def test_make_inverse_operator_fixed():
"""Test MNE inverse computation (fixed orientation)
"""
fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
fwd_1 = read_forward_solution(fname_fwd, surf_ori=False, force_fixed=False)
fwd_2 = read_forward_solution(fname_fwd, surf_ori=False, force_fixed=True)
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
# can't make depth-weighted fixed inv without surf ori fwd
assert_raises(ValueError, make_inverse_operator, evoked.info, fwd_1,
noise_cov, depth=0.8, loose=None, fixed=True)
# can't make fixed inv with depth weighting without free ori fwd
assert_raises(ValueError, make_inverse_operator, evoked.info, fwd_2,
noise_cov, depth=0.8, loose=None, fixed=True)
# compare to C solution w/fixed
inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov, depth=0.8,
loose=None, fixed=True)
_compare_io(inv_op)
inverse_operator_fixed = read_inverse_operator(fname_inv_fixed)
_compare_inverses_approx(inverse_operator_fixed, inv_op, evoked, 2)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)
# now compare to C solution
# note that the forward solution must not be surface-oriented
# to get equivalency (surf_ori=True changes the normals)
inv_op = make_inverse_operator(evoked.info, fwd_2, noise_cov, depth=None,
loose=None, fixed=True)
inverse_operator_nodepth = read_inverse_operator(fname_inv_nodepth)
_compare_inverses_approx(inverse_operator_nodepth, inv_op, evoked, 2)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)
def test_make_inverse_operator_fixed(self):
"""Test MNE inverse computation w/ fixed orientation (& no depth
weighting)
"""
# can't make fixed inv without surf ori fwd
assert_raises(ValueError, make_inverse_operator, evoked.info,
self.fwd_1, noise_cov, depth=0.8, loose=None, fixed=True)
# can't make fixed inv with depth weighting without free ori fwd
assert_raises(ValueError, make_inverse_operator, evoked.info,
self.fwd_2, noise_cov, depth=0.8, loose=None, fixed=True)
inv_op = make_inverse_operator(evoked.info, self.fwd_op, noise_cov,
depth=0.8, loose=None, fixed=True)
_compare_io(inv_op)
inverse_operator_fixed = read_inverse_operator(fname_inv_fixed)
_compare_inverses_approx(inverse_operator_fixed, inv_op, evoked, 2)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)
# Now without depth weighting, these should be equivalent
inv_op = make_inverse_operator(evoked.info, self.fwd_2, noise_cov,
depth=None, loose=None, fixed=True)
inv_2 = make_inverse_operator(evoked.info, self.fwd_op, noise_cov,
depth=None, loose=None, fixed=True)
_compare_inverses_approx(inv_op, inv_2, evoked, 2)
_compare_io(inv_op)
# now compare to C solution
inverse_operator_nodepth = read_inverse_operator(fname_inv_nodepth)
_compare_inverses_approx(inverse_operator_nodepth, inv_op, evoked, 2)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)
def test_apply_inverse_operator():
"""Test MNE inverse application
"""
inverse_operator = read_inverse_operator(fname_full)
evoked = _get_evoked()
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10e-9)
assert_true(stc.data.mean() > 1e-11)
# test if using prepared and not prepared inverse operator give the same
# result
inv_op = prepare_inverse_operator(inverse_operator,
nave=evoked.nave,
lambda2=lambda2,
method="MNE")
stc2 = apply_inverse(evoked, inv_op, lambda2, "MNE")
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10.0)
assert_true(stc.data.mean() > 0.1)
stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 35)
assert_true(stc.data.mean() > 0.1)
# test without using a label (so delayed computation is used)
label = read_label(fname_label % 'Aud-lh')
stc = apply_inverse(evoked, inv_op, lambda2, "MNE")
stc_label = apply_inverse(evoked, inv_op, lambda2, "MNE", label=label)
assert_equal(stc_label.subject, 'sample')
label_stc = stc.in_label(label)
assert_true(label_stc.subject == 'sample')
assert_array_almost_equal(stc_label.data, label_stc.data)
# Test we get errors when using custom ref or no average proj is present
evoked.info['custom_ref_applied'] = True
assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
evoked.info['custom_ref_applied'] = False
evoked.info['projs'] = [] # remove EEG proj
assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
def test_make_inverse_operator_fixed():
"""Test MNE inverse computation (fixed orientation)
"""
fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
fwd_1 = read_forward_solution(fname_fwd, surf_ori=False, force_fixed=False)
fwd_2 = read_forward_solution(fname_fwd, surf_ori=False, force_fixed=True)
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
# can't make depth-weighted fixed inv without surf ori fwd
assert_raises(ValueError,
make_inverse_operator,
evoked.info,
fwd_1,
noise_cov,
depth=0.8,
loose=None,
fixed=True)
# can't make fixed inv with depth weighting without free ori fwd
assert_raises(ValueError,
make_inverse_operator,
evoked.info,
fwd_2,
noise_cov,
depth=0.8,
loose=None,
fixed=True)
# compare to C solution w/fixed
inv_op = make_inverse_operator(evoked.info,
fwd_op,
noise_cov,
depth=0.8,
loose=None,
fixed=True)
_compare_io(inv_op)
inverse_operator_fixed = read_inverse_operator(fname_inv_fixed)
_compare_inverses_approx(inverse_operator_fixed, inv_op, evoked, 2)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)
# now compare to C solution
# note that the forward solution must not be surface-oriented
# to get equivalency (surf_ori=True changes the normals)
inv_op = make_inverse_operator(evoked.info,
fwd_2,
noise_cov,
depth=None,
loose=None,
fixed=True)
inverse_operator_nodepth = read_inverse_operator(fname_inv_nodepth)
_compare_inverses_approx(inverse_operator_nodepth, inv_op, evoked, 2)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)
def test_apply_inverse_operator():
"""Test MNE inverse application
"""
inverse_operator = read_inverse_operator(fname_full)
evoked = _get_evoked()
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10e-9)
assert_true(stc.data.mean() > 1e-11)
# test if using prepared and not prepared inverse operator give the same
# result
inv_op = prepare_inverse_operator(inverse_operator, nave=evoked.nave,
lambda2=lambda2, method="MNE")
stc2 = apply_inverse(evoked, inv_op, lambda2, "MNE")
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10.0)
assert_true(stc.data.mean() > 0.1)
stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 35)
assert_true(stc.data.mean() > 0.1)
# test without using a label (so delayed computation is used)
label = read_label(fname_label % 'Aud-lh')
stc = apply_inverse(evoked, inv_op, lambda2, "MNE")
stc_label = apply_inverse(evoked, inv_op, lambda2, "MNE",
label=label)
assert_equal(stc_label.subject, 'sample')
label_stc = stc.in_label(label)
assert_true(label_stc.subject == 'sample')
assert_array_almost_equal(stc_label.data, label_stc.data)
# Test we get errors when using custom ref or no average proj is present
evoked.info['custom_ref_applied'] = True
assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
evoked.info['custom_ref_applied'] = False
evoked.info['projs'] = [] # remove EEG proj
assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
def test_inverse_operator_noise_cov_rank():
"""Test MNE inverse operator with a specified noise cov rank."""
fwd_op = read_forward_solution_meg(fname_fwd, surf_ori=True)
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
inv = make_inverse_operator(evoked.info, fwd_op, noise_cov, rank=64)
assert (compute_rank_inverse(inv) == 64)
fwd_op = read_forward_solution_eeg(fname_fwd, surf_ori=True)
inv = make_inverse_operator(evoked.info, fwd_op, noise_cov,
rank=dict(eeg=20))
assert (compute_rank_inverse(inv) == 20)
def test_apply_inverse_operator():
"""Test MNE inverse computation (precomputed and non-precomputed)
"""
inverse_operator = read_inverse_operator(fname_inv)
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
# Test old version of inverse computation starting from forward operator
fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
my_inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
loose=0.2, depth=0.8,
limit_depth_chs=False)
_compare_io(my_inv_op)
assert_true(inverse_operator['units'] == 'Am')
_compare_inverses_approx(my_inv_op, inverse_operator, evoked, 2,
check_depth=False)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
# Test MNE inverse computation starting from forward operator
my_inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
loose=0.2, depth=0.8)
_compare_io(my_inv_op)
_compare_inverses_approx(my_inv_op, inverse_operator, evoked, 2)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10e-10)
assert_true(stc.data.mean() > 1e-11)
stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10.0)
assert_true(stc.data.mean() > 0.1)
stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 35)
assert_true(stc.data.mean() > 0.1)
my_stc = apply_inverse(evoked, my_inv_op, lambda2, "dSPM")
assert_true('dev_head_t' in my_inv_op['info'])
assert_true('mri_head_t' in my_inv_op)
assert_true(my_stc.subject == 'sample')
assert_equal(stc.times, my_stc.times)
assert_array_almost_equal(stc.data, my_stc.data, 2)
def test_apply_inverse_operator():
"""Test MNE inverse computation (precomputed and non-precomputed)
"""
inverse_operator = read_inverse_operator(fname_inv)
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
# Test old version of inverse computation starting from forward operator
fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
my_inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
loose=0.2, depth=0.8,
limit_depth_chs=False)
_compare_io(my_inv_op)
assert_true(inverse_operator['units'] == 'Am')
_compare_inverses_approx(my_inv_op, inverse_operator, evoked, 2,
check_depth=False)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
# Test MNE inverse computation starting from forward operator
my_inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
loose=0.2, depth=0.8)
_compare_io(my_inv_op)
_compare_inverses_approx(my_inv_op, inverse_operator, evoked, 2)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10e-10)
assert_true(stc.data.mean() > 1e-11)
stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10.0)
assert_true(stc.data.mean() > 0.1)
stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 35)
assert_true(stc.data.mean() > 0.1)
my_stc = apply_inverse(evoked, my_inv_op, lambda2, "dSPM")
assert_true('dev_head_t' in my_inv_op['info'])
assert_true('mri_head_t' in my_inv_op)
assert_true(my_stc.subject == 'sample')
assert_equal(stc.times, my_stc.times)
assert_array_almost_equal(stc.data, my_stc.data, 2)
def test_inverse_operator_noise_cov_rank(evoked, noise_cov):
"""Test MNE inverse operator with a specified noise cov rank."""
fwd_op = read_forward_solution_meg(fname_fwd, surf_ori=True)
with pytest.deprecated_call(): # rank int
inv = make_inverse_operator(evoked.info, fwd_op, noise_cov, rank=64)
assert (compute_rank_inverse(inv) == 64)
inv = make_inverse_operator(evoked.info, fwd_op, noise_cov,
rank=dict(meg=64))
assert (compute_rank_inverse(inv) == 64)
fwd_op = read_forward_solution_eeg(fname_fwd, surf_ori=True)
inv = make_inverse_operator(evoked.info, fwd_op, noise_cov,
rank=dict(eeg=20))
assert (compute_rank_inverse(inv) == 20)
def test_inverse_operator_noise_cov_rank(evoked, noise_cov):
"""Test MNE inverse operator with a specified noise cov rank."""
fwd_op = read_forward_solution_meg(fname_fwd, surf_ori=True)
with pytest.deprecated_call(): # rank int
inv = make_inverse_operator(evoked.info, fwd_op, noise_cov, rank=64)
assert (compute_rank_inverse(inv) == 64)
inv = make_inverse_operator(evoked.info, fwd_op, noise_cov,
rank=dict(meg=64))
assert (compute_rank_inverse(inv) == 64)
fwd_op = read_forward_solution_eeg(fname_fwd, surf_ori=True)
inv = make_inverse_operator(evoked.info, fwd_op, noise_cov,
rank=dict(eeg=20))
assert (compute_rank_inverse(inv) == 20)
def test_make_inverse_operator_fixed():
"""Test MNE inverse computation (fixed orientation)."""
fwd = read_forward_solution_meg(fname_fwd)
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
# can't make fixed inv with depth weighting without free ori fwd
fwd_fixed = convert_forward_solution(fwd, force_fixed=True, use_cps=True)
assert_raises(ValueError,
make_inverse_operator,
evoked.info,
fwd_fixed,
noise_cov,
depth=0.8,
fixed=True)
# now compare to C solution
# note that the forward solution must not be surface-oriented
# to get equivalency (surf_ori=True changes the normals)
inv_op = make_inverse_operator(evoked.info,
fwd,
noise_cov,
depth=None,
fixed=True,
use_cps=False)
assert 'EEG channels: 0' in repr(inv_op)
assert 'MEG channels: 305' in repr(inv_op)
del fwd_fixed
inverse_operator_nodepth = read_inverse_operator(fname_inv_fixed_nodepth)
# XXX We should have this but we don't (MNE-C doesn't restrict info):
# assert 'EEG channels: 0' in repr(inverse_operator_nodepth)
assert 'MEG channels: 305' in repr(inverse_operator_nodepth)
_compare_inverses_approx(inverse_operator_nodepth,
inv_op,
evoked,
rtol=1e-5,
atol=1e-4)
# Inverse has 306 channels - 6 proj = 302
assert_true(compute_rank_inverse(inverse_operator_nodepth) == 302)
# Now with depth
fwd_surf = convert_forward_solution(fwd, surf_ori=True) # not fixed
for kwargs, use_fwd in zip(
[dict(fixed=True), dict(loose=0.)],
[fwd, fwd_surf]): # Should be equiv.
inv_op_depth = make_inverse_operator(evoked.info,
use_fwd,
noise_cov,
depth=0.8,
use_cps=True,
**kwargs)
inverse_operator_depth = read_inverse_operator(fname_inv_fixed_depth)
# Normals should be the adjusted ones
assert_allclose(inverse_operator_depth['source_nn'],
fwd_surf['source_nn'][2::3],
atol=1e-5)
_compare_inverses_approx(inverse_operator_depth,
inv_op_depth,
evoked,
rtol=1e-3,
atol=1e-4)
def test_apply_inverse_operator(self):
"""Test MNE inverse computation (precomputed and non-precomputed)
"""
# Test old version of inverse computation starting from forward
# operator
my_inv_op = make_inverse_operator(evoked.info, self.fwd_op, noise_cov,
loose=0.2, depth=0.8,
limit_depth_chs=False)
_compare_io(my_inv_op)
_compare_inverses_approx(my_inv_op, self.inv_op, evoked, 2,
check_depth=False)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(self.inv_op) == 302)
# Test MNE inverse computation starting from forward operator
my_inv_op = make_inverse_operator(evoked.info, self.fwd_op, noise_cov,
loose=0.2, depth=0.8)
_compare_io(my_inv_op)
_compare_inverses_approx(my_inv_op, self.inv_op, evoked, 2)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(self.inv_op) == 302)
stc = apply_inverse(evoked, self.inv_op, lambda2, "MNE")
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10e-10)
assert_true(stc.data.mean() > 1e-11)
stc = apply_inverse(evoked, self.inv_op, lambda2, "sLORETA")
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 9.0)
assert_true(stc.data.mean() > 0.1)
stc = apply_inverse(evoked, self.inv_op, lambda2, "dSPM")
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 35)
assert_true(stc.data.mean() > 0.1)
my_stc = apply_inverse(evoked, my_inv_op, lambda2, "dSPM")
assert_true('dev_head_t' in my_inv_op['info'])
assert_true('mri_head_t' in my_inv_op)
assert_equal(stc.times, my_stc.times)
assert_array_almost_equal(stc.data, my_stc.data, 2)
def test_make_inverse_operator_diag():
"""Test MNE inverse computation with diagonal noise cov
"""
fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov.as_diag(),
loose=0.2, depth=0.8)
_compare_io(inv_op)
inverse_operator_diag = read_inverse_operator(fname_inv_diag)
# This one's only good to zero decimal places, roundoff error (?)
_compare_inverses_approx(inverse_operator_diag, inv_op, evoked, 0)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator_diag) == 302)
def test_make_inverse_operator_diag(self):
"""Test MNE inverse computation with diagonal noise cov
"""
inv_op = make_inverse_operator(evoked.info, self.fwd_op,
noise_cov.as_diag(), loose=0.2,
depth=0.8)
_compare_io(inv_op)
inverse_operator_diag = read_inverse_operator(fname_inv_diag)
# This one's only good to zero decimal places, roundoff error (?)
_compare_inverses_approx(inverse_operator_diag, inv_op, evoked, 0)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator_diag) == 302)
def test_apply_inverse_operator():
"""Test MNE inverse application
"""
inverse_operator = read_inverse_operator(fname_full)
evoked = _get_evoked()
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10e-9)
assert_true(stc.data.mean() > 1e-11)
# test if using prepared and not prepared inverse operator give the same
# result
inv_op = prepare_inverse_operator(inverse_operator,
nave=evoked.nave,
lambda2=lambda2,
method="MNE")
stc2 = apply_inverse(evoked, inv_op, lambda2, "MNE")
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10.0)
assert_true(stc.data.mean() > 0.1)
stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 35)
assert_true(stc.data.mean() > 0.1)
def test_make_inverse_operator_diag():
"""Test MNE inverse computation with diagonal noise cov
"""
evoked = _get_evoked()
noise_cov = read_cov(fname_cov).as_diag(copy=False)
fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
loose=0.2, depth=0.8)
_compare_io(inv_op)
inverse_operator_diag = read_inverse_operator(fname_inv_meeg_diag)
# This one's only good to zero decimal places, roundoff error (?)
_compare_inverses_approx(inverse_operator_diag, inv_op, evoked, 0, 1e0)
# Inverse has 366 channels - 6 proj = 360
assert_true(compute_rank_inverse(inverse_operator_diag) == 360)
def test_make_inverse_operator_diag():
"""Test MNE inverse computation with diagonal noise cov
"""
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov.as_diag(),
loose=0.2, depth=0.8)
_compare_io(inv_op)
inverse_operator_diag = read_inverse_operator(fname_inv_meeg_diag)
# This one's only good to zero decimal places, roundoff error (?)
_compare_inverses_approx(inverse_operator_diag, inv_op, evoked, 0, 1e0)
# Inverse has 366 channels - 6 proj = 360
assert_true(compute_rank_inverse(inverse_operator_diag) == 360)
def test_make_inverse_operator_diag(evoked, noise_cov):
"""Test MNE inverse computation with diagonal noise cov."""
noise_cov = noise_cov.as_diag()
fwd_op = convert_forward_solution(read_forward_solution(fname_fwd),
surf_ori=True)
inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
loose=0.2, depth=0.8)
_compare_io(inv_op)
inverse_operator_diag = read_inverse_operator(fname_inv_meeg_diag)
# This one is pretty bad
_compare_inverses_approx(inverse_operator_diag, inv_op, evoked,
rtol=1e-1, atol=1e-1, ctol=0.99, check_K=False)
# Inverse has 366 channels - 6 proj = 360
assert (compute_rank_inverse(inverse_operator_diag) == 360)
def test_make_inverse_operator_diag(evoked, noise_cov):
"""Test MNE inverse computation with diagonal noise cov."""
noise_cov = noise_cov.as_diag()
fwd_op = convert_forward_solution(read_forward_solution(fname_fwd),
surf_ori=True)
inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
loose=0.2, depth=0.8)
_compare_io(inv_op)
inverse_operator_diag = read_inverse_operator(fname_inv_meeg_diag)
# This one is pretty bad
_compare_inverses_approx(inverse_operator_diag, inv_op, evoked,
rtol=1e-1, atol=1e-1, ctol=0.99, check_K=False)
# Inverse has 366 channels - 6 proj = 360
assert (compute_rank_inverse(inverse_operator_diag) == 360)
def test_apply_inverse_operator():
"""Test MNE inverse application
"""
inverse_operator = read_inverse_operator(fname_full)
evoked = _get_evoked()
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10e-9)
assert_true(stc.data.mean() > 1e-11)
# test if using prepared and not prepared inverse operator give the same
# result
inv_op = prepare_inverse_operator(inverse_operator, nave=evoked.nave,
lambda2=lambda2, method="MNE")
stc2 = apply_inverse(evoked, inv_op, lambda2, "MNE")
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10.0)
assert_true(stc.data.mean() > 0.1)
stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 35)
assert_true(stc.data.mean() > 0.1)
def test_make_inverse_operator_fixed():
"""Test MNE inverse computation (fixed orientation)."""
fwd = read_forward_solution_meg(fname_fwd)
evoked = _get_evoked()
noise_cov = read_cov(fname_cov)
# can't make fixed inv with depth weighting without free ori fwd
fwd_fixed = convert_forward_solution(fwd, force_fixed=True,
use_cps=True)
pytest.raises(ValueError, make_inverse_operator, evoked.info, fwd_fixed,
noise_cov, depth=0.8, fixed=True)
# now compare to C solution
# note that the forward solution must not be surface-oriented
# to get equivalency (surf_ori=True changes the normals)
with catch_logging() as log:
inv_op = make_inverse_operator( # test depth=0. alias for depth=None
evoked.info, fwd, noise_cov, depth=0., fixed=True,
use_cps=False, verbose=True)
log = log.getvalue()
assert 'rank 302 (3 small eigenvalues omitted)' in log
assert 'EEG channels: 0' in repr(inv_op)
assert 'MEG channels: 305' in repr(inv_op)
del fwd_fixed
inverse_operator_nodepth = read_inverse_operator(fname_inv_fixed_nodepth)
# XXX We should have this but we don't (MNE-C doesn't restrict info):
# assert 'EEG channels: 0' in repr(inverse_operator_nodepth)
assert 'MEG channels: 305' in repr(inverse_operator_nodepth)
_compare_inverses_approx(inverse_operator_nodepth, inv_op, evoked,
rtol=1e-5, atol=1e-4)
# Inverse has 306 channels - 6 proj = 302
assert (compute_rank_inverse(inverse_operator_nodepth) == 302)
# Now with depth
fwd_surf = convert_forward_solution(fwd, surf_ori=True) # not fixed
for kwargs, use_fwd in zip([dict(fixed=True), dict(loose=0.)],
[fwd, fwd_surf]): # Should be equiv.
inv_op_depth = make_inverse_operator(
evoked.info, use_fwd, noise_cov, depth=0.8, use_cps=True,
**kwargs)
inverse_operator_depth = read_inverse_operator(fname_inv_fixed_depth)
# Normals should be the adjusted ones
assert_allclose(inverse_operator_depth['source_nn'],
fwd_surf['source_nn'][2::3], atol=1e-5)
_compare_inverses_approx(inverse_operator_depth, inv_op_depth, evoked,
rtol=1e-3, atol=1e-4)
def test_apply_inverse_operator(evoked):
"""Test MNE inverse application."""
# use fname_inv as it will be faster than fname_full (fewer verts and chs)
inverse_operator = read_inverse_operator(fname_inv)
# Inverse has 306 channels - 4 proj = 302
assert (compute_rank_inverse(inverse_operator) == 302)
# Inverse has 306 channels - 4 proj = 302
assert (compute_rank_inverse(inverse_operator) == 302)
stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
assert stc.subject == 'sample'
assert stc.data.min() > 0
assert stc.data.max() < 13e-9
assert stc.data.mean() > 1e-11
# test if using prepared and not prepared inverse operator give the same
# result
inv_op = prepare_inverse_operator(inverse_operator,
nave=evoked.nave,
lambda2=lambda2,
method="MNE")
stc2 = apply_inverse(evoked, inv_op, lambda2, "MNE")
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
# This is little more than a smoke test...
stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
assert stc.subject == 'sample'
assert stc.data.min() > 0
assert stc.data.max() < 10.0
assert stc.data.mean() > 0.1
stc = apply_inverse(evoked, inverse_operator, lambda2, "eLORETA")
assert stc.subject == 'sample'
assert stc.data.min() > 0
assert stc.data.max() < 3.0
assert stc.data.mean() > 0.1
stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
assert stc.subject == 'sample'
assert stc.data.min() > 0
assert stc.data.max() < 35
assert stc.data.mean() > 0.1
# test without using a label (so delayed computation is used)
label = read_label(fname_label % 'Aud-lh')
stc = apply_inverse(evoked, inv_op, lambda2, "MNE")
stc_label = apply_inverse(evoked, inv_op, lambda2, "MNE", label=label)
assert_equal(stc_label.subject, 'sample')
label_stc = stc.in_label(label)
assert label_stc.subject == 'sample'
assert_allclose(stc_label.data, label_stc.data)
# Test that no errors are raised with loose inverse ops and picking normals
noise_cov = read_cov(fname_cov)
fwd = read_forward_solution_meg(fname_fwd)
inv_op_meg = make_inverse_operator(evoked.info,
fwd,
noise_cov,
loose=1,
fixed='auto',
depth=None)
apply_inverse(evoked, inv_op_meg, 1 / 9., method='MNE', pick_ori='normal')
# Test we get errors when using custom ref or no average proj is present
evoked.info['custom_ref_applied'] = True
pytest.raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
evoked.info['custom_ref_applied'] = False
evoked.info['projs'] = [] # remove EEG proj
pytest.raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
# But test that we do not get EEG-related errors on MEG-only inv (gh-4650)
apply_inverse(evoked, inv_op_meg, 1. / 9.)
def test_apply_inverse_operator():
"""Test MNE inverse application."""
inverse_operator = read_inverse_operator(fname_full)
evoked = _get_evoked()
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10e-9)
assert_true(stc.data.mean() > 1e-11)
# test if using prepared and not prepared inverse operator give the same
# result
inv_op = prepare_inverse_operator(inverse_operator,
nave=evoked.nave,
lambda2=lambda2,
method="MNE")
stc2 = apply_inverse(evoked, inv_op, lambda2, "MNE")
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10.0)
assert_true(stc.data.mean() > 0.1)
stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 35)
assert_true(stc.data.mean() > 0.1)
# test without using a label (so delayed computation is used)
label = read_label(fname_label % 'Aud-lh')
stc = apply_inverse(evoked, inv_op, lambda2, "MNE")
stc_label = apply_inverse(evoked, inv_op, lambda2, "MNE", label=label)
assert_equal(stc_label.subject, 'sample')
label_stc = stc.in_label(label)
assert_true(label_stc.subject == 'sample')
assert_allclose(stc_label.data, label_stc.data)
# Test that no errors are raised with loose inverse ops and picking normals
noise_cov = read_cov(fname_cov)
fwd = read_forward_solution_meg(fname_fwd)
inv_op2 = make_inverse_operator(evoked.info,
fwd,
noise_cov,
loose=1,
fixed='auto',
depth=None)
apply_inverse(evoked, inv_op2, 1 / 9., method='MNE', pick_ori='normal')
# Test we get errors when using custom ref or no average proj is present
evoked.info['custom_ref_applied'] = True
assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
evoked.info['custom_ref_applied'] = False
evoked.info['projs'] = [] # remove EEG proj
assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
def test_apply_inverse_operator():
"""Test MNE inverse application."""
inverse_operator = read_inverse_operator(fname_full)
evoked = _get_evoked()
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
# Inverse has 306 channels - 4 proj = 302
assert_true(compute_rank_inverse(inverse_operator) == 302)
stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10e-9)
assert_true(stc.data.mean() > 1e-11)
# test if using prepared and not prepared inverse operator give the same
# result
inv_op = prepare_inverse_operator(inverse_operator, nave=evoked.nave,
lambda2=lambda2, method="MNE")
stc2 = apply_inverse(evoked, inv_op, lambda2, "MNE")
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 10.0)
assert_true(stc.data.mean() > 0.1)
stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
assert_true(stc.subject == 'sample')
assert_true(stc.data.min() > 0)
assert_true(stc.data.max() < 35)
assert_true(stc.data.mean() > 0.1)
# test without using a label (so delayed computation is used)
label = read_label(fname_label % 'Aud-lh')
stc = apply_inverse(evoked, inv_op, lambda2, "MNE")
stc_label = apply_inverse(evoked, inv_op, lambda2, "MNE",
label=label)
assert_equal(stc_label.subject, 'sample')
label_stc = stc.in_label(label)
assert_true(label_stc.subject == 'sample')
assert_allclose(stc_label.data, label_stc.data)
# Test that no errors are raised with loose inverse ops and picking normals
noise_cov = read_cov(fname_cov)
fwd = read_forward_solution_meg(fname_fwd)
inv_op2 = make_inverse_operator(evoked.info, fwd, noise_cov, loose=1,
fixed='auto', depth=None)
apply_inverse(evoked, inv_op2, 1 / 9., method='MNE',
pick_ori='normal')
# Test we get errors when using custom ref or no average proj is present
evoked.info['custom_ref_applied'] = True
assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
evoked.info['custom_ref_applied'] = False
evoked.info['projs'] = [] # remove EEG proj
assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
def test_apply_inverse_operator():
"""Test MNE inverse application."""
# use fname_inv as it will be faster than fname_full (fewer verts and chs)
inverse_operator = read_inverse_operator(fname_inv)
evoked = _get_evoked()
# Inverse has 306 channels - 4 proj = 302
assert (compute_rank_inverse(inverse_operator) == 302)
# Inverse has 306 channels - 4 proj = 302
assert (compute_rank_inverse(inverse_operator) == 302)
stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
assert stc.subject == 'sample'
assert stc.data.min() > 0
assert stc.data.max() < 13e-9
assert stc.data.mean() > 1e-11
# test if using prepared and not prepared inverse operator give the same
# result
inv_op = prepare_inverse_operator(inverse_operator, nave=evoked.nave,
lambda2=lambda2, method="MNE")
stc2 = apply_inverse(evoked, inv_op, lambda2, "MNE")
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
# This is little more than a smoke test...
stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
assert stc.subject == 'sample'
assert stc.data.min() > 0
assert stc.data.max() < 10.0
assert stc.data.mean() > 0.1
stc = apply_inverse(evoked, inverse_operator, lambda2, "eLORETA")
assert stc.subject == 'sample'
assert stc.data.min() > 0
assert stc.data.max() < 3.0
assert stc.data.mean() > 0.1
stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
assert stc.subject == 'sample'
assert stc.data.min() > 0
assert stc.data.max() < 35
assert stc.data.mean() > 0.1
# test without using a label (so delayed computation is used)
label = read_label(fname_label % 'Aud-lh')
stc = apply_inverse(evoked, inv_op, lambda2, "MNE")
stc_label = apply_inverse(evoked, inv_op, lambda2, "MNE",
label=label)
assert_equal(stc_label.subject, 'sample')
label_stc = stc.in_label(label)
assert label_stc.subject == 'sample'
assert_allclose(stc_label.data, label_stc.data)
# Test that no errors are raised with loose inverse ops and picking normals
noise_cov = read_cov(fname_cov)
fwd = read_forward_solution_meg(fname_fwd)
inv_op_meg = make_inverse_operator(evoked.info, fwd, noise_cov, loose=1,
fixed='auto', depth=None)
apply_inverse(evoked, inv_op_meg, 1 / 9., method='MNE', pick_ori='normal')
# Test we get errors when using custom ref or no average proj is present
evoked.info['custom_ref_applied'] = True
pytest.raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
evoked.info['custom_ref_applied'] = False
evoked.info['projs'] = [] # remove EEG proj
pytest.raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
# But test that we do not get EEG-related errors on MEG-only inv (gh-4650)
apply_inverse(evoked, inv_op_meg, 1. / 9.)
