def test_maxfilter_get_rank(n_proj, fname, rank_orig, meg):
"""Test maxfilter rank lookup."""
raw = read_raw_fif(fname).crop(0, 5).load_data().pick_types()
assert raw.info['projs'] == []
mf = raw.info['proc_history'][0]['max_info']
assert mf['sss_info']['nfree'] == rank_orig
assert _get_rank_sss(raw) == rank_orig
mult = 1 + (meg == 'separate')
rank = rank_orig - mult * n_proj
if n_proj > 0:
# Let's do some projection
raw.add_proj(
compute_proj_raw(raw,
n_mag=n_proj,
n_grad=n_proj,
meg=meg,
verbose=True))
raw.apply_proj()
data_orig = raw[:][0]
# degenerate cases
with pytest.raises(ValueError, match='tol must be'):
_estimate_rank_raw(raw, tol='foo')
with pytest.raises(TypeError, match='must be a string or a number'):
_estimate_rank_raw(raw, tol=None)
allowed_rank = [rank_orig if meg == 'separate' else rank]
if fname == mf_fif_fname:
# Here we permit a -1 because for mf_fif_fname we miss by 1, which is
# probably acceptable. If we use the entire duration instead of 5 sec
# this problem goes away, but the test is much slower.
allowed_rank.append(allowed_rank[0] - 1)
# multiple ways of hopefully getting the same thing
# default tol=1e-4, scalings='norm'
rank_new = _estimate_rank_raw(raw)
assert rank_new in allowed_rank
tol = 'float32' # temporary option until we can fix things
rank_new = _estimate_rank_raw(raw, tol=tol)
assert rank_new in allowed_rank
rank_new = _estimate_rank_raw(raw, scalings=dict(), tol=tol)
assert rank_new in allowed_rank
scalings = dict(grad=1e13, mag=1e15)
rank_new = _compute_rank_int(raw,
None,
scalings=scalings,
tol=tol,
verbose='debug')
assert rank_new in allowed_rank
# XXX default scalings mis-estimate sometimes :(
if fname == hp_fif_fname:
allowed_rank.append(allowed_rank[0] - 2)
rank_new = _compute_rank_int(raw, None, tol=tol, verbose='debug')
assert rank_new in allowed_rank
del allowed_rank
rank_new = _compute_rank_int(raw, 'info')
assert rank_new == rank
assert_array_equal(raw[:][0], data_orig)
def test_ica_rank_reduction(method):
"""Test recovery ICA rank reduction."""
_skip_check_picard(method)
# Most basic recovery
raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data()
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[:10]
n_components = 5
max_pca_components = len(picks)
for n_pca_components in [6, 10]:
with pytest.warns(UserWarning, match='did not converge'):
ica = ICA(n_components=n_components,
max_pca_components=max_pca_components,
n_pca_components=n_pca_components,
method=method, max_iter=1).fit(raw, picks=picks)
rank_before = _compute_rank_int(raw.copy().pick(picks), proj=False)
assert_equal(rank_before, len(picks))
raw_clean = ica.apply(raw.copy())
rank_after = _compute_rank_int(raw_clean.copy().pick(picks),
proj=False)
# interaction between ICA rejection and PCA components difficult
# to preduct. Rank_after often seems to be 1 higher then
# n_pca_components
assert (n_components < n_pca_components <= rank_after <=
rank_before)
def test_ica_rank_reduction(method):
"""Test recovery ICA rank reduction."""
_skip_check_picard(method)
# Most basic recovery
raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data()
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')[:10]
n_components = 5
max_pca_components = len(picks)
for n_pca_components in [6, 10]:
with pytest.warns(UserWarning, match='did not converge'):
ica = ICA(n_components=n_components,
max_pca_components=max_pca_components,
n_pca_components=n_pca_components,
method=method,
max_iter=1).fit(raw, picks=picks)
rank_before = _compute_rank_int(raw.copy().pick(picks), proj=False)
assert_equal(rank_before, len(picks))
raw_clean = ica.apply(raw.copy())
rank_after = _compute_rank_int(raw_clean.copy().pick(picks),
proj=False)
# interaction between ICA rejection and PCA components difficult
# to preduct. Rank_after often seems to be 1 higher then
# n_pca_components
assert (n_components < n_pca_components <= rank_after <= rank_before)
def test_maxfilter_get_rank(n_proj, fname, rank_orig, meg):
"""Test maxfilter rank lookup."""
raw = read_raw_fif(fname).crop(0, 5).load_data().pick_types()
assert raw.info['projs'] == []
mf = raw.info['proc_history'][0]['max_info']
assert mf['sss_info']['nfree'] == rank_orig
assert _get_rank_sss(raw) == rank_orig
mult = 1 + (meg == 'separate')
rank = rank_orig - mult * n_proj
if n_proj > 0:
# Let's do some projection
raw.add_proj(compute_proj_raw(raw, n_mag=n_proj, n_grad=n_proj,
meg=meg, verbose=True))
raw.apply_proj()
data_orig = raw[:][0]
# degenerate cases
with pytest.raises(ValueError, match='tol must be'):
_estimate_rank_raw(raw, tol='foo')
with pytest.raises(TypeError, match='must be a string or a number'):
_estimate_rank_raw(raw, tol=None)
allowed_rank = [rank_orig if meg == 'separate' else rank]
if fname == mf_fif_fname:
# Here we permit a -1 because for mf_fif_fname we miss by 1, which is
# probably acceptable. If we use the entire duration instead of 5 sec
# this problem goes away, but the test is much slower.
allowed_rank.append(allowed_rank[0] - 1)
# multiple ways of hopefully getting the same thing
# default tol=1e-4, scalings='norm'
rank_new = _estimate_rank_raw(raw)
assert rank_new in allowed_rank
tol = 'float32' # temporary option until we can fix things
rank_new = _estimate_rank_raw(raw, tol=tol)
assert rank_new in allowed_rank
rank_new = _estimate_rank_raw(raw, scalings=dict(), tol=tol)
assert rank_new in allowed_rank
scalings = dict(grad=1e13, mag=1e15)
rank_new = _compute_rank_int(raw, None, scalings=scalings, tol=tol,
verbose='debug')
assert rank_new in allowed_rank
# XXX default scalings mis-estimate sometimes :(
if fname == hp_fif_fname:
allowed_rank.append(allowed_rank[0] - 2)
rank_new = _compute_rank_int(raw, None, tol=tol, verbose='debug')
assert rank_new in allowed_rank
del allowed_rank
rank_new = _compute_rank_int(raw, 'info')
assert rank_new == rank
assert_array_equal(raw[:][0], data_orig)
def test_spatiotemporal_only():
"""Test tSSS-only processing."""
# Load raw testing data
tmax = 0.5
raw = read_crop(raw_fname, (0, tmax)).load_data()
picks = pick_types(raw.info, meg=True, exclude='bads')[::2]
raw.pick_channels([raw.ch_names[pick] for pick in picks])
mag_picks = pick_types(raw.info, meg='mag', exclude=())
power = np.sqrt(np.sum(raw[mag_picks][0] ** 2))
# basics
raw_tsss = maxwell_filter(raw, st_duration=tmax / 2., st_only=True)
assert len(raw.info['projs']) == len(raw_tsss.info['projs'])
assert _compute_rank_int(raw_tsss, proj=False) == len(picks)
_assert_shielding(raw_tsss, power, 9)
# with movement
head_pos = read_head_pos(pos_fname)
raw_tsss = maxwell_filter(raw, st_duration=tmax / 2., st_only=True,
head_pos=head_pos)
assert _compute_rank_int(raw_tsss, proj=False) == len(picks)
_assert_shielding(raw_tsss, power, 9)
with pytest.warns(RuntimeWarning, match='st_fixed'):
raw_tsss = maxwell_filter(raw, st_duration=tmax / 2., st_only=True,
head_pos=head_pos, st_fixed=False)
assert _compute_rank_int(raw_tsss, proj=False) == len(picks)
_assert_shielding(raw_tsss, power, 9)
# should do nothing
raw_tsss = maxwell_filter(raw, st_duration=tmax, st_correlation=1.,
st_only=True)
assert_allclose(raw[:][0], raw_tsss[:][0])
# degenerate
pytest.raises(ValueError, maxwell_filter, raw, st_only=True) # no ST
# two-step process equivalent to single-step process
raw_tsss = maxwell_filter(raw, st_duration=tmax, st_only=True)
raw_tsss = maxwell_filter(raw_tsss)
raw_tsss_2 = maxwell_filter(raw, st_duration=tmax)
assert_meg_snr(raw_tsss, raw_tsss_2, 1e5)
# now also with head movement, and a bad MEG channel
assert len(raw.info['bads']) == 0
bads = [raw.ch_names[0]]
raw.info['bads'] = list(bads)
raw_tsss = maxwell_filter(raw, st_duration=tmax, st_only=True,
head_pos=head_pos)
assert raw.info['bads'] == bads
assert raw_tsss.info['bads'] == bads # don't reset
raw_tsss = maxwell_filter(raw_tsss, head_pos=head_pos)
assert raw_tsss.info['bads'] == [] # do reset MEG bads
raw_tsss_2 = maxwell_filter(raw, st_duration=tmax, head_pos=head_pos)
assert raw_tsss_2.info['bads'] == []
assert_meg_snr(raw_tsss, raw_tsss_2, 1e5)
def test_spatiotemporal_only():
"""Test tSSS-only processing."""
# Load raw testing data
tmax = 0.5
raw = read_crop(raw_fname, (0, tmax)).load_data()
picks = pick_types(raw.info, meg=True, exclude='bads')[::2]
raw.pick_channels([raw.ch_names[pick] for pick in picks])
mag_picks = pick_types(raw.info, meg='mag', exclude=())
power = np.sqrt(np.sum(raw[mag_picks][0] ** 2))
# basics
raw_tsss = maxwell_filter(raw, st_duration=tmax / 2., st_only=True)
assert len(raw.info['projs']) == len(raw_tsss.info['projs'])
assert _compute_rank_int(raw_tsss, proj=False) == len(picks)
_assert_shielding(raw_tsss, power, 9)
# with movement
head_pos = read_head_pos(pos_fname)
raw_tsss = maxwell_filter(raw, st_duration=tmax / 2., st_only=True,
head_pos=head_pos)
assert _compute_rank_int(raw_tsss, proj=False) == len(picks)
_assert_shielding(raw_tsss, power, 9)
with pytest.warns(RuntimeWarning, match='st_fixed'):
raw_tsss = maxwell_filter(raw, st_duration=tmax / 2., st_only=True,
head_pos=head_pos, st_fixed=False)
assert _compute_rank_int(raw_tsss, proj=False) == len(picks)
_assert_shielding(raw_tsss, power, 9)
# should do nothing
raw_tsss = maxwell_filter(raw, st_duration=tmax, st_correlation=1.,
st_only=True)
assert_allclose(raw[:][0], raw_tsss[:][0])
# degenerate
pytest.raises(ValueError, maxwell_filter, raw, st_only=True) # no ST
# two-step process equivalent to single-step process
raw_tsss = maxwell_filter(raw, st_duration=tmax, st_only=True)
raw_tsss = maxwell_filter(raw_tsss)
raw_tsss_2 = maxwell_filter(raw, st_duration=tmax)
assert_meg_snr(raw_tsss, raw_tsss_2, 1e5)
# now also with head movement, and a bad MEG channel
assert len(raw.info['bads']) == 0
bads = [raw.ch_names[0]]
raw.info['bads'] = list(bads)
raw_tsss = maxwell_filter(raw, st_duration=tmax, st_only=True,
head_pos=head_pos)
assert raw.info['bads'] == bads
assert raw_tsss.info['bads'] == bads # don't reset
raw_tsss = maxwell_filter(raw_tsss, head_pos=head_pos)
assert raw_tsss.info['bads'] == [] # do reset MEG bads
raw_tsss_2 = maxwell_filter(raw, st_duration=tmax, head_pos=head_pos)
assert raw_tsss_2.info['bads'] == []
assert_meg_snr(raw_tsss, raw_tsss_2, 1e5)
def test_sss_proj():
"""Test `meg` proj option."""
raw = read_raw_fif(raw_fname)
raw.crop(0, 1.0).load_data().pick_types(meg=True, exclude=())
raw.pick_channels(raw.ch_names[:51]).del_proj()
raw_sss = maxwell_filter(raw, int_order=5, ext_order=2)
sss_rank = 21 # really low due to channel picking
assert len(raw_sss.info['projs']) == 0
for meg, n_proj, want_rank in (('separate', 6, sss_rank), ('combined', 3,
sss_rank - 3)):
proj = compute_proj_raw(raw_sss,
n_grad=3,
n_mag=3,
meg=meg,
verbose='error')
this_raw = raw_sss.copy().add_proj(proj).apply_proj()
assert len(this_raw.info['projs']) == n_proj
sss_proj_rank = _compute_rank_int(this_raw)
cov = compute_raw_covariance(this_raw, verbose='error')
W, ch_names, rank = compute_whitener(cov,
this_raw.info,
return_rank=True)
assert ch_names == this_raw.ch_names
assert want_rank == sss_proj_rank == rank # proper reduction
if meg == 'combined':
assert this_raw.info['projs'][0]['data']['col_names'] == ch_names
else:
mag_names = ch_names[2::3]
assert this_raw.info['projs'][3]['data']['col_names'] == mag_names
def test_sss_proj():
"""Test `meg` proj option."""
raw = read_raw_fif(raw_fname)
raw.crop(0, 1.0).load_data().pick_types(exclude=())
raw.pick_channels(raw.ch_names[:51]).del_proj()
with pytest.raises(ValueError, match='can only be used with Maxfiltered'):
compute_proj_raw(raw, meg='combined')
raw_sss = maxwell_filter(raw, int_order=5, ext_order=2)
sss_rank = 21 # really low due to channel picking
assert len(raw_sss.info['projs']) == 0
for meg, n_proj, want_rank in (('separate', 6, sss_rank),
('combined', 3, sss_rank - 3)):
proj = compute_proj_raw(raw_sss, n_grad=3, n_mag=3, meg=meg,
verbose='error')
this_raw = raw_sss.copy().add_proj(proj).apply_proj()
assert len(this_raw.info['projs']) == n_proj
sss_proj_rank = _compute_rank_int(this_raw)
cov = compute_raw_covariance(this_raw, verbose='error')
W, ch_names, rank = compute_whitener(cov, this_raw.info,
return_rank=True)
assert ch_names == this_raw.ch_names
assert want_rank == sss_proj_rank == rank # proper reduction
if meg == 'combined':
assert this_raw.info['projs'][0]['data']['col_names'] == ch_names
else:
mag_names = ch_names[2::3]
assert this_raw.info['projs'][3]['data']['col_names'] == mag_names
def test_maxwell_filter_additional(tmpdir):
"""Test processing of Maxwell filtered data."""
# TODO: Future tests integrate with mne/io/tests/test_proc_history
# Load testing data (raw, SSS std origin, SSS non-standard origin)
data_path = op.join(testing.data_path(download=False))
file_name = 'test_move_anon'
raw_fname = op.join(data_path, 'SSS', file_name + '_raw.fif')
# Use 2.0 seconds of data to get stable cov. estimate
raw = read_crop(raw_fname, (0., 2.))
# Get MEG channels, compute Maxwell filtered data
raw.load_data()
raw.pick_types(meg=True, eeg=False)
int_order = 8
raw_sss = maxwell_filter(raw, origin=mf_head_origin, regularize=None,
bad_condition='ignore')
# Test io on processed data
tempdir = str(tmpdir)
test_outname = op.join(tempdir, 'test_raw_sss.fif')
raw_sss.save(test_outname)
raw_sss_loaded = read_crop(test_outname).load_data()
# Some numerical imprecision since save uses 'single' fmt
assert_allclose(raw_sss_loaded[:][0], raw_sss[:][0],
rtol=1e-6, atol=1e-20)
# Test rank of covariance matrices for raw and SSS processed data
cov_raw = compute_raw_covariance(raw)
cov_sss = compute_raw_covariance(raw_sss)
scalings = None
cov_raw_rank = _compute_rank_int(
cov_raw, scalings=scalings, info=raw.info, proj=False)
cov_sss_rank = _compute_rank_int(
cov_sss, scalings=scalings, info=raw_sss.info, proj=False)
assert cov_raw_rank == raw.info['nchan']
assert cov_sss_rank == _get_n_moments(int_order)
def test_maxwell_filter_additional(tmpdir):
"""Test processing of Maxwell filtered data."""
# TODO: Future tests integrate with mne/io/tests/test_proc_history
# Load testing data (raw, SSS std origin, SSS non-standard origin)
data_path = op.join(testing.data_path(download=False))
file_name = 'test_move_anon'
raw_fname = op.join(data_path, 'SSS', file_name + '_raw.fif')
# Use 2.0 seconds of data to get stable cov. estimate
raw = read_crop(raw_fname, (0., 2.))
# Get MEG channels, compute Maxwell filtered data
raw.load_data()
raw.pick_types(meg=True, eeg=False)
int_order = 8
raw_sss = maxwell_filter(raw, origin=mf_head_origin, regularize=None,
bad_condition='ignore')
# Test io on processed data
tempdir = str(tmpdir)
test_outname = op.join(tempdir, 'test_raw_sss.fif')
raw_sss.save(test_outname)
raw_sss_loaded = read_crop(test_outname).load_data()
# Some numerical imprecision since save uses 'single' fmt
assert_allclose(raw_sss_loaded[:][0], raw_sss[:][0],
rtol=1e-6, atol=1e-20)
# Test rank of covariance matrices for raw and SSS processed data
cov_raw = compute_raw_covariance(raw)
cov_sss = compute_raw_covariance(raw_sss)
scalings = None
cov_raw_rank = _compute_rank_int(
cov_raw, scalings=scalings, info=raw.info, proj=False)
cov_sss_rank = _compute_rank_int(
cov_sss, scalings=scalings, info=raw_sss.info, proj=False)
assert cov_raw_rank == raw.info['nchan']
assert cov_sss_rank == _get_n_moments(int_order)
def test_spatiotemporal():
"""Test Maxwell filter (tSSS) spatiotemporal processing."""
# Load raw testing data
raw = read_crop(raw_fname)
# Test that window is less than length of data
with pytest.raises(ValueError, match='duration'):
maxwell_filter(raw, st_duration=1000.)
# We could check both 4 and 10 seconds because Elekta handles them
# differently (to ensure that std/non-std tSSS windows are correctly
# handled), but the 4-sec case should hopefully be sufficient.
st_durations = [4.] # , 10.]
tols = [(80, 100)] # , 200.]
kwargs = dict(origin=mf_head_origin, regularize=None,
bad_condition='ignore')
for st_duration, tol in zip(st_durations, tols):
# Load tSSS data depending on st_duration and get data
tSSS_fname = op.join(sss_path,
'test_move_anon_st%0ds_raw_sss.fif' % st_duration)
tsss_bench = read_crop(tSSS_fname)
# Because Elekta's tSSS sometimes(!) lumps the tail window of data
# onto the previous buffer if it's shorter than st_duration, we have to
# crop the data here to compensate for Elekta's tSSS behavior.
# if st_duration == 10.:
# tsss_bench.crop(0, st_duration)
# raw.crop(0, st_duration)
# Test sss computation at the standard head origin. Same cropping issue
# as mentioned above.
raw_tsss = maxwell_filter(
raw, st_duration=st_duration, **kwargs)
assert _compute_rank_int(raw_tsss, proj=False) == 140
assert_meg_snr(raw_tsss, tsss_bench, *tol)
py_st = raw_tsss.info['proc_history'][0]['max_info']['max_st']
assert (len(py_st) > 0)
assert py_st['buflen'] == st_duration
assert py_st['subspcorr'] == 0.98
# Degenerate cases
pytest.raises(ValueError, maxwell_filter, raw, st_duration=10.,
st_correlation=0.)
def test_spatiotemporal():
"""Test Maxwell filter (tSSS) spatiotemporal processing."""
# Load raw testing data
raw = read_crop(raw_fname)
# Test that window is less than length of data
with pytest.raises(ValueError, match='duration'):
maxwell_filter(raw, st_duration=1000.)
# We could check both 4 and 10 seconds because Elekta handles them
# differently (to ensure that std/non-std tSSS windows are correctly
# handled), but the 4-sec case should hopefully be sufficient.
st_durations = [4.] # , 10.]
tols = [(80, 100)] # , 200.]
kwargs = dict(origin=mf_head_origin, regularize=None,
bad_condition='ignore')
for st_duration, tol in zip(st_durations, tols):
# Load tSSS data depending on st_duration and get data
tSSS_fname = op.join(sss_path,
'test_move_anon_st%0ds_raw_sss.fif' % st_duration)
tsss_bench = read_crop(tSSS_fname)
# Because Elekta's tSSS sometimes(!) lumps the tail window of data
# onto the previous buffer if it's shorter than st_duration, we have to
# crop the data here to compensate for Elekta's tSSS behavior.
# if st_duration == 10.:
# tsss_bench.crop(0, st_duration)
# raw.crop(0, st_duration)
# Test sss computation at the standard head origin. Same cropping issue
# as mentioned above.
raw_tsss = maxwell_filter(
raw, st_duration=st_duration, **kwargs)
assert _compute_rank_int(raw_tsss, proj=False) == 140
assert_meg_snr(raw_tsss, tsss_bench, *tol)
py_st = raw_tsss.info['proc_history'][0]['max_info']['max_st']
assert (len(py_st) > 0)
assert py_st['buflen'] == st_duration
assert py_st['subspcorr'] == 0.98
# Degenerate cases
pytest.raises(ValueError, maxwell_filter, raw, st_duration=10.,
st_correlation=0.)
def test_low_rank_cov(raw_epochs_events):
"""Test additional properties of low rank computations."""
raw, epochs, events = raw_epochs_events
sss_proj_rank = 139 # 80 MEG + 60 EEG - 1 proj
n_ch = 366
proj_rank = 365 # one EEG proj
with pytest.warns(RuntimeWarning, match='Too few samples'):
emp_cov = compute_covariance(epochs)
# Test equivalence with mne.cov.regularize subspace
with pytest.raises(ValueError, match='are dependent.*must equal'):
regularize(emp_cov, epochs.info, rank=None, mag=0.1, grad=0.2)
assert _cov_rank(emp_cov, epochs.info) == sss_proj_rank
reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
assert _cov_rank(reg_cov, epochs.info) == proj_rank
with pytest.warns(RuntimeWarning, match='exceeds the theoretical'):
_compute_rank_int(reg_cov, info=epochs.info)
del reg_cov
with catch_logging() as log:
reg_r_cov = regularize(emp_cov,
epochs.info,
proj=True,
rank=None,
verbose=True)
log = log.getvalue()
assert 'jointly' in log
assert _cov_rank(reg_r_cov, epochs.info) == sss_proj_rank
reg_r_only_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
assert _cov_rank(reg_r_only_cov, epochs.info) == sss_proj_rank
assert_allclose(reg_r_only_cov['data'], reg_r_cov['data'])
del reg_r_only_cov, reg_r_cov
# test that rank=306 is same as rank='full'
epochs_meg = epochs.copy().pick_types(meg=True)
assert len(epochs_meg.ch_names) == 306
epochs_meg.info.update(bads=[], projs=[])
cov_full = compute_covariance(epochs_meg,
method='oas',
rank='full',
verbose='error')
assert _cov_rank(cov_full, epochs_meg.info) == 306
with pytest.warns(RuntimeWarning, match='few samples'):
cov_dict = compute_covariance(epochs_meg,
method='oas',
rank=dict(meg=306))
assert _cov_rank(cov_dict, epochs_meg.info) == 306
assert_allclose(cov_full['data'], cov_dict['data'])
cov_dict = compute_covariance(epochs_meg,
method='oas',
rank=dict(meg=306),
verbose='error')
assert _cov_rank(cov_dict, epochs_meg.info) == 306
assert_allclose(cov_full['data'], cov_dict['data'])
# Work with just EEG data to simplify projection / rank reduction
raw = raw.copy().pick_types(meg=False, eeg=True)
n_proj = 2
raw.add_proj(compute_proj_raw(raw, n_eeg=n_proj))
n_ch = len(raw.ch_names)
rank = n_ch - n_proj - 1 # plus avg proj
assert len(raw.info['projs']) == 3
epochs = Epochs(raw, events, tmin=-0.2, tmax=0, preload=True)
assert len(raw.ch_names) == n_ch
emp_cov = compute_covariance(epochs, rank='full', verbose='error')
assert _cov_rank(emp_cov, epochs.info) == rank
reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
assert _cov_rank(reg_cov, epochs.info) == rank
reg_r_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
assert _cov_rank(reg_r_cov, epochs.info) == rank
dia_cov = compute_covariance(epochs,
rank=None,
method='diagonal_fixed',
verbose='error')
assert _cov_rank(dia_cov, epochs.info) == rank
assert_allclose(dia_cov['data'], reg_cov['data'])
epochs.pick_channels(epochs.ch_names[:103])
# degenerate
with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
compute_covariance(epochs, rank=None, method='pca')
with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
compute_covariance(epochs, rank=None, method='factor_analysis')
def _cov_rank(cov, info, proj=True):
# ignore warnings about rank mismatches: sometimes we will intentionally
# violate the computed/info assumption, such as when using SSS with
# `rank='full'`
with pytest.warns(None):
return _compute_rank_int(cov, info=info, proj=proj)
def test_low_rank_cov(raw_epochs_events):
"""Test additional properties of low rank computations."""
raw, epochs, events = raw_epochs_events
sss_proj_rank = 139 # 80 MEG + 60 EEG - 1 proj
n_ch = 366
proj_rank = 365 # one EEG proj
with pytest.warns(RuntimeWarning, match='Too few samples'):
emp_cov = compute_covariance(epochs)
# Test equivalence with mne.cov.regularize subspace
with pytest.raises(ValueError, match='are dependent.*must equal'):
regularize(emp_cov, epochs.info, rank=None, mag=0.1, grad=0.2)
assert _cov_rank(emp_cov, epochs.info) == sss_proj_rank
reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
assert _cov_rank(reg_cov, epochs.info) == proj_rank
with pytest.warns(RuntimeWarning, match='exceeds the theoretical'):
_compute_rank_int(reg_cov, info=epochs.info)
del reg_cov
with catch_logging() as log:
reg_r_cov = regularize(emp_cov, epochs.info, proj=True, rank=None,
verbose=True)
log = log.getvalue()
assert 'jointly' in log
assert _cov_rank(reg_r_cov, epochs.info) == sss_proj_rank
reg_r_only_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
assert _cov_rank(reg_r_only_cov, epochs.info) == sss_proj_rank
assert_allclose(reg_r_only_cov['data'], reg_r_cov['data'])
del reg_r_only_cov, reg_r_cov
# test that rank=306 is same as rank='full'
epochs_meg = epochs.copy().pick_types()
assert len(epochs_meg.ch_names) == 306
epochs_meg.info.update(bads=[], projs=[])
cov_full = compute_covariance(epochs_meg, method='oas',
rank='full', verbose='error')
assert _cov_rank(cov_full, epochs_meg.info) == 306
with pytest.deprecated_call(match='int is deprecated'):
cov_dict = compute_covariance(epochs_meg, method='oas', rank=306)
assert _cov_rank(cov_dict, epochs_meg.info) == 306
assert_allclose(cov_full['data'], cov_dict['data'])
cov_dict = compute_covariance(epochs_meg, method='oas',
rank=dict(meg=306), verbose='error')
assert _cov_rank(cov_dict, epochs_meg.info) == 306
assert_allclose(cov_full['data'], cov_dict['data'])
# Work with just EEG data to simplify projection / rank reduction
raw = raw.copy().pick_types(meg=False, eeg=True)
n_proj = 2
raw.add_proj(compute_proj_raw(raw, n_eeg=n_proj))
n_ch = len(raw.ch_names)
rank = n_ch - n_proj - 1 # plus avg proj
assert len(raw.info['projs']) == 3
epochs = Epochs(raw, events, tmin=-0.2, tmax=0, preload=True)
assert len(raw.ch_names) == n_ch
emp_cov = compute_covariance(epochs, rank='full', verbose='error')
assert _cov_rank(emp_cov, epochs.info) == rank
reg_cov = regularize(emp_cov, epochs.info, proj=True, rank='full')
assert _cov_rank(reg_cov, epochs.info) == rank
reg_r_cov = regularize(emp_cov, epochs.info, proj=False, rank=None)
assert _cov_rank(reg_r_cov, epochs.info) == rank
dia_cov = compute_covariance(epochs, rank=None, method='diagonal_fixed',
verbose='error')
assert _cov_rank(dia_cov, epochs.info) == rank
assert_allclose(dia_cov['data'], reg_cov['data'])
# test our deprecation: can simply remove later
epochs.pick_channels(epochs.ch_names[:103])
# degenerate
with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
compute_covariance(epochs, rank=None, method='pca')
with pytest.raises(ValueError, match='can.*only be used with rank="full"'):
compute_covariance(epochs, rank=None, method='factor_analysis')
def _cov_rank(cov, info, proj=True):
# ignore warnings about rank mismatches: sometimes we will intentionally
# violate the computed/info assumption, such as when using SSS with
# `rank='full'`
with pytest.warns(None):
return _compute_rank_int(cov, info=info, proj=proj)