def test_explicit_bads_pick():
"""Test when bads channels are explicitly passed + default picks=None."""
raw = read_raw_fif(raw_fname, preload=True)
raw.pick_types(eeg=True, meg=True, ref_meg=True)
# Covariance
# Default picks=None
raw.info['bads'] = list()
noise_cov_1 = compute_raw_covariance(raw, picks=None)
rank = compute_rank(noise_cov_1, info=raw.info)
assert rank == dict(meg=303, eeg=60)
assert raw.info['bads'] == []
raw.info['bads'] = ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122']
noise_cov = compute_raw_covariance(raw, picks=None)
rank = compute_rank(noise_cov, info=raw.info)
assert rank == dict(meg=302, eeg=57)
assert raw.info['bads'] == ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122']
# Explicit picks
picks = pick_types(raw.info, meg=True, eeg=True, exclude=[])
noise_cov_2 = compute_raw_covariance(raw, picks=picks)
rank = compute_rank(noise_cov_2, info=raw.info)
assert rank == dict(meg=303, eeg=60)
assert raw.info['bads'] == ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122']
assert_array_equal(noise_cov_1['data'], noise_cov_2['data'])
assert noise_cov_1['names'] == noise_cov_2['names']
# Raw
raw.info['bads'] = list()
rank = compute_rank(raw)
assert rank == dict(meg=303, eeg=60)
raw.info['bads'] = ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122']
rank = compute_rank(raw)
assert rank == dict(meg=302, eeg=57)
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_maxfilter_get_rank(n_proj, fname, rank_orig, meg, tol_kind, tol):
"""Test maxfilter rank lookup."""
raw = read_raw_fif(fname).crop(0, 5).load_data().pick_types(meg=True)
assert raw.info['projs'] == []
mf = raw.info['proc_history'][0]['max_info']
assert mf['sss_info']['nfree'] == rank_orig
assert compute_rank(raw, 'info')['meg'] == rank_orig
assert compute_rank(raw.copy().pick('grad'), 'info')['grad'] == rank_orig
assert compute_rank(raw.copy().pick('mag'), 'info')['mag'] == 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'):
_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, tol_kind=tol_kind)
assert rank_new in allowed_rank
rank_new = _estimate_rank_raw(raw, tol=tol, tol_kind=tol_kind)
if fname == mf_fif_fname and tol_kind == 'relative' and tol != 'auto':
pass # does not play nicely with row norms of _estimate_rank_raw
else:
assert rank_new in allowed_rank
rank_new = _estimate_rank_raw(raw,
scalings=dict(),
tol=tol,
tol_kind=tol_kind)
assert rank_new in allowed_rank
scalings = dict(grad=1e13, mag=1e15)
rank_new = _compute_rank_int(raw,
None,
scalings=scalings,
tol=tol,
tol_kind=tol_kind,
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,
tol_kind=tol_kind,
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_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