def test_cov_ctf():
"""Test basic cov computation on ctf data with/without compensation."""
raw = read_raw_ctf(ctf_fname).crop(0., 2.).load_data()
events = make_fixed_length_events(raw, 99999)
assert len(events) == 2
ch_names = [
raw.info['ch_names'][pick]
for pick in pick_types(raw.info, meg=True, eeg=False, ref_meg=False)
]
for comp in [0, 1]:
raw.apply_gradient_compensation(comp)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
with pytest.warns(RuntimeWarning, match='Too few samples'):
noise_cov = compute_covariance(epochs,
tmax=0.,
method=['empirical'])
prepare_noise_cov(noise_cov, raw.info, ch_names)
raw.apply_gradient_compensation(0)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
with pytest.warns(RuntimeWarning, match='Too few samples'):
noise_cov = compute_covariance(epochs, tmax=0., method=['empirical'])
raw.apply_gradient_compensation(1)
# TODO This next call in principle should fail.
prepare_noise_cov(noise_cov, raw.info, ch_names)
# make sure comps matrices was not removed from raw
assert raw.info['comps'], 'Comps matrices removed'
def test_cov_ctf():
"""Test basic cov computation on ctf data with/without compensation."""
raw = read_raw_ctf(ctf_fname).crop(0., 2.).load_data()
events = make_fixed_length_events(raw, 99999)
assert len(events) == 2
ch_names = [raw.info['ch_names'][pick]
for pick in pick_types(raw.info, meg=True, eeg=False,
ref_meg=False)]
for comp in [0, 1]:
raw.apply_gradient_compensation(comp)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
with pytest.warns(RuntimeWarning, match='Too few samples'):
noise_cov = compute_covariance(epochs, tmax=0.,
method=['empirical'])
prepare_noise_cov(noise_cov, raw.info, ch_names)
raw.apply_gradient_compensation(0)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
with pytest.warns(RuntimeWarning, match='Too few samples'):
noise_cov = compute_covariance(epochs, tmax=0., method=['empirical'])
raw.apply_gradient_compensation(1)
# TODO This next call in principle should fail.
prepare_noise_cov(noise_cov, raw.info, ch_names)
# make sure comps matrices was not removed from raw
assert raw.info['comps'], 'Comps matrices removed'
def test_cov_ctf():
"""Test basic cov computation on ctf data with/without compensation."""
raw = read_raw_ctf(ctf_fname, preload=True)
events = make_fixed_length_events(raw, 99999)
ch_names = [
raw.info['ch_names'][pick]
for pick in pick_types(raw.info, meg=True, eeg=False, ref_meg=False)
]
for comp in [0, 1]:
raw.apply_gradient_compensation(comp)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
noise_cov = compute_covariance(epochs, tmax=0., method=['shrunk'])
prepare_noise_cov(noise_cov, raw.info, ch_names)
raw.apply_gradient_compensation(0)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
noise_cov = compute_covariance(epochs, tmax=0., method=['shrunk'])
raw.apply_gradient_compensation(1)
# TODO This next call in principle should fail.
prepare_noise_cov(noise_cov, raw.info, ch_names)
# make sure comps matrices was not removed from raw
if not raw.info['comps']:
raise RuntimeError('Comps matrices removed')
def test_cov_order():
"""Test covariance ordering."""
raw = read_raw_fif(raw_fname)
raw.set_eeg_reference(projection=True)
info = raw.info
# add MEG channel with low enough index number to affect EEG if
# order is incorrect
info['bads'] += ['MEG 0113']
ch_names = [info['ch_names'][pick]
for pick in pick_types(info, meg=False, eeg=True)]
cov = read_cov(cov_fname)
# no avg ref present warning
prepare_noise_cov(cov, info, ch_names, verbose='error')
# big reordering
cov_reorder = cov.copy()
order = np.random.RandomState(0).permutation(np.arange(len(cov.ch_names)))
cov_reorder['names'] = [cov['names'][ii] for ii in order]
cov_reorder['data'] = cov['data'][order][:, order]
# Make sure we did this properly
_assert_reorder(cov_reorder, cov, order)
# Now check some functions that should get the same result for both
# regularize
with pytest.raises(ValueError, match='rank, if str'):
regularize(cov, info, rank='foo')
with pytest.raises(TypeError, match='rank must be'):
regularize(cov, info, rank=False)
with pytest.raises(TypeError, match='rank must be'):
regularize(cov, info, rank=1.)
cov_reg = regularize(cov, info, rank='full')
cov_reg_reorder = regularize(cov_reorder, info, rank='full')
_assert_reorder(cov_reg_reorder, cov_reg, order)
# prepare_noise_cov
cov_prep = prepare_noise_cov(cov, info, ch_names)
cov_prep_reorder = prepare_noise_cov(cov, info, ch_names)
_assert_reorder(cov_prep, cov_prep_reorder,
order=np.arange(len(cov_prep['names'])))
# compute_whitener
whitener, w_ch_names, n_nzero = compute_whitener(
cov, info, return_rank=True)
assert whitener.shape[0] == whitener.shape[1]
whitener_2, w_ch_names_2, n_nzero_2 = compute_whitener(
cov_reorder, info, return_rank=True)
assert_array_equal(w_ch_names_2, w_ch_names)
assert_allclose(whitener_2, whitener, rtol=1e-6)
assert n_nzero == n_nzero_2
# with pca
assert n_nzero < whitener.shape[0]
whitener_pca, w_ch_names_pca, n_nzero_pca = compute_whitener(
cov, info, pca=True, return_rank=True)
assert_array_equal(w_ch_names_pca, w_ch_names)
assert n_nzero_pca == n_nzero
assert whitener_pca.shape == (n_nzero_pca, len(w_ch_names))
# whiten_evoked
evoked = read_evokeds(ave_fname)[0]
evoked_white = whiten_evoked(evoked, cov)
evoked_white_2 = whiten_evoked(evoked, cov_reorder)
assert_allclose(evoked_white_2.data, evoked_white.data, atol=1e-7)
def test_cov_order():
"""Test covariance ordering."""
raw = read_raw_fif(raw_fname)
raw.set_eeg_reference(projection=True)
info = raw.info
# add MEG channel with low enough index number to affect EEG if
# order is incorrect
info['bads'] += ['MEG 0113']
ch_names = [info['ch_names'][pick]
for pick in pick_types(info, meg=False, eeg=True)]
cov = read_cov(cov_fname)
# no avg ref present warning
prepare_noise_cov(cov, info, ch_names, verbose='error')
# big reordering
cov_reorder = cov.copy()
order = np.random.RandomState(0).permutation(np.arange(len(cov.ch_names)))
cov_reorder['names'] = [cov['names'][ii] for ii in order]
cov_reorder['data'] = cov['data'][order][:, order]
# Make sure we did this properly
_assert_reorder(cov_reorder, cov, order)
# Now check some functions that should get the same result for both
# regularize
with pytest.raises(ValueError, match='rank, if str'):
regularize(cov, info, rank='foo')
with pytest.raises(TypeError, match='rank must be'):
regularize(cov, info, rank=False)
with pytest.raises(TypeError, match='rank must be'):
regularize(cov, info, rank=1.)
cov_reg = regularize(cov, info, rank='full')
cov_reg_reorder = regularize(cov_reorder, info, rank='full')
_assert_reorder(cov_reg_reorder, cov_reg, order)
# prepare_noise_cov
cov_prep = prepare_noise_cov(cov, info, ch_names)
cov_prep_reorder = prepare_noise_cov(cov, info, ch_names)
_assert_reorder(cov_prep, cov_prep_reorder,
order=np.arange(len(cov_prep['names'])))
# compute_whitener
whitener, w_ch_names, n_nzero = compute_whitener(
cov, info, return_rank=True)
assert whitener.shape[0] == whitener.shape[1]
whitener_2, w_ch_names_2, n_nzero_2 = compute_whitener(
cov_reorder, info, return_rank=True)
assert_array_equal(w_ch_names_2, w_ch_names)
assert_allclose(whitener_2, whitener)
assert n_nzero == n_nzero_2
# with pca
assert n_nzero < whitener.shape[0]
whitener_pca, w_ch_names_pca, n_nzero_pca = compute_whitener(
cov, info, pca=True, return_rank=True)
assert_array_equal(w_ch_names_pca, w_ch_names)
assert n_nzero_pca == n_nzero
assert whitener_pca.shape == (n_nzero_pca, len(w_ch_names))
# whiten_evoked
evoked = read_evokeds(ave_fname)[0]
evoked_white = whiten_evoked(evoked, cov)
evoked_white_2 = whiten_evoked(evoked, cov_reorder)
assert_allclose(evoked_white_2.data, evoked_white.data)
def test_cov_order():
"""Test covariance ordering."""
info = read_info(raw_fname)
# add MEG channel with low enough index number to affect EEG if
# order is incorrect
info["bads"] += ["MEG 0113"]
ch_names = [info["ch_names"][pick] for pick in pick_types(info, meg=False, eeg=True)]
cov = read_cov(cov_fname)
# no avg ref present warning
prepare_noise_cov(cov, info, ch_names, verbose="error")
def test_cov_order():
"""Test covariance ordering"""
info = read_info(raw_fname)
# add MEG channel with low enough index number to affect EEG if
# order is incorrect
info['bads'] += ['MEG 0113']
ch_names = [info['ch_names'][pick]
for pick in pick_types(info, meg=False, eeg=True)]
cov = read_cov(cov_fname)
with warnings.catch_warnings(record=True): # no avg ref present
prepare_noise_cov(cov, info, ch_names, verbose='error')
def test_cov_order():
"""Test covariance ordering."""
info = read_info(raw_fname)
# add MEG channel with low enough index number to affect EEG if
# order is incorrect
info['bads'] += ['MEG 0113']
ch_names = [
info['ch_names'][pick]
for pick in pick_types(info, meg=False, eeg=True)
]
cov = read_cov(cov_fname)
# no avg ref present warning
prepare_noise_cov(cov, info, ch_names, verbose='error')
def test_cov_order():
"""Test covariance ordering."""
info = read_info(raw_fname)
# add MEG channel with low enough index number to affect EEG if
# order is incorrect
info['bads'] += ['MEG 0113']
ch_names = [
info['ch_names'][pick]
for pick in pick_types(info, meg=False, eeg=True)
]
cov = read_cov(cov_fname)
# no avg ref present warning
prepare_noise_cov(cov, info, ch_names, verbose='error')
# big reordering
cov_reorder = cov.copy()
order = np.random.RandomState(0).permutation(np.arange(len(cov.ch_names)))
cov_reorder['names'] = [cov['names'][ii] for ii in order]
cov_reorder['data'] = cov['data'][order][:, order]
# Make sure we did this properly
_assert_reorder(cov_reorder, cov, order)
# Now check some functions that should get the same result for both
# regularize
cov_reg = regularize(cov, info)
cov_reg_reorder = regularize(cov_reorder, info)
_assert_reorder(cov_reg_reorder, cov_reg, order)
# prepare_noise_cov
cov_prep = prepare_noise_cov(cov, info, ch_names)
cov_prep_reorder = prepare_noise_cov(cov, info, ch_names)
_assert_reorder(cov_prep,
cov_prep_reorder,
order=np.arange(len(cov_prep['names'])))
# compute_whitener
whitener, w_ch_names = compute_whitener(cov, info)
whitener_2, w_ch_names_2 = compute_whitener(cov_reorder, info)
assert_array_equal(w_ch_names_2, w_ch_names)
assert_allclose(whitener_2, whitener)
# whiten_evoked
evoked = read_evokeds(ave_fname)[0]
evoked_white = whiten_evoked(evoked, cov)
evoked_white_2 = whiten_evoked(evoked, cov_reorder)
assert_allclose(evoked_white_2.data, evoked_white.data)
def test_cov_order():
"""Test covariance ordering."""
info = read_info(raw_fname)
# add MEG channel with low enough index number to affect EEG if
# order is incorrect
info['bads'] += ['MEG 0113']
ch_names = [info['ch_names'][pick]
for pick in pick_types(info, meg=False, eeg=True)]
cov = read_cov(cov_fname)
# no avg ref present warning
prepare_noise_cov(cov, info, ch_names, verbose='error')
# big reordering
cov_reorder = cov.copy()
order = np.random.RandomState(0).permutation(np.arange(len(cov.ch_names)))
cov_reorder['names'] = [cov['names'][ii] for ii in order]
cov_reorder['data'] = cov['data'][order][:, order]
# Make sure we did this properly
_assert_reorder(cov_reorder, cov, order)
# Now check some functions that should get the same result for both
# regularize
cov_reg = regularize(cov, info)
cov_reg_reorder = regularize(cov_reorder, info)
_assert_reorder(cov_reg_reorder, cov_reg, order)
# prepare_noise_cov
cov_prep = prepare_noise_cov(cov, info, ch_names)
cov_prep_reorder = prepare_noise_cov(cov, info, ch_names)
_assert_reorder(cov_prep, cov_prep_reorder,
order=np.arange(len(cov_prep['names'])))
# compute_whitener
whitener, w_ch_names = compute_whitener(cov, info)
whitener_2, w_ch_names_2 = compute_whitener(cov_reorder, info)
assert_array_equal(w_ch_names_2, w_ch_names)
assert_allclose(whitener_2, whitener)
# whiten_evoked
evoked = read_evokeds(ave_fname)[0]
evoked_white = whiten_evoked(evoked, cov)
evoked_white_2 = whiten_evoked(evoked, cov_reorder)
assert_allclose(evoked_white_2.data, evoked_white.data)
def mSSP_make_whitener(noise_cov, info, pca=False, rank=None, verbose=None):
"""get whitening matrix from covariance matrix
ripped from mne.minimum_norm.inverse._prepare_forward
I still don't understand this - doesn't seem to work
e.g. whitener.dot(noise_cov.data).dot(whitener) is not near to identity
"""
ch_names = [
c['ch_name'] for c in info['chs']
if ((c['ch_name'] not in info['bads'] and c['ch_name'] not in
noise_cov['bads']) and (c['ch_name'] in noise_cov.ch_names))
]
if not len(info['bads']) == len(noise_cov['bads']) or \
not all(b in noise_cov['bads'] for b in info['bads']):
logger.info('info["bads"] and noise_cov["bads"] do not match, '
'excluding bad channels from both')
n_chan = len(ch_names)
logger.info("Computing whitener with %d channels." % n_chan)
#
# Handle noise cov
#
noise_cov = prepare_noise_cov(noise_cov, info, ch_names, rank)
# Omit the zeroes due to projection
eig = noise_cov['eig']
nzero = (eig > 0)
n_nzero = sum(nzero)
if pca:
# Rows of eigvec are the eigenvectors
whitener = noise_cov['eigvec'][nzero] / np.sqrt(eig[nzero])[:, None]
logger.info('Reducing data rank to %d' % n_nzero)
else:
whitener = np.zeros((n_chan, n_chan), dtype=np.float)
whitener[nzero, nzero] = 1.0 / np.sqrt(eig[nzero])
# Rows of eigvec are the eigenvectors
whitener = np.dot(whitener, noise_cov['eigvec'])
logger.info('Total rank is %d' % n_nzero)
return whitener, noise_cov, n_nzero
def test_rank():
"""Test cov rank estimation."""
# Test that our rank estimation works properly on a simple case
evoked = read_evokeds(ave_fname,
condition=0,
baseline=(None, 0),
proj=False)
cov = read_cov(cov_fname)
ch_names = [
ch for ch in evoked.info['ch_names']
if '053' not in ch and ch.startswith('EEG')
]
cov = prepare_noise_cov(cov, evoked.info, ch_names, None)
assert_equal(cov['eig'][0], 0.) # avg projector should set this to zero
assert_true((cov['eig'][1:] > 0).all()) # all else should be > 0
# Now do some more comprehensive tests
raw_sample = read_raw_fif(raw_fname)
raw_sss = read_raw_fif(hp_fif_fname)
raw_sss.add_proj(compute_proj_raw(raw_sss))
cov_sample = compute_raw_covariance(raw_sample)
cov_sample_proj = compute_raw_covariance(raw_sample.copy().apply_proj())
cov_sss = compute_raw_covariance(raw_sss)
cov_sss_proj = compute_raw_covariance(raw_sss.copy().apply_proj())
picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)
info_sample = pick_info(raw_sample.info, picks_all_sample)
picks_stack_sample = [('eeg', pick_types(info_sample, meg=False,
eeg=True))]
picks_stack_sample += [('meg', pick_types(info_sample, meg=True))]
picks_stack_sample += [('all', pick_types(info_sample, meg=True,
eeg=True))]
info_sss = pick_info(raw_sss.info, picks_all_sss)
picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))]
picks_stack_somato += [('meg', pick_types(info_sss, meg=True))]
picks_stack_somato += [('all', pick_types(info_sss, meg=True, eeg=True))]
iter_tests = list(
itt.product(
[(cov_sample, picks_stack_sample, info_sample),
(cov_sample_proj, picks_stack_sample, info_sample),
(cov_sss, picks_stack_somato, info_sss),
(cov_sss_proj, picks_stack_somato, info_sss)], # sss
[dict(mag=1e15, grad=1e13, eeg=1e6)]))
for (cov, picks_list, this_info), scalings in iter_tests:
for ch_type, picks in picks_list:
this_very_info = pick_info(this_info, picks)
# compute subset of projs
this_projs = [
c['active'] and len(
set(c['data']['col_names']).intersection(
set(this_very_info['ch_names']))) > 0
for c in cov['projs']
]
n_projs = sum(this_projs)
# count channel types
ch_types = [
channel_type(this_very_info, idx) for idx in range(len(picks))
]
n_eeg, n_mag, n_grad = [
ch_types.count(k) for k in ['eeg', 'mag', 'grad']
]
n_meg = n_mag + n_grad
if ch_type in ('all', 'eeg'):
n_projs_eeg = 1
else:
n_projs_eeg = 0
# check sss
if len(this_very_info['proc_history']) > 0:
mf = this_very_info['proc_history'][0]['max_info']
n_free = _get_sss_rank(mf)
if 'mag' not in ch_types and 'grad' not in ch_types:
n_free = 0
# - n_projs XXX clarify
expected_rank = n_free + n_eeg
if n_projs > 0 and ch_type in ('all', 'eeg'):
expected_rank -= n_projs_eeg
else:
expected_rank = n_meg + n_eeg - n_projs
C = cov['data'][np.ix_(picks, picks)]
est_rank = _estimate_rank_meeg_cov(C,
this_very_info,
scalings=scalings)
assert_equal(expected_rank, est_rank)
def test_cov_rank_estimation(rank_method, proj, meg):
"""Test cov rank estimation."""
# Test that our rank estimation works properly on a simple case
evoked = read_evokeds(ave_fname,
condition=0,
baseline=(None, 0),
proj=False)
cov = read_cov(cov_fname)
ch_names = [
ch for ch in evoked.info['ch_names']
if '053' not in ch and ch.startswith('EEG')
]
cov = prepare_noise_cov(cov, evoked.info, ch_names, None)
assert cov['eig'][0] <= 1e-25 # avg projector should set this to zero
assert (cov['eig'][1:] > 1e-16).all() # all else should be > 0
# Now do some more comprehensive tests
raw_sample = read_raw_fif(raw_fname)
assert not _has_eeg_average_ref_proj(raw_sample.info['projs'])
raw_sss = read_raw_fif(hp_fif_fname)
assert not _has_eeg_average_ref_proj(raw_sss.info['projs'])
raw_sss.add_proj(compute_proj_raw(raw_sss, meg=meg))
cov_sample = compute_raw_covariance(raw_sample)
cov_sample_proj = compute_raw_covariance(raw_sample.copy().apply_proj())
cov_sss = compute_raw_covariance(raw_sss)
cov_sss_proj = compute_raw_covariance(raw_sss.copy().apply_proj())
picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)
info_sample = pick_info(raw_sample.info, picks_all_sample)
picks_stack_sample = [('eeg', pick_types(info_sample, meg=False,
eeg=True))]
picks_stack_sample += [('meg', pick_types(info_sample, meg=True))]
picks_stack_sample += [('all', pick_types(info_sample, meg=True,
eeg=True))]
info_sss = pick_info(raw_sss.info, picks_all_sss)
picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))]
picks_stack_somato += [('meg', pick_types(info_sss, meg=True))]
picks_stack_somato += [('all', pick_types(info_sss, meg=True, eeg=True))]
iter_tests = list(
itt.product(
[(cov_sample, picks_stack_sample, info_sample),
(cov_sample_proj, picks_stack_sample, info_sample),
(cov_sss, picks_stack_somato, info_sss),
(cov_sss_proj, picks_stack_somato, info_sss)], # sss
[dict(mag=1e15, grad=1e13, eeg=1e6)],
))
for (cov, picks_list, iter_info), scalings in iter_tests:
rank = compute_rank(cov, rank_method, scalings, iter_info, proj=proj)
rank['all'] = sum(rank.values())
for ch_type, picks in picks_list:
this_info = pick_info(iter_info, picks)
# compute subset of projs, active and inactive
n_projs_applied = sum(proj['active'] and len(
set(proj['data']['col_names'])
& set(this_info['ch_names'])) > 0 for proj in cov['projs'])
n_projs_info = sum(
len(
set(proj['data']['col_names'])
& set(this_info['ch_names'])) > 0
for proj in this_info['projs'])
# count channel types
ch_types = _get_channel_types(this_info)
n_eeg, n_mag, n_grad = [
ch_types.count(k) for k in ['eeg', 'mag', 'grad']
]
n_meg = n_mag + n_grad
has_sss = (n_meg > 0 and len(this_info['proc_history']) > 0)
if has_sss:
n_meg = _get_rank_sss(this_info)
expected_rank = n_meg + n_eeg
if rank_method is None:
if meg == 'combined' or not has_sss:
if proj:
expected_rank -= n_projs_info
else:
expected_rank -= n_projs_applied
else:
# XXX for now it just uses the total count
assert rank_method == 'info'
if proj:
expected_rank -= n_projs_info
assert rank[ch_type] == expected_rank
def test_rank():
"""Test cov rank estimation."""
# Test that our rank estimation works properly on a simple case
evoked = read_evokeds(ave_fname, condition=0, baseline=(None, 0),
proj=False)
cov = read_cov(cov_fname)
ch_names = [ch for ch in evoked.info['ch_names'] if '053' not in ch and
ch.startswith('EEG')]
cov = prepare_noise_cov(cov, evoked.info, ch_names, None)
assert_equal(cov['eig'][0], 0.) # avg projector should set this to zero
assert_true((cov['eig'][1:] > 0).all()) # all else should be > 0
# Now do some more comprehensive tests
raw_sample = read_raw_fif(raw_fname)
raw_sss = read_raw_fif(hp_fif_fname)
raw_sss.add_proj(compute_proj_raw(raw_sss))
cov_sample = compute_raw_covariance(raw_sample)
cov_sample_proj = compute_raw_covariance(
raw_sample.copy().apply_proj())
cov_sss = compute_raw_covariance(raw_sss)
cov_sss_proj = compute_raw_covariance(
raw_sss.copy().apply_proj())
picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)
info_sample = pick_info(raw_sample.info, picks_all_sample)
picks_stack_sample = [('eeg', pick_types(info_sample, meg=False,
eeg=True))]
picks_stack_sample += [('meg', pick_types(info_sample, meg=True))]
picks_stack_sample += [('all',
pick_types(info_sample, meg=True, eeg=True))]
info_sss = pick_info(raw_sss.info, picks_all_sss)
picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))]
picks_stack_somato += [('meg', pick_types(info_sss, meg=True))]
picks_stack_somato += [('all',
pick_types(info_sss, meg=True, eeg=True))]
iter_tests = list(itt.product(
[(cov_sample, picks_stack_sample, info_sample),
(cov_sample_proj, picks_stack_sample, info_sample),
(cov_sss, picks_stack_somato, info_sss),
(cov_sss_proj, picks_stack_somato, info_sss)], # sss
[dict(mag=1e15, grad=1e13, eeg=1e6)]
))
for (cov, picks_list, this_info), scalings in iter_tests:
for ch_type, picks in picks_list:
this_very_info = pick_info(this_info, picks)
# compute subset of projs
this_projs = [c['active'] and
len(set(c['data']['col_names'])
.intersection(set(this_very_info['ch_names']))) >
0 for c in cov['projs']]
n_projs = sum(this_projs)
# count channel types
ch_types = [channel_type(this_very_info, idx)
for idx in range(len(picks))]
n_eeg, n_mag, n_grad = [ch_types.count(k) for k in
['eeg', 'mag', 'grad']]
n_meg = n_mag + n_grad
if ch_type in ('all', 'eeg'):
n_projs_eeg = 1
else:
n_projs_eeg = 0
# check sss
if len(this_very_info['proc_history']) > 0:
mf = this_very_info['proc_history'][0]['max_info']
n_free = _get_sss_rank(mf)
if 'mag' not in ch_types and 'grad' not in ch_types:
n_free = 0
# - n_projs XXX clarify
expected_rank = n_free + n_eeg
if n_projs > 0 and ch_type in ('all', 'eeg'):
expected_rank -= n_projs_eeg
else:
expected_rank = n_meg + n_eeg - n_projs
C = cov['data'][np.ix_(picks, picks)]
est_rank = _estimate_rank_meeg_cov(C, this_very_info,
scalings=scalings)
assert_equal(expected_rank, est_rank)
def mSSP_make_beamformer(cov, topos, regpars):
"""Make beamformer estimator given covariance matrix and topography(ies)
inclulde MLE depending on covmat
Parameters
----------
cov : instance of Covariance matrix
Covariance matrix for beamformer, assumed to be regularised.
topos : instance of Evoked
One beamformer per topography will be computed, with this toography as target.
regpars : float or dict of float
regularisation parameters for covariance matrices (per channel type)
if float, one regpar for all channel types
if dict, needs entries for all channel types in cov ('mag', 'grad', 'eeg')
Returns
-------
spatfilt: Evoked
spatial filters as topographies."""
# evoked object to contain spatial filters
spatfilt = copy.deepcopy(topos)
# make sure covariance matrix and topos agree in channels
# assumes noise_cov has at least the channels of topos
ch_names = topos.ch_names
# channel indices for different channel types
ch_idx = mSSP_get_channel_types(ch_names)
print "Preparing covariance matrix"
cov = prepare_noise_cov(cov, info=topos.info, ch_names=ch_names, rank=None)
# noise covariance matrix as np.array
cov_mat = cov.data
print "Getting sub-matrices for channel types"
cov_dict = {}
if ch_idx['mag']:
cov_dict['mag'] = cov_mat[np.ix_(ch_idx['mag'], ch_idx['mag'])]
if ch_idx['grad']:
cov_dict['grad'] = cov_mat[np.ix_(ch_idx['grad'], ch_idx['grad'])]
if ch_idx['eeg']:
cov_dict['eeg'] = cov_mat[np.ix_(ch_idx['eeg'], ch_idx['eeg'])]
### if using whitener-approach (not happy with it):
# cov_inv_dict = {}
# if ch_idx['mag']:
# chs = [ch_names[idx] for idx in ch_idx['mag']]
# cov_now = pick_channels_cov(cov, include=chs)
# [cov_inv_now, _, _] = mSSP_make_whitener(cov_now, topos.info, rank=None)
# cov_inv_dict['mag'] = cov_inv_now
# if ch_idx['grad']:
# chs = [ch_names[idx] for idx in ch_idx['grad']]
# cov_now = pick_channels_cov(cov, include=chs)
# [cov_inv_now, _, _] = mSSP_make_whitener(cov_now, topos.info, rank=None)
# cov_inv_dict['grad'] = cov_inv_now
# if ch_idx['eeg']:
# chs = [ch_names[idx] for idx in ch_idx['eeg']]
# cov_now = pick_channels_cov(cov, include=chs)
# [cov_inv_now, _, _] = mSSP_make_whitener(cov_now, topos.info, rank=None)
# cov_inv_dict['eeg'] = cov_inv_now
print "Computing spatial filters"
sf_dict = {} # spatial filters per channel type
for ch_type in cov_dict:
print ch_type
topo_now = topos.data[ch_idx[ch_type], :]
[n_c, n_t] = topo_now.shape
sf_dict[ch_type] = np.zeros([n_c, n_t])
# # take square of whitener for beamformer
# cov_dict_inv = cov_inv_dict[ch_type].dot(cov_inv_dict[ch_type])
# (pseudo)invert covariance matrix
# cov_dict_inv = pinv(cov_dict[ch_type])
# using eigenvalue truncation
# cov_dict_inv = mSSP_invert_SVD_trunc(cov_dict[ch_type], n_comp=30)
# using Tikhonov regularisation
if type(regpars) == dict: # if reg params specified per channel type
regpar = regpars[ch_type]
else: # if only one reg param specified: use for all channel types
regpar = regpars
cov_dict_inv = mSSP_invert_square_Tikhonov(cov_dict[ch_type],
regpar=regpar)
for tt in range(n_t):
targ = topo_now[:, tt]
denom = targ.T.dot(cov_dict_inv).dot(targ)
sf_dict[ch_type][:, tt] = cov_dict_inv.dot(targ) / denom
spatfilt.data[ch_idx[ch_type], :] = sf_dict[ch_type]
return spatfilt
def test_cov_rank_estimation(rank_method, proj, meg):
"""Test cov rank estimation."""
# Test that our rank estimation works properly on a simple case
evoked = read_evokeds(ave_fname, condition=0, baseline=(None, 0),
proj=False)
cov = read_cov(cov_fname)
ch_names = [ch for ch in evoked.info['ch_names'] if '053' not in ch and
ch.startswith('EEG')]
cov = prepare_noise_cov(cov, evoked.info, ch_names, None)
assert cov['eig'][0] <= 1e-25 # avg projector should set this to zero
assert (cov['eig'][1:] > 1e-16).all() # all else should be > 0
# Now do some more comprehensive tests
raw_sample = read_raw_fif(raw_fname)
assert not _has_eeg_average_ref_proj(raw_sample.info['projs'])
raw_sss = read_raw_fif(hp_fif_fname)
assert not _has_eeg_average_ref_proj(raw_sss.info['projs'])
raw_sss.add_proj(compute_proj_raw(raw_sss, meg=meg))
cov_sample = compute_raw_covariance(raw_sample)
cov_sample_proj = compute_raw_covariance(raw_sample.copy().apply_proj())
cov_sss = compute_raw_covariance(raw_sss)
cov_sss_proj = compute_raw_covariance(raw_sss.copy().apply_proj())
picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)
info_sample = pick_info(raw_sample.info, picks_all_sample)
picks_stack_sample = [('eeg', pick_types(info_sample, meg=False,
eeg=True))]
picks_stack_sample += [('meg', pick_types(info_sample, meg=True))]
picks_stack_sample += [('all',
pick_types(info_sample, meg=True, eeg=True))]
info_sss = pick_info(raw_sss.info, picks_all_sss)
picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))]
picks_stack_somato += [('meg', pick_types(info_sss, meg=True))]
picks_stack_somato += [('all',
pick_types(info_sss, meg=True, eeg=True))]
iter_tests = list(itt.product(
[(cov_sample, picks_stack_sample, info_sample),
(cov_sample_proj, picks_stack_sample, info_sample),
(cov_sss, picks_stack_somato, info_sss),
(cov_sss_proj, picks_stack_somato, info_sss)], # sss
[dict(mag=1e15, grad=1e13, eeg=1e6)],
))
for (cov, picks_list, iter_info), scalings in iter_tests:
rank = compute_rank(cov, rank_method, scalings, iter_info,
proj=proj)
rank['all'] = sum(rank.values())
for ch_type, picks in picks_list:
this_info = pick_info(iter_info, picks)
# compute subset of projs, active and inactive
n_projs_applied = sum(proj['active'] and
len(set(proj['data']['col_names']) &
set(this_info['ch_names'])) > 0
for proj in cov['projs'])
n_projs_info = sum(len(set(proj['data']['col_names']) &
set(this_info['ch_names'])) > 0
for proj in this_info['projs'])
# count channel types
ch_types = [channel_type(this_info, idx)
for idx in range(len(picks))]
n_eeg, n_mag, n_grad = [ch_types.count(k) for k in
['eeg', 'mag', 'grad']]
n_meg = n_mag + n_grad
has_sss = (n_meg > 0 and len(this_info['proc_history']) > 0)
if has_sss:
n_meg = _get_rank_sss(this_info)
expected_rank = n_meg + n_eeg
if rank_method is None:
if meg == 'combined' or not has_sss:
if proj:
expected_rank -= n_projs_info
else:
expected_rank -= n_projs_applied
else:
# XXX for now it just uses the total count
assert rank_method == 'info'
if proj:
expected_rank -= n_projs_info
assert rank[ch_type] == expected_rank
