def _read_evoked(fname, sensor_mode, info):
""" helper to read evokeds """
data = scio.loadmat(fname, squeeze_me=True)['data']
ch_names = [ch for ch in data['label'].tolist()]
times = data['time'].tolist()
sfreq = 1. / np.diff(times)[0]
if info is None:
info = create_info(
ch_names, ch_types=[sensor_mode] * len(ch_names),
sfreq=sfreq)
else:
info = _hcp_pick_info(info, ch_names)
info['sfreq'] = sfreq
orig_labels = list(data['grad'].tolist()['label'].tolist())
sel = [orig_labels.index(ch) for ch in ch_names]
pos = data['grad'].tolist()['chanpos'].tolist()[sel]
out = list()
comment = ('_'.join(fname.split('/')[-1].split('_')[2:])
.replace('.mat', '')
.replace('_eravg_', '_')
.replace('[', '')
.replace(']', ''))
for key, kind in (('var', 'standard_error'), ('avg', 'average')):
evoked = EvokedArray(
data=data[key].tolist(), info=info, tmin=min(times),
kind=kind, comment=comment)
evoked._set_channel_positions(pos, ch_names)
out.append(evoked)
return out
def test_montage():
"""Test making montages"""
tempdir = _TempDir()
# no pep8
input_str = [
'FidNz 0.00000 10.56381 -2.05108\nFidT9 -7.82694 0.45386 -3.76056\n'
'very_very_very_long_name 7.82694 0.45386 -3.76056',
'// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa
'E2 44.600 -0.880 1.000 0.7119 0.7021 -0.0154 0.00000000000000000\n' # noqa
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000', # noqa
'# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n'
'NumberPositions= 68\nPositions\n-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n0.0083 86.8110 -39.9830\n'
'Labels\nLPA\nRPA\nNz\n',
'# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n'
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'Labels\nLPA\nRPA\nNz\n',
'Site Theta Phi\nFp1 -92 -72\nFp2 92 72\n'
'very_very_very_long_name -60 -51\n',
'346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n',
'eeg Fp1 -95.0 -31.0 -3.0\neeg AF7 -81 -59 -3\neeg AF3 -87 -41 28\n'
]
kinds = ['test.sfp', 'test.csd', 'test_mm.elc', 'test_m.elc', 'test.txt',
'test.elp', 'test.hpts']
for kind, text in zip(kinds, input_str):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if ".sfp" in kind or ".txt" in kind:
assert_true('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 3)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (3, 3))
assert_equal(montage.kind, op.splitext(kind)[0])
if kind.endswith('csd'):
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
pos2 = np.c_[table['x'], table['y'], table['z']]
assert_array_almost_equal(pos2, montage.pos, 4)
if kind.endswith('elc'):
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1,
montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
"""
kind = 'test_fid.hpts'
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, 'test_fid.hpts'), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index('2')
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index('1')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index('3')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, 'test_fid.hpts')
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(
montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(
data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in the montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
continue
raw_fname = paths('sss', subject=meg_subject, block=1)
inv_fname = paths('inv', subject=meg_subject)
cov = load('cov', subject=meg_subject)
fwd = load('fwd', subject=meg_subject)
info = read_info(raw_fname)
inv = make_inverse_operator(info, fwd, cov, loose=0.2, depth=0.8)
save(inv, 'inv', subject=meg_subject, overwrite=True)
# Morph anatomy into common model ---------------------------------------------
from mne import EvokedArray
from mne import compute_morph_matrix
from mne.minimum_norm import apply_inverse
if True:
for meg_subject, subject in zip(range(1, 21), subjects_id):
if subject in bad_mri:
continue
raw_fname = paths('sss', subject=meg_subject, block=1)
info = read_info(raw_fname)
# precompute morphing matrix for faster processing
inv = load('inv', subject=meg_subject)
evoked = EvokedArray(np.zeros((len(info['chs']), 2)), info, 0)
evoked.pick_types(eeg=False, meg=True)
stc = apply_inverse(evoked, inv, lambda2=1.0 / (2 ** 3.0),
method='dSPM', pick_ori=None)
morph = compute_morph_matrix(subject, 'fsaverage', stc.vertices,
vertices_to=[np.arange(10242)] * 2,
subjects_dir=subjects_dir)
save(morph, 'morph', subject=meg_subject)
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
inputs = dict(
sfp='FidNz 0 9.071585155 -2.359754454\n'
'FidT9 -6.711765 0.040402876 -3.251600355\n'
'very_very_very_long_name -5.831241498 -4.494821698 4.955347697\n'
'Cz 0 0 8.899186843',
csd='// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa: E501
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa: E501
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa: E501
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n' # noqa: E501
'E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000\n' # noqa: E501
'E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022', # noqa: E501
mm_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n' # noqa:E501
'NumberPositions= 68\n'
'Positions\n'
'-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n'
'0.0083 86.8110 -39.9830\n'
'-86.0761 -24.9897 -67.9860\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
m_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n' # noqa:E501
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'.08 -.02 -.04\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
txt='Site Theta Phi\n'
'Fp1 -92 -72\n'
'Fp2 92 72\n'
'very_very_very_long_name -92 72\n'
'O2 92 -90\n',
elp='346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n'
'EEG\t FCz\t 68.01\t 58.103\t 85\n',
hpts='eeg Fp1 -95.0 -3. -3.\n'
'eeg AF7 -1 -1 -3\n'
'eeg A3 -2 -2 2\n'
'eeg A 0 0 0',
)
# Get actual positions and save them for checking
# csd comes from the string above, all others come from commit 2fa35d4
poss = dict(
sfp=[[0.0, 9.07159, -2.35975], [-6.71176, 0.0404, -3.2516],
[-5.83124, -4.49482, 4.95535], [0.0, 0.0, 8.89919]],
mm_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.08681, -0.03998], [-0.08608, -0.02499, -0.06799]],
m_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.00868, -0.03998], [0.08, -0.02, -0.04]],
txt=[[-26.25044, 80.79056, -2.96646], [26.25044, 80.79056, -2.96646],
[-26.25044, -80.79056, -2.96646], [0.0, -84.94822, -2.96646]],
elp=[[-48.20043, 57.55106, 39.86971], [0.0, 60.73848, 59.4629],
[48.1426, 57.58403, 39.89198], [41.64599, 66.91489, 31.8278]],
hpts=[[-95, -3, -3], [-1, -1., -3.], [-2, -2, 2.], [0, 0, 0]],
)
for key, text in inputs.items():
kind = key.split('_')[-1]
fname = op.join(tempdir, 'test.' + kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if kind in ('sfp', 'txt'):
assert_true('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, 'test')
if kind == 'csd':
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
poss['csd'] = np.c_[table['x'], table['y'], table['z']]
if kind == 'elc':
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1,
montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
assert_array_almost_equal(poss[key], montage.pos, 4, err_msg=key)
# Test reading in different letter case.
ch_names = ["F3", "FZ", "F4", "FC3", "FCz", "FC4", "C3", "CZ", "C4", "CP3",
"CPZ", "CP4", "P3", "PZ", "P4", "O1", "OZ", "O2"]
montage = read_montage('standard_1020', ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_strs = ["""
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
""", """
Fp1 -95.0 -31.0 -3.0
AF7 -81 -59 -3
AF3 -87 -41 28
nasion -91 0 -42
lpa 0 -91 -42
rpa 0 91 -42
"""]
all_fiducials = [['2', '1', '3'], ['nasion', 'lpa', 'rpa']]
kinds = ['test_fid.hpts', 'test_fid.sfp']
for kind, fiducials, input_str in zip(kinds, all_fiducials, input_strs):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, kind), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index(fiducials[0])
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index(fiducials[1])
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index(fiducials[2])
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, kind)
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3,
['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(
montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(
data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
# Channel names can be treated case insensitive
with warnings.catch_warnings(record=True) as w:
info = create_info(['FP1', 'af7', 'AF3'], 1e3, ['eeg'] * 3)
_set_montage(info, montage)
assert_true(len(w) == 0)
# Unless there is a collision in names
with warnings.catch_warnings(record=True) as w:
info = create_info(['FP1', 'Fp1', 'AF3'], 1e3, ['eeg'] * 3)
_set_montage(info, montage)
assert_true(len(w) == 1)
with warnings.catch_warnings(record=True) as w:
montage.ch_names = ['FP1', 'Fp1', 'AF3']
info = create_info(['fp1', 'AF3'], 1e3, ['eeg', 'eeg'])
_set_montage(info, montage)
assert_true(len(w) == 1)
aspect='auto',
extent=[epochs.times[0], epochs.times[-1], 1,
len(X_transform)],
cmap='RdBu_r')
plt.xlabel('Time (ms)')
plt.ylabel('Trials (reordered by condition)')
# Plot average response
plt.figure()
plt.title('Average EMS signal')
mappings = [(key, value) for key, value in event_ids.items()]
for key, value in mappings:
ems_ave = X_transform[y == value]
plt.plot(epochs.times, ems_ave.mean(0), label=key)
plt.xlabel('Time (ms)')
plt.ylabel('a.u.')
plt.legend(loc='best')
plt.show()
# Visualize spatial filters across time
evoked = EvokedArray(filters, epochs.info, tmin=epochs.tmin)
evoked.plot_topomap()
#############################################################################
# Note that a similar transformation can be applied with `compute_ems`
# However, this function replicates Schurger et al's original paper, and thus
# applies the normalization outside a leave-one-out cross-validation, which we
# recommend not to do.
epochs.equalize_event_counts(event_ids)
X_transform, filters, classes = compute_ems(epochs)
plt.title('single trial surrogates')
plt.imshow(X_transform[y.argsort()], origin='lower', aspect='auto',
extent=[epochs.times[0], epochs.times[-1], 1, len(X_transform)],
cmap='RdBu_r')
plt.xlabel('Time (ms)')
plt.ylabel('Trials (reordered by condition)')
# Plot average response
plt.figure()
plt.title('Average EMS signal')
mappings = [(key, value) for key, value in event_ids.items()]
for key, value in mappings:
ems_ave = X_transform[y == value]
plt.plot(epochs.times, ems_ave.mean(0), label=key)
plt.xlabel('Time (ms)')
plt.ylabel('a.u.')
plt.legend(loc='best')
plt.show()
# Visualize spatial filters across time
evoked = EvokedArray(filters, epochs.info, tmin=epochs.tmin)
evoked.plot_topomap(time_unit='s')
#############################################################################
# Note that a similar transformation can be applied with `compute_ems`
# However, this function replicates Schurger et al's original paper, and thus
# applies the normalization outside a leave-one-out cross-validation, which we
# recommend not to do.
epochs.equalize_event_counts(event_ids)
X_transform, filters, classes = compute_ems(epochs)
# fit the classifier on MEG data
X = scaler.fit_transform(meg_data)
model.fit(X, labels)
# Extract and plot spatial filters and spatial patterns
for name, coef in (('patterns', model.patterns_), ('filters', model.filters_)):
# We fitted the linear model onto Z-scored data. To make the filters
# interpretable, we must reverse this normalization step
coef = scaler.inverse_transform([coef])[0]
# The data was vectorized to fit a single model across all time points and
# all channels. We thus reshape it:
coef = coef.reshape(len(meg_epochs.ch_names), -1)
# Plot
evoked = EvokedArray(coef, meg_epochs.info, tmin=epochs.tmin)
evoked.plot_topomap(title='MEG %s' % name)
###############################################################################
# Let's do the same on EEG data using a scikit-learn pipeline
X = epochs.pick_types(meg=False, eeg=True)
y = epochs.events[:, 2]
# Define a unique pipeline to sequentially:
clf = make_pipeline(
Vectorizer(), # 1) vectorize across time and channels
StandardScaler(), # 2) normalize features across trials
LinearModel(LogisticRegression())) # 3) fits a logistic regression
clf.fit(X, y)
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
inputs = dict(
sfp="FidNz 0 9.071585155 -2.359754454\n"
"FidT9 -6.711765 0.040402876 -3.251600355\n"
"very_very_very_long_name -5.831241498 -4.494821698 4.955347697\n"
"Cz 0 0 8.899186843",
csd="// MatLab Sphere coordinates [degrees] Cartesian coordinates\n" # noqa: E501
"// Label Theta Phi Radius X Y Z off sphere surface\n" # noqa: E501
"E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n" # noqa: E501
"E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n" # noqa: E501
"E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000\n" # noqa: E501
"E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022", # noqa: E501
mm_elc="# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n" # noqa:E501
"NumberPositions= 68\n"
"Positions\n"
"-86.0761 -19.9897 -47.9860\n"
"85.7939 -20.0093 -48.0310\n"
"0.0083 86.8110 -39.9830\n"
"-86.0761 -24.9897 -67.9860\n"
"Labels\nLPA\nRPA\nNz\nDummy\n",
m_elc="# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n"
"NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n" # noqa:E501
".0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n"
".08 -.02 -.04\n"
"Labels\nLPA\nRPA\nNz\nDummy\n",
txt="Site Theta Phi\n"
"Fp1 -92 -72\n"
"Fp2 92 72\n"
"very_very_very_long_name -92 72\n"
"O2 92 -90\n",
elp="346\n"
"EEG\t F3\t -62.027\t -50.053\t 85\n"
"EEG\t Fz\t 45.608\t 90\t 85\n"
"EEG\t F4\t 62.01\t 50.103\t 85\n"
"EEG\t FCz\t 68.01\t 58.103\t 85\n",
hpts="eeg Fp1 -95.0 -3. -3.\n" "eeg AF7 -1 -1 -3\n" "eeg A3 -2 -2 2\n" "eeg A 0 0 0",
)
# Get actual positions and save them for checking
# csd comes from the string above, all others come from commit 2fa35d4
poss = dict(
sfp=[[0.0, 9.07159, -2.35975], [-6.71176, 0.0404, -3.2516], [-5.83124, -4.49482, 4.95535], [0.0, 0.0, 8.89919]],
mm_elc=[
[-0.08608, -0.01999, -0.04799],
[0.08579, -0.02001, -0.04803],
[1e-05, 0.08681, -0.03998],
[-0.08608, -0.02499, -0.06799],
],
m_elc=[
[-0.08608, -0.01999, -0.04799],
[0.08579, -0.02001, -0.04803],
[1e-05, 0.00868, -0.03998],
[0.08, -0.02, -0.04],
],
txt=[
[-26.25044, 80.79056, -2.96646],
[26.25044, 80.79056, -2.96646],
[-26.25044, -80.79056, -2.96646],
[0.0, -84.94822, -2.96646],
],
elp=[
[-48.20043, 57.55106, 39.86971],
[0.0, 60.73848, 59.4629],
[48.1426, 57.58403, 39.89198],
[41.64599, 66.91489, 31.8278],
],
hpts=[[-95, -3, -3], [-1, -1.0, -3.0], [-2, -2, 2.0], [0, 0, 0]],
)
for key, text in inputs.items():
kind = key.split("_")[-1]
fname = op.join(tempdir, "test." + kind)
with open(fname, "w") as fid:
fid.write(text)
montage = read_montage(fname)
if kind in ("sfp", "txt"):
assert_true("very_very_very_long_name" in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, "test")
if kind == "csd":
dtype = [
("label", "S4"),
("theta", "f8"),
("phi", "f8"),
("radius", "f8"),
("x", "f8"),
("y", "f8"),
("z", "f8"),
("off_sph", "f8"),
]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
poss["csd"] = np.c_[table["x"], table["y"], table["z"]]
if kind == "elc":
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(np.linalg.norm, 1, montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
assert_array_almost_equal(poss[key], montage.pos, 4, err_msg=key)
# Test reading in different letter case.
ch_names = [
"F3",
"FZ",
"F4",
"FC3",
"FCz",
"FC4",
"C3",
"CZ",
"C4",
"CP3",
"CPZ",
"CP4",
"P3",
"PZ",
"P4",
"O1",
"OZ",
"O2",
]
montage = read_montage("standard_1020", ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
"""
fname = op.join(tempdir, "test_fid.hpts")
with open(fname, "w") as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, "test_fid.hpts"), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index("2")
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index("1")
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index("3")
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, "test_fid.hpts")
montage = read_montage(montage_fname, unit="mm")
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ["eeg"] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c["loc"][:3] for c in info["chs"]])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info["ch_names"])
info = create_info(montage.ch_names, 1e3, ["eeg"] * len(montage.ch_names))
evoked = EvokedArray(data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c["loc"][:3] for c in evoked.info["chs"]])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info["ch_names"])
# Warning should be raised when some EEG are not specified in the montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ["foo", "bar"], 1e3, ["eeg"] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
# fit the classifier on MEG data
X = scaler.fit_transform(meg_data)
model.fit(X, labels)
# Extract and plot spatial filters and spatial patterns
for name, coef in (('patterns', model.patterns_), ('filters', model.filters_)):
# We fitted the linear model onto Z-scored data. To make the filters
# interpretable, we must reverse this normalization step
coef = scaler.inverse_transform([coef])[0]
# The data was vectorized to fit a single model across all time points and
# all channels. We thus reshape it:
coef = coef.reshape(len(meg_epochs.ch_names), -1)
# Plot
evoked = EvokedArray(coef, meg_epochs.info, tmin=epochs.tmin)
evoked.plot_topomap(title='MEG %s' % name, time_unit='s')
###############################################################################
# Let's do the same on EEG data using a scikit-learn pipeline
X = epochs.pick_types(meg=False, eeg=True)
y = epochs.events[:, 2]
# Define a unique pipeline to sequentially:
clf = make_pipeline(
Vectorizer(), # 1) vectorize across time and channels
StandardScaler(), # 2) normalize features across trials
LinearModel(
LogisticRegression(solver='lbfgs'))) # 3) fits a logistic regression
clf.fit(X, y)
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
inputs = dict(
sfp='FidNz 0 9.071585155 -2.359754454\n'
'FidT9 -6.711765 0.040402876 -3.251600355\n'
'very_very_very_long_name -5.831241498 -4.494821698 4.955347697\n'
'Cz 0 0 8.899186843',
csd=
'// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa: E501
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa: E501
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa: E501
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n' # noqa: E501
'E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000\n' # noqa: E501
'E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022', # noqa: E501
mm_elc=
'# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n' # noqa:E501
'NumberPositions= 68\n'
'Positions\n'
'-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n'
'0.0083 86.8110 -39.9830\n'
'-86.0761 -24.9897 -67.9860\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
m_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n' # noqa:E501
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'.08 -.02 -.04\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
txt='Site Theta Phi\n'
'Fp1 -92 -72\n'
'Fp2 92 72\n'
'very_very_very_long_name -92 72\n'
'O2 92 -90\n',
elp='346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n'
'EEG\t FCz\t 68.01\t 58.103\t 85\n',
hpts='eeg Fp1 -95.0 -3. -3.\n'
'eeg AF7 -1 -1 -3\n'
'eeg A3 -2 -2 2\n'
'eeg A 0 0 0',
bvef='<?xml version="1.0" encoding="UTF-8" standalone="yes"?>\n'
'<!-- Generated by EasyCap Configurator 19.05.2014 -->\n'
'<Electrodes defaults="false">\n'
' <Electrode>\n'
' <Name>Fp1</Name>\n'
' <Theta>-90</Theta>\n'
' <Phi>-72</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>1</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>Fz</Name>\n'
' <Theta>45</Theta>\n'
' <Phi>90</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>2</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>F3</Name>\n'
' <Theta>-60</Theta>\n'
' <Phi>-51</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>3</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>F7</Name>\n'
' <Theta>-90</Theta>\n'
' <Phi>-36</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>4</Number>\n'
' </Electrode>\n'
'</Electrodes>',
)
# Get actual positions and save them for checking
# csd comes from the string above, all others come from commit 2fa35d4
poss = dict(
sfp=[[0.0, 9.07159, -2.35975], [-6.71176, 0.0404, -3.2516],
[-5.83124, -4.49482, 4.95535], [0.0, 0.0, 8.89919]],
mm_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.08681, -0.03998], [-0.08608, -0.02499, -0.06799]],
m_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.00868, -0.03998], [0.08, -0.02, -0.04]],
txt=[[-26.25044, 80.79056, -2.96646], [26.25044, 80.79056, -2.96646],
[-26.25044, -80.79056, -2.96646], [0.0, -84.94822, -2.96646]],
elp=[[-48.20043, 57.55106, 39.86971], [0.0, 60.73848, 59.4629],
[48.1426, 57.58403, 39.89198], [41.64599, 66.91489, 31.8278]],
hpts=[[-95, -3, -3], [-1, -1., -3.], [-2, -2, 2.], [0, 0, 0]],
bvef=[[-26.266444, 80.839803, 5.204748e-15],
[3.680313e-15, 60.104076, 60.104076],
[-46.325632, 57.207392, 42.500000],
[-68.766444, 49.961746, 5.204748e-15]],
)
for key, text in inputs.items():
kind = key.split('_')[-1]
fname = op.join(tempdir, 'test.' + kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if kind in ('sfp', 'txt'):
assert ('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, 'test')
if kind == 'csd':
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
poss['csd'] = np.c_[table['x'], table['y'], table['z']]
if kind == 'elc':
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1, montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
assert_array_almost_equal(poss[key], montage.pos, 4, err_msg=key)
# Test reading in different letter case.
ch_names = [
"F3", "FZ", "F4", "FC3", "FCz", "FC4", "C3", "CZ", "C4", "CP3", "CPZ",
"CP4", "P3", "PZ", "P4", "O1", "OZ", "O2"
]
montage = read_montage('standard_1020', ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_strs = [
"""
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
""", """
Fp1 -95.0 -31.0 -3.0
AF7 -81 -59 -3
AF3 -87 -41 28
FidNz -91 0 -42
FidT9 0 -91 -42
FidT10 0 91 -42
"""
]
# sfp files seem to have Nz, T9, and T10 as fiducials:
# https://github.com/mne-tools/mne-python/pull/4482#issuecomment-321980611
kinds = ['test_fid.hpts', 'test_fid.sfp']
for kind, input_str in zip(kinds, input_strs):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, kind), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
assert_array_equal(montage.nasion[[0, 2]], [0, 0])
assert_array_equal(montage.lpa[[1, 2]], [0, 0])
assert_array_equal(montage.rpa[[1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, kind)
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3,
['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(montage.ch_names, 1e3,
['eeg'] * len(montage.ch_names))
evoked = EvokedArray(data=np.zeros((len(montage.ch_names), 1)),
info=info,
tmin=0)
# test return type as well as set montage
assert (isinstance(evoked.set_montage(montage), type(evoked)))
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in montage
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage)
# Channel names can be treated case insensitive
info = create_info(['FP1', 'af7', 'AF3'], 1e3, ['eeg'] * 3)
_set_montage(info, montage)
# Unless there is a collision in names
info = create_info(['FP1', 'Fp1', 'AF3'], 1e3, ['eeg'] * 3)
assert (info['dig'] is None)
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage)
assert len(info['dig']) == 5 # 2 EEG w/pos, 3 fiducials
montage.ch_names = ['FP1', 'Fp1', 'AF3']
info = create_info(['fp1', 'AF3'], 1e3, ['eeg', 'eeg'])
assert (info['dig'] is None)
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage, set_dig=False)
assert (info['dig'] is None)
# test get_pos2d method
montage = read_montage("standard_1020")
c3 = montage.get_pos2d()[montage.ch_names.index("C3")]
c4 = montage.get_pos2d()[montage.ch_names.index("C4")]
fz = montage.get_pos2d()[montage.ch_names.index("Fz")]
oz = montage.get_pos2d()[montage.ch_names.index("Oz")]
f1 = montage.get_pos2d()[montage.ch_names.index("F1")]
assert (c3[0] < 0) # left hemisphere
assert (c4[0] > 0) # right hemisphere
assert (fz[1] > 0) # frontal
assert (oz[1] < 0) # occipital
assert_allclose(fz[0], 0, atol=1e-2) # midline
assert_allclose(oz[0], 0, atol=1e-2) # midline
assert (f1[0] < 0 and f1[1] > 0) # left frontal
# test get_builtin_montages function
montages = get_builtin_montages()
assert (len(montages) > 0) # MNE should always ship with montages
assert ("standard_1020" in montages) # 10/20 montage
assert ("standard_1005" in montages) # 10/05 montage
plt.yticks(tick_marks, target_names)
fig.tight_layout()
ax.set(ylabel='True label', xlabel='Predicted label')
# %%
# The ``patterns_`` attribute of a fitted Xdawn instance (here from the last
# cross-validation fold) can be used for visualization.
fig, axes = plt.subplots(nrows=len(event_id),
ncols=n_filter,
figsize=(n_filter, len(event_id) * 2))
fitted_xdawn = clf.steps[0][1]
tmp_info = epochs.info.copy()
tmp_info['sfreq'] = 1.
for ii, cur_class in enumerate(sorted(event_id)):
cur_patterns = fitted_xdawn.patterns_[cur_class]
pattern_evoked = EvokedArray(cur_patterns[:n_filter].T, tmp_info, tmin=0)
pattern_evoked.plot_topomap(times=np.arange(n_filter),
time_format='Component %d' if ii == 0 else '',
colorbar=False,
show_names=False,
axes=axes[ii],
show=False)
axes[ii, 0].set(ylabel=cur_class)
fig.tight_layout(h_pad=1.0, w_pad=1.0, pad=0.1)
# %%
# References
# ----------
# .. footbibliography::
def plot_filters(self,
info,
components=None,
fband=None,
ch_type=None,
layout=None,
vmin=None,
vmax=None,
cmap='RdBu_r',
sensors=True,
colorbar=True,
scalings=None,
units='a.u.',
res=64,
size=1,
cbar_fmt='%3.1f',
name_format='CSP%01d',
show=True,
show_names=False,
title=None,
mask=None,
mask_params=None,
outlines='head',
contours=6,
image_interp='bilinear',
average=None,
head_pos=None,
axes=None):
if components is None:
components = np.arange(self.n_compo)
filters = self.filters_[self.fbands.index(fband)]
# set sampling frequency to have 1 component per time point
info = cp.deepcopy(info)
info['sfreq'] = 1.
filters = EvokedArray(filters, info, tmin=0)
return filters.plot_topomap(times=components,
ch_type=ch_type,
layout=layout,
vmin=vmin,
vmax=vmax,
cmap=cmap,
colorbar=colorbar,
res=res,
cbar_fmt=cbar_fmt,
sensors=sensors,
scalings=scalings,
units=units,
time_unit='s',
time_format=name_format,
size=size,
show_names=show_names,
title=title,
mask_params=mask_params,
mask=mask,
outlines=outlines,
contours=contours,
image_interp=image_interp,
show=show,
average=average,
head_pos=head_pos,
axes=axes)
plt.title('single trial surrogates')
plt.imshow(X_transform[y.argsort()], origin='lower', aspect='auto',
extent=[epochs.times[0], epochs.times[-1], 1, len(X_transform)],
cmap='RdBu_r')
plt.xlabel('Time (ms)')
plt.ylabel('Trials (reordered by condition)')
# Plot average response
plt.figure()
plt.title('Average EMS signal')
mappings = [(key, value) for key, value in event_ids.items()]
for key, value in mappings:
ems_ave = X_transform[y == value]
plt.plot(epochs.times, ems_ave.mean(0), label=key)
plt.xlabel('Time (ms)')
plt.ylabel('a.u.')
plt.legend(loc='best')
plt.show()
# Visualize spatial filters across time
evoked = EvokedArray(filters, epochs.info, tmin=epochs.tmin)
evoked.plot_topomap(time_unit='s', scalings=1)
#############################################################################
# Note that a similar transformation can be applied with `compute_ems`
# However, this function replicates Schurger et al's original paper, and thus
# applies the normalization outside a leave-one-out cross-validation, which we
# recommend not to do.
epochs.equalize_event_counts(event_ids)
X_transform, filters, classes = compute_ems(epochs)
t_stL = np.c_[t_th_stL,t_a_stL,t_b1_stL, t_b2_stL]
t_sinR = np.c_[t_th_sinR,t_a_sinR,t_b1_sinR, t_b2_sinR]
t_sinL = np.c_[t_th_sinL,t_a_sinL,t_b1_sinL, t_b2_sinL]
t_rest = np.c_[t_th_rest,t_a_rest,t_b1_rest, t_b2_rest]
for p in range(4):
_, p_corr = fdrcorrection(p_stR[:,p])
p_stR[:,p] = p_corr
_, p_corr = fdrcorrection(p_stL[:,p])
p_stL[:,p] = p_corr
_, p_corr = fdrcorrection(p_sinR[:,p])
p_sinR[:,p] = p_corr
_, p_corr = fdrcorrection(p_sinL[:,p])
p_sinL[:,p] = p_corr
_, p_corr = fdrcorrection(p_rest[:,p])
p_rest[:,p] = p_corr
t_stR = EvokedArray(t_stR, info, tmin=0)
t_stL = EvokedArray(t_stL, info, tmin=0)
t_sinR = EvokedArray(t_sinR, info, tmin=0)
t_sinL = EvokedArray(t_sinL, info, tmin=0)
t_rest = EvokedArray(t_rest, info, tmin=0)
mask_stR = p_stR <= 0.05
mask_stL = p_stL <=0.05
mask_sinR = p_sinR <= 0.05
mask_sinL = p_sinL <=0.05
mask_rest = p_rest <= 0.05
fig1 = t_stR.plot_topomap(ch_type='eeg', scalings=1,
time_format=' ', vmin=-4.5, vmax=4.5,
units='t_values', mask=mask_stR,
size=3,
time_unit='s', title = None, nrows = 4, res = 1000)
def _get_matrix_from_inverse_operator(inverse_operator,
forward,
method='dSPM',
lambda2=1. / 9.):
"""Get inverse matrix from an inverse operator.
Currently works only for fixed/loose orientation constraints
For loose orientation constraint, the CTFs are computed for the normal
component (pick_ori='normal').
Parameters
----------
inverse_operator : instance of InverseOperator
The inverse operator.
forward : instance of Forward
The forward operator.
method : 'MNE' | 'dSPM' | 'sLORETA'
Inverse methods (for apply_inverse).
lambda2 : float
The regularization parameter (for apply_inverse).
Returns
-------
invmat : array, shape (n_dipoles, n_channels)
Inverse matrix associated with inverse operator and specified
parameters.
"""
# make sure forward and inverse operators match with respect to
# surface orientation
_convert_forward_match_inv(forward, inverse_operator)
info_inv = _prepare_info(inverse_operator)
# only use channels that are good for inverse operator and forward sol
ch_names_inv = info_inv['ch_names']
n_chs_inv = len(ch_names_inv)
bads_inv = inverse_operator['info']['bads']
# indices of bad channels
ch_idx_bads = [ch_names_inv.index(ch) for ch in bads_inv]
# create identity matrix as input for inverse operator
# set elements to zero for non-selected channels
id_mat = np.eye(n_chs_inv)
# convert identity matrix to evoked data type (pretending it's an epoch)
ev_id = EvokedArray(id_mat, info=info_inv, tmin=0.)
# apply inverse operator to identity matrix in order to get inverse matrix
# free orientation constraint not possible because apply_inverse would
# combine components
# check if inverse operator uses fixed source orientations
is_fixed_inv = _check_fixed_ori(inverse_operator)
# choose pick_ori according to inverse operator
if is_fixed_inv:
pick_ori = None
else:
pick_ori = 'vector'
# columns for bad channels will be zero
invmat_op = apply_inverse(ev_id,
inverse_operator,
lambda2=lambda2,
method=method,
pick_ori=pick_ori)
# turn source estimate into numpy array
invmat = invmat_op.data
# remove columns for bad channels
# take into account it may be 3D array
invmat = np.delete(invmat, ch_idx_bads, axis=invmat.ndim - 1)
# if 3D array, i.e. multiple values per location (fixed and loose),
# reshape into 2D array
if invmat.ndim == 3:
v0o1 = invmat[0, 1].copy()
v3o2 = invmat[3, 2].copy()
shape = invmat.shape
invmat = invmat.reshape(shape[0] * shape[1], shape[2])
# make sure that reshaping worked
assert np.array_equal(v0o1, invmat[1])
assert np.array_equal(v3o2, invmat[11])
logger.info("Dimension of Inverse Matrix: %s" % str(invmat.shape))
return invmat
# fit the classifier on MEG data
X = scaler.fit_transform(meg_data)
model.fit(X, labels)
# Extract and plot spatial filters and spatial patterns
for name, coef in (('patterns', model.patterns_), ('filters', model.filters_)):
# We fitted the linear model onto Z-scored data. To make the filters
# interpretable, we must reverse this normalization step
coef = scaler.inverse_transform([coef])[0]
# The data was vectorized to fit a single model across all time points and
# all channels. We thus reshape it:
coef = coef.reshape(len(meg_epochs.ch_names), -1)
# Plot
evoked = EvokedArray(coef, meg_epochs.info, tmin=epochs.tmin)
evoked.plot_topomap(title='MEG %s' % name, time_unit='s')
###############################################################################
# Let's do the same on EEG data using a scikit-learn pipeline
X = epochs.pick_types(meg=False, eeg=True)
y = epochs.events[:, 2]
# Define a unique pipeline to sequentially:
clf = make_pipeline(
Vectorizer(), # 1) vectorize across time and channels
StandardScaler(), # 2) normalize features across trials
LinearModel(
LogisticRegression(solver='lbfgs'))) # 3) fits a logistic regression
clf.fit(X, y)
def test_montage():
"""Test making montages"""
tempdir = _TempDir()
# no pep8
input_str = ["""FidNz 0.00000 10.56381 -2.05108
FidT9 -7.82694 0.45386 -3.76056
FidT10 7.82694 0.45386 -3.76056""",
"""// MatLab Sphere coordinates [degrees] Cartesian coordinates
// Label Theta Phi Radius X Y Z off sphere surface
E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011
E2 44.600 -0.880 1.000 0.7119 0.7021 -0.0154 0.00000000000000000
E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000""", # noqa
"""# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 68
Positions
-86.0761 -19.9897 -47.9860
85.7939 -20.0093 -48.0310
0.0083 86.8110 -39.9830
Labels
LPA
RPA
Nz
""",
"""Site Theta Phi
Fp1 -92 -72
Fp2 92 72
F3 -60 -51
""",
"""346
EEG F3 -62.027 -50.053 85
EEG Fz 45.608 90 85
EEG F4 62.01 50.103 85
""",
"""
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
"""]
kinds = ['test.sfp', 'test.csd', 'test.elc', 'test.txt', 'test.elp',
'test.hpts']
for kind, text in zip(kinds, input_str):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
assert_equal(len(montage.ch_names), 3)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (3, 3))
assert_equal(montage.kind, op.splitext(kind)[0])
if kind.endswith('csd'):
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
pos2 = np.c_[table['x'], table['y'], table['z']]
assert_array_almost_equal(pos2, montage.pos, 4)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
"""
kind = 'test_fid.hpts'
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, 'test_fid.hpts'), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index('2')
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index('1')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index('3')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, 'test_fid.hpts')
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(
montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(
data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in the montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
# no pep8
input_str = [
'FidNz 0.00000 10.56381 -2.05108\nFidT9 -7.82694 0.45386 -3.76056\n'
'very_very_very_long_name 7.82694 0.45386 -3.76056',
'// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa: E501
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa: E501
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa: E501
'E2 44.600 -0.880 1.000 0.7119 0.7021 -0.0154 0.00000000000000000\n' # noqa: E501
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000', # noqa: E501
'# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n'
'NumberPositions= 68\nPositions\n-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n0.0083 86.8110 -39.9830\n'
'Labels\nLPA\nRPA\nNz\n',
'# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n'
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'Labels\nLPA\nRPA\nNz\n',
'Site Theta Phi\nFp1 -92 -72\nFp2 92 72\n'
'very_very_very_long_name -60 -51\n',
'346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n',
'eeg Fp1 -95.0 -31.0 -3.0\neeg AF7 -81 -59 -3\neeg AF3 -87 -41 28\n'
]
kinds = ['test.sfp', 'test.csd', 'test_mm.elc', 'test_m.elc', 'test.txt',
'test.elp', 'test.hpts']
for kind, text in zip(kinds, input_str):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if ".sfp" in kind or ".txt" in kind:
assert_true('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 3)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (3, 3))
assert_equal(montage.kind, op.splitext(kind)[0])
if kind.endswith('csd'):
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
pos2 = np.c_[table['x'], table['y'], table['z']]
assert_array_almost_equal(pos2, montage.pos, 4)
if kind.endswith('elc'):
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1,
montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
"""
kind = 'test_fid.hpts'
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, 'test_fid.hpts'), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index('2')
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index('1')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index('3')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, 'test_fid.hpts')
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(
montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(
data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in the montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
def _compare_inverses_approx(inv_1,
inv_2,
evoked,
rtol,
atol,
depth_atol=1e-6,
ctol=0.999999,
check_nn=True,
check_K=True):
"""Compare inverses."""
# depth prior
if inv_1['depth_prior'] is not None:
assert_allclose(inv_1['depth_prior']['data'],
inv_2['depth_prior']['data'],
atol=depth_atol)
else:
assert (inv_2['depth_prior'] is None)
# orient prior
if inv_1['orient_prior'] is not None:
assert_allclose(inv_1['orient_prior']['data'],
inv_2['orient_prior']['data'],
atol=1e-7)
else:
assert (inv_2['orient_prior'] is None)
# source cov
assert_allclose(inv_1['source_cov']['data'],
inv_2['source_cov']['data'],
atol=1e-7)
for key in ('units', 'eigen_leads_weighted', 'nsource', 'coord_frame'):
assert_equal(inv_1[key], inv_2[key], err_msg=key)
assert_equal(inv_1['eigen_leads']['ncol'], inv_2['eigen_leads']['ncol'])
K_1 = np.dot(inv_1['eigen_leads']['data'] * inv_1['sing'].astype(float),
inv_1['eigen_fields']['data'])
K_2 = np.dot(inv_2['eigen_leads']['data'] * inv_2['sing'].astype(float),
inv_2['eigen_fields']['data'])
# for free + surf ori, we only care about the ::2
# (the other two dimensions have arbitrary direction)
if inv_1['nsource'] * 3 == inv_1['source_nn'].shape[0]:
# Technically this undersamples the free-orientation, non-surf-ori
# inverse, but it's probably okay
sl = slice(2, None, 3)
else:
sl = slice(None)
if check_nn:
assert_allclose(inv_1['source_nn'][sl],
inv_2['source_nn'][sl],
atol=1e-4)
if check_K:
assert_allclose(np.abs(K_1[sl]), np.abs(K_2[sl]), rtol=rtol, atol=atol)
# Now let's do some practical tests, too
evoked = EvokedArray(np.eye(len(evoked.ch_names)), evoked.info)
for method in ('MNE', 'dSPM'):
stc_1 = apply_inverse(evoked, inv_1, lambda2, method)
stc_2 = apply_inverse(evoked, inv_2, lambda2, method)
assert_equal(stc_1.subject, stc_2.subject)
assert_equal(stc_1.times, stc_2.times)
stc_1 = stc_1.data
stc_2 = stc_2.data
norms = np.max(stc_1, axis=-1, keepdims=True)
stc_1 /= norms
stc_2 /= norms
corr = np.corrcoef(stc_1.ravel(), stc_2.ravel())[0, 1]
assert corr > ctol
assert_allclose(stc_1,
stc_2,
rtol=rtol,
atol=atol,
err_msg='%s: %s' % (method, corr))
def test_localization_bias():
"""Test inverse localization bias for minimum-norm solvers."""
# Identity input
evoked = _get_evoked()
evoked.pick_types(meg=True, eeg=True, exclude=())
evoked = EvokedArray(np.eye(len(evoked.data)), evoked.info)
noise_cov = read_cov(fname_cov)
# restrict to limited set of verts (small src here) and one hemi for speed
fwd_orig = read_forward_solution(fname_fwd)
vertices = [fwd_orig['src'][0]['vertno'].copy(), []]
stc = SourceEstimate(np.zeros((sum(len(v) for v in vertices), 1)),
vertices, 0., 1.)
fwd_orig = restrict_forward_to_stc(fwd_orig, stc)
#
# Fixed orientation (not very different)
#
fwd = fwd_orig.copy()
inv_fixed = make_inverse_operator(evoked.info,
fwd,
noise_cov,
loose=0.,
depth=0.8)
fwd = convert_forward_solution(fwd, force_fixed=True, surf_ori=True)
fwd = fwd['sol']['data']
want = np.arange(fwd.shape[1])
for method, lower, upper in (('MNE', 83, 87), ('dSPM', 96, 98),
('sLORETA', 100, 100), ('eLORETA', 100, 100)):
inv_op = apply_inverse(evoked, inv_fixed, lambda2, method).data
loc = np.abs(np.dot(inv_op, fwd))
# Compute the percentage of sources for which there is no localization
# bias:
perc = (want == np.argmax(loc, axis=0)).mean() * 100
assert lower <= perc <= upper, method
#
# Loose orientation
#
fwd = fwd_orig.copy()
inv_free = make_inverse_operator(evoked.info,
fwd,
noise_cov,
loose=0.2,
depth=0.8)
fwd = fwd['sol']['data']
want = np.arange(fwd.shape[1]) // 3
for method, lower, upper in (('MNE', 25, 35), ('dSPM', 25, 35),
('sLORETA', 35, 40), ('eLORETA', 40, 45)):
inv_op = apply_inverse(evoked,
inv_free,
lambda2,
method,
pick_ori='vector').data
loc = np.linalg.norm(np.einsum('vos,sx->vxo', inv_op, fwd), axis=-1)
# Compute the percentage of sources for which there is no localization
# bias:
perc = (want == np.argmax(loc, axis=0)).mean() * 100
assert lower <= perc <= upper, method
#
# Free orientation
#
fwd = fwd_orig.copy()
inv_free = make_inverse_operator(evoked.info,
fwd,
noise_cov,
loose=1.,
depth=0.8)
fwd = fwd['sol']['data']
want = np.arange(fwd.shape[1]) // 3
force_kwargs = dict(method_params=dict(force_equal=True))
for method, lower, upper, kwargs in (
('MNE', 45, 55, {}),
('dSPM', 40, 45, {}),
('sLORETA', 90, 95, {}),
('eLORETA', 90, 95, force_kwargs),
('eLORETA', 100, 100, {}),
):
inv_op = apply_inverse(evoked,
inv_free,
lambda2,
method,
pick_ori='vector',
**kwargs).data
loc = np.linalg.norm(np.einsum('vos,sx->vxo', inv_op, fwd), axis=-1)
# Compute the percentage of sources for which there is no localization
# bias:
perc = (want == np.argmax(loc, axis=0)).mean() * 100
assert lower <= perc <= upper, method
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
inputs = dict(
sfp='FidNz 0 9.071585155 -2.359754454\n'
'FidT9 -6.711765 0.040402876 -3.251600355\n'
'very_very_very_long_name -5.831241498 -4.494821698 4.955347697\n'
'Cz 0 0 8.899186843',
csd='// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa: E501
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa: E501
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa: E501
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n' # noqa: E501
'E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000\n' # noqa: E501
'E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022', # noqa: E501
mm_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n' # noqa:E501
'NumberPositions= 68\n'
'Positions\n'
'-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n'
'0.0083 86.8110 -39.9830\n'
'-86.0761 -24.9897 -67.9860\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
m_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n' # noqa:E501
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'.08 -.02 -.04\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
txt='Site Theta Phi\n'
'Fp1 -92 -72\n'
'Fp2 92 72\n'
'very_very_very_long_name -92 72\n'
'O2 92 -90\n',
elp='346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n'
'EEG\t FCz\t 68.01\t 58.103\t 85\n',
hpts='eeg Fp1 -95.0 -3. -3.\n'
'eeg AF7 -1 -1 -3\n'
'eeg A3 -2 -2 2\n'
'eeg A 0 0 0',
bvef='<?xml version="1.0" encoding="UTF-8" standalone="yes"?>\n'
'<!-- Generated by EasyCap Configurator 19.05.2014 -->\n'
'<Electrodes defaults="false">\n'
' <Electrode>\n'
' <Name>Fp1</Name>\n'
' <Theta>-90</Theta>\n'
' <Phi>-72</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>1</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>Fz</Name>\n'
' <Theta>45</Theta>\n'
' <Phi>90</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>2</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>F3</Name>\n'
' <Theta>-60</Theta>\n'
' <Phi>-51</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>3</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>F7</Name>\n'
' <Theta>-90</Theta>\n'
' <Phi>-36</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>4</Number>\n'
' </Electrode>\n'
'</Electrodes>',
)
# Get actual positions and save them for checking
# csd comes from the string above, all others come from commit 2fa35d4
poss = dict(
sfp=[[0.0, 9.07159, -2.35975], [-6.71176, 0.0404, -3.2516],
[-5.83124, -4.49482, 4.95535], [0.0, 0.0, 8.89919]],
mm_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.08681, -0.03998], [-0.08608, -0.02499, -0.06799]],
m_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.00868, -0.03998], [0.08, -0.02, -0.04]],
txt=[[-26.25044, 80.79056, -2.96646], [26.25044, 80.79056, -2.96646],
[-26.25044, -80.79056, -2.96646], [0.0, -84.94822, -2.96646]],
elp=[[-48.20043, 57.55106, 39.86971], [0.0, 60.73848, 59.4629],
[48.1426, 57.58403, 39.89198], [41.64599, 66.91489, 31.8278]],
hpts=[[-95, -3, -3], [-1, -1., -3.], [-2, -2, 2.], [0, 0, 0]],
bvef=[[-26.266444, 80.839803, 5.204748e-15],
[3.680313e-15, 60.104076, 60.104076],
[-46.325632, 57.207392, 42.500000],
[-68.766444, 49.961746, 5.204748e-15]],
)
for key, text in inputs.items():
kind = key.split('_')[-1]
fname = op.join(tempdir, 'test.' + kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if kind in ('sfp', 'txt'):
assert ('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, 'test')
if kind == 'csd':
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
poss['csd'] = np.c_[table['x'], table['y'], table['z']]
if kind == 'elc':
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1,
montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
assert_array_almost_equal(poss[key], montage.pos, 4, err_msg=key)
# Test reading in different letter case.
ch_names = ["F3", "FZ", "F4", "FC3", "FCz", "FC4", "C3", "CZ", "C4", "CP3",
"CPZ", "CP4", "P3", "PZ", "P4", "O1", "OZ", "O2"]
montage = read_montage('standard_1020', ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_strs = ["""
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
""", """
Fp1 -95.0 -31.0 -3.0
AF7 -81 -59 -3
AF3 -87 -41 28
FidNz -91 0 -42
FidT9 0 -91 -42
FidT10 0 91 -42
"""]
# sfp files seem to have Nz, T9, and T10 as fiducials:
# https://github.com/mne-tools/mne-python/pull/4482#issuecomment-321980611
kinds = ['test_fid.hpts', 'test_fid.sfp']
for kind, input_str in zip(kinds, input_strs):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, kind), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
assert_array_equal(montage.nasion[[0, 2]], [0, 0])
assert_array_equal(montage.lpa[[1, 2]], [0, 0])
assert_array_equal(montage.rpa[[1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, kind)
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3,
['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(
montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(
data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
# test return type as well as set montage
assert (isinstance(evoked.set_montage(montage), type(evoked)))
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in montage
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage)
# Channel names can be treated case insensitive
info = create_info(['FP1', 'af7', 'AF3'], 1e3, ['eeg'] * 3)
_set_montage(info, montage)
# Unless there is a collision in names
info = create_info(['FP1', 'Fp1', 'AF3'], 1e3, ['eeg'] * 3)
assert (info['dig'] is None)
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage)
assert len(info['dig']) == 5 # 2 EEG w/pos, 3 fiducials
montage.ch_names = ['FP1', 'Fp1', 'AF3']
info = create_info(['fp1', 'AF3'], 1e3, ['eeg', 'eeg'])
assert (info['dig'] is None)
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage, set_dig=False)
assert (info['dig'] is None)
# test get_pos2d method
montage = read_montage("standard_1020")
c3 = montage.get_pos2d()[montage.ch_names.index("C3")]
c4 = montage.get_pos2d()[montage.ch_names.index("C4")]
fz = montage.get_pos2d()[montage.ch_names.index("Fz")]
oz = montage.get_pos2d()[montage.ch_names.index("Oz")]
f1 = montage.get_pos2d()[montage.ch_names.index("F1")]
assert (c3[0] < 0) # left hemisphere
assert (c4[0] > 0) # right hemisphere
assert (fz[1] > 0) # frontal
assert (oz[1] < 0) # occipital
assert_allclose(fz[0], 0, atol=1e-2) # midline
assert_allclose(oz[0], 0, atol=1e-2) # midline
assert (f1[0] < 0 and f1[1] > 0) # left frontal
# test get_builtin_montages function
montages = get_builtin_montages()
assert (len(montages) > 0) # MNE should always ship with montages
assert ("standard_1020" in montages) # 10/20 montage
assert ("standard_1005" in montages) # 10/05 montage
def test_ica_additional(method):
"""Test additional ICA functionality."""
_skip_check_picard(method)
tempdir = _TempDir()
stop2 = 500
raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
raw.del_proj() # avoid warnings
raw.set_annotations(Annotations([0.5], [0.5], ['BAD']))
# XXX This breaks the tests :(
# raw.info['bads'] = [raw.ch_names[1]]
test_cov = read_cov(test_cov_name)
events = read_events(event_name)
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')[1::2]
epochs = Epochs(raw,
events,
None,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True,
proj=False)
epochs.decimate(3, verbose='error')
assert len(epochs) == 4
# test if n_components=None works
ica = ICA(n_components=None,
max_pca_components=None,
n_pca_components=None,
random_state=0,
method=method,
max_iter=1)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(epochs)
# for testing eog functionality
picks2 = np.concatenate([picks, pick_types(raw.info, False, eog=True)])
epochs_eog = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks2,
baseline=(None, 0),
preload=True)
del picks2
test_cov2 = test_cov.copy()
ica = ICA(noise_cov=test_cov2,
n_components=3,
max_pca_components=4,
n_pca_components=4,
method=method)
assert (ica.info is None)
with pytest.warns(RuntimeWarning, match='normalize_proj'):
ica.fit(raw, picks[:5])
assert (isinstance(ica.info, Info))
assert (ica.n_components_ < 5)
ica = ICA(n_components=3,
max_pca_components=4,
method=method,
n_pca_components=4,
random_state=0)
pytest.raises(RuntimeError, ica.save, '')
ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2)
# check passing a ch_name to find_bads_ecg
with pytest.warns(RuntimeWarning, match='longer'):
_, scores_1 = ica.find_bads_ecg(raw)
_, scores_2 = ica.find_bads_ecg(raw, raw.ch_names[1])
assert scores_1[0] != scores_2[0]
# test corrmap
ica2 = ica.copy()
ica3 = ica.copy()
corrmap([ica, ica2], (0, 0),
threshold='auto',
label='blinks',
plot=True,
ch_type="mag")
corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False)
assert (ica.labels_["blinks"] == ica2.labels_["blinks"])
assert (0 in ica.labels_["blinks"])
# test retrieval of component maps as arrays
components = ica.get_components()
template = components[:, 0]
EvokedArray(components, ica.info, tmin=0.).plot_topomap([0], time_unit='s')
corrmap([ica, ica3],
template,
threshold='auto',
label='blinks',
plot=True,
ch_type="mag")
assert (ica2.labels_["blinks"] == ica3.labels_["blinks"])
plt.close('all')
# test warnings on bad filenames
ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz')
with pytest.warns(RuntimeWarning, match='-ica.fif'):
ica.save(ica_badname)
with pytest.warns(RuntimeWarning, match='-ica.fif'):
read_ica(ica_badname)
# test decim
ica = ICA(n_components=3,
max_pca_components=4,
n_pca_components=4,
method=method,
max_iter=1)
raw_ = raw.copy()
for _ in range(3):
raw_.append(raw_)
n_samples = raw_._data.shape[1]
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw, picks=picks[:5], decim=3)
assert raw_._data.shape[1] == n_samples
# test expl var
ica = ICA(n_components=1.0,
max_pca_components=4,
n_pca_components=4,
method=method,
max_iter=1)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw, picks=None, decim=3)
assert (ica.n_components_ == 4)
ica_var = _ica_explained_variance(ica, raw, normalize=True)
assert (np.all(ica_var[:-1] >= ica_var[1:]))
# test ica sorting
ica.exclude = [0]
ica.labels_ = dict(blink=[0], think=[1])
ica_sorted = _sort_components(ica, [3, 2, 1, 0], copy=True)
assert_equal(ica_sorted.exclude, [3])
assert_equal(ica_sorted.labels_, dict(blink=[3], think=[2]))
# epochs extraction from raw fit
pytest.raises(RuntimeError, ica.get_sources, epochs)
# test reading and writing
test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif')
for cov in (None, test_cov):
ica = ICA(noise_cov=cov,
n_components=2,
max_pca_components=4,
n_pca_components=4,
method=method,
max_iter=1)
with pytest.warns(None): # ICA does not converge
ica.fit(raw, picks=picks[:10], start=start, stop=stop2)
sources = ica.get_sources(epochs).get_data()
assert (ica.mixing_matrix_.shape == (2, 2))
assert (ica.unmixing_matrix_.shape == (2, 2))
assert (ica.pca_components_.shape == (4, 10))
assert (sources.shape[1] == ica.n_components_)
for exclude in [[], [0], np.array([1, 2, 3])]:
ica.exclude = exclude
ica.labels_ = {'foo': [0]}
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert (list(ica.exclude) == ica_read.exclude)
assert_equal(ica.labels_, ica_read.labels_)
ica.apply(raw)
ica.exclude = []
ica.apply(raw, exclude=[1])
assert (ica.exclude == [])
ica.exclude = [0, 1]
ica.apply(raw, exclude=[1])
assert (ica.exclude == [0, 1])
ica_raw = ica.get_sources(raw)
assert (ica.exclude == [
ica_raw.ch_names.index(e) for e in ica_raw.info['bads']
])
# test filtering
d1 = ica_raw._data[0].copy()
ica_raw.filter(4, 20, fir_design='firwin2')
assert_equal(ica_raw.info['lowpass'], 20.)
assert_equal(ica_raw.info['highpass'], 4.)
assert ((d1 != ica_raw._data[0]).any())
d1 = ica_raw._data[0].copy()
ica_raw.notch_filter([10], trans_bandwidth=10, fir_design='firwin')
assert ((d1 != ica_raw._data[0]).any())
ica.n_pca_components = 2
ica.method = 'fake'
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert (ica.n_pca_components == ica_read.n_pca_components)
assert_equal(ica.method, ica_read.method)
assert_equal(ica.labels_, ica_read.labels_)
# check type consistency
attrs = ('mixing_matrix_ unmixing_matrix_ pca_components_ '
'pca_explained_variance_ pre_whitener_')
def f(x, y):
return getattr(x, y).dtype
for attr in attrs.split():
assert_equal(f(ica_read, attr), f(ica, attr))
ica.n_pca_components = 4
ica_read.n_pca_components = 4
ica.exclude = []
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
for attr in [
'mixing_matrix_', 'unmixing_matrix_', 'pca_components_',
'pca_mean_', 'pca_explained_variance_', 'pre_whitener_'
]:
assert_array_almost_equal(getattr(ica, attr),
getattr(ica_read, attr))
assert (ica.ch_names == ica_read.ch_names)
assert (isinstance(ica_read.info, Info))
sources = ica.get_sources(raw)[:, :][0]
sources2 = ica_read.get_sources(raw)[:, :][0]
assert_array_almost_equal(sources, sources2)
_raw1 = ica.apply(raw, exclude=[1])
_raw2 = ica_read.apply(raw, exclude=[1])
assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])
os.remove(test_ica_fname)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(raw,
target='EOG 061',
score_func=func,
start=0,
stop=10)
assert (ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(raw, score_func=stats.skew)
# check exception handling
pytest.raises(ValueError, ica.score_sources, raw, target=np.arange(1))
params = []
params += [(None, -1, slice(2), [0, 1])] # variance, kurtosis params
params += [(None, 'MEG 1531')] # ECG / EOG channel params
for idx, ch_name in product(*params):
ica.detect_artifacts(raw,
start_find=0,
stop_find=50,
ecg_ch=ch_name,
eog_ch=ch_name,
skew_criterion=idx,
var_criterion=idx,
kurt_criterion=idx)
evoked = epochs.average()
evoked_data = evoked.data.copy()
raw_data = raw[:][0].copy()
epochs_data = epochs.get_data().copy()
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_ecg(raw, method='ctps')
assert_equal(len(scores), ica.n_components_)
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_ecg(raw, method='correlation')
assert_equal(len(scores), ica.n_components_)
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(epochs, method='ctps')
assert_equal(len(scores), ica.n_components_)
pytest.raises(ValueError,
ica.find_bads_ecg,
epochs.average(),
method='ctps')
pytest.raises(ValueError, ica.find_bads_ecg, raw, method='crazy-coupling')
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202
with pytest.warns(RuntimeWarning, match='longer'):
idx, scores = ica.find_bads_eog(raw)
assert (isinstance(scores, list))
assert_equal(len(scores[0]), ica.n_components_)
idx, scores = ica.find_bads_eog(evoked, ch_name='MEG 1441')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(evoked, method='correlation')
assert_equal(len(scores), ica.n_components_)
assert_array_equal(raw_data, raw[:][0])
assert_array_equal(epochs_data, epochs.get_data())
assert_array_equal(evoked_data, evoked.data)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(epochs_eog,
target='EOG 061',
score_func=func)
assert (ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(epochs, score_func=stats.skew)
# check exception handling
pytest.raises(ValueError, ica.score_sources, epochs, target=np.arange(1))
# ecg functionality
ecg_scores = ica.score_sources(raw,
target='MEG 1531',
score_func='pearsonr')
with pytest.warns(RuntimeWarning, match='longer'):
ecg_events = ica_find_ecg_events(raw,
sources[np.abs(ecg_scores).argmax()])
assert (ecg_events.ndim == 2)
# eog functionality
eog_scores = ica.score_sources(raw,
target='EOG 061',
score_func='pearsonr')
with pytest.warns(RuntimeWarning, match='longer'):
eog_events = ica_find_eog_events(raw,
sources[np.abs(eog_scores).argmax()])
assert (eog_events.ndim == 2)
# Test ica fiff export
ica_raw = ica.get_sources(raw, start=0, stop=100)
assert (ica_raw.last_samp - ica_raw.first_samp == 100)
assert_equal(len(ica_raw._filenames), 1) # API consistency
ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch]
assert (ica.n_components_ == len(ica_chans))
test_ica_fname = op.join(op.abspath(op.curdir), 'test-ica_raw.fif')
ica.n_components = np.int32(ica.n_components)
ica_raw.save(test_ica_fname, overwrite=True)
ica_raw2 = read_raw_fif(test_ica_fname, preload=True)
assert_allclose(ica_raw._data, ica_raw2._data, rtol=1e-5, atol=1e-4)
ica_raw2.close()
os.remove(test_ica_fname)
# Test ica epochs export
ica_epochs = ica.get_sources(epochs)
assert (ica_epochs.events.shape == epochs.events.shape)
ica_chans = [ch for ch in ica_epochs.ch_names if 'ICA' in ch]
assert (ica.n_components_ == len(ica_chans))
assert (ica.n_components_ == ica_epochs.get_data().shape[1])
assert (ica_epochs._raw is None)
assert (ica_epochs.preload is True)
# test float n pca components
ica.pca_explained_variance_ = np.array([0.2] * 5)
ica.n_components_ = 0
for ncomps, expected in [[0.3, 1], [0.9, 4], [1, 1]]:
ncomps_ = ica._check_n_pca_components(ncomps)
assert (ncomps_ == expected)
ica = ICA(method=method)
with pytest.warns(None): # sometimes does not converge
ica.fit(raw, picks=picks[:5])
with pytest.warns(RuntimeWarning, match='longer'):
ica.find_bads_ecg(raw)
ica.find_bads_eog(epochs, ch_name='MEG 0121')
assert_array_equal(raw_data, raw[:][0])
raw.drop_channels(['MEG 0122'])
pytest.raises(RuntimeError, ica.find_bads_eog, raw)
with pytest.warns(RuntimeWarning, match='longer'):
pytest.raises(RuntimeError, ica.find_bads_ecg, raw)
plt.title('single trial surrogates')
plt.imshow(X_transform[y.argsort()], origin='lower', aspect='auto',
extent=[epochs.times[0], epochs.times[-1], 1, len(X_transform)],
cmap='RdBu_r')
plt.xlabel('Time (ms)')
plt.ylabel('Trials (reordered by condition)')
# Plot average response
plt.figure()
plt.title('Average EMS signal')
mappings = [(key, value) for key, value in event_ids.items()]
for key, value in mappings:
ems_ave = X_transform[y == value]
plt.plot(epochs.times, ems_ave.mean(0), label=key)
plt.xlabel('Time (ms)')
plt.ylabel('a.u.')
plt.legend(loc='best')
plt.show()
# Visualize spatial filters across time
evoked = EvokedArray(filters, epochs.info, tmin=epochs.tmin)
evoked.plot_topomap()
#############################################################################
# Note that a similar transformation can be applied with `compute_ems`
# However, this function replicates Schurger et al's original paper, and thus
# applies the normalization outside a leave-one-out cross-validation, which we
# recommend not to do.
epochs.equalize_event_counts(event_ids)
X_transform, filters, classes = compute_ems(epochs)
def test_montage():
"""Test making montages"""
tempdir = _TempDir()
# no pep8
input_str = [
"""FidNz 0.00000 10.56381 -2.05108
FidT9 -7.82694 0.45386 -3.76056
FidT10 7.82694 0.45386 -3.76056""",
"""// MatLab Sphere coordinates [degrees] Cartesian coordinates
// Label Theta Phi Radius X Y Z off sphere surface
E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011
E2 44.600 -0.880 1.000 0.7119 0.7021 -0.0154 0.00000000000000000
E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000""", # noqa
"""# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 68
Positions
-86.0761 -19.9897 -47.9860
85.7939 -20.0093 -48.0310
0.0083 86.8110 -39.9830
Labels
LPA
RPA
Nz
""",
"""Site Theta Phi
Fp1 -92 -72
Fp2 92 72
F3 -60 -51
""",
"""346
EEG F3 -62.027 -50.053 85
EEG Fz 45.608 90 85
EEG F4 62.01 50.103 85
""",
"""
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
"""
]
kinds = [
'test.sfp', 'test.csd', 'test.elc', 'test.txt', 'test.elp', 'test.hpts'
]
for kind, text in zip(kinds, input_str):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
assert_equal(len(montage.ch_names), 3)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (3, 3))
assert_equal(montage.kind, op.splitext(kind)[0])
if kind.endswith('csd'):
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
pos2 = np.c_[table['x'], table['y'], table['z']]
assert_array_almost_equal(pos2, montage.pos, 4)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
"""
kind = 'test_fid.hpts'
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, 'test_fid.hpts'), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index('2')
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index('1')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index('3')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, 'test_fid.hpts')
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(data=np.zeros((len(montage.ch_names), 1)),
info=info,
tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in the montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
