def test_no_conversion():
""" Test bti no-conversion option """
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
raw = read_raw_bti(pdf, config, hs, convert=False)
raw_con = read_raw_bti(pdf, config, hs, convert=True)
bti_info = _read_bti_header(pdf, config)
dev_ctf_t = _correct_trans(bti_info["bti_transform"][0])
assert_array_equal(dev_ctf_t, raw.info["dev_ctf_t"]["trans"])
assert_array_equal(raw.info["dev_head_t"]["trans"], np.eye(4))
assert_array_equal(raw.info["ctf_head_t"]["trans"], np.eye(4))
dig, t = _process_bti_headshape(hs, convert=False, use_hpi=False)
assert_array_equal(t["trans"], np.eye(4))
for ii, (old, new, con) in enumerate(zip(dig, raw.info["dig"], raw_con.info["dig"])):
assert_equal(old["ident"], new["ident"])
assert_array_equal(old["r"], new["r"])
assert_true(not np.allclose(old["r"], con["r"]))
if ii > 10:
break
ch_map = dict((ch["chan_label"], ch["coil_trans"]) for ch in bti_info["chs"])
for ii, ch_label in enumerate(raw.bti_ch_labels):
if not ch_label.startswith("A"):
continue
t1 = _correct_trans(ch_map[ch_label])
t2 = raw.info["chs"][ii]["coil_trans"]
t3 = raw_con.info["chs"][ii]["coil_trans"]
assert_array_equal(t1, t2)
assert_true(not np.allclose(t1, t3))
def test_info_no_rename_no_reorder_no_pdf():
"""Test private renaming, reordering and partial construction option."""
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
info, bti_info = _get_bti_info(
pdf_fname=pdf, config_fname=config, head_shape_fname=hs,
rotation_x=0.0, translation=(0.0, 0.02, 0.11), convert=False,
ecg_ch='E31', eog_ch=('E63', 'E64'),
rename_channels=False, sort_by_ch_name=False)
info2, bti_info = _get_bti_info(
pdf_fname=None, config_fname=config, head_shape_fname=hs,
rotation_x=0.0, translation=(0.0, 0.02, 0.11), convert=False,
ecg_ch='E31', eog_ch=('E63', 'E64'),
rename_channels=False, sort_by_ch_name=False)
assert_equal(info['ch_names'],
[ch['ch_name'] for ch in info['chs']])
assert_equal([n for n in info['ch_names'] if n.startswith('A')][:5],
['A22', 'A2', 'A104', 'A241', 'A138'])
assert_equal([n for n in info['ch_names'] if n.startswith('A')][-5:],
['A133', 'A158', 'A44', 'A134', 'A216'])
info = pick_info(info, pick_types(info, meg=True, stim=True,
resp=True))
info2 = pick_info(info2, pick_types(info2, meg=True, stim=True,
resp=True))
assert_true(info['sfreq'] is not None)
assert_true(info['lowpass'] is not None)
assert_true(info['highpass'] is not None)
assert_true(info['meas_date'] is not None)
assert_equal(info2['sfreq'], None)
assert_equal(info2['lowpass'], None)
assert_equal(info2['highpass'], None)
assert_equal(info2['meas_date'], None)
assert_equal(info['ch_names'], info2['ch_names'])
assert_equal(info['ch_names'], info2['ch_names'])
for key in ['dev_ctf_t', 'dev_head_t', 'ctf_head_t']:
assert_array_equal(info[key]['trans'], info2[key]['trans'])
assert_array_equal(
np.array([ch['loc'] for ch in info['chs']]),
np.array([ch['loc'] for ch in info2['chs']]))
# just check reading data | corner case
raw1 = read_raw_bti(
pdf_fname=pdf, config_fname=config, head_shape_fname=None,
sort_by_ch_name=False, preload=True)
# just check reading data | corner case
raw2 = read_raw_bti(
pdf_fname=pdf, config_fname=config, head_shape_fname=None,
rename_channels=False,
sort_by_ch_name=True, preload=True)
sort_idx = [raw1.bti_ch_labels.index(ch) for ch in raw2.bti_ch_labels]
raw1._data = raw1._data[sort_idx]
assert_array_equal(raw1._data, raw2._data)
assert_array_equal(raw2.bti_ch_labels, raw2.ch_names)
def test_info_no_rename_no_reorder_no_pdf():
"""Test private renaming, reordering and partial construction option."""
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
info, bti_info = _get_bti_info(
pdf_fname=pdf, config_fname=config, head_shape_fname=hs,
rotation_x=0.0, translation=(0.0, 0.02, 0.11), convert=False,
ecg_ch='E31', eog_ch=('E63', 'E64'),
rename_channels=False, sort_by_ch_name=False)
info2, bti_info = _get_bti_info(
pdf_fname=None, config_fname=config, head_shape_fname=hs,
rotation_x=0.0, translation=(0.0, 0.02, 0.11), convert=False,
ecg_ch='E31', eog_ch=('E63', 'E64'),
rename_channels=False, sort_by_ch_name=False)
assert_equal(info['ch_names'],
[ch['ch_name'] for ch in info['chs']])
assert_equal([n for n in info['ch_names'] if n.startswith('A')][:5],
['A22', 'A2', 'A104', 'A241', 'A138'])
assert_equal([n for n in info['ch_names'] if n.startswith('A')][-5:],
['A133', 'A158', 'A44', 'A134', 'A216'])
info = pick_info(info, pick_types(info, meg=True, stim=True,
resp=True))
info2 = pick_info(info2, pick_types(info2, meg=True, stim=True,
resp=True))
assert (info['sfreq'] is not None)
assert (info['lowpass'] is not None)
assert (info['highpass'] is not None)
assert (info['meas_date'] is not None)
assert_equal(info2['sfreq'], None)
assert_equal(info2['lowpass'], None)
assert_equal(info2['highpass'], None)
assert_equal(info2['meas_date'], None)
assert_equal(info['ch_names'], info2['ch_names'])
assert_equal(info['ch_names'], info2['ch_names'])
for key in ['dev_ctf_t', 'dev_head_t', 'ctf_head_t']:
assert_array_equal(info[key]['trans'], info2[key]['trans'])
assert_array_equal(
np.array([ch['loc'] for ch in info['chs']]),
np.array([ch['loc'] for ch in info2['chs']]))
# just check reading data | corner case
raw1 = read_raw_bti(
pdf_fname=pdf, config_fname=config, head_shape_fname=None,
sort_by_ch_name=False, preload=True)
# just check reading data | corner case
raw2 = read_raw_bti(
pdf_fname=pdf, config_fname=config, head_shape_fname=None,
rename_channels=False,
sort_by_ch_name=True, preload=True)
sort_idx = [raw1.bti_ch_labels.index(ch) for ch in raw2.bti_ch_labels]
raw1._data = raw1._data[sort_idx]
assert_array_equal(raw1._data, raw2._data)
assert_array_equal(raw2.bti_ch_labels, raw2.ch_names)
def test_bytes_io():
""" Test bti bytes-io API """
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
raw = read_raw_bti(pdf, config, hs, convert=True)
with open(pdf, 'rb') as fid:
pdf = six.BytesIO(fid.read())
with open(config, 'rb') as fid:
config = six.BytesIO(fid.read())
with open(hs, 'rb') as fid:
hs = six.BytesIO(fid.read())
raw2 = read_raw_bti(pdf, config, hs, convert=True)
repr(raw2)
assert_array_equal(raw._data, raw2._data)
def test_bytes_io():
""" Test bti bytes-io API """
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
raw = read_raw_bti(pdf, config, hs, convert=True, preload=False)
with open(pdf, 'rb') as fid:
pdf = six.BytesIO(fid.read())
with open(config, 'rb') as fid:
config = six.BytesIO(fid.read())
with open(hs, 'rb') as fid:
hs = six.BytesIO(fid.read())
raw2 = read_raw_bti(pdf, config, hs, convert=True, preload=False)
repr(raw2)
assert_array_equal(raw[:][0], raw2[:][0])
def test_bytes_io():
""" Test bti bytes-io API """
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
raw = read_raw_bti(pdf, config, hs, convert=True)
with open(pdf, "rb") as fid:
pdf = six.BytesIO(fid.read())
with open(config, "rb") as fid:
config = six.BytesIO(fid.read())
with open(hs, "rb") as fid:
hs = six.BytesIO(fid.read())
raw2 = read_raw_bti(pdf, config, hs, convert=True)
repr(raw2)
assert_array_equal(raw._data, raw2._data)
def test_bytes_io():
"""Test bti bytes-io API."""
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
raw = read_raw_bti(pdf, config, hs, convert=True, preload=False)
with open(pdf, 'rb') as fid:
pdf = BytesIO(fid.read())
with open(config, 'rb') as fid:
config = BytesIO(fid.read())
with open(hs, 'rb') as fid:
hs = BytesIO(fid.read())
raw2 = read_raw_bti(pdf, config, hs, convert=True, preload=False)
repr(raw2)
assert_array_equal(raw[:][0], raw2[:][0])
def _read_raw(raw_fname, electrode=None, hsp=None, hpi=None, config=None,
verbose=None):
"""Read a raw file into MNE, making inferences based on extension."""
fname, ext = _parse_ext(raw_fname)
# KIT systems
if ext in ['.con', '.sqd']:
raw = io.read_raw_kit(raw_fname, elp=electrode, hsp=hsp,
mrk=hpi, preload=False)
# Neuromag or converted-to-fif systems
elif ext in ['.fif', '.gz']:
raw = io.read_raw_fif(raw_fname, preload=False)
# BTi systems
elif ext == '.pdf':
if os.path.isfile(raw_fname):
raw = io.read_raw_bti(raw_fname, config_fname=config,
head_shape_fname=hsp,
preload=False, verbose=verbose)
# CTF systems
elif ext == '.ds':
raw = io.read_raw_ctf(raw_fname)
else:
raise ValueError("Raw file name extension must be one of %\n"
"Got %" % (ALLOWED_EXTENSIONS, ext))
return raw
def test_bytes_io():
""" Test bti bytes-io API """
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
with warnings.catch_warnings(record=True): # weight tables
raw = read_raw_bti(pdf, config, hs, convert=True, preload=False)
with open(pdf, 'rb') as fid:
pdf = six.BytesIO(fid.read())
with open(config, 'rb') as fid:
config = six.BytesIO(fid.read())
with open(hs, 'rb') as fid:
hs = six.BytesIO(fid.read())
with warnings.catch_warnings(record=True): # weight tables
raw2 = read_raw_bti(pdf, config, hs, convert=True, preload=False)
repr(raw2)
assert_array_equal(raw[:][0], raw2[:][0])
def test_raw():
""" Test bti conversion to Raw object """
for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
exported_fnames):
# rx = 2 if 'linux' in pdf else 0
assert_raises(ValueError, read_raw_bti, pdf, 'eggs')
assert_raises(ValueError, read_raw_bti, pdf, config, 'spam')
if op.exists(tmp_raw_fname):
os.remove(tmp_raw_fname)
with Raw(exported, preload=True) as ex:
with read_raw_bti(pdf, config, hs) as ra:
assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
assert_array_almost_equal(ex.info['dev_head_t']['trans'],
ra.info['dev_head_t']['trans'], 7)
dig1, dig2 = [np.array([d['r'] for d in r_.info['dig']])
for r_ in (ra, ex)]
assert_array_equal(dig1, dig2)
coil1, coil2 = [np.concatenate([d['coil_trans'].flatten()
for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)]
assert_array_almost_equal(coil1, coil2, 7)
loc1, loc2 = [np.concatenate([d['loc'].flatten()
for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)]
assert_array_equal(loc1, loc2)
assert_array_equal(ra._data[:NCH], ex._data[:NCH])
assert_array_equal(ra.cals[:NCH], ex.cals[:NCH])
ra.save(tmp_raw_fname)
with Raw(tmp_raw_fname) as r:
print(r)
os.remove(tmp_raw_fname)
def test_plot_alignment_meg(renderer, system):
"""Test plotting of MEG sensors + helmet."""
if system == 'Neuromag':
this_info = read_info(evoked_fname)
elif system == 'CTF':
this_info = read_raw_ctf(ctf_fname).info
elif system == 'BTi':
this_info = read_raw_bti(pdf_fname,
config_fname,
hs_fname,
convert=True,
preload=False).info
else:
assert system == 'KIT'
this_info = read_raw_kit(sqd_fname).info
meg = ['helmet', 'sensors']
if system == 'KIT':
meg.append('ref')
fig = plot_alignment(this_info,
read_trans(trans_fname),
subject='sample',
subjects_dir=subjects_dir,
meg=meg,
eeg=False)
# count the number of objects: should be n_meg_ch + 1 (helmet) + 1 (head)
use_info = pick_info(
this_info,
pick_types(this_info,
meg=True,
eeg=False,
ref_meg='ref' in meg,
exclude=()))
n_actors = use_info['nchan'] + 2
_assert_n_actors(fig, renderer, n_actors)
def test_bytes_io():
""" Test bti bytes-io API """
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
with warnings.catch_warnings(record=True): # weight tables
raw = read_raw_bti(pdf, config, hs, convert=True, preload=False)
with open(pdf, "rb") as fid:
pdf = six.BytesIO(fid.read())
with open(config, "rb") as fid:
config = six.BytesIO(fid.read())
with open(hs, "rb") as fid:
hs = six.BytesIO(fid.read())
with warnings.catch_warnings(record=True): # weight tables
raw2 = read_raw_bti(pdf, config, hs, convert=True, preload=False)
repr(raw2)
assert_array_equal(raw[:][0], raw2[:][0])
def test_pick_refs():
"""Test picking of reference sensors
"""
infos = list()
# KIT
kit_dir = op.join(io_dir, 'kit', 'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
raw_kit = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
infos.append(raw_kit.info)
# BTi
bti_dir = op.join(io_dir, 'bti', 'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
with warnings.catch_warnings(record=True): # weight tables
raw_bti = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
infos.append(raw_bti.info)
# CTF
fname_ctf_raw = op.join(io_dir, 'tests', 'data', 'test_ctf_comp_raw.fif')
raw_ctf = read_raw_fif(fname_ctf_raw, add_eeg_ref=False)
raw_ctf.apply_gradient_compensation(2)
infos.append(raw_ctf.info)
for info in infos:
info['bads'] = []
assert_raises(ValueError, pick_types, info, meg='foo')
assert_raises(ValueError, pick_types, info, ref_meg='foo')
picks_meg_ref = pick_types(info, meg=True, ref_meg=True)
picks_meg = pick_types(info, meg=True, ref_meg=False)
picks_ref = pick_types(info, meg=False, ref_meg=True)
assert_array_equal(picks_meg_ref,
np.sort(np.concatenate([picks_meg, picks_ref])))
picks_grad = pick_types(info, meg='grad', ref_meg=False)
picks_ref_grad = pick_types(info, meg=False, ref_meg='grad')
picks_meg_ref_grad = pick_types(info, meg='grad', ref_meg='grad')
assert_array_equal(
picks_meg_ref_grad,
np.sort(np.concatenate([picks_grad, picks_ref_grad])))
picks_mag = pick_types(info, meg='mag', ref_meg=False)
picks_ref_mag = pick_types(info, meg=False, ref_meg='mag')
picks_meg_ref_mag = pick_types(info, meg='mag', ref_meg='mag')
assert_array_equal(picks_meg_ref_mag,
np.sort(np.concatenate([picks_mag, picks_ref_mag])))
assert_array_equal(picks_meg,
np.sort(np.concatenate([picks_mag, picks_grad])))
assert_array_equal(
picks_ref, np.sort(np.concatenate([picks_ref_mag,
picks_ref_grad])))
assert_array_equal(
picks_meg_ref,
np.sort(
np.concatenate(
[picks_grad, picks_mag, picks_ref_grad, picks_ref_mag])))
for pick in (picks_meg_ref, picks_meg, picks_ref, picks_grad,
picks_ref_grad, picks_meg_ref_grad, picks_mag,
picks_ref_mag, picks_meg_ref_mag):
if len(pick) > 0:
pick_info(info, pick)
def test_plot_trans():
"""Test plotting of -trans.fif files and MEG sensor layouts
"""
evoked = read_evokeds(evoked_fname)[0]
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
ref_meg = False if system == 'KIT' else True
plot_trans(info, trans_fname, subject='sample', meg_sensors=True,
subjects_dir=subjects_dir, ref_meg=ref_meg)
# KIT ref sensor coil def not defined
assert_raises(RuntimeError, plot_trans, infos['KIT'], None,
meg_sensors=True, ref_meg=True)
info = infos['Neuromag']
assert_raises(ValueError, plot_trans, info, trans_fname,
subject='sample', subjects_dir=subjects_dir,
ch_type='bad-chtype')
assert_raises(TypeError, plot_trans, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
# EEG only with strange options
with warnings.catch_warnings(record=True) as w:
plot_trans(evoked.copy().pick_types(meg=False, eeg=True).info,
trans=trans_fname, meg_sensors=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
def test_find_ch_connectivity():
"""Test computing the connectivity matrix."""
data_path = testing.data_path()
raw = read_raw_fif(raw_fname, preload=True)
sizes = {'mag': 828, 'grad': 1700, 'eeg': 386}
nchans = {'mag': 102, 'grad': 204, 'eeg': 60}
for ch_type in ['mag', 'grad', 'eeg']:
conn, ch_names = find_ch_connectivity(raw.info, ch_type)
# Silly test for checking the number of neighbors.
assert_equal(conn.getnnz(), sizes[ch_type])
assert_equal(len(ch_names), nchans[ch_type])
pytest.raises(ValueError, find_ch_connectivity, raw.info, None)
# Test computing the conn matrix with gradiometers.
conn, ch_names = _compute_ch_connectivity(raw.info, 'grad')
assert_equal(conn.getnnz(), 2680)
# Test ch_type=None.
raw.pick_types(meg='mag')
find_ch_connectivity(raw.info, None)
bti_fname = op.join(data_path, 'BTi', 'erm_HFH', 'c,rfDC')
bti_config_name = op.join(data_path, 'BTi', 'erm_HFH', 'config')
raw = read_raw_bti(bti_fname, bti_config_name, None)
_, ch_names = find_ch_connectivity(raw.info, 'mag')
assert 'A1' in ch_names
ctf_fname = op.join(data_path, 'CTF', 'testdata_ctf_short.ds')
raw = read_raw_ctf(ctf_fname)
_, ch_names = find_ch_connectivity(raw.info, 'mag')
assert 'MLC11' in ch_names
pytest.raises(ValueError, find_ch_connectivity, raw.info, 'eog')
def test_raw():
"""Test bti conversion to Raw object."""
for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
exported_fnames):
# rx = 2 if 'linux' in pdf else 0
pytest.raises(ValueError, read_raw_bti, pdf, 'eggs', preload=False)
pytest.raises(ValueError,
read_raw_bti,
pdf,
config,
'spam',
preload=False)
if op.exists(tmp_raw_fname):
os.remove(tmp_raw_fname)
ex = read_raw_fif(exported, preload=True)
ra = read_raw_bti(pdf, config, hs, preload=False)
assert ('RawBTi' in repr(ra))
assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
assert_array_almost_equal(ex.info['dev_head_t']['trans'],
ra.info['dev_head_t']['trans'], 7)
assert len(ex.info['dig']) in (3563, 5154)
assert_dig_allclose(ex.info, ra.info, limit=100)
coil1, coil2 = [
np.concatenate([d['loc'].flatten() for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)
]
assert_array_almost_equal(coil1, coil2, 7)
loc1, loc2 = [
np.concatenate([d['loc'].flatten() for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)
]
assert_allclose(loc1, loc2)
assert_allclose(ra[:NCH][0], ex[:NCH][0])
assert_array_equal([c['range'] for c in ra.info['chs'][:NCH]],
[c['range'] for c in ex.info['chs'][:NCH]])
assert_array_equal([c['cal'] for c in ra.info['chs'][:NCH]],
[c['cal'] for c in ex.info['chs'][:NCH]])
assert_array_equal(ra._cals[:NCH], ex._cals[:NCH])
# check our transforms
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
if ex.info[key] is None:
pass
else:
assert (ra.info[key] is not None)
for ent in ('to', 'from', 'trans'):
assert_allclose(ex.info[key][ent], ra.info[key][ent])
ra.save(tmp_raw_fname)
re = read_raw_fif(tmp_raw_fname)
print(re)
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert (isinstance(re.info[key], dict))
this_t = re.info[key]['trans']
assert_equal(this_t.shape, (4, 4))
# check that matrix by is not identity
assert (not np.allclose(this_t, np.eye(4)))
os.remove(tmp_raw_fname)
def test_find_ch_connectivity():
"""Test computing the connectivity matrix."""
data_path = testing.data_path()
raw = read_raw_fif(raw_fname, preload=True)
sizes = {'mag': 828, 'grad': 1700, 'eeg': 386}
nchans = {'mag': 102, 'grad': 204, 'eeg': 60}
for ch_type in ['mag', 'grad', 'eeg']:
conn, ch_names = find_ch_connectivity(raw.info, ch_type)
# Silly test for checking the number of neighbors.
assert_equal(conn.getnnz(), sizes[ch_type])
assert_equal(len(ch_names), nchans[ch_type])
assert_raises(ValueError, find_ch_connectivity, raw.info, None)
# Test computing the conn matrix with gradiometers.
conn, ch_names = _compute_ch_connectivity(raw.info, 'grad')
assert_equal(conn.getnnz(), 2680)
# Test ch_type=None.
raw.pick_types(meg='mag')
find_ch_connectivity(raw.info, None)
bti_fname = op.join(data_path, 'BTi', 'erm_HFH', 'c,rfDC')
bti_config_name = op.join(data_path, 'BTi', 'erm_HFH', 'config')
raw = read_raw_bti(bti_fname, bti_config_name, None)
_, ch_names = find_ch_connectivity(raw.info, 'mag')
assert_true('A1' in ch_names)
ctf_fname = op.join(data_path, 'CTF', 'testdata_ctf_short.ds')
raw = read_raw_ctf(ctf_fname)
_, ch_names = find_ch_connectivity(raw.info, 'mag')
assert_true('MLC11' in ch_names)
assert_raises(ValueError, find_ch_connectivity, raw.info, 'eog')
def test_no_conversion():
""" Test bti no-conversion option """
get_info = partial(_get_bti_info,
rotation_x=0.0,
translation=(0.0, 0.02, 0.11),
convert=False,
ecg_ch='E31',
eog_ch=('E63', 'E64'),
rename_channels=False,
sort_by_ch_name=False)
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
raw_info, _ = get_info(pdf, config, hs, convert=False)
raw_info_con = read_raw_bti(pdf_fname=pdf,
config_fname=config,
head_shape_fname=hs,
convert=True,
preload=False).info
pick_info(raw_info_con,
pick_types(raw_info_con, meg=True, ref_meg=True),
copy=False)
pick_info(raw_info,
pick_types(raw_info, meg=True, ref_meg=True),
copy=False)
bti_info = _read_bti_header(pdf, config)
dev_ctf_t = _correct_trans(bti_info['bti_transform'][0])
assert_array_equal(dev_ctf_t, raw_info['dev_ctf_t']['trans'])
assert_array_equal(raw_info['dev_head_t']['trans'], np.eye(4))
assert_array_equal(raw_info['ctf_head_t']['trans'], np.eye(4))
dig, t = _process_bti_headshape(hs, convert=False, use_hpi=False)
assert_array_equal(t['trans'], np.eye(4))
for ii, (old, new, con) in enumerate(
zip(dig, raw_info['dig'], raw_info_con['dig'])):
assert_equal(old['ident'], new['ident'])
assert_array_equal(old['r'], new['r'])
assert_true(not np.allclose(old['r'], con['r']))
if ii > 10:
break
ch_map = dict((ch['chan_label'], ch['loc']) for ch in bti_info['chs'])
for ii, ch_label in enumerate(raw_info['ch_names']):
if not ch_label.startswith('A'):
continue
t1 = ch_map[ch_label] # correction already performed in bti_info
t2 = raw_info['chs'][ii]['loc']
t3 = raw_info_con['chs'][ii]['loc']
assert_allclose(t1, t2, atol=1e-15)
assert_true(not np.allclose(t1, t3))
idx_a = raw_info_con['ch_names'].index('MEG 001')
idx_b = raw_info['ch_names'].index('A22')
assert_equal(raw_info_con['chs'][idx_a]['coord_frame'],
FIFF.FIFFV_COORD_DEVICE)
assert_equal(raw_info['chs'][idx_b]['coord_frame'],
FIFF.FIFFV_MNE_COORD_4D_HEAD)
def test_no_conversion():
""" Test bti no-conversion option """
get_info = partial(
_get_bti_info,
rotation_x=0.0, translation=(0.0, 0.02, 0.11), convert=False,
ecg_ch='E31', eog_ch=('E63', 'E64'),
rename_channels=False, sort_by_ch_name=False)
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
with warnings.catch_warnings(record=True): # weight tables
raw_info, _ = get_info(pdf, config, hs, convert=False)
with warnings.catch_warnings(record=True): # weight tables
raw_info_con = read_raw_bti(
pdf_fname=pdf, config_fname=config, head_shape_fname=hs,
convert=True, preload=False).info
pick_info(raw_info_con,
pick_types(raw_info_con, meg=True, ref_meg=True),
copy=False)
pick_info(raw_info,
pick_types(raw_info, meg=True, ref_meg=True), copy=False)
bti_info = _read_bti_header(pdf, config)
dev_ctf_t = _correct_trans(bti_info['bti_transform'][0])
assert_array_equal(dev_ctf_t, raw_info['dev_ctf_t']['trans'])
assert_array_equal(raw_info['dev_head_t']['trans'], np.eye(4))
assert_array_equal(raw_info['ctf_head_t']['trans'], np.eye(4))
dig, t = _process_bti_headshape(hs, convert=False, use_hpi=False)
assert_array_equal(t['trans'], np.eye(4))
for ii, (old, new, con) in enumerate(zip(
dig, raw_info['dig'], raw_info_con['dig'])):
assert_equal(old['ident'], new['ident'])
assert_array_equal(old['r'], new['r'])
assert_true(not np.allclose(old['r'], con['r']))
if ii > 10:
break
ch_map = dict((ch['chan_label'],
ch['loc']) for ch in bti_info['chs'])
for ii, ch_label in enumerate(raw_info['ch_names']):
if not ch_label.startswith('A'):
continue
t1 = ch_map[ch_label] # correction already performed in bti_info
t2 = raw_info['chs'][ii]['loc']
t3 = raw_info_con['chs'][ii]['loc']
assert_allclose(t1, t2, atol=1e-15)
assert_true(not np.allclose(t1, t3))
idx_a = raw_info_con['ch_names'].index('MEG 001')
idx_b = raw_info['ch_names'].index('A22')
assert_equal(
raw_info_con['chs'][idx_a]['coord_frame'],
FIFF.FIFFV_COORD_DEVICE)
assert_equal(
raw_info['chs'][idx_b]['coord_frame'],
FIFF.FIFFV_MNE_COORD_4D_HEAD)
def test_pick_refs():
"""Test picking of reference sensors
"""
infos = list()
# KIT
kit_dir = op.join(io_dir, 'kit', 'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
raw_kit = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
infos.append(raw_kit.info)
# BTi
bti_dir = op.join(io_dir, 'bti', 'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
with warnings.catch_warnings(record=True): # weight tables
raw_bti = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
infos.append(raw_bti.info)
# CTF
fname_ctf_raw = op.join(io_dir, 'tests', 'data', 'test_ctf_comp_raw.fif')
raw_ctf = read_raw_fif(fname_ctf_raw, add_eeg_ref=False)
raw_ctf.apply_gradient_compensation(2)
infos.append(raw_ctf.info)
for info in infos:
info['bads'] = []
assert_raises(ValueError, pick_types, info, meg='foo')
assert_raises(ValueError, pick_types, info, ref_meg='foo')
picks_meg_ref = pick_types(info, meg=True, ref_meg=True)
picks_meg = pick_types(info, meg=True, ref_meg=False)
picks_ref = pick_types(info, meg=False, ref_meg=True)
assert_array_equal(picks_meg_ref,
np.sort(np.concatenate([picks_meg, picks_ref])))
picks_grad = pick_types(info, meg='grad', ref_meg=False)
picks_ref_grad = pick_types(info, meg=False, ref_meg='grad')
picks_meg_ref_grad = pick_types(info, meg='grad', ref_meg='grad')
assert_array_equal(picks_meg_ref_grad,
np.sort(np.concatenate([picks_grad,
picks_ref_grad])))
picks_mag = pick_types(info, meg='mag', ref_meg=False)
picks_ref_mag = pick_types(info, meg=False, ref_meg='mag')
picks_meg_ref_mag = pick_types(info, meg='mag', ref_meg='mag')
assert_array_equal(picks_meg_ref_mag,
np.sort(np.concatenate([picks_mag,
picks_ref_mag])))
assert_array_equal(picks_meg,
np.sort(np.concatenate([picks_mag, picks_grad])))
assert_array_equal(picks_ref,
np.sort(np.concatenate([picks_ref_mag,
picks_ref_grad])))
assert_array_equal(picks_meg_ref, np.sort(np.concatenate(
[picks_grad, picks_mag, picks_ref_grad, picks_ref_mag])))
for pick in (picks_meg_ref, picks_meg, picks_ref,
picks_grad, picks_ref_grad, picks_meg_ref_grad,
picks_mag, picks_ref_mag, picks_meg_ref_mag):
if len(pick) > 0:
pick_info(info, pick)
def test_raw():
""" Test bti conversion to Raw object """
for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
exported_fnames):
# rx = 2 if 'linux' in pdf else 0
assert_raises(ValueError, read_raw_bti, pdf, 'eggs')
assert_raises(ValueError, read_raw_bti, pdf, config, 'spam')
if op.exists(tmp_raw_fname):
os.remove(tmp_raw_fname)
ex = Raw(exported, preload=True)
ra = read_raw_bti(pdf, config, hs)
assert_true('RawBTi' in repr(ra))
assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
assert_array_almost_equal(ex.info['dev_head_t']['trans'],
ra.info['dev_head_t']['trans'], 7)
dig1, dig2 = [
np.array([d['r'] for d in r_.info['dig']]) for r_ in (ra, ex)
]
assert_array_almost_equal(dig1, dig2, 18)
coil1, coil2 = [
np.concatenate([d['loc'].flatten() for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)
]
assert_array_almost_equal(coil1, coil2, 7)
loc1, loc2 = [
np.concatenate([d['loc'].flatten() for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)
]
assert_allclose(loc1, loc2)
assert_array_equal(ra._data[:NCH], ex._data[:NCH])
assert_array_equal(ra._cals[:NCH], ex._cals[:NCH])
# check our transforms
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
if ex.info[key] is None:
pass
else:
assert_true(ra.info[key] is not None)
for ent in ('to', 'from', 'trans'):
assert_allclose(ex.info[key][ent], ra.info[key][ent])
# Make sure concatenation works
raw_concat = concatenate_raws([ra.copy(), ra])
assert_equal(raw_concat.n_times, 2 * ra.n_times)
ra.save(tmp_raw_fname)
re = Raw(tmp_raw_fname)
print(re)
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_true(isinstance(re.info[key], dict))
this_t = re.info[key]['trans']
assert_equal(this_t.shape, (4, 4))
# cehck that matrix by is not identity
assert_true(not np.allclose(this_t, np.eye(4)))
os.remove(tmp_raw_fname)
def run():
"""Run command."""
from mne.commands.utils import get_optparser
parser = get_optparser(__file__)
parser.add_option('-p', '--pdf', dest='pdf_fname',
help='Input data file name', metavar='FILE')
parser.add_option('-c', '--config', dest='config_fname',
help='Input config file name', metavar='FILE',
default='config')
parser.add_option('--head_shape', dest='head_shape_fname',
help='Headshape file name', metavar='FILE',
default='hs_file')
parser.add_option('-o', '--out_fname', dest='out_fname',
help='Name of the resulting fiff file',
default='as_data_fname')
parser.add_option('-r', '--rotation_x', dest='rotation_x', type='float',
help='Compensatory rotation about Neuromag x axis, deg',
default=2.0)
parser.add_option('-T', '--translation', dest='translation', type='str',
help='Default translation, meter',
default=(0.00, 0.02, 0.11))
parser.add_option('--ecg_ch', dest='ecg_ch', type='str',
help='4D ECG channel name',
default='E31')
parser.add_option('--eog_ch', dest='eog_ch', type='str',
help='4D EOG channel names',
default='E63,E64')
options, args = parser.parse_args()
pdf_fname = options.pdf_fname
if pdf_fname is None:
parser.print_help()
sys.exit(1)
config_fname = options.config_fname
head_shape_fname = options.head_shape_fname
out_fname = options.out_fname
rotation_x = options.rotation_x
translation = options.translation
ecg_ch = options.ecg_ch
eog_ch = options.ecg_ch.split(',')
if out_fname == 'as_data_fname':
out_fname = pdf_fname + '_raw.fif'
raw = read_raw_bti(pdf_fname=pdf_fname, config_fname=config_fname,
head_shape_fname=head_shape_fname,
rotation_x=rotation_x, translation=translation,
ecg_ch=ecg_ch, eog_ch=eog_ch)
raw.save(out_fname)
raw.close()
if is_main:
sys.exit(0)
def test_raw():
""" Test bti conversion to Raw object """
for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
exported_fnames):
# rx = 2 if 'linux' in pdf else 0
assert_raises(ValueError, read_raw_bti, pdf, 'eggs', preload=False)
assert_raises(ValueError, read_raw_bti, pdf, config, 'spam',
preload=False)
if op.exists(tmp_raw_fname):
os.remove(tmp_raw_fname)
ex = Raw(exported, preload=True)
with warnings.catch_warnings(record=True): # weight tables
ra = read_raw_bti(pdf, config, hs, preload=False)
assert_true('RawBTi' in repr(ra))
assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
assert_array_almost_equal(ex.info['dev_head_t']['trans'],
ra.info['dev_head_t']['trans'], 7)
with warnings.catch_warnings(record=True): # headshape
assert_dig_allclose(ex.info, ra.info)
coil1, coil2 = [np.concatenate([d['loc'].flatten()
for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)]
assert_array_almost_equal(coil1, coil2, 7)
loc1, loc2 = [np.concatenate([d['loc'].flatten()
for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)]
assert_allclose(loc1, loc2)
assert_allclose(ra[:NCH][0], ex[:NCH][0])
assert_array_equal([c['range'] for c in ra.info['chs'][:NCH]],
[c['range'] for c in ex.info['chs'][:NCH]])
assert_array_equal([c['cal'] for c in ra.info['chs'][:NCH]],
[c['cal'] for c in ex.info['chs'][:NCH]])
assert_array_equal(ra._cals[:NCH], ex._cals[:NCH])
# check our transforms
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
if ex.info[key] is None:
pass
else:
assert_true(ra.info[key] is not None)
for ent in ('to', 'from', 'trans'):
assert_allclose(ex.info[key][ent],
ra.info[key][ent])
ra.save(tmp_raw_fname)
re = Raw(tmp_raw_fname)
print(re)
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_true(isinstance(re.info[key], dict))
this_t = re.info[key]['trans']
assert_equal(this_t.shape, (4, 4))
# cehck that matrix by is not identity
assert_true(not np.allclose(this_t, np.eye(4)))
os.remove(tmp_raw_fname)
def test_crop():
""" Test crop raw """
raw = read_raw_bti(pdf_fnames[0], config_fnames[0], hs_fnames[0])
y, t = raw[:]
t0, t1 = 0.25 * t[-1], 0.75 * t[-1]
mask = (t0 <= t) * (t <= t1)
raw_ = raw.crop(t0, t1)
y_, _ = raw_[:]
assert_true(y_.shape[1] == mask.sum())
assert_true(y_.shape[0] == y.shape[0])
def test_crop():
""" Test crop raw """
raw = read_raw_bti(pdf_fnames[0], config_fnames[0], hs_fnames[0])
y, t = raw[:]
t0, t1 = 0.25 * t[-1], 0.75 * t[-1]
mask = (t0 <= t) * (t <= t1)
raw_ = raw.crop(t0, t1)
y_, _ = raw_[:]
assert_true(y_.shape[1] == mask.sum())
assert_true(y_.shape[0] == y.shape[0])
def run():
"""Run command."""
from mne.commands.utils import get_optparser
parser = get_optparser(__file__)
parser.add_option('-p', '--pdf', dest='pdf_fname',
help='Input data file name', metavar='FILE')
parser.add_option('-c', '--config', dest='config_fname',
help='Input config file name', metavar='FILE',
default='config')
parser.add_option('--head_shape', dest='head_shape_fname',
help='Headshape file name', metavar='FILE',
default='hs_file')
parser.add_option('-o', '--out_fname', dest='out_fname',
help='Name of the resulting fiff file',
default='as_data_fname')
parser.add_option('-r', '--rotation_x', dest='rotation_x', type='float',
help='Compensatory rotation about Neuromag x axis, deg',
default=2.0)
parser.add_option('-T', '--translation', dest='translation', type='str',
help='Default translation, meter',
default=(0.00, 0.02, 0.11))
parser.add_option('--ecg_ch', dest='ecg_ch', type='str',
help='4D ECG channel name',
default='E31')
parser.add_option('--eog_ch', dest='eog_ch', type='str',
help='4D EOG channel names',
default='E63,E64')
options, args = parser.parse_args()
pdf_fname = options.pdf_fname
if pdf_fname is None:
parser.print_help()
sys.exit(1)
config_fname = options.config_fname
head_shape_fname = options.head_shape_fname
out_fname = options.out_fname
rotation_x = options.rotation_x
translation = options.translation
ecg_ch = options.ecg_ch
eog_ch = options.ecg_ch.split(',')
if out_fname == 'as_data_fname':
out_fname = pdf_fname + '_raw.fif'
raw = read_raw_bti(pdf_fname=pdf_fname, config_fname=config_fname,
head_shape_fname=head_shape_fname,
rotation_x=rotation_x, translation=translation,
ecg_ch=ecg_ch, eog_ch=eog_ch)
raw.save(out_fname)
raw.close()
def test_raw():
""" Test bti conversion to Raw object """
for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
exported_fnames):
# rx = 2 if 'linux' in pdf else 0
assert_raises(ValueError, read_raw_bti, pdf, 'eggs')
assert_raises(ValueError, read_raw_bti, pdf, config, 'spam')
if op.exists(tmp_raw_fname):
os.remove(tmp_raw_fname)
ex = Raw(exported, preload=True)
ra = read_raw_bti(pdf, config, hs)
assert_true('RawBTi' in repr(ra))
assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
assert_array_almost_equal(ex.info['dev_head_t']['trans'],
ra.info['dev_head_t']['trans'], 7)
dig1, dig2 = [np.array([d['r'] for d in r_.info['dig']])
for r_ in (ra, ex)]
assert_array_almost_equal(dig1, dig2, 18)
coil1, coil2 = [np.concatenate([d['loc'].flatten()
for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)]
assert_array_almost_equal(coil1, coil2, 7)
loc1, loc2 = [np.concatenate([d['loc'].flatten()
for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)]
assert_allclose(loc1, loc2)
assert_array_equal(ra._data[:NCH], ex._data[:NCH])
assert_array_equal(ra._cals[:NCH], ex._cals[:NCH])
# check our transforms
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
if ex.info[key] is None:
pass
else:
assert_true(ra.info[key] is not None)
for ent in ('to', 'from', 'trans'):
assert_allclose(ex.info[key][ent],
ra.info[key][ent])
# Make sure concatenation works
raw_concat = concatenate_raws([ra.copy(), ra])
assert_equal(raw_concat.n_times, 2 * ra.n_times)
ra.save(tmp_raw_fname)
re = Raw(tmp_raw_fname)
print(re)
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_true(isinstance(re.info[key], dict))
this_t = re.info[key]['trans']
assert_equal(this_t.shape, (4, 4))
# cehck that matrix by is not identity
assert_true(not np.allclose(this_t, np.eye(4)))
os.remove(tmp_raw_fname)
def _read_raw(raw_fpath,
electrode=None,
hsp=None,
hpi=None,
allow_maxshield=False,
config=None,
**kwargs):
"""Read a raw file into MNE, making inferences based on extension."""
_, ext = _parse_ext(raw_fpath)
# KIT systems
if ext in ['.con', '.sqd']:
raw = io.read_raw_kit(raw_fpath,
elp=electrode,
hsp=hsp,
mrk=hpi,
preload=False,
**kwargs)
# BTi systems
elif ext == '.pdf':
raw = io.read_raw_bti(raw_fpath,
config_fname=config,
head_shape_fname=hsp,
preload=False,
**kwargs)
elif ext == '.fif':
raw = reader[ext](raw_fpath, allow_maxshield, **kwargs)
elif ext in ['.ds', '.vhdr', '.set', '.edf', '.bdf', '.EDF']:
raw_fpath = Path(raw_fpath)
# handle EDF extension upper/lower casing
if ext == '.edf' and not raw_fpath.exists():
raw_fpath = raw_fpath.with_suffix('.EDF')
elif ext == '.EDF' and not raw_fpath.exists():
raw_fpath = raw_fpath.with_suffix('.edf')
raw = reader[ext](raw_fpath, **kwargs)
# MEF and NWB are allowed, but not yet implemented
elif ext in ['.mef', '.nwb']:
raise ValueError(f'Got "{ext}" as extension. This is an allowed '
f'extension but there is no IO support for this '
f'file format yet.')
# No supported data found ...
# ---------------------------
else:
raise ValueError(f'Raw file name extension must be one '
f'of {ALLOWED_DATATYPE_EXTENSIONS}\n'
f'Got {ext}')
return raw
def _read_raw(raw_path,
electrode=None,
hsp=None,
hpi=None,
allow_maxshield=False,
config_path=None,
**kwargs):
"""Read a raw file into MNE, making inferences based on extension."""
_, ext = _parse_ext(raw_path)
# KIT systems
if ext in ['.con', '.sqd']:
raw = io.read_raw_kit(raw_path,
elp=electrode,
hsp=hsp,
mrk=hpi,
preload=False,
**kwargs)
# BTi systems
elif ext == '.pdf':
raw = io.read_raw_bti(
pdf_fname=str(raw_path), # FIXME MNE should accept Path!
config_fname=str(config_path), # FIXME MNE should accept Path!
head_shape_fname=hsp,
preload=False,
**kwargs)
elif ext == '.fif':
raw = reader[ext](raw_path, allow_maxshield, **kwargs)
elif ext in ['.ds', '.vhdr', '.set', '.edf', '.bdf', '.EDF', '.snirf']:
if (ext == '.snirf'
and not check_version('mne', '1.0')): # pragma: no cover
raise RuntimeError(
'fNIRS support in MNE-BIDS requires MNE-Python version 1.0')
raw_path = Path(raw_path)
raw = reader[ext](raw_path, **kwargs)
# MEF and NWB are allowed, but not yet implemented
elif ext in ['.mef', '.nwb']:
raise ValueError(f'Got "{ext}" as extension. This is an allowed '
f'extension but there is no IO support for this '
f'file format yet.')
# No supported data found ...
# ---------------------------
else:
raise ValueError(f'Raw file name extension must be one '
f'of {ALLOWED_DATATYPE_EXTENSIONS}\n'
f'Got {ext}')
return raw
def _read_raw(raw_fpath,
electrode=None,
hsp=None,
hpi=None,
config=None,
verbose=None,
**kwargs):
"""Read a raw file into MNE, making inferences based on extension."""
_, ext = _parse_ext(raw_fpath)
# KIT systems
if ext in ['.con', '.sqd']:
raw = io.read_raw_kit(raw_fpath,
elp=electrode,
hsp=hsp,
mrk=hpi,
preload=False,
**kwargs)
# BTi systems
elif ext == '.pdf':
raw = io.read_raw_bti(raw_fpath,
config_fname=config,
head_shape_fname=hsp,
preload=False,
verbose=verbose,
**kwargs)
elif ext == '.fif':
raw = reader[ext](raw_fpath, **kwargs)
elif ext in ['.ds', '.vhdr', '.set']:
raw = reader[ext](raw_fpath, **kwargs)
# EDF (european data format) or BDF (biosemi) format
# TODO: integrate with lines above once MNE can read
# annotations with preload=False
elif ext in ['.edf', '.bdf']:
raw = reader[ext](raw_fpath, preload=True, **kwargs)
# MEF and NWB are allowed, but not yet implemented
elif ext in ['.mef', '.nwb']:
raise ValueError(
'Got "{}" as extension. This is an allowed extension '
'but there is no IO support for this file format yet.'.format(ext))
# No supported data found ...
# ---------------------------
else:
raise ValueError('Raw file name extension must be one of {}\n'
'Got {}'.format(ALLOWED_EXTENSIONS, ext))
return raw
def _read_raw_bti(raw_fid, config_fid, convert, verbose=None):
"""Convert and raw file from HCP input"""
raw = read_raw_bti( # no convrt + no rename for HCP compatibility
raw_fid,
config_fid,
convert=convert,
head_shape_fname=None,
sort_by_ch_name=False,
rename_channels=False,
preload=False,
verbose=verbose)
return raw
def test_crop_append():
""" Test crop and append raw """
raw = read_raw_bti(pdf_fnames[0], config_fnames[0], hs_fnames[0])
raw.preload_data() # currently does nothing
y, t = raw[:]
t0, t1 = 0.25 * t[-1], 0.75 * t[-1]
mask = (t0 <= t) * (t <= t1)
raw_ = raw.crop(t0, t1)
y_, _ = raw_[:]
assert_true(y_.shape[1] == mask.sum())
assert_true(y_.shape[0] == y.shape[0])
raw2 = raw.copy()
assert_raises(RuntimeError, raw.append, raw2, preload=False)
raw.append(raw2)
assert_allclose(np.tile(raw2[:, :][0], (1, 2)), raw[:, :][0])
def test_crop_append():
""" Test crop and append raw """
raw = read_raw_bti(pdf_fnames[0], config_fnames[0], hs_fnames[0])
raw.load_data() # currently does nothing
y, t = raw[:]
t0, t1 = 0.25 * t[-1], 0.75 * t[-1]
mask = (t0 <= t) * (t <= t1)
raw_ = raw.crop(t0, t1)
y_, _ = raw_[:]
assert_true(y_.shape[1] == mask.sum())
assert_true(y_.shape[0] == y.shape[0])
raw2 = raw.copy()
assert_raises(RuntimeError, raw.append, raw2, preload=False)
raw.append(raw2)
assert_allclose(np.tile(raw2[:, :][0], (1, 2)), raw[:, :][0])
def test_plot_trans():
"""Test plotting of -trans.fif files and MEG sensor layouts."""
from mayavi import mlab
evoked = read_evokeds(evoked_fname)[0]
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
ref_meg = False if system == 'KIT' else True
plot_trans(info, trans_fname, subject='sample', meg_sensors=True,
subjects_dir=subjects_dir, ref_meg=ref_meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
mlab.close(all=True)
info = infos['Neuromag']
assert_raises(ValueError, plot_trans, info, trans_fname,
subject='sample', subjects_dir=subjects_dir,
ch_type='bad-chtype')
assert_raises(TypeError, plot_trans, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
mlab.close(all=True)
# all coord frames
for coord_frame in ('meg', 'head', 'mri'):
plot_trans(info, meg_sensors=True, dig=True, coord_frame=coord_frame,
trans=trans_fname, subject='sample',
subjects_dir=subjects_dir)
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog.set_channel_types({'EEG 001': 'ecog'})
with warnings.catch_warnings(record=True) as w:
plot_trans(evoked_eeg_ecog.info, subject='sample', trans=trans_fname,
source='outer_skin', meg_sensors=True, skull=True,
eeg_sensors=['original', 'projected'], ecog_sensors=True,
brain='white', head=True, subjects_dir=subjects_dir)
mlab.close(all=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
def test_raw():
""" Test bti conversion to Raw object """
for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
exported_fnames):
# rx = 2 if 'linux' in pdf else 0
assert_raises(ValueError, read_raw_bti, pdf, 'eggs')
assert_raises(ValueError, read_raw_bti, pdf, config, 'spam')
if op.exists(tmp_raw_fname):
os.remove(tmp_raw_fname)
with Raw(exported, preload=True) as ex:
with read_raw_bti(pdf, config, hs) as ra:
assert_true('RawBTi' in repr(ra))
assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
assert_array_almost_equal(ex.info['dev_head_t']['trans'],
ra.info['dev_head_t']['trans'], 7)
dig1, dig2 = [
np.array([d['r'] for d in r_.info['dig']])
for r_ in (ra, ex)
]
assert_array_equal(dig1, dig2)
coil1, coil2 = [
np.concatenate([
d['coil_trans'].flatten() for d in r_.info['chs'][:NCH]
]) for r_ in (ra, ex)
]
assert_array_almost_equal(coil1, coil2, 7)
loc1, loc2 = [
np.concatenate(
[d['loc'].flatten() for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)
]
assert_array_equal(loc1, loc2)
assert_array_equal(ra._data[:NCH], ex._data[:NCH])
assert_array_equal(ra._cals[:NCH], ex._cals[:NCH])
# Make sure concatenation works
raw_concat = concatenate_raws([ra.copy(), ra])
assert_equal(raw_concat.n_times, 2 * ra.n_times)
ra.save(tmp_raw_fname)
with Raw(tmp_raw_fname) as r:
print(r)
os.remove(tmp_raw_fname)
def test_transforms():
""" Test transformations """
bti_trans = (0.0, 0.02, 0.11)
bti_dev_t = Transform("ctf_meg", "meg", _get_bti_dev_t(0.0, bti_trans))
for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
raw = read_raw_bti(pdf, config, hs)
dev_ctf_t = raw.info["dev_ctf_t"]
dev_head_t_old = raw.info["dev_head_t"]
ctf_head_t = raw.info["ctf_head_t"]
# 1) get BTI->Neuromag
bti_dev_t = Transform("ctf_meg", "meg", _get_bti_dev_t(0.0, bti_trans))
# 2) get Neuromag->BTI head
t = combine_transforms(invert_transform(bti_dev_t), dev_ctf_t, "meg", "ctf_head")
# 3) get Neuromag->head
dev_head_t_new = combine_transforms(t, ctf_head_t, "meg", "head")
assert_array_equal(dev_head_t_new["trans"], dev_head_t_old["trans"])
def test_no_loc_none(monkeypatch):
"""Test that we don't set loc to None when no trans is found."""
ch_name = 'MLzA'
def _read_config_bad(*args, **kwargs):
cfg = _read_config(*args, **kwargs)
idx = [ch['name'] for ch in cfg['chs']].index(ch_name)
del cfg['chs'][idx]['dev']['transform']
return cfg
monkeypatch.setattr(mne.io.bti.bti, '_read_config', _read_config_bad)
kwargs = dict(pdf_fname=pdf_fnames[0],
config_fname=config_fnames[0],
head_shape_fname=hs_fnames[0],
rename_channels=False,
sort_by_ch_name=False)
raw = read_raw_bti(**kwargs)
idx = raw.ch_names.index(ch_name)
assert_allclose(raw.info['chs'][idx]['loc'], np.full(12, np.nan))
def test_transforms():
"""Test transformations."""
bti_trans = (0.0, 0.02, 0.11)
bti_dev_t = Transform('ctf_meg', 'meg', _get_bti_dev_t(0.0, bti_trans))
for pdf, config, hs, in zip(pdf_fnames, config_fnames, hs_fnames):
raw = read_raw_bti(pdf, config, hs, preload=False)
dev_ctf_t = raw.info['dev_ctf_t']
dev_head_t_old = raw.info['dev_head_t']
ctf_head_t = raw.info['ctf_head_t']
# 1) get BTI->Neuromag
bti_dev_t = Transform('ctf_meg', 'meg', _get_bti_dev_t(0.0, bti_trans))
# 2) get Neuromag->BTI head
t = combine_transforms(invert_transform(bti_dev_t), dev_ctf_t, 'meg',
'ctf_head')
# 3) get Neuromag->head
dev_head_t_new = combine_transforms(t, ctf_head_t, 'meg', 'head')
assert_array_equal(dev_head_t_new['trans'], dev_head_t_old['trans'])
def test_transforms():
"""Test transformations."""
bti_trans = (0.0, 0.02, 0.11)
bti_dev_t = Transform('ctf_meg', 'meg', _get_bti_dev_t(0.0, bti_trans))
for pdf, config, hs, in zip(pdf_fnames, config_fnames, hs_fnames):
raw = read_raw_bti(pdf, config, hs, preload=False)
dev_ctf_t = raw.info['dev_ctf_t']
dev_head_t_old = raw.info['dev_head_t']
ctf_head_t = raw.info['ctf_head_t']
# 1) get BTI->Neuromag
bti_dev_t = Transform('ctf_meg', 'meg', _get_bti_dev_t(0.0, bti_trans))
# 2) get Neuromag->BTI head
t = combine_transforms(invert_transform(bti_dev_t), dev_ctf_t,
'meg', 'ctf_head')
# 3) get Neuromag->head
dev_head_t_new = combine_transforms(t, ctf_head_t, 'meg', 'head')
assert_array_equal(dev_head_t_new['trans'], dev_head_t_old['trans'])
def test_find_ch_adjacency():
"""Test computing the adjacency matrix."""
raw = read_raw_fif(raw_fname, preload=True)
sizes = {'mag': 828, 'grad': 1700, 'eeg': 384}
nchans = {'mag': 102, 'grad': 204, 'eeg': 60}
for ch_type in ['mag', 'grad', 'eeg']:
conn, ch_names = find_ch_adjacency(raw.info, ch_type)
# Silly test for checking the number of neighbors.
assert_equal(conn.getnnz(), sizes[ch_type])
assert_equal(len(ch_names), nchans[ch_type])
pytest.raises(ValueError, find_ch_adjacency, raw.info, None)
# Test computing the conn matrix with gradiometers.
conn, ch_names = _compute_ch_adjacency(raw.info, 'grad')
assert_equal(conn.getnnz(), 2680)
# Test ch_type=None.
raw.pick_types(meg='mag')
find_ch_adjacency(raw.info, None)
bti_fname = op.join(testing_path, 'BTi', 'erm_HFH', 'c,rfDC')
bti_config_name = op.join(testing_path, 'BTi', 'erm_HFH', 'config')
raw = read_raw_bti(bti_fname, bti_config_name, None)
_, ch_names = find_ch_adjacency(raw.info, 'mag')
assert 'A1' in ch_names
ctf_fname = op.join(testing_path, 'CTF', 'testdata_ctf_short.ds')
raw = read_raw_ctf(ctf_fname)
_, ch_names = find_ch_adjacency(raw.info, 'mag')
assert 'MLC11' in ch_names
pytest.raises(ValueError, find_ch_adjacency, raw.info, 'eog')
raw_kit = read_raw_kit(fname_kit_157)
neighb, ch_names = find_ch_adjacency(raw_kit.info, 'mag')
assert neighb.data.size == 1329
assert ch_names[0] == 'MEG 001'
def test_plot_alignment():
"""Test plotting of -trans.fif files and MEG sensor layouts."""
# generate fiducials file for testing
tempdir = _TempDir()
fiducials_path = op.join(tempdir, 'fiducials.fif')
fid = [{'coord_frame': 5, 'ident': 1, 'kind': 1,
'r': [-0.08061612, -0.02908875, -0.04131077]},
{'coord_frame': 5, 'ident': 2, 'kind': 1,
'r': [0.00146763, 0.08506715, -0.03483611]},
{'coord_frame': 5, 'ident': 3, 'kind': 1,
'r': [0.08436285, -0.02850276, -0.04127743]}]
write_dig(fiducials_path, fid, 5)
mlab = _import_mlab()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
meg = ['helmet', 'sensors']
if system == 'KIT':
meg.append('ref')
plot_alignment(info, trans_fname, subject='sample',
subjects_dir=subjects_dir, meg=meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
mlab.close(all=True)
info = infos['Neuromag']
assert_raises(TypeError, plot_alignment, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
assert_raises(TypeError, plot_alignment, info, trans_fname,
subject='sample', subjects_dir=subjects_dir, src='foo')
assert_raises(ValueError, plot_alignment, info, trans_fname,
subject='fsaverage', subjects_dir=subjects_dir,
src=sample_src)
sample_src.plot(subjects_dir=subjects_dir, head=True, skull=True,
brain='white')
mlab.close(all=True)
# no-head version
mlab.close(all=True)
# all coord frames
assert_raises(ValueError, plot_alignment, info)
plot_alignment(info, surfaces=[])
for coord_frame in ('meg', 'head', 'mri'):
plot_alignment(info, meg=['helmet', 'sensors'], dig=True,
coord_frame=coord_frame, trans=trans_fname,
subject='sample', mri_fiducials=fiducials_path,
subjects_dir=subjects_dir, src=sample_src)
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog.set_channel_types({'EEG 001': 'ecog'})
with warnings.catch_warnings(record=True) as w:
plot_alignment(evoked_eeg_ecog.info, subject='sample',
trans=trans_fname, subjects_dir=subjects_dir,
surfaces=['white', 'outer_skin', 'outer_skull'],
meg=['helmet', 'sensors'],
eeg=['original', 'projected'], ecog=True)
mlab.close(all=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
sphere = make_sphere_model(info=evoked.info, r0='auto', head_radius='auto')
bem_sol = read_bem_solution(op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem-sol.fif'))
bem_surfs = read_bem_surfaces(op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem.fif'))
sample_src[0]['coord_frame'] = 4 # hack for coverage
plot_alignment(info, subject='sample', eeg='projected',
meg='helmet', bem=sphere, dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull',
'outer_skin'])
plot_alignment(info, trans_fname, subject='sample', meg='helmet',
subjects_dir=subjects_dir, eeg='projected', bem=sphere,
surfaces=['head', 'brain'], src=sample_src)
plot_alignment(info, trans_fname, subject='sample', meg=[],
subjects_dir=subjects_dir, bem=bem_sol, eeg=True,
surfaces=['head', 'inflated', 'outer_skull', 'inner_skull'])
plot_alignment(info, trans_fname, subject='sample',
meg=True, subjects_dir=subjects_dir,
surfaces=['head', 'inner_skull'], bem=bem_surfs)
sphere = make_sphere_model('auto', 'auto', evoked.info)
src = setup_volume_source_space(sphere=sphere)
plot_alignment(info, eeg='projected', meg='helmet', bem=sphere,
src=src, dig=True, surfaces=['brain', 'inner_skull',
'outer_skull', 'outer_skin'])
sphere = make_sphere_model('auto', None, evoked.info) # one layer
plot_alignment(info, trans_fname, subject='sample', meg=False,
coord_frame='mri', subjects_dir=subjects_dir,
surfaces=['brain'], bem=sphere, show_axes=True)
# one layer bem with skull surfaces:
assert_raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['brain', 'head', 'inner_skull'], bem=sphere)
# wrong eeg value:
assert_raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir, eeg='foo')
# wrong meg value:
assert_raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir, meg='bar')
# multiple brain surfaces:
assert_raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['white', 'pial'])
assert_raises(TypeError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=[1])
assert_raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['foo'])
mlab.close(all=True)
def test_plot_alignment(tmpdir, renderer):
"""Test plotting of -trans.fif files and MEG sensor layouts."""
# generate fiducials file for testing
tempdir = str(tmpdir)
fiducials_path = op.join(tempdir, 'fiducials.fif')
fid = [{
'coord_frame': 5,
'ident': 1,
'kind': 1,
'r': [-0.08061612, -0.02908875, -0.04131077]
}, {
'coord_frame': 5,
'ident': 2,
'kind': 1,
'r': [0.00146763, 0.08506715, -0.03483611]
}, {
'coord_frame': 5,
'ident': 3,
'kind': 1,
'r': [0.08436285, -0.02850276, -0.04127743]
}]
write_dig(fiducials_path, fid, 5)
renderer._close_all()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
bti = read_raw_bti(pdf_fname,
config_fname,
hs_fname,
convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
meg = ['helmet', 'sensors']
if system == 'KIT':
meg.append('ref')
fig = plot_alignment(info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
meg=meg)
rend = renderer._Renderer(fig=fig)
rend.close()
# KIT ref sensor coil def is defined
renderer._close_all()
info = infos['Neuromag']
pytest.raises(TypeError,
plot_alignment,
'foo',
trans_fname,
subject='sample',
subjects_dir=subjects_dir)
pytest.raises(OSError,
plot_alignment,
info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
src='foo')
pytest.raises(ValueError,
plot_alignment,
info,
trans_fname,
subject='fsaverage',
subjects_dir=subjects_dir,
src=sample_src)
sample_src.plot(subjects_dir=subjects_dir,
head=True,
skull=True,
brain='white')
renderer._close_all()
# no-head version
renderer._close_all()
# all coord frames
pytest.raises(ValueError, plot_alignment, info)
plot_alignment(info, surfaces=[])
for coord_frame in ('meg', 'head', 'mri'):
fig = plot_alignment(info,
meg=['helmet', 'sensors'],
dig=True,
coord_frame=coord_frame,
trans=trans_fname,
subject='sample',
mri_fiducials=fiducials_path,
subjects_dir=subjects_dir,
src=src_fname)
renderer._close_all()
# EEG only with strange options
evoked_eeg_ecog_seeg = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog_seeg.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog_seeg.set_channel_types({
'EEG 001': 'ecog',
'EEG 002': 'seeg'
})
with pytest.warns(RuntimeWarning, match='Cannot plot MEG'):
plot_alignment(evoked_eeg_ecog_seeg.info,
subject='sample',
trans=trans_fname,
subjects_dir=subjects_dir,
surfaces=['white', 'outer_skin', 'outer_skull'],
meg=['helmet', 'sensors'],
eeg=['original', 'projected'],
ecog=True,
seeg=True)
renderer._close_all()
sphere = make_sphere_model(info=evoked.info, r0='auto', head_radius='auto')
bem_sol = read_bem_solution(
op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem-sol.fif'))
bem_surfs = read_bem_surfaces(
op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem.fif'))
sample_src[0]['coord_frame'] = 4 # hack for coverage
plot_alignment(
info,
subject='sample',
eeg='projected',
meg='helmet',
bem=sphere,
dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull', 'outer_skin'])
plot_alignment(info,
trans_fname,
subject='sample',
meg='helmet',
subjects_dir=subjects_dir,
eeg='projected',
bem=sphere,
surfaces=['head', 'brain'],
src=sample_src)
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info,
trans_fname,
subject='sample',
meg=[],
subjects_dir=subjects_dir,
bem=bem_sol,
eeg=True,
surfaces=['head', 'inflated', 'outer_skull', 'inner_skull'])
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info,
trans_fname,
subject='sample',
meg=True,
subjects_dir=subjects_dir,
surfaces=['head', 'inner_skull'],
bem=bem_surfs)
# single-layer BEM can still plot head surface
assert bem_surfs[-1]['id'] == FIFF.FIFFV_BEM_SURF_ID_BRAIN
bem_sol_homog = read_bem_solution(
op.join(subjects_dir, 'sample', 'bem', 'sample-1280-bem-sol.fif'))
for use_bem in (bem_surfs[-1:], bem_sol_homog):
with catch_logging() as log:
plot_alignment(info,
trans_fname,
subject='sample',
meg=True,
subjects_dir=subjects_dir,
surfaces=['head', 'inner_skull'],
bem=use_bem,
verbose=True)
log = log.getvalue()
assert 'not find the surface for head in the provided BEM model' in log
# sphere model
sphere = make_sphere_model('auto', 'auto', evoked.info)
src = setup_volume_source_space(sphere=sphere)
plot_alignment(
info,
eeg='projected',
meg='helmet',
bem=sphere,
src=src,
dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull', 'outer_skin'])
sphere = make_sphere_model('auto', None, evoked.info) # one layer
# no info is permitted
fig = plot_alignment(trans=trans_fname,
subject='sample',
meg=False,
coord_frame='mri',
subjects_dir=subjects_dir,
surfaces=['brain'],
bem=sphere,
show_axes=True)
renderer._close_all()
if renderer.get_3d_backend() == 'mayavi':
import mayavi # noqa: F401 analysis:ignore
assert isinstance(fig, mayavi.core.scene.Scene)
# 3D coil with no defined draw (ConvexHull)
info_cube = pick_info(info, [0])
info['dig'] = None
info_cube['chs'][0]['coil_type'] = 9999
with pytest.raises(RuntimeError, match='coil definition not found'):
plot_alignment(info_cube, meg='sensors', surfaces=())
coil_def_fname = op.join(tempdir, 'temp')
with open(coil_def_fname, 'w') as fid:
fid.write(coil_3d)
with use_coil_def(coil_def_fname):
plot_alignment(info_cube, meg='sensors', surfaces=(), dig=True)
# one layer bem with skull surfaces:
with pytest.raises(ValueError, match='sphere conductor model must have'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['brain', 'head', 'inner_skull'],
bem=sphere)
# wrong eeg value:
with pytest.raises(ValueError, match='eeg must only contain'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
eeg='foo')
# wrong meg value:
with pytest.raises(ValueError, match='meg must only contain'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
meg='bar')
# multiple brain surfaces:
with pytest.raises(ValueError, match='Only one brain surface can be plot'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['white', 'pial'])
with pytest.raises(TypeError, match='all entries in surfaces must be'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=[1])
with pytest.raises(ValueError, match='Unknown surface type'):
plot_alignment(info=info,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['foo'])
fwd_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-4-fwd.fif')
fwd = read_forward_solution(fwd_fname)
plot_alignment(subject='sample',
subjects_dir=subjects_dir,
trans=trans_fname,
fwd=fwd,
surfaces='white',
coord_frame='head')
fwd = convert_forward_solution(fwd, force_fixed=True)
plot_alignment(subject='sample',
subjects_dir=subjects_dir,
trans=trans_fname,
fwd=fwd,
surfaces='white',
coord_frame='head')
renderer._close_all()
def test_plot_trans():
"""Test plotting of -trans.fif files and MEG sensor layouts."""
mlab = _import_mlab()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
with warnings.catch_warnings(record=True): # 4D weight tables
bti = read_raw_bti(pdf_fname,
config_fname,
hs_fname,
convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
ref_meg = False if system == 'KIT' else True
plot_trans(info,
trans_fname,
subject='sample',
meg_sensors=True,
subjects_dir=subjects_dir,
ref_meg=ref_meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
mlab.close(all=True)
info = infos['Neuromag']
assert_raises(ValueError,
plot_trans,
info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
ch_type='bad-chtype')
assert_raises(TypeError,
plot_trans,
'foo',
trans_fname,
subject='sample',
subjects_dir=subjects_dir)
assert_raises(TypeError,
plot_trans,
info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
src='foo')
assert_raises(ValueError,
plot_trans,
info,
trans_fname,
subject='fsaverage',
subjects_dir=subjects_dir,
src=sample_src)
sample_src.plot(subjects_dir=subjects_dir)
mlab.close(all=True)
# no-head version
plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
mlab.close(all=True)
# all coord frames
for coord_frame in ('meg', 'head', 'mri'):
plot_trans(info,
meg_sensors=True,
dig=True,
coord_frame=coord_frame,
trans=trans_fname,
subject='sample',
subjects_dir=subjects_dir)
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog.set_channel_types({'EEG 001': 'ecog'})
with warnings.catch_warnings(record=True) as w:
plot_trans(evoked_eeg_ecog.info,
subject='sample',
trans=trans_fname,
source='outer_skin',
meg_sensors=True,
skull=True,
eeg_sensors=['original', 'projected'],
ecog_sensors=True,
brain='white',
head=True,
subjects_dir=subjects_dir)
mlab.close(all=True)
assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])
def test_pick_refs():
"""Test picking of reference sensors."""
infos = list()
# KIT
kit_dir = op.join(io_dir, 'kit', 'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
raw_kit = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
infos.append(raw_kit.info)
# BTi
bti_dir = op.join(io_dir, 'bti', 'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
raw_bti = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
infos.append(raw_bti.info)
# CTF
fname_ctf_raw = op.join(io_dir, 'tests', 'data', 'test_ctf_comp_raw.fif')
raw_ctf = read_raw_fif(fname_ctf_raw)
raw_ctf.apply_gradient_compensation(2)
for info in infos:
info['bads'] = []
pytest.raises(ValueError, pick_types, info, meg='foo')
pytest.raises(ValueError, pick_types, info, ref_meg='foo')
picks_meg_ref = pick_types(info, meg=True, ref_meg=True)
picks_meg = pick_types(info, meg=True, ref_meg=False)
picks_ref = pick_types(info, meg=False, ref_meg=True)
assert_array_equal(picks_meg_ref,
np.sort(np.concatenate([picks_meg, picks_ref])))
picks_grad = pick_types(info, meg='grad', ref_meg=False)
picks_ref_grad = pick_types(info, meg=False, ref_meg='grad')
picks_meg_ref_grad = pick_types(info, meg='grad', ref_meg='grad')
assert_array_equal(picks_meg_ref_grad,
np.sort(np.concatenate([picks_grad,
picks_ref_grad])))
picks_mag = pick_types(info, meg='mag', ref_meg=False)
picks_ref_mag = pick_types(info, meg=False, ref_meg='mag')
picks_meg_ref_mag = pick_types(info, meg='mag', ref_meg='mag')
assert_array_equal(picks_meg_ref_mag,
np.sort(np.concatenate([picks_mag,
picks_ref_mag])))
assert_array_equal(picks_meg,
np.sort(np.concatenate([picks_mag, picks_grad])))
assert_array_equal(picks_ref,
np.sort(np.concatenate([picks_ref_mag,
picks_ref_grad])))
assert_array_equal(picks_meg_ref, np.sort(np.concatenate(
[picks_grad, picks_mag, picks_ref_grad, picks_ref_mag])))
for pick in (picks_meg_ref, picks_meg, picks_ref,
picks_grad, picks_ref_grad, picks_meg_ref_grad,
picks_mag, picks_ref_mag, picks_meg_ref_mag):
if len(pick) > 0:
pick_info(info, pick)
# test CTF expected failures directly
info = raw_ctf.info
info['bads'] = []
picks_meg_ref = pick_types(info, meg=True, ref_meg=True)
picks_meg = pick_types(info, meg=True, ref_meg=False)
picks_ref = pick_types(info, meg=False, ref_meg=True)
picks_mag = pick_types(info, meg='mag', ref_meg=False)
picks_ref_mag = pick_types(info, meg=False, ref_meg='mag')
picks_meg_ref_mag = pick_types(info, meg='mag', ref_meg='mag')
for pick in (picks_meg_ref, picks_ref, picks_ref_mag, picks_meg_ref_mag):
if len(pick) > 0:
pick_info(info, pick)
for pick in (picks_meg, picks_mag):
if len(pick) > 0:
with catch_logging() as log:
pick_info(info, pick, verbose=True)
assert ('Removing {} compensators'.format(len(info['comps']))
in log.getvalue())
from mne.datasets import sample, spm_face, testing
from mne.viz import plot_alignment, set_3d_title
print(__doc__)
bti_path = op.abspath(op.dirname(mne.__file__)) + '/io/bti/tests/data/'
kit_path = op.abspath(op.dirname(mne.__file__)) + '/io/kit/tests/data/'
raws = {
'Neuromag':
read_raw_fif(sample.data_path() + '/MEG/sample/sample_audvis_raw.fif'),
'CTF 275':
read_raw_ctf(spm_face.data_path() +
'/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds'),
'Magnes 3600wh':
read_raw_bti(op.join(bti_path, 'test_pdf_linux'),
op.join(bti_path, 'test_config_linux'),
op.join(bti_path, 'test_hs_linux')),
'KIT':
read_raw_kit(op.join(kit_path, 'test.sqd')),
'Artemis123':
read_raw_artemis123(
op.join(testing.data_path(), 'ARTEMIS123',
'Artemis_Data_2017-04-14-10h-38m-59s_Phantom_1k_HPI_1s.bin')),
}
for system, raw in sorted(raws.items()):
meg = ['helmet', 'sensors']
# We don't have coil definitions for KIT refs, so exclude them
if system != 'KIT':
meg.append('ref')
fig = plot_alignment(raw.info,
def test_plot_alignment(tmpdir):
"""Test plotting of -trans.fif files and MEG sensor layouts."""
# generate fiducials file for testing
tempdir = str(tmpdir)
fiducials_path = op.join(tempdir, 'fiducials.fif')
fid = [{'coord_frame': 5, 'ident': 1, 'kind': 1,
'r': [-0.08061612, -0.02908875, -0.04131077]},
{'coord_frame': 5, 'ident': 2, 'kind': 1,
'r': [0.00146763, 0.08506715, -0.03483611]},
{'coord_frame': 5, 'ident': 3, 'kind': 1,
'r': [0.08436285, -0.02850276, -0.04127743]}]
write_dig(fiducials_path, fid, 5)
mlab = _import_mlab()
evoked = read_evokeds(evoked_fname)[0]
sample_src = read_source_spaces(src_fname)
bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
preload=False).info
infos = dict(
Neuromag=evoked.info,
CTF=read_raw_ctf(ctf_fname).info,
BTi=bti,
KIT=read_raw_kit(sqd_fname).info,
)
for system, info in infos.items():
meg = ['helmet', 'sensors']
if system == 'KIT':
meg.append('ref')
plot_alignment(info, trans_fname, subject='sample',
subjects_dir=subjects_dir, meg=meg)
mlab.close(all=True)
# KIT ref sensor coil def is defined
mlab.close(all=True)
info = infos['Neuromag']
pytest.raises(TypeError, plot_alignment, 'foo', trans_fname,
subject='sample', subjects_dir=subjects_dir)
pytest.raises(TypeError, plot_alignment, info, trans_fname,
subject='sample', subjects_dir=subjects_dir, src='foo')
pytest.raises(ValueError, plot_alignment, info, trans_fname,
subject='fsaverage', subjects_dir=subjects_dir,
src=sample_src)
sample_src.plot(subjects_dir=subjects_dir, head=True, skull=True,
brain='white')
mlab.close(all=True)
# no-head version
mlab.close(all=True)
# all coord frames
pytest.raises(ValueError, plot_alignment, info)
plot_alignment(info, surfaces=[])
for coord_frame in ('meg', 'head', 'mri'):
plot_alignment(info, meg=['helmet', 'sensors'], dig=True,
coord_frame=coord_frame, trans=trans_fname,
subject='sample', mri_fiducials=fiducials_path,
subjects_dir=subjects_dir, src=sample_src)
mlab.close(all=True)
# EEG only with strange options
evoked_eeg_ecog_seeg = evoked.copy().pick_types(meg=False, eeg=True)
evoked_eeg_ecog_seeg.info['projs'] = [] # "remove" avg proj
evoked_eeg_ecog_seeg.set_channel_types({'EEG 001': 'ecog',
'EEG 002': 'seeg'})
with pytest.warns(RuntimeWarning, match='Cannot plot MEG'):
plot_alignment(evoked_eeg_ecog_seeg.info, subject='sample',
trans=trans_fname, subjects_dir=subjects_dir,
surfaces=['white', 'outer_skin', 'outer_skull'],
meg=['helmet', 'sensors'],
eeg=['original', 'projected'], ecog=True, seeg=True)
mlab.close(all=True)
sphere = make_sphere_model(info=evoked.info, r0='auto', head_radius='auto')
bem_sol = read_bem_solution(op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem-sol.fif'))
bem_surfs = read_bem_surfaces(op.join(subjects_dir, 'sample', 'bem',
'sample-1280-1280-1280-bem.fif'))
sample_src[0]['coord_frame'] = 4 # hack for coverage
plot_alignment(info, subject='sample', eeg='projected',
meg='helmet', bem=sphere, dig=True,
surfaces=['brain', 'inner_skull', 'outer_skull',
'outer_skin'])
plot_alignment(info, trans_fname, subject='sample', meg='helmet',
subjects_dir=subjects_dir, eeg='projected', bem=sphere,
surfaces=['head', 'brain'], src=sample_src)
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info, trans_fname, subject='sample', meg=[],
subjects_dir=subjects_dir, bem=bem_sol, eeg=True,
surfaces=['head', 'inflated', 'outer_skull', 'inner_skull'])
assert all(surf['coord_frame'] == FIFF.FIFFV_COORD_MRI
for surf in bem_sol['surfs'])
plot_alignment(info, trans_fname, subject='sample',
meg=True, subjects_dir=subjects_dir,
surfaces=['head', 'inner_skull'], bem=bem_surfs)
sphere = make_sphere_model('auto', 'auto', evoked.info)
src = setup_volume_source_space(sphere=sphere)
plot_alignment(info, eeg='projected', meg='helmet', bem=sphere,
src=src, dig=True, surfaces=['brain', 'inner_skull',
'outer_skull', 'outer_skin'])
sphere = make_sphere_model('auto', None, evoked.info) # one layer
plot_alignment(info, trans_fname, subject='sample', meg=False,
coord_frame='mri', subjects_dir=subjects_dir,
surfaces=['brain'], bem=sphere, show_axes=True)
# 3D coil with no defined draw (ConvexHull)
info_cube = pick_info(info, [0])
info['dig'] = None
info_cube['chs'][0]['coil_type'] = 9999
with pytest.raises(RuntimeError, match='coil definition not found'):
plot_alignment(info_cube, meg='sensors', surfaces=())
coil_def_fname = op.join(tempdir, 'temp')
with open(coil_def_fname, 'w') as fid:
fid.write(coil_3d)
with use_coil_def(coil_def_fname):
plot_alignment(info_cube, meg='sensors', surfaces=(), dig=True)
# one layer bem with skull surfaces:
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['brain', 'head', 'inner_skull'], bem=sphere)
# wrong eeg value:
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir, eeg='foo')
# wrong meg value:
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir, meg='bar')
# multiple brain surfaces:
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['white', 'pial'])
pytest.raises(TypeError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=[1])
pytest.raises(ValueError, plot_alignment, info=info, trans=trans_fname,
subject='sample', subjects_dir=subjects_dir,
surfaces=['foo'])
mlab.close(all=True)
def test_other_systems():
"""Test Maxwell filtering on KIT, BTI, and CTF files
"""
io_dir = op.join(op.dirname(__file__), '..', '..', 'io')
# KIT
kit_dir = op.join(io_dir, 'kit', 'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
raw_kit = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
with warnings.catch_warnings(record=True): # head fit
assert_raises(RuntimeError, maxwell_filter, raw_kit)
raw_sss = maxwell_filter(raw_kit, origin=(0., 0., 0.04), ignore_ref=True)
_assert_n_free(raw_sss, 65, 65)
# XXX this KIT origin fit is terrible! Eventually we should get a
# corrected HSP file with proper coverage
with warnings.catch_warnings(record=True):
with catch_logging() as log_file:
assert_raises(RuntimeError, maxwell_filter, raw_kit,
ignore_ref=True, regularize=None) # bad condition
raw_sss = maxwell_filter(raw_kit, origin='auto',
ignore_ref=True, bad_condition='warning',
verbose='warning')
log_file = log_file.getvalue()
assert_true('badly conditioned' in log_file)
assert_true('more than 20 mm from' in log_file)
# fits can differ slightly based on scipy version, so be lenient here
_assert_n_free(raw_sss, 28, 34) # bad origin == brutal reg
# Let's set the origin
with warnings.catch_warnings(record=True):
with catch_logging() as log_file:
raw_sss = maxwell_filter(raw_kit, origin=(0., 0., 0.04),
ignore_ref=True, bad_condition='warning',
regularize=None, verbose='warning')
log_file = log_file.getvalue()
assert_true('badly conditioned' in log_file)
_assert_n_free(raw_sss, 80)
# Now with reg
with warnings.catch_warnings(record=True):
with catch_logging() as log_file:
raw_sss = maxwell_filter(raw_kit, origin=(0., 0., 0.04),
ignore_ref=True, verbose=True)
log_file = log_file.getvalue()
assert_true('badly conditioned' not in log_file)
_assert_n_free(raw_sss, 65)
# BTi
bti_dir = op.join(io_dir, 'bti', 'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
with warnings.catch_warnings(record=True): # weght table
raw_bti = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
raw_sss = maxwell_filter(raw_bti)
_assert_n_free(raw_sss, 70)
# CTF
fname_ctf_raw = op.join(io_dir, 'tests', 'data', 'test_ctf_comp_raw.fif')
raw_ctf = Raw(fname_ctf_raw, compensation=2)
assert_raises(RuntimeError, maxwell_filter, raw_ctf) # compensated
raw_ctf = Raw(fname_ctf_raw)
assert_raises(ValueError, maxwell_filter, raw_ctf) # cannot fit headshape
raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04))
_assert_n_free(raw_sss, 68)
raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04), ignore_ref=True)
_assert_n_free(raw_sss, 70)
def test_other_systems():
"""Test Maxwell filtering on KIT, BTI, and CTF files
"""
io_dir = op.join(op.dirname(__file__), "..", "..", "io")
# KIT
kit_dir = op.join(io_dir, "kit", "tests", "data")
sqd_path = op.join(kit_dir, "test.sqd")
mrk_path = op.join(kit_dir, "test_mrk.sqd")
elp_path = op.join(kit_dir, "test_elp.txt")
hsp_path = op.join(kit_dir, "test_hsp.txt")
raw_kit = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
assert_raises(RuntimeError, maxwell_filter, raw_kit)
raw_sss = maxwell_filter(raw_kit, origin=(0.0, 0.0, 0.04), ignore_ref=True)
_assert_n_free(raw_sss, 65)
# XXX this KIT origin fit is terrible! Eventually we should get a
# corrected HSP file with proper coverage
with catch_logging() as log_file:
assert_raises(RuntimeError, maxwell_filter, raw_kit, ignore_ref=True, regularize=None) # bad condition
raw_sss = maxwell_filter(raw_kit, origin="auto", ignore_ref=True, bad_condition="warning", verbose="warning")
log_file = log_file.getvalue()
assert_true("badly conditioned" in log_file)
assert_true("more than 20 mm from" in log_file)
# fits can differ slightly based on scipy version, so be lenient here
_assert_n_free(raw_sss, 28, 34) # bad origin == brutal reg
# Let's set the origin
with catch_logging() as log_file:
raw_sss = maxwell_filter(
raw_kit,
origin=(0.0, 0.0, 0.04),
ignore_ref=True,
bad_condition="warning",
regularize=None,
verbose="warning",
)
log_file = log_file.getvalue()
assert_true("badly conditioned" in log_file)
_assert_n_free(raw_sss, 80)
# Now with reg
with catch_logging() as log_file:
raw_sss = maxwell_filter(raw_kit, origin=(0.0, 0.0, 0.04), ignore_ref=True, verbose=True)
log_file = log_file.getvalue()
assert_true("badly conditioned" not in log_file)
_assert_n_free(raw_sss, 65)
# BTi
bti_dir = op.join(io_dir, "bti", "tests", "data")
bti_pdf = op.join(bti_dir, "test_pdf_linux")
bti_config = op.join(bti_dir, "test_config_linux")
bti_hs = op.join(bti_dir, "test_hs_linux")
raw_bti = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
raw_sss = maxwell_filter(raw_bti)
_assert_n_free(raw_sss, 70)
# CTF
fname_ctf_raw = op.join(io_dir, "tests", "data", "test_ctf_comp_raw.fif")
raw_ctf = Raw(fname_ctf_raw, compensation=2)
assert_raises(RuntimeError, maxwell_filter, raw_ctf) # compensated
raw_ctf = Raw(fname_ctf_raw)
assert_raises(ValueError, maxwell_filter, raw_ctf) # cannot fit headshape
raw_sss = maxwell_filter(raw_ctf, origin=(0.0, 0.0, 0.04))
_assert_n_free(raw_sss, 68)
raw_sss = maxwell_filter(raw_ctf, origin=(0.0, 0.0, 0.04), ignore_ref=True)
_assert_n_free(raw_sss, 70)
def test_make_forward_solution_kit():
"""Test making fwd using KIT, BTI, and CTF (compensated) files
"""
kit_dir = op.join(op.dirname(__file__), "..", "..", "io", "kit", "tests", "data")
sqd_path = op.join(kit_dir, "test.sqd")
mrk_path = op.join(kit_dir, "test_mrk.sqd")
elp_path = op.join(kit_dir, "test_elp.txt")
hsp_path = op.join(kit_dir, "test_hsp.txt")
trans_path = op.join(kit_dir, "trans-sample.fif")
fname_kit_raw = op.join(kit_dir, "test_bin_raw.fif")
bti_dir = op.join(op.dirname(__file__), "..", "..", "io", "bti", "tests", "data")
bti_pdf = op.join(bti_dir, "test_pdf_linux")
bti_config = op.join(bti_dir, "test_config_linux")
bti_hs = op.join(bti_dir, "test_hs_linux")
fname_bti_raw = op.join(bti_dir, "exported4D_linux_raw.fif")
fname_ctf_raw = op.join(op.dirname(__file__), "..", "..", "io", "tests", "data", "test_ctf_comp_raw.fif")
# first set up a small testing source space
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, "sample-oct-2-src.fif")
src = setup_source_space("sample", fname_src_small, "oct2", subjects_dir=subjects_dir, add_dist=False)
n_src = 108 # this is the resulting # of verts in fwd
# first use mne-C: convert file, make forward solution
fwd = _do_forward_solution(
"sample",
fname_kit_raw,
src=fname_src_small,
bem=fname_bem_meg,
mri=trans_path,
eeg=False,
meg=True,
subjects_dir=subjects_dir,
)
assert_true(isinstance(fwd, Forward))
# now let's use python with the same raw file
fwd_py = make_forward_solution(fname_kit_raw, trans_path, src, fname_bem_meg, eeg=False, meg=True)
_compare_forwards(fwd, fwd_py, 157, n_src)
assert_true(isinstance(fwd_py, Forward))
# now let's use mne-python all the way
raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
# without ignore_ref=True, this should throw an error:
assert_raises(
NotImplementedError,
make_forward_solution,
raw_py.info,
src=src,
eeg=False,
meg=True,
bem=fname_bem_meg,
trans=trans_path,
)
# check that asking for eeg channels (even if they don't exist) is handled
meg_only_info = pick_info(raw_py.info, pick_types(raw_py.info, meg=True, eeg=False))
fwd_py = make_forward_solution(
meg_only_info, src=src, meg=True, eeg=True, bem=fname_bem_meg, trans=trans_path, ignore_ref=True
)
_compare_forwards(fwd, fwd_py, 157, n_src, meg_rtol=1e-3, meg_atol=1e-7)
# BTI python end-to-end versus C
fwd = _do_forward_solution(
"sample",
fname_bti_raw,
src=fname_src_small,
bem=fname_bem_meg,
mri=trans_path,
eeg=False,
meg=True,
subjects_dir=subjects_dir,
)
with warnings.catch_warnings(record=True): # weight tables
raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
fwd_py = make_forward_solution(raw_py.info, src=src, eeg=False, meg=True, bem=fname_bem_meg, trans=trans_path)
_compare_forwards(fwd, fwd_py, 248, n_src)
# now let's test CTF w/compensation
fwd_py = make_forward_solution(fname_ctf_raw, fname_trans, src, fname_bem_meg, eeg=False, meg=True)
fwd = _do_forward_solution(
"sample",
fname_ctf_raw,
mri=fname_trans,
src=fname_src_small,
bem=fname_bem_meg,
eeg=False,
meg=True,
subjects_dir=subjects_dir,
)
_compare_forwards(fwd, fwd_py, 274, n_src)
# CTF with compensation changed in python
ctf_raw = Raw(fname_ctf_raw, compensation=2)
fwd_py = make_forward_solution(ctf_raw.info, fname_trans, src, fname_bem_meg, eeg=False, meg=True)
with warnings.catch_warnings(record=True):
fwd = _do_forward_solution(
"sample",
ctf_raw,
mri=fname_trans,
src=fname_src_small,
bem=fname_bem_meg,
eeg=False,
meg=True,
subjects_dir=subjects_dir,
)
_compare_forwards(fwd, fwd_py, 274, n_src)
import os.path as op
from mayavi import mlab
import mne
from mne.io import Raw, read_raw_ctf, read_raw_bti, read_raw_kit
from mne.datasets import sample, spm_face
from mne.viz import plot_trans
print(__doc__)
bti_path = op.abspath(op.dirname(mne.__file__)) + '/io/bti/tests/data/'
kit_path = op.abspath(op.dirname(mne.__file__)) + '/io/kit/tests/data/'
raws = dict(
Neuromag=Raw(sample.data_path() + '/MEG/sample/sample_audvis_raw.fif'),
CTF_275=read_raw_ctf(spm_face.data_path() +
'/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds'),
Magnes_3600wh=read_raw_bti(bti_path + 'test_pdf_linux',
bti_path + 'test_config_linux',
bti_path + 'test_hs_linux'),
KIT=read_raw_kit(kit_path + 'test.sqd'),
)
for system, raw in raws.items():
# We don't have coil definitions for KIT refs, so exclude them
ref_meg = False if system == 'KIT' else True
fig = plot_trans(raw.info, trans=None, dig=False, eeg_sensors=False,
meg_sensors=True, coord_frame='meg', ref_meg=ref_meg)
mlab.title(system)
parser.add_option('--eog_ch', dest='eog_ch', type='str',
help='4D EOG channel names',
default='E63,E64')
options, args = parser.parse_args()
pdf_fname = options.pdf_fname
if pdf_fname is None:
parser.print_help()
sys.exit(1)
config_fname = options.config_fname
head_shape_fname = options.head_shape_fname
out_fname = options.out_fname
rotation_x = options.rotation_x
translation = options.translation
ecg_ch = options.ecg_ch
eog_ch = options.ecg_ch.split(',')
if out_fname == 'as_data_fname':
out_fname = pdf_fname + '_raw.fif'
raw = read_raw_bti(pdf_fname=pdf_fname, config_fname=config_fname,
head_shape_fname=head_shape_fname,
rotation_x=rotation_x, translation=translation,
ecg_ch=ecg_ch, eog_ch=eog_ch)
raw.save(out_fname)
raw.close()
sys.exit(0)
def test_other_systems():
"""Test Maxwell filtering on KIT, BTI, and CTF files."""
# KIT
kit_dir = op.join(io_dir, 'kit', 'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
raw_kit = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
with warnings.catch_warnings(record=True): # head fit
assert_raises(RuntimeError, maxwell_filter, raw_kit)
raw_sss = maxwell_filter(raw_kit, origin=(0., 0., 0.04), ignore_ref=True)
_assert_n_free(raw_sss, 65, 65)
raw_sss_auto = maxwell_filter(raw_kit,
origin=(0., 0., 0.04),
ignore_ref=True,
mag_scale='auto')
assert_allclose(raw_sss._data, raw_sss_auto._data)
# XXX this KIT origin fit is terrible! Eventually we should get a
# corrected HSP file with proper coverage
with warnings.catch_warnings(record=True):
with catch_logging() as log_file:
assert_raises(RuntimeError,
maxwell_filter,
raw_kit,
ignore_ref=True,
regularize=None) # bad condition
raw_sss = maxwell_filter(raw_kit,
origin='auto',
ignore_ref=True,
bad_condition='warning',
verbose='warning')
log_file = log_file.getvalue()
assert_true('badly conditioned' in log_file)
assert_true('more than 20 mm from' in log_file)
# fits can differ slightly based on scipy version, so be lenient here
_assert_n_free(raw_sss, 28, 34) # bad origin == brutal reg
# Let's set the origin
with warnings.catch_warnings(record=True):
with catch_logging() as log_file:
raw_sss = maxwell_filter(raw_kit,
origin=(0., 0., 0.04),
ignore_ref=True,
bad_condition='warning',
regularize=None,
verbose='warning')
log_file = log_file.getvalue()
assert_true('badly conditioned' in log_file)
_assert_n_free(raw_sss, 80)
# Now with reg
with warnings.catch_warnings(record=True):
with catch_logging() as log_file:
raw_sss = maxwell_filter(raw_kit,
origin=(0., 0., 0.04),
ignore_ref=True,
verbose=True)
log_file = log_file.getvalue()
assert_true('badly conditioned' not in log_file)
_assert_n_free(raw_sss, 65)
# BTi
bti_dir = op.join(io_dir, 'bti', 'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
with warnings.catch_warnings(record=True): # weght table
raw_bti = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
picks = pick_types(raw_bti.info, meg='mag', exclude=())
power = np.sqrt(np.sum(raw_bti[picks][0]**2))
raw_sss = maxwell_filter(raw_bti)
_assert_n_free(raw_sss, 70)
_assert_shielding(raw_sss, power, 0.5)
raw_sss_auto = maxwell_filter(raw_bti, mag_scale='auto', verbose=True)
_assert_shielding(raw_sss_auto, power, 0.7)
# CTF
raw_ctf = read_crop(fname_ctf_raw)
assert_equal(raw_ctf.compensation_grade, 3)
assert_raises(RuntimeError, maxwell_filter, raw_ctf) # compensated
raw_ctf.apply_gradient_compensation(0)
assert_raises(ValueError, maxwell_filter, raw_ctf) # cannot fit headshape
raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04))
_assert_n_free(raw_sss, 68)
_assert_shielding(raw_sss, raw_ctf, 1.8)
raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04), ignore_ref=True)
_assert_n_free(raw_sss, 70)
_assert_shielding(raw_sss, raw_ctf, 12)
raw_sss_auto = maxwell_filter(raw_ctf,
origin=(0., 0., 0.04),
ignore_ref=True,
mag_scale='auto')
assert_allclose(raw_sss._data, raw_sss_auto._data)
def test_other_systems():
"""Test Maxwell filtering on KIT, BTI, and CTF files."""
# KIT
kit_dir = op.join(io_dir, 'kit', 'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
raw_kit = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
with pytest.warns(RuntimeWarning, match='fit'):
pytest.raises(RuntimeError, maxwell_filter, raw_kit)
with catch_logging() as log:
raw_sss = maxwell_filter(raw_kit, origin=(0., 0., 0.04),
ignore_ref=True, verbose=True)
assert '12/15 out' in log.getvalue() # homogeneous fields removed
_assert_n_free(raw_sss, 65, 65)
raw_sss_auto = maxwell_filter(raw_kit, origin=(0., 0., 0.04),
ignore_ref=True, mag_scale='auto')
assert_allclose(raw_sss._data, raw_sss_auto._data)
# XXX this KIT origin fit is terrible! Eventually we should get a
# corrected HSP file with proper coverage
with pytest.warns(RuntimeWarning, match='more than 20 mm'):
with catch_logging() as log:
pytest.raises(RuntimeError, maxwell_filter, raw_kit,
ignore_ref=True, regularize=None) # bad condition
raw_sss = maxwell_filter(raw_kit, origin='auto',
ignore_ref=True, bad_condition='info',
verbose=True)
log = log.getvalue()
assert 'badly conditioned' in log
assert 'more than 20 mm from' in log
# fits can differ slightly based on scipy version, so be lenient here
_assert_n_free(raw_sss, 28, 34) # bad origin == brutal reg
# Let's set the origin
with catch_logging() as log:
raw_sss = maxwell_filter(raw_kit, origin=(0., 0., 0.04),
ignore_ref=True, bad_condition='info',
regularize=None, verbose=True)
log = log.getvalue()
assert 'badly conditioned' in log
assert '80/80 in, 12/15 out' in log
_assert_n_free(raw_sss, 80)
# Now with reg
with catch_logging() as log:
raw_sss = maxwell_filter(raw_kit, origin=(0., 0., 0.04),
ignore_ref=True, verbose=True)
log = log.getvalue()
assert 'badly conditioned' not in log
assert '12/15 out' in log
_assert_n_free(raw_sss, 65)
# BTi
bti_dir = op.join(io_dir, 'bti', 'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
raw_bti = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
picks = pick_types(raw_bti.info, meg='mag', exclude=())
power = np.sqrt(np.sum(raw_bti[picks][0] ** 2))
raw_sss = maxwell_filter(raw_bti)
_assert_n_free(raw_sss, 70)
_assert_shielding(raw_sss, power, 0.5)
raw_sss_auto = maxwell_filter(raw_bti, mag_scale='auto', verbose=True)
_assert_shielding(raw_sss_auto, power, 0.7)
# CTF
raw_ctf = read_crop(fname_ctf_raw)
assert_equal(raw_ctf.compensation_grade, 3)
pytest.raises(RuntimeError, maxwell_filter, raw_ctf) # compensated
raw_ctf.apply_gradient_compensation(0)
pytest.raises(ValueError, maxwell_filter, raw_ctf) # cannot fit headshape
raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04))
_assert_n_free(raw_sss, 68)
_assert_shielding(raw_sss, raw_ctf, 1.8)
with catch_logging() as log:
raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04),
ignore_ref=True, verbose=True)
assert ', 12/15 out' in log.getvalue() # homogeneous fields removed
_assert_n_free(raw_sss, 70)
_assert_shielding(raw_sss, raw_ctf, 12)
raw_sss_auto = maxwell_filter(raw_ctf, origin=(0., 0., 0.04),
ignore_ref=True, mag_scale='auto')
assert_allclose(raw_sss._data, raw_sss_auto._data)
with catch_logging() as log:
maxwell_filter(raw_ctf, origin=(0., 0., 0.04), regularize=None,
ignore_ref=True, verbose=True)
assert '80/80 in, 12/15 out' in log.getvalue() # homogeneous fields
from mne.io import read_raw_fif, read_raw_ctf, read_raw_bti, read_raw_kit
from mne.io import read_raw_artemis123
from mne.datasets import sample, spm_face, testing
from mne.viz import plot_alignment
print(__doc__)
bti_path = op.abspath(op.dirname(mne.__file__)) + '/io/bti/tests/data/'
kit_path = op.abspath(op.dirname(mne.__file__)) + '/io/kit/tests/data/'
raws = {
'Neuromag': read_raw_fif(sample.data_path() +
'/MEG/sample/sample_audvis_raw.fif'),
'CTF 275': read_raw_ctf(spm_face.data_path() +
'/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds'),
'Magnes 3600wh': read_raw_bti(op.join(bti_path, 'test_pdf_linux'),
op.join(bti_path, 'test_config_linux'),
op.join(bti_path, 'test_hs_linux')),
'KIT': read_raw_kit(op.join(kit_path, 'test.sqd')),
'Artemis123': read_raw_artemis123(op.join(
testing.data_path(), 'ARTEMIS123',
'Artemis_Data_2017-04-14-10h-38m-59s_Phantom_1k_HPI_1s.bin')),
}
for system, raw in sorted(raws.items()):
meg = ['helmet', 'sensors']
# We don't have coil definitions for KIT refs, so exclude them
if system != 'KIT':
meg.append('ref')
fig = plot_alignment(raw.info, trans=None, dig=False, eeg=False,
surfaces=[], meg=meg, coord_frame='meg',
verbose=True)
def test_make_forward_solution_kit():
"""Test making fwd using KIT, BTI, and CTF (compensated) files."""
kit_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'kit',
'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
trans_path = op.join(kit_dir, 'trans-sample.fif')
fname_kit_raw = op.join(kit_dir, 'test_bin_raw.fif')
bti_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'bti',
'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
fname_bti_raw = op.join(bti_dir, 'exported4D_linux_raw.fif')
fname_ctf_raw = op.join(op.dirname(__file__), '..', '..', 'io', 'tests',
'data', 'test_ctf_comp_raw.fif')
# first set up a small testing source space
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir,
add_dist=False)
write_source_spaces(fname_src_small, src) # to enable working with MNE-C
n_src = 108 # this is the resulting # of verts in fwd
# first use mne-C: convert file, make forward solution
fwd = _do_forward_solution('sample', fname_kit_raw, src=fname_src_small,
bem=fname_bem_meg, mri=trans_path,
eeg=False, meg=True, subjects_dir=subjects_dir)
assert (isinstance(fwd, Forward))
# now let's use python with the same raw file
fwd_py = make_forward_solution(fname_kit_raw, trans_path, src,
fname_bem_meg, eeg=False, meg=True)
_compare_forwards(fwd, fwd_py, 157, n_src)
assert (isinstance(fwd_py, Forward))
# now let's use mne-python all the way
raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
# without ignore_ref=True, this should throw an error:
pytest.raises(NotImplementedError, make_forward_solution, raw_py.info,
src=src, eeg=False, meg=True,
bem=fname_bem_meg, trans=trans_path)
# check that asking for eeg channels (even if they don't exist) is handled
meg_only_info = pick_info(raw_py.info, pick_types(raw_py.info, meg=True,
eeg=False))
fwd_py = make_forward_solution(meg_only_info, src=src, meg=True, eeg=True,
bem=fname_bem_meg, trans=trans_path,
ignore_ref=True)
_compare_forwards(fwd, fwd_py, 157, n_src,
meg_rtol=1e-3, meg_atol=1e-7)
# BTI python end-to-end versus C
fwd = _do_forward_solution('sample', fname_bti_raw, src=fname_src_small,
bem=fname_bem_meg, mri=trans_path,
eeg=False, meg=True, subjects_dir=subjects_dir)
raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
fwd_py = make_forward_solution(raw_py.info, src=src, eeg=False, meg=True,
bem=fname_bem_meg, trans=trans_path)
_compare_forwards(fwd, fwd_py, 248, n_src)
# now let's test CTF w/compensation
fwd_py = make_forward_solution(fname_ctf_raw, fname_trans, src,
fname_bem_meg, eeg=False, meg=True)
fwd = _do_forward_solution('sample', fname_ctf_raw, mri=fname_trans,
src=fname_src_small, bem=fname_bem_meg,
eeg=False, meg=True, subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
# CTF with compensation changed in python
ctf_raw = read_raw_fif(fname_ctf_raw)
ctf_raw.info['bads'] = ['MRO24-2908'] # test that it works with some bads
ctf_raw.apply_gradient_compensation(2)
fwd_py = make_forward_solution(ctf_raw.info, fname_trans, src,
fname_bem_meg, eeg=False, meg=True)
fwd = _do_forward_solution('sample', ctf_raw, mri=fname_trans,
src=fname_src_small, bem=fname_bem_meg,
eeg=False, meg=True,
subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
temp_dir = _TempDir()
fname_temp = op.join(temp_dir, 'test-ctf-fwd.fif')
write_forward_solution(fname_temp, fwd_py)
fwd_py2 = read_forward_solution(fname_temp)
_compare_forwards(fwd_py, fwd_py2, 274, n_src)
repr(fwd_py)
logging.getLogger('log').addHandler(console)
# print a.ch_names
# print len(a)
# print a[0][0]
# print len(a[0][0])
# print len(a[0][1])
# print a.info
for pre, con in paths:
for ind in con:
logger.info('Individual %s', ind)
indf = pre + ind
a = read_raw_bti(smaqepath + indf + '/e,rfhp1.0Hz', verbose=False)
for band, lf, hf in lband:
fa = mne.filter.band_pass_filter(a)
nchannels = len([cn for cn in a.info['ch_names'] if 'MEG' in cn])
data = np.zeros((nchannels, len(a)))
lcnames = []
ic = 0
for i, cn in enumerate(a.ch_names):
if 'MEG' in cn:
#print cn
lcnames.append(cn)
data[ic] = a[i][0][0]
ic += 1
def test_make_forward_solution_kit():
"""Test making fwd using KIT, BTI, and CTF (compensated) files."""
kit_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'kit', 'tests',
'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
trans_path = op.join(kit_dir, 'trans-sample.fif')
fname_kit_raw = op.join(kit_dir, 'test_bin_raw.fif')
bti_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'bti', 'tests',
'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
fname_bti_raw = op.join(bti_dir, 'exported4D_linux_raw.fif')
fname_ctf_raw = op.join(op.dirname(__file__), '..', '..', 'io', 'tests',
'data', 'test_ctf_comp_raw.fif')
# first set up a small testing source space
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample',
'oct2',
subjects_dir=subjects_dir,
add_dist=False)
write_source_spaces(fname_src_small, src) # to enable working with MNE-C
n_src = 108 # this is the resulting # of verts in fwd
# first use mne-C: convert file, make forward solution
fwd = _do_forward_solution('sample',
fname_kit_raw,
src=fname_src_small,
bem=fname_bem_meg,
mri=trans_path,
eeg=False,
meg=True,
subjects_dir=subjects_dir)
assert_true(isinstance(fwd, Forward))
# now let's use python with the same raw file
fwd_py = make_forward_solution(fname_kit_raw,
trans_path,
src,
fname_bem_meg,
eeg=False,
meg=True)
_compare_forwards(fwd, fwd_py, 157, n_src)
assert_true(isinstance(fwd_py, Forward))
# now let's use mne-python all the way
raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
# without ignore_ref=True, this should throw an error:
assert_raises(NotImplementedError,
make_forward_solution,
raw_py.info,
src=src,
eeg=False,
meg=True,
bem=fname_bem_meg,
trans=trans_path)
# check that asking for eeg channels (even if they don't exist) is handled
meg_only_info = pick_info(raw_py.info,
pick_types(raw_py.info, meg=True, eeg=False))
fwd_py = make_forward_solution(meg_only_info,
src=src,
meg=True,
eeg=True,
bem=fname_bem_meg,
trans=trans_path,
ignore_ref=True)
_compare_forwards(fwd, fwd_py, 157, n_src, meg_rtol=1e-3, meg_atol=1e-7)
# BTI python end-to-end versus C
fwd = _do_forward_solution('sample',
fname_bti_raw,
src=fname_src_small,
bem=fname_bem_meg,
mri=trans_path,
eeg=False,
meg=True,
subjects_dir=subjects_dir)
with warnings.catch_warnings(record=True): # weight tables
raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
fwd_py = make_forward_solution(raw_py.info,
src=src,
eeg=False,
meg=True,
bem=fname_bem_meg,
trans=trans_path)
_compare_forwards(fwd, fwd_py, 248, n_src)
# now let's test CTF w/compensation
fwd_py = make_forward_solution(fname_ctf_raw,
fname_trans,
src,
fname_bem_meg,
eeg=False,
meg=True)
fwd = _do_forward_solution('sample',
fname_ctf_raw,
mri=fname_trans,
src=fname_src_small,
bem=fname_bem_meg,
eeg=False,
meg=True,
subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
# CTF with compensation changed in python
ctf_raw = read_raw_fif(fname_ctf_raw)
ctf_raw.apply_gradient_compensation(2)
fwd_py = make_forward_solution(ctf_raw.info,
fname_trans,
src,
fname_bem_meg,
eeg=False,
meg=True)
with warnings.catch_warnings(record=True):
fwd = _do_forward_solution('sample',
ctf_raw,
mri=fname_trans,
src=fname_src_small,
bem=fname_bem_meg,
eeg=False,
meg=True,
subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
def test_make_forward_solution_kit():
"""Test making fwd using KIT, BTI, and CTF (compensated) files
"""
fname_bem = op.join(subjects_dir, 'sample', 'bem',
'sample-5120-bem-sol.fif')
kit_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'kit',
'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
mri_path = op.join(kit_dir, 'trans-sample.fif')
fname_kit_raw = op.join(kit_dir, 'test_bin_raw.fif')
bti_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'bti',
'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
fname_bti_raw = op.join(bti_dir, 'exported4D_linux_raw.fif')
fname_ctf_raw = op.join(op.dirname(__file__), '..', '..', 'io', 'tests',
'data', 'test_ctf_comp_raw.fif')
# first set up a testing source space
fname_src = op.join(temp_dir, 'oct2-src.fif')
src = setup_source_space('sample', fname_src, 'oct2',
subjects_dir=subjects_dir)
# first use mne-C: convert file, make forward solution
fwd = do_forward_solution('sample', fname_kit_raw, src=fname_src,
mindist=0.0, bem=fname_bem, mri=mri_path,
eeg=False, meg=True, subjects_dir=subjects_dir)
assert_true(isinstance(fwd, Forward))
# now let's use python with the same raw file
fwd_py = make_forward_solution(fname_kit_raw, mindist=0.0,
src=src, eeg=False, meg=True,
bem=fname_bem, mri=mri_path)
_compare_forwards(fwd, fwd_py, 157, 108)
assert_true(isinstance(fwd_py, Forward))
# now let's use mne-python all the way
raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
# without ignore_ref=True, this should throw an error:
assert_raises(NotImplementedError, make_forward_solution, raw_py.info,
mindist=0.0, src=src, eeg=False, meg=True,
bem=fname_bem, mri=mri_path)
fwd_py = make_forward_solution(raw_py.info, mindist=0.0,
src=src, eeg=False, meg=True,
bem=fname_bem, mri=mri_path,
ignore_ref=True)
_compare_forwards(fwd, fwd_py, 157, 108,
meg_rtol=1e-3, meg_atol=1e-7)
# BTI python end-to-end versus C
fwd = do_forward_solution('sample', fname_bti_raw, src=fname_src,
mindist=0.0, bem=fname_bem, mri=mri_path,
eeg=False, meg=True, subjects_dir=subjects_dir)
raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs)
fwd_py = make_forward_solution(raw_py.info, mindist=0.0,
src=src, eeg=False, meg=True,
bem=fname_bem, mri=mri_path)
_compare_forwards(fwd, fwd_py, 248, 108)
# now let's test CTF w/compensation
fwd_py = make_forward_solution(fname_ctf_raw, mindist=0.0,
src=src, eeg=False, meg=True,
bem=fname_bem, mri=fname_mri)
fwd = do_forward_solution('sample', fname_ctf_raw, src=fname_src,
mindist=0.0, bem=fname_bem, mri=fname_mri,
eeg=False, meg=True, subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, 108)
# CTF with compensation changed in python
ctf_raw = Raw(fname_ctf_raw, compensation=2)
fwd_py = make_forward_solution(ctf_raw.info, mindist=0.0,
src=src, eeg=False, meg=True,
bem=fname_bem, mri=fname_mri)
with warnings.catch_warnings(record=True):
fwd = do_forward_solution('sample', ctf_raw, src=fname_src,
mindist=0.0, bem=fname_bem, mri=fname_mri,
eeg=False, meg=True,
subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, 108)