def test_multipolar_bases():
"""Test multipolar moment basis calculation using sensor information"""
from scipy.io import loadmat
# Test our basis calculations
info = read_info(raw_fname)
coils = _prep_meg_channels(info, accurate=True, elekta_defs=True)[0]
# Check against a known benchmark
sss_data = loadmat(bases_fname)
for origin in ((0, 0, 0.04), (0, 0.02, 0.02)):
o_str = ''.join('%d' % (1000 * n) for n in origin)
S_tot = _sss_basis_basic(origin,
coils,
int_order,
ext_order,
method='alternative')
# Test our real<->complex conversion functions
S_tot_complex = _bases_real_to_complex(S_tot, int_order, ext_order)
S_tot_round = _bases_complex_to_real(S_tot_complex, int_order,
ext_order)
assert_allclose(S_tot, S_tot_round, atol=1e-7)
S_tot_mat = np.concatenate(
[sss_data['Sin' + o_str], sss_data['Sout' + o_str]], axis=1)
S_tot_mat_real = _bases_complex_to_real(S_tot_mat, int_order,
ext_order)
S_tot_mat_round = _bases_real_to_complex(S_tot_mat_real, int_order,
ext_order)
assert_allclose(S_tot_mat, S_tot_mat_round, atol=1e-7)
assert_allclose(S_tot_complex, S_tot_mat, rtol=1e-4, atol=1e-8)
assert_allclose(S_tot, S_tot_mat_real, rtol=1e-4, atol=1e-8)
# Now normalize our columns
S_tot /= np.sqrt(np.sum(S_tot * S_tot, axis=0))[np.newaxis]
S_tot_complex /= np.sqrt(
np.sum((S_tot_complex * S_tot_complex.conj()).real,
axis=0))[np.newaxis]
# Check against a known benchmark
S_tot_mat = np.concatenate(
[sss_data['SNin' + o_str], sss_data['SNout' + o_str]], axis=1)
# Check this roundtrip
S_tot_mat_real = _bases_complex_to_real(S_tot_mat, int_order,
ext_order)
S_tot_mat_round = _bases_real_to_complex(S_tot_mat_real, int_order,
ext_order)
assert_allclose(S_tot_mat, S_tot_mat_round, atol=1e-7)
assert_allclose(S_tot_complex, S_tot_mat, rtol=1e-4, atol=1e-8)
# Now test our optimized version
S_tot = _sss_basis_basic(origin, coils, int_order, ext_order)
S_tot_fast = _sss_basis(origin, coils, int_order, ext_order)
S_tot_fast *= _get_coil_scale(coils)
# there are some sign differences for columns (order/degrees)
# in here, likely due to Condon-Shortley. Here we use a
# Magnetometer channel to figure out the flips because the
# gradiometer channels have effectively zero values for first three
# external components (i.e., S_tot[grad_picks, 80:83])
flips = (np.sign(S_tot_fast[2]) != np.sign(S_tot[2]))
flips = 1 - 2 * flips
assert_allclose(S_tot, S_tot_fast * flips, atol=1e-16)
def test_multipolar_bases():
"""Test multipolar moment basis calculation using sensor information"""
from scipy.io import loadmat
# Test our basis calculations
info = read_info(raw_fname)
coils = _prep_meg_channels(info, accurate=True, elekta_defs=True)[0]
# Check against a known benchmark
sss_data = loadmat(bases_fname)
for origin in ((0, 0, 0.04), (0, 0.02, 0.02)):
o_str = ''.join('%d' % (1000 * n) for n in origin)
S_tot = _sss_basis_basic(origin, coils, int_order, ext_order,
method='alternative')
# Test our real<->complex conversion functions
S_tot_complex = _bases_real_to_complex(S_tot, int_order, ext_order)
S_tot_round = _bases_complex_to_real(S_tot_complex,
int_order, ext_order)
assert_allclose(S_tot, S_tot_round, atol=1e-7)
S_tot_mat = np.concatenate([sss_data['Sin' + o_str],
sss_data['Sout' + o_str]], axis=1)
S_tot_mat_real = _bases_complex_to_real(S_tot_mat,
int_order, ext_order)
S_tot_mat_round = _bases_real_to_complex(S_tot_mat_real,
int_order, ext_order)
assert_allclose(S_tot_mat, S_tot_mat_round, atol=1e-7)
assert_allclose(S_tot_complex, S_tot_mat, rtol=1e-4, atol=1e-8)
assert_allclose(S_tot, S_tot_mat_real, rtol=1e-4, atol=1e-8)
# Now normalize our columns
S_tot /= np.sqrt(np.sum(S_tot * S_tot, axis=0))[np.newaxis]
S_tot_complex /= np.sqrt(np.sum(
(S_tot_complex * S_tot_complex.conj()).real, axis=0))[np.newaxis]
# Check against a known benchmark
S_tot_mat = np.concatenate([sss_data['SNin' + o_str],
sss_data['SNout' + o_str]], axis=1)
# Check this roundtrip
S_tot_mat_real = _bases_complex_to_real(S_tot_mat,
int_order, ext_order)
S_tot_mat_round = _bases_real_to_complex(S_tot_mat_real,
int_order, ext_order)
assert_allclose(S_tot_mat, S_tot_mat_round, atol=1e-7)
assert_allclose(S_tot_complex, S_tot_mat, rtol=1e-4, atol=1e-8)
# Now test our optimized version
S_tot = _sss_basis_basic(origin, coils, int_order, ext_order)
S_tot_fast = _sss_basis(origin, coils, int_order, ext_order)
S_tot_fast *= _get_coil_scale(coils)
# there are some sign differences for columns (order/degrees)
# in here, likely due to Condon-Shortley. Here we use a
# Magnetometer channel to figure out the flips because the
# gradiometer channels have effectively zero values for first three
# external components (i.e., S_tot[grad_picks, 80:83])
flips = (np.sign(S_tot_fast[2]) != np.sign(S_tot[2]))
flips = 1 - 2 * flips
assert_allclose(S_tot, S_tot_fast * flips, atol=1e-16)
def test_multipolar_bases():
"""Test multipolar moment basis calculation using sensor information"""
from scipy.io import loadmat
# Test our basis calculations
info = read_info(raw_fname)
coils = _prep_meg_channels(info, accurate=True, elekta_defs=True,
verbose=False)[0]
# Check against a known benchmark
sss_data = loadmat(bases_fname)
for origin in ((0, 0, 0.04), (0, 0.02, 0.02)):
o_str = ''.join('%d' % (1000 * n) for n in origin)
S_tot = _sss_basis(origin, coils, int_order, ext_order,
method='alternative')
# Test our real<->complex conversion functions
S_tot_complex = _bases_real_to_complex(S_tot, int_order, ext_order)
S_tot_round = _bases_complex_to_real(S_tot_complex,
int_order, ext_order)
assert_allclose(S_tot, S_tot_round, atol=1e-7)
S_tot_mat = np.concatenate([sss_data['Sin' + o_str],
sss_data['Sout' + o_str]], axis=1)
mu0 = 4e-7 * np.pi # Permeability of vacuum
S_tot_mat /= mu0 # divide out the magnetic permeability
S_tot_mat_real = _bases_complex_to_real(S_tot_mat,
int_order, ext_order)
S_tot_mat_round = _bases_real_to_complex(S_tot_mat_real,
int_order, ext_order)
assert_allclose(S_tot_mat, S_tot_mat_round, atol=1e-7)
assert_allclose(S_tot_complex, S_tot_mat, rtol=1e-4, atol=1e-8)
assert_allclose(S_tot, S_tot_mat_real, rtol=1e-4, atol=1e-8)
# Now normalize our columns
S_tot /= np.sqrt(np.sum(S_tot * S_tot, axis=0))[np.newaxis]
S_tot_complex /= np.sqrt(np.sum(
(S_tot_complex * S_tot_complex.conj()).real, axis=0))[np.newaxis]
# Check against a known benchmark
S_tot_mat = np.concatenate([sss_data['SNin' + o_str],
sss_data['SNout' + o_str]], axis=1)
# Check this roundtrip
S_tot_mat_real = _bases_complex_to_real(S_tot_mat,
int_order, ext_order)
S_tot_mat_round = _bases_real_to_complex(S_tot_mat_real,
int_order, ext_order)
assert_allclose(S_tot_mat, S_tot_mat_round, atol=1e-7)
assert_allclose(S_tot_complex, S_tot_mat, rtol=1e-4, atol=1e-8)
def test_maxwell_filter():
"""Test multipolar moment and Maxwell filter"""
# TODO: Future tests integrate with mne/io/tests/test_proc_history
# Load testing data (raw, SSS std origin, SSS non-standard origin)
with warnings.catch_warnings(record=True): # maxshield
raw = Raw(raw_fname, preload=False, proj=False,
allow_maxshield=True).crop(0., 1., False)
raw.preload_data()
with warnings.catch_warnings(record=True): # maxshield, naming
sss_std = Raw(sss_std_fname,
preload=True,
proj=False,
allow_maxshield=True)
sss_nonStd = Raw(sss_nonstd_fname,
preload=True,
proj=False,
allow_maxshield=True)
raw_err = Raw(raw_fname,
preload=False,
proj=True,
allow_maxshield=True).crop(0., 0.1, False)
assert_raises(RuntimeError, maxwell.maxwell_filter, raw_err)
# Create coils
all_coils, _, _, meg_info = _prep_meg_channels(raw.info,
ignore_ref=True,
elekta_defs=True)
picks = [
raw.info['ch_names'].index(ch)
for ch in [coil['chname'] for coil in all_coils]
]
coils = [all_coils[ci] for ci in picks]
ncoils = len(coils)
int_order, ext_order = 8, 3
n_int_bases = int_order**2 + 2 * int_order
n_ext_bases = ext_order**2 + 2 * ext_order
nbases = n_int_bases + n_ext_bases
# Check number of bases computed correctly
assert_equal(maxwell.get_num_moments(int_order, ext_order), nbases)
# Check multipolar moment basis set
S_in, S_out = maxwell._sss_basis(origin=np.array([0, 0, 40]),
coils=coils,
int_order=int_order,
ext_order=ext_order)
assert_equal(S_in.shape, (ncoils, n_int_bases), 'S_in has incorrect shape')
assert_equal(S_out.shape, (ncoils, n_ext_bases),
'S_out has incorrect shape')
# Test sss computation at the standard head origin
raw_sss = maxwell.maxwell_filter(raw,
origin=[0., 0., 40.],
int_order=int_order,
ext_order=ext_order)
assert_array_almost_equal(raw_sss._data[picks, :],
sss_std._data[picks, :],
decimal=11,
err_msg='Maxwell filtered data at '
'standard origin incorrect.')
# Confirm SNR is above 100
bench_rms = np.sqrt(np.mean(sss_std._data[picks, :]**2, axis=1))
error = raw_sss._data[picks, :] - sss_std._data[picks, :]
error_rms = np.sqrt(np.mean(error**2, axis=1))
assert_true(np.mean(bench_rms / error_rms) > 1000, 'SNR < 1000')
# Test sss computation at non-standard head origin
raw_sss = maxwell.maxwell_filter(raw,
origin=[0., 20., 20.],
int_order=int_order,
ext_order=ext_order)
assert_array_almost_equal(raw_sss._data[picks, :],
sss_nonStd._data[picks, :],
decimal=11,
err_msg='Maxwell filtered data at non-std '
'origin incorrect.')
# Confirm SNR is above 100
bench_rms = np.sqrt(np.mean(sss_nonStd._data[picks, :]**2, axis=1))
error = raw_sss._data[picks, :] - sss_nonStd._data[picks, :]
error_rms = np.sqrt(np.mean(error**2, axis=1))
assert_true(np.mean(bench_rms / error_rms) > 1000, 'SNR < 1000')
# Check against SSS functions from proc_history
sss_info = raw_sss.info['proc_history'][0]['max_info']
assert_equal(maxwell.get_num_moments(int_order, 0),
proc_history._get_sss_rank(sss_info))
def test_maxwell_filter():
"""Test multipolar moment and Maxwell filter"""
# TODO: Future tests integrate with mne/io/tests/test_proc_history
# Load testing data (raw, SSS std origin, SSS non-standard origin)
with warnings.catch_warnings(record=True): # maxshield
raw = Raw(raw_fname, preload=False, proj=False,
allow_maxshield=True).crop(0., 1., False)
raw.load_data()
with warnings.catch_warnings(record=True): # maxshield, naming
sss_std = Raw(sss_std_fname, preload=True, proj=False,
allow_maxshield=True)
sss_nonStd = Raw(sss_nonstd_fname, preload=True, proj=False,
allow_maxshield=True)
raw_err = Raw(raw_fname, preload=False, proj=True,
allow_maxshield=True).crop(0., 0.1, False)
assert_raises(RuntimeError, maxwell.maxwell_filter, raw_err)
# Create coils
all_coils, _, _, meg_info = _prep_meg_channels(raw.info, ignore_ref=True,
elekta_defs=True)
picks = [raw.info['ch_names'].index(ch) for ch in [coil['chname']
for coil in all_coils]]
coils = [all_coils[ci] for ci in picks]
ncoils = len(coils)
int_order, ext_order = 8, 3
n_int_bases = int_order ** 2 + 2 * int_order
n_ext_bases = ext_order ** 2 + 2 * ext_order
nbases = n_int_bases + n_ext_bases
# Check number of bases computed correctly
assert_equal(maxwell.get_num_moments(int_order, ext_order), nbases)
# Check multipolar moment basis set
S_in, S_out = maxwell._sss_basis(origin=np.array([0, 0, 40]), coils=coils,
int_order=int_order, ext_order=ext_order)
assert_equal(S_in.shape, (ncoils, n_int_bases), 'S_in has incorrect shape')
assert_equal(S_out.shape, (ncoils, n_ext_bases),
'S_out has incorrect shape')
# Test sss computation at the standard head origin
raw_sss = maxwell.maxwell_filter(raw, origin=[0., 0., 40.],
int_order=int_order, ext_order=ext_order)
assert_array_almost_equal(raw_sss._data[picks, :], sss_std._data[picks, :],
decimal=11, err_msg='Maxwell filtered data at '
'standard origin incorrect.')
# Confirm SNR is above 100
bench_rms = np.sqrt(np.mean(sss_std._data[picks, :] ** 2, axis=1))
error = raw_sss._data[picks, :] - sss_std._data[picks, :]
error_rms = np.sqrt(np.mean(error ** 2, axis=1))
assert_true(np.mean(bench_rms / error_rms) > 1000, 'SNR < 1000')
# Test sss computation at non-standard head origin
raw_sss = maxwell.maxwell_filter(raw, origin=[0., 20., 20.],
int_order=int_order, ext_order=ext_order)
assert_array_almost_equal(raw_sss._data[picks, :],
sss_nonStd._data[picks, :], decimal=11,
err_msg='Maxwell filtered data at non-std '
'origin incorrect.')
# Confirm SNR is above 100
bench_rms = np.sqrt(np.mean(sss_nonStd._data[picks, :] ** 2, axis=1))
error = raw_sss._data[picks, :] - sss_nonStd._data[picks, :]
error_rms = np.sqrt(np.mean(error ** 2, axis=1))
assert_true(np.mean(bench_rms / error_rms) > 1000, 'SNR < 1000')
# Check against SSS functions from proc_history
sss_info = raw_sss.info['proc_history'][0]['max_info']
assert_equal(maxwell.get_num_moments(int_order, 0),
proc_history._get_sss_rank(sss_info))