] * factor, "gc-dists (ori): %s" % gc_dists)
assert_true(amp_errs[0] >= amp_errs[1] * factor, "amplitude errors: %s" % amp_errs)
assert_true(gofs[0] <= gofs[1] / factor, "gof: %s" % gofs)
def test_dipole_fitting(tmp_path):
"""Test dipole fitting."""
amp = 100e-9
tempdir = str(tmp_path)
rng = np.random.RandomState(0)
fname_dtemp = op.join(tempdir, 'test.dip')
fname_sim = op.join(tempdir, 'test-ave.fif')
fwd = convert_forward_solution(read_forward_solution(fname_fwd),
surf_ori=False,
force_fixed=True,
use_cps=True)
evoked = read_evokeds(fname_evo)[0]
cov = read_cov(fname_cov)
n_per_hemi = 5
vertices = [
np.sort(rng.permutation(s['vertno'])[:n_per_hemi]) for s in fwd['src']
]
nv = sum(len(v) for v in vertices)
stc = SourceEstimate(amp * np.eye(nv), vertices, 0, 0.001)
evoked = simulate_evoked(fwd,
stc,
evoked.info,
cov,
nave=evoked.nave,
random_state=rng)
# For speed, let's use a subset of channels (strange but works)
picks = np.sort(
np.concatenate([
pick_types(evoked.info, meg=True, eeg=False)[::2],
pick_types(evoked.info, meg=False, eeg=True)[::2]
]))
evoked.pick_channels([evoked.ch_names[p] for p in picks])
evoked.add_proj(make_eeg_average_ref_proj(evoked.info))
write_evokeds(fname_sim, evoked)
# Run MNE-C version
run_subprocess([
'mne_dipole_fit',
'--meas',
fname_sim,
'--meg',
'--eeg',
'--noise',
fname_cov,
'--dip',
fname_dtemp,
'--mri',
fname_fwd,
'--reg',
'0',
'--tmin',
'0',
])
dip_c = read_dipole(fname_dtemp)
# Run mne-python version
sphere = make_sphere_model(head_radius=0.1)
with pytest.warns(RuntimeWarning, match='projection'):
dip, residual = fit_dipole(evoked, cov, sphere, fname_fwd,
rank='info') # just to test rank support
assert isinstance(residual, Evoked)
# Test conversion of dip.pos to MNI coordinates.
dip_mni_pos = dip.to_mni('sample', fname_trans, subjects_dir=subjects_dir)
head_to_mni_dip_pos = head_to_mni(dip.pos,
'sample',
fwd['mri_head_t'],
subjects_dir=subjects_dir)
assert_allclose(dip_mni_pos, head_to_mni_dip_pos, rtol=1e-3, atol=0)
# Test finding label for dip.pos in an aseg, also tests `to_mri`
target_labels = [
'Left-Cerebral-Cortex', 'Unknown', 'Left-Cerebral-Cortex',
'Right-Cerebral-Cortex', 'Left-Cerebral-Cortex', 'Unknown', 'Unknown',
'Unknown', 'Right-Cerebral-White-Matter', 'Right-Cerebral-Cortex'
]
labels = dip.to_volume_labels(fname_trans,
subject='fsaverage',
aseg="aseg",
subjects_dir=subjects_dir)
assert labels == target_labels
# Sanity check: do our residuals have less power than orig data?
data_rms = np.sqrt(np.sum(evoked.data**2, axis=0))
resi_rms = np.sqrt(np.sum(residual.data**2, axis=0))
assert (data_rms > resi_rms * 0.95).all(), \
'%s (factor: %s)' % ((data_rms / resi_rms).min(), 0.95)
# Compare to original points
transform_surface_to(fwd['src'][0], 'head', fwd['mri_head_t'])
transform_surface_to(fwd['src'][1], 'head', fwd['mri_head_t'])
assert fwd['src'][0]['coord_frame'] == FIFF.FIFFV_COORD_HEAD
src_rr = np.concatenate([s['rr'][v] for s, v in zip(fwd['src'], vertices)],
axis=0)
src_nn = np.concatenate([s['nn'][v] for s, v in zip(fwd['src'], vertices)],
axis=0)
# MNE-C skips the last "time" point :(
out = dip.crop(dip_c.times[0], dip_c.times[-1])
assert (dip is out)
src_rr, src_nn = src_rr[:-1], src_nn[:-1]
# check that we did about as well
corrs, dists, gc_dists, amp_errs, gofs = [], [], [], [], []
for d in (dip_c, dip):
new = d.pos
diffs = new - src_rr
corrs += [np.corrcoef(src_rr.ravel(), new.ravel())[0, 1]]
dists += [np.sqrt(np.mean(np.sum(diffs * diffs, axis=1)))]
gc_dists += [
180 / np.pi * np.mean(np.arccos(np.sum(src_nn * d.ori, axis=1)))
]
amp_errs += [np.sqrt(np.mean((amp - d.amplitude)**2))]
gofs += [np.mean(d.gof)]
# XXX possibly some OpenBLAS numerical differences make
# things slightly worse for us
factor = 0.7
assert dists[0] / factor >= dists[1], 'dists: %s' % dists
assert corrs[0] * factor <= corrs[1], 'corrs: %s' % corrs
assert gc_dists[0] / factor >= gc_dists[1] * 0.8, \
'gc-dists (ori): %s' % gc_dists
assert amp_errs[0] / factor >= amp_errs[1],\
'amplitude errors: %s' % amp_errs
# This one is weird because our cov/sim/picking is weird
assert gofs[0] * factor <= gofs[1] * 2, 'gof: %s' % gofs
def test_dipole_fitting():
"""Test dipole fitting."""
amp = 100e-9
tempdir = _TempDir()
rng = np.random.RandomState(0)
fname_dtemp = op.join(tempdir, 'test.dip')
fname_sim = op.join(tempdir, 'test-ave.fif')
fwd = convert_forward_solution(read_forward_solution(fname_fwd),
surf_ori=False, force_fixed=True,
use_cps=True)
evoked = read_evokeds(fname_evo)[0]
cov = read_cov(fname_cov)
n_per_hemi = 5
vertices = [np.sort(rng.permutation(s['vertno'])[:n_per_hemi])
for s in fwd['src']]
nv = sum(len(v) for v in vertices)
stc = SourceEstimate(amp * np.eye(nv), vertices, 0, 0.001)
evoked = simulate_evoked(fwd, stc, evoked.info, cov, nave=evoked.nave,
random_state=rng)
# For speed, let's use a subset of channels (strange but works)
picks = np.sort(np.concatenate([
pick_types(evoked.info, meg=True, eeg=False)[::2],
pick_types(evoked.info, meg=False, eeg=True)[::2]]))
evoked.pick_channels([evoked.ch_names[p] for p in picks])
evoked.add_proj(make_eeg_average_ref_proj(evoked.info))
write_evokeds(fname_sim, evoked)
# Run MNE-C version
run_subprocess([
'mne_dipole_fit', '--meas', fname_sim, '--meg', '--eeg',
'--noise', fname_cov, '--dip', fname_dtemp,
'--mri', fname_fwd, '--reg', '0', '--tmin', '0',
])
dip_c = read_dipole(fname_dtemp)
# Run mne-python version
sphere = make_sphere_model(head_radius=0.1)
with pytest.warns(RuntimeWarning, match='projection'):
dip, residual = fit_dipole(evoked, cov, sphere, fname_fwd)
assert isinstance(residual, Evoked)
# Sanity check: do our residuals have less power than orig data?
data_rms = np.sqrt(np.sum(evoked.data ** 2, axis=0))
resi_rms = np.sqrt(np.sum(residual.data ** 2, axis=0))
assert (data_rms > resi_rms * 0.95).all(), \
'%s (factor: %s)' % ((data_rms / resi_rms).min(), 0.95)
# Compare to original points
transform_surface_to(fwd['src'][0], 'head', fwd['mri_head_t'])
transform_surface_to(fwd['src'][1], 'head', fwd['mri_head_t'])
assert_equal(fwd['src'][0]['coord_frame'], FIFF.FIFFV_COORD_HEAD)
src_rr = np.concatenate([s['rr'][v] for s, v in zip(fwd['src'], vertices)],
axis=0)
src_nn = np.concatenate([s['nn'][v] for s, v in zip(fwd['src'], vertices)],
axis=0)
# MNE-C skips the last "time" point :(
out = dip.crop(dip_c.times[0], dip_c.times[-1])
assert (dip is out)
src_rr, src_nn = src_rr[:-1], src_nn[:-1]
# check that we did about as well
corrs, dists, gc_dists, amp_errs, gofs = [], [], [], [], []
for d in (dip_c, dip):
new = d.pos
diffs = new - src_rr
corrs += [np.corrcoef(src_rr.ravel(), new.ravel())[0, 1]]
dists += [np.sqrt(np.mean(np.sum(diffs * diffs, axis=1)))]
gc_dists += [180 / np.pi * np.mean(np.arccos(np.sum(src_nn * d.ori,
axis=1)))]
amp_errs += [np.sqrt(np.mean((amp - d.amplitude) ** 2))]
gofs += [np.mean(d.gof)]
# XXX possibly some OpenBLAS numerical differences make
# things slightly worse for us
factor = 0.7
assert dists[0] / factor >= dists[1], 'dists: %s' % dists
assert corrs[0] * factor <= corrs[1], 'corrs: %s' % corrs
assert gc_dists[0] / factor >= gc_dists[1] * 0.8, \
'gc-dists (ori): %s' % gc_dists
assert amp_errs[0] / factor >= amp_errs[1],\
'amplitude errors: %s' % amp_errs
# This one is weird because our cov/sim/picking is weird
assert gofs[0] * factor <= gofs[1] * 2, 'gof: %s' % gofs
def DeFleCT_make_estimator(forward, noise_cov, labels, lambda2_cov=3/10.,
lambda2_S=1/9., pick_meg=True, pick_eeg=False,
mode='svd', n_svd_comp=1, verbose=None):
"""
Create the DeFleCT estimator for a set of labels
Parameters:
-----------
forward: forward solution (assumes surf_ori=True)
noise_cov: noise covariance matrix
lambda2_cov: regularisation paramter for noise covariance matrix (whitening)
pick_meg: Which MEG channels to pick (True/False/'grad'/'mag')
pick_eeg: Which EEG channels to pick (True/False)
labels: list of labels, first one is the target for DeFleCT
mode : 'mean' | 'sum' | 'svd' |
PSFs can be computed for different summary measures with labels:
'sum' or 'mean': sum or means of sub-leadfields for labels
This corresponds to situations where labels can be assumed to be
homogeneously activated.
'svd': SVD components of sub-leadfields for labels
This is better suited for situations where activation patterns are
assumed to be more variable.
"sub-leadfields" are the parts of the forward solutions that belong to
vertices within invidual labels.
n_svd_comp : integer
Number of SVD components for which PSFs will be computed and output
(irrelevant for 'sum' and 'mean'). Explained variances within
sub-leadfields are shown in screen output.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Returns:
--------
w: np-array (1xn_chan), spatial filter for first column of P
F: np-array, whitened leadfield matrix (n_chan x n_vert)
P: np-array, whitened projection matrix (n_chan x n_comp)
noise_cov_mat: noise covariance matrix as used in DeFleCT
whitener: whitening matrix as used in DeFleCT
"""
# get wanted channels
picks = pick_types(forward['info'], meg=pick_meg, eeg=pick_eeg, eog=False,
stim=False, exclude='bads')
fwd_ch_names_all = [c['ch_name'] for c in forward['info']['chs']]
fwd_ch_names = [fwd_ch_names_all[pp] for pp in picks]
ch_names = [c for c in fwd_ch_names
if ((c not in noise_cov['bads']) and
(c not in forward['info']['bads'])) and
(c in noise_cov.ch_names)]
if not len(forward['info']['bads']) == len(noise_cov['bads']) or \
not all([b in noise_cov['bads'] for b in forward['info']['bads']]):
logger.info('\nforward["info"]["bads"] and noise_cov["bads"] do not '
'match excluding bad channels from both')
# reduce forward to desired channels
forward = pick_channels_forward(forward, ch_names)
noise_cov = pick_channels_cov(noise_cov, ch_names)
logger.info("\nNoise covariance matrix has %d channels." %
noise_cov.data.shape[0] )
info_fwd = deepcopy(forward['info'])
info_fwd['sfreq'] = 1000.
if pick_eeg:
avgproj = make_eeg_average_ref_proj(info_fwd, activate=True)
info_fwd['projs'] = []
info_fwd['projs'].append(avgproj)
else:
info_fwd['projs'] = noise_cov['projs']
if lambda2_cov: # regularize covariance matrix "old style"
lbd = lambda2_cov
noise_cov_reg = cov_regularize(noise_cov, info_fwd, mag=lbd['mag'],
grad=lbd['gra'], eeg=lbd['eeg'], proj=True)
else: # use cov_mat as is
noise_cov_reg = noise_cov
fwd_info, leadfield, noise_cov_fwd, whitener, n_nzero = _prepare_forward(
forward, info_fwd, noise_cov_reg,
pca=False, rank=None, verbose=None)
leadfield = leadfield[:,2::3] # assumes surf_ori=True, (normal component)
n_chan, n_vert = leadfield.shape
logger.info("\nLeadfield has dimensions %d by %d\n" % (n_chan, n_vert))
# if EEG present: remove mean of columns for EEG (average-reference)
if pick_eeg:
print "\nReferening EEG \n"
EEG_idx = [cc for cc in range(len(ch_names)) if ch_names[cc][:3]=='EEG']
nr_eeg = len(EEG_idx)
lfdmean = leadfield[EEG_idx,:].mean(axis=0)
leadfield[EEG_idx,:] = leadfield[EEG_idx,:] - lfdmean[np.newaxis,:]
#### CREATE SUBLEADFIELDs FOR LABELS
# extract SUBLEADFIELDS for labels
label_lfd_summary = DeFleCT_make_subleadfields(labels, forward, leadfield,
mode='svd', n_svd_comp=n_svd_comp, verbose=None)
#### COMPUTE DEFLECT ESTIMATOR
# rename variables to match paper
F = np.dot( whitener, leadfield )
P = np.dot( whitener, label_lfd_summary )
nr_comp = P.shape[1]
i = np.eye( nr_comp )[0,:].T # desired sensitivity to columns in P
t = np.zeros(n_vert).T[np.newaxis,:] # desired CTF associated with w
# Compute DeFleCT ESTIMATOR
w = DeFleCT_matrix(F, P, i, t, lambda2_S)
# add whitener on the right (i.e. input should be unwhitened)
w = w.dot(whitener)
return w, ch_names, leadfield, label_lfd_summary, noise_cov_fwd, whitener
