def test_maxfilter_get_rank():
"""test maxfilter rank lookup"""
raw = io.read_raw_fif(raw_fname)
mf = raw.info['proc_history'][0]['max_info']
rank1 = mf['sss_info']['nfree']
rank2 = _get_sss_rank(mf)
assert_equal(rank1, rank2)
def test_rank_estimation():
"""Test raw rank estimation
"""
iter_tests = itt.product([fif_fname, hp_fif_fname], ["norm", dict(mag=1e11, grad=1e9, eeg=1e5)]) # sss
for fname, scalings in iter_tests:
raw = Raw(fname)
(_, picks_meg), (_, picks_eeg) = _picks_by_type(raw.info, meg_combined=True)
n_meg = len(picks_meg)
n_eeg = len(picks_eeg)
raw = Raw(fname, preload=True)
if "proc_history" not in raw.info:
expected_rank = n_meg + n_eeg
else:
mf = raw.info["proc_history"][0]["max_info"]
expected_rank = _get_sss_rank(mf) + n_eeg
assert_array_equal(raw.estimate_rank(scalings=scalings), expected_rank)
assert_array_equal(raw.estimate_rank(picks=picks_eeg, scalings=scalings), n_eeg)
raw = Raw(fname, preload=False)
if "sss" in fname:
tstart, tstop = 0.0, 30.0
raw.add_proj(compute_proj_raw(raw))
raw.apply_proj()
else:
tstart, tstop = 10.0, 20.0
raw.apply_proj()
n_proj = len(raw.info["projs"])
assert_array_equal(
raw.estimate_rank(tstart=tstart, tstop=tstop, scalings=scalings),
expected_rank - (1 if "sss" in fname else n_proj),
)
def test_basic():
"""Test Maxwell filter basic version"""
# Load testing data (raw, SSS std origin, SSS non-standard origin)
with warnings.catch_warnings(record=True): # maxshield
raw = Raw(raw_fname, allow_maxshield=True).crop(0., 1., False)
raw_err = Raw(raw_fname, proj=True, allow_maxshield=True)
raw_erm = Raw(erm_fname, allow_maxshield=True)
assert_raises(RuntimeError, maxwell_filter, raw_err)
assert_raises(TypeError, maxwell_filter, 1.) # not a raw
assert_raises(ValueError, maxwell_filter, raw, int_order=20) # too many
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(_get_n_moments([int_order, ext_order]).sum(), nbases)
# Test SSS computation at the standard head origin
raw_sss = maxwell_filter(raw, origin=mf_head_origin, regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, Raw(sss_std_fname), 200., 1000.)
py_cal = raw_sss.info['proc_history'][0]['max_info']['sss_cal']
assert_equal(len(py_cal), 0)
py_ctc = raw_sss.info['proc_history'][0]['max_info']['sss_ctc']
assert_equal(len(py_ctc), 0)
py_st = raw_sss.info['proc_history'][0]['max_info']['max_st']
assert_equal(len(py_st), 0)
assert_raises(RuntimeError, maxwell_filter, raw_sss)
# Test SSS computation at non-standard head origin
raw_sss = maxwell_filter(raw, origin=[0., 0.02, 0.02], regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, Raw(sss_nonstd_fname), 250., 700.)
# Test SSS computation at device origin
sss_erm_std = Raw(sss_erm_std_fname)
raw_sss = maxwell_filter(raw_erm, coord_frame='meg',
origin=mf_meg_origin, regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, sss_erm_std, 100., 900.)
for key in ('job', 'frame'):
vals = [x.info['proc_history'][0]['max_info']['sss_info'][key]
for x in [raw_sss, sss_erm_std]]
assert_equal(vals[0], vals[1])
# Check against SSS functions from proc_history
sss_info = raw_sss.info['proc_history'][0]['max_info']
assert_equal(_get_n_moments(int_order),
proc_history._get_sss_rank(sss_info))
# Degenerate cases
raw_bad = raw.copy()
raw_bad.comp = True
assert_raises(RuntimeError, maxwell_filter, raw_bad)
del raw_bad
assert_raises(ValueError, maxwell_filter, raw, coord_frame='foo')
assert_raises(ValueError, maxwell_filter, raw, origin='foo')
assert_raises(ValueError, maxwell_filter, raw, origin=[0] * 4)
def test_basic():
"""Test Maxwell filter basic version."""
# Load testing data (raw, SSS std origin, SSS non-standard origin)
raw = read_crop(raw_fname, (0., 1.))
raw_err = read_crop(raw_fname).apply_proj()
raw_erm = read_crop(erm_fname)
assert_raises(RuntimeError, maxwell_filter, raw_err)
assert_raises(TypeError, maxwell_filter, 1.) # not a raw
assert_raises(ValueError, maxwell_filter, raw, int_order=20) # too many
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(_get_n_moments([int_order, ext_order]).sum(), nbases)
# Test SSS computation at the standard head origin
assert_equal(len(raw.info['projs']), 12) # 11 MEG projs + 1 AVG EEG
raw_sss = maxwell_filter(raw, origin=mf_head_origin, regularize=None,
bad_condition='ignore')
assert_equal(len(raw_sss.info['projs']), 1) # avg EEG
assert_equal(raw_sss.info['projs'][0]['desc'], 'Average EEG reference')
assert_meg_snr(raw_sss, read_crop(sss_std_fname), 200., 1000.)
py_cal = raw_sss.info['proc_history'][0]['max_info']['sss_cal']
assert_equal(len(py_cal), 0)
py_ctc = raw_sss.info['proc_history'][0]['max_info']['sss_ctc']
assert_equal(len(py_ctc), 0)
py_st = raw_sss.info['proc_history'][0]['max_info']['max_st']
assert_equal(len(py_st), 0)
assert_raises(RuntimeError, maxwell_filter, raw_sss)
# Test SSS computation at non-standard head origin
raw_sss = maxwell_filter(raw, origin=[0., 0.02, 0.02], regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, read_crop(sss_nonstd_fname), 250., 700.)
# Test SSS computation at device origin
sss_erm_std = read_crop(sss_erm_std_fname)
raw_sss = maxwell_filter(raw_erm, coord_frame='meg',
origin=mf_meg_origin, regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, sss_erm_std, 100., 900.)
for key in ('job', 'frame'):
vals = [x.info['proc_history'][0]['max_info']['sss_info'][key]
for x in [raw_sss, sss_erm_std]]
assert_equal(vals[0], vals[1])
# Check against SSS functions from proc_history
sss_info = raw_sss.info['proc_history'][0]['max_info']
assert_equal(_get_n_moments(int_order),
proc_history._get_sss_rank(sss_info))
# Degenerate cases
assert_raises(ValueError, maxwell_filter, raw, coord_frame='foo')
assert_raises(ValueError, maxwell_filter, raw, origin='foo')
assert_raises(ValueError, maxwell_filter, raw, origin=[0] * 4)
assert_raises(ValueError, maxwell_filter, raw, mag_scale='foo')
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_basic():
"""Test Maxwell filter basic version."""
# Load testing data (raw, SSS std origin, SSS non-standard origin)
raw = read_crop(raw_fname, (0., 1.))
raw_err = read_crop(raw_fname).apply_proj()
raw_erm = read_crop(erm_fname)
pytest.raises(RuntimeError, maxwell_filter, raw_err)
pytest.raises(TypeError, maxwell_filter, 1.) # not a raw
pytest.raises(ValueError, maxwell_filter, raw, int_order=20) # too many
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(_get_n_moments([int_order, ext_order]).sum(), nbases)
# Test SSS computation at the standard head origin
assert_equal(len(raw.info['projs']), 12) # 11 MEG projs + 1 AVG EEG
with use_coil_def(elekta_def_fname):
raw_sss = maxwell_filter(raw, origin=mf_head_origin, regularize=None,
bad_condition='ignore')
assert_equal(len(raw_sss.info['projs']), 1) # avg EEG
assert_equal(raw_sss.info['projs'][0]['desc'], 'Average EEG reference')
assert_meg_snr(raw_sss, read_crop(sss_std_fname), 200., 1000.)
py_cal = raw_sss.info['proc_history'][0]['max_info']['sss_cal']
assert_equal(len(py_cal), 0)
py_ctc = raw_sss.info['proc_history'][0]['max_info']['sss_ctc']
assert_equal(len(py_ctc), 0)
py_st = raw_sss.info['proc_history'][0]['max_info']['max_st']
assert_equal(len(py_st), 0)
pytest.raises(RuntimeError, maxwell_filter, raw_sss)
# Test SSS computation at non-standard head origin
with use_coil_def(elekta_def_fname):
raw_sss = maxwell_filter(raw, origin=[0., 0.02, 0.02], regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, read_crop(sss_nonstd_fname), 250., 700.)
# Test SSS computation at device origin
sss_erm_std = read_crop(sss_erm_std_fname)
raw_sss = maxwell_filter(raw_erm, coord_frame='meg',
origin=mf_meg_origin, regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, sss_erm_std, 70., 260.)
for key in ('job', 'frame'):
vals = [x.info['proc_history'][0]['max_info']['sss_info'][key]
for x in [raw_sss, sss_erm_std]]
assert_equal(vals[0], vals[1])
# Two equivalent things: at device origin in device coords (0., 0., 0.)
# and at device origin at head coords info['dev_head_t'][:3, 3]
raw_sss_meg = maxwell_filter(
raw, coord_frame='meg', origin=(0., 0., 0.))
raw_sss_head = maxwell_filter(
raw, origin=raw.info['dev_head_t']['trans'][:3, 3])
assert_meg_snr(raw_sss_meg, raw_sss_head, 100., 900.)
# Check against SSS functions from proc_history
sss_info = raw_sss.info['proc_history'][0]['max_info']
assert_equal(_get_n_moments(int_order),
proc_history._get_sss_rank(sss_info))
# Degenerate cases
pytest.raises(ValueError, maxwell_filter, raw, coord_frame='foo')
pytest.raises(ValueError, maxwell_filter, raw, origin='foo')
pytest.raises(ValueError, maxwell_filter, raw, origin=[0] * 4)
pytest.raises(ValueError, maxwell_filter, raw, mag_scale='foo')
raw_missing = raw.copy().load_data()
raw_missing.info['bads'] = ['MEG0111']
raw_missing.pick_types(meg=True) # will be missing the bad
maxwell_filter(raw_missing)
with pytest.warns(RuntimeWarning, match='not in data'):
maxwell_filter(raw_missing, calibration=fine_cal_fname)
def test_rank():
"""Test cov rank estimation"""
raw_sample = Raw(raw_fname)
raw_sss = Raw(hp_fif_fname)
raw_sss.add_proj(compute_proj_raw(raw_sss))
cov_sample = compute_raw_data_covariance(raw_sample)
cov_sample_proj = compute_raw_data_covariance(
raw_sample.copy().apply_proj())
cov_sss = compute_raw_data_covariance(raw_sss)
cov_sss_proj = compute_raw_data_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 'proc_history' in this_very_info:
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_basic():
"""Test Maxwell filter basic version."""
# Load testing data (raw, SSS std origin, SSS non-standard origin)
raw = read_crop(raw_fname, (0., 1.))
raw_err = read_crop(raw_fname).apply_proj()
raw_erm = read_crop(erm_fname)
pytest.raises(RuntimeError, maxwell_filter, raw_err)
pytest.raises(TypeError, maxwell_filter, 1.) # not a raw
pytest.raises(ValueError, maxwell_filter, raw, int_order=20) # too many
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(_get_n_moments([int_order, ext_order]).sum(), nbases)
# Test SSS computation at the standard head origin
assert_equal(len(raw.info['projs']), 12) # 11 MEG projs + 1 AVG EEG
raw_sss = maxwell_filter(raw,
origin=mf_head_origin,
regularize=None,
bad_condition='ignore')
assert_equal(len(raw_sss.info['projs']), 1) # avg EEG
assert_equal(raw_sss.info['projs'][0]['desc'], 'Average EEG reference')
assert_meg_snr(raw_sss, read_crop(sss_std_fname), 200., 1000.)
py_cal = raw_sss.info['proc_history'][0]['max_info']['sss_cal']
assert_equal(len(py_cal), 0)
py_ctc = raw_sss.info['proc_history'][0]['max_info']['sss_ctc']
assert_equal(len(py_ctc), 0)
py_st = raw_sss.info['proc_history'][0]['max_info']['max_st']
assert_equal(len(py_st), 0)
pytest.raises(RuntimeError, maxwell_filter, raw_sss)
# Test SSS computation at non-standard head origin
raw_sss = maxwell_filter(raw,
origin=[0., 0.02, 0.02],
regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, read_crop(sss_nonstd_fname), 250., 700.)
# Test SSS computation at device origin
sss_erm_std = read_crop(sss_erm_std_fname)
raw_sss = maxwell_filter(raw_erm,
coord_frame='meg',
origin=mf_meg_origin,
regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, sss_erm_std, 100., 900.)
for key in ('job', 'frame'):
vals = [
x.info['proc_history'][0]['max_info']['sss_info'][key]
for x in [raw_sss, sss_erm_std]
]
assert_equal(vals[0], vals[1])
# Two equivalent things: at device origin in device coords (0., 0., 0.)
# and at device origin at head coords info['dev_head_t'][:3, 3]
raw_sss_meg = maxwell_filter(raw, coord_frame='meg', origin=(0., 0., 0.))
raw_sss_head = maxwell_filter(raw,
origin=raw.info['dev_head_t']['trans'][:3,
3])
assert_meg_snr(raw_sss_meg, raw_sss_head, 100., 900.)
# Check against SSS functions from proc_history
sss_info = raw_sss.info['proc_history'][0]['max_info']
assert_equal(_get_n_moments(int_order),
proc_history._get_sss_rank(sss_info))
# Degenerate cases
pytest.raises(ValueError, maxwell_filter, raw, coord_frame='foo')
pytest.raises(ValueError, maxwell_filter, raw, origin='foo')
pytest.raises(ValueError, maxwell_filter, raw, origin=[0] * 4)
pytest.raises(ValueError, maxwell_filter, raw, mag_scale='foo')
raw_missing = raw.copy().load_data()
raw_missing.info['bads'] = ['MEG0111']
raw_missing.pick_types(meg=True) # will be missing the bad
maxwell_filter(raw_missing)
with pytest.warns(RuntimeWarning, match='not in data'):
maxwell_filter(raw_missing, calibration=fine_cal_fname)
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 (cov['eig'][1:] > 0).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))
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
# 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
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_basic():
"""Test Maxwell filter basic version"""
# Load testing data (raw, SSS std origin, SSS non-standard origin)
raw = Raw(raw_fname, allow_maxshield='yes').crop(0., 1., copy=False)
raw_err = Raw(raw_fname, proj=True, allow_maxshield='yes')
raw_erm = Raw(erm_fname, allow_maxshield='yes')
assert_raises(RuntimeError, maxwell_filter, raw_err)
assert_raises(TypeError, maxwell_filter, 1.) # not a raw
assert_raises(ValueError, maxwell_filter, raw, int_order=20) # too many
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(_get_n_moments([int_order, ext_order]).sum(), nbases)
# Test SSS computation at the standard head origin
assert_equal(len(raw.info['projs']), 12) # 11 MEG projs + 1 AVG EEG
raw_sss = maxwell_filter(raw,
origin=mf_head_origin,
regularize=None,
bad_condition='ignore')
assert_equal(len(raw_sss.info['projs']), 1) # avg EEG
assert_equal(raw_sss.info['projs'][0]['desc'], 'Average EEG reference')
assert_meg_snr(raw_sss, Raw(sss_std_fname), 200., 1000.)
py_cal = raw_sss.info['proc_history'][0]['max_info']['sss_cal']
assert_equal(len(py_cal), 0)
py_ctc = raw_sss.info['proc_history'][0]['max_info']['sss_ctc']
assert_equal(len(py_ctc), 0)
py_st = raw_sss.info['proc_history'][0]['max_info']['max_st']
assert_equal(len(py_st), 0)
assert_raises(RuntimeError, maxwell_filter, raw_sss)
# Test SSS computation at non-standard head origin
raw_sss = maxwell_filter(raw,
origin=[0., 0.02, 0.02],
regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, Raw(sss_nonstd_fname), 250., 700.)
# Test SSS computation at device origin
sss_erm_std = Raw(sss_erm_std_fname)
raw_sss = maxwell_filter(raw_erm,
coord_frame='meg',
origin=mf_meg_origin,
regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, sss_erm_std, 100., 900.)
for key in ('job', 'frame'):
vals = [
x.info['proc_history'][0]['max_info']['sss_info'][key]
for x in [raw_sss, sss_erm_std]
]
assert_equal(vals[0], vals[1])
# Check against SSS functions from proc_history
sss_info = raw_sss.info['proc_history'][0]['max_info']
assert_equal(_get_n_moments(int_order),
proc_history._get_sss_rank(sss_info))
# Degenerate cases
assert_raises(ValueError, maxwell_filter, raw, coord_frame='foo')
assert_raises(ValueError, maxwell_filter, raw, origin='foo')
assert_raises(ValueError, maxwell_filter, raw, origin=[0] * 4)
assert_raises(ValueError, maxwell_filter, raw, mag_scale='foo')
def test_maxfilter_get_rank():
"""Test maxfilter rank lookup."""
mf = read_info(raw_fname)['proc_history'][0]['max_info']
rank1 = mf['sss_info']['nfree']
rank2 = _get_sss_rank(mf)
assert_equal(rank1, rank2)
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))
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')
# Check normalization
assert_allclose(np.sum(S_in**2, axis=0),
np.ones((S_in.shape[1])),
rtol=1e-12,
err_msg='S_in normalization error')
assert_allclose(np.sum(S_out**2, axis=0),
np.ones((S_out.shape[1])),
rtol=1e-12,
err_msg='S_out normalization error')
# 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_rank():
"""Test cov rank estimation"""
raw_sample = Raw(raw_fname)
raw_sss = Raw(hp_fif_fname)
raw_sss.add_proj(compute_proj_raw(raw_sss))
cov_sample = compute_raw_data_covariance(raw_sample)
cov_sample_proj = compute_raw_data_covariance(
raw_sample.copy().apply_proj())
cov_sss = compute_raw_data_covariance(raw_sss)
cov_sss_proj = compute_raw_data_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 'proc_history' in this_very_info:
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_maxfilter_get_rank():
"""Test maxfilter rank lookup."""
mf = read_info(raw_fname)['proc_history'][0]['max_info']
rank1 = mf['sss_info']['nfree']
rank2 = _get_sss_rank(mf)
assert_equal(rank1, rank2)
