def test_saving_picked():
"""Test saving picked CTF instances."""
temp_dir = _TempDir()
out_fname = op.join(temp_dir, 'test_py_raw.fif')
raw = read_raw_ctf(op.join(ctf_dir, ctf_fname_1_trial))
raw.crop(0, 1).load_data()
assert raw.compensation_grade == get_current_comp(raw.info) == 0
assert len(raw.info['comps']) == 5
pick_kwargs = dict(meg=True, ref_meg=False, verbose=True)
for comp_grade in [0, 1]:
raw.apply_gradient_compensation(comp_grade)
with catch_logging() as log:
raw_pick = raw.copy().pick_types(**pick_kwargs)
assert len(raw.info['comps']) == 5
assert len(raw_pick.info['comps']) == 0
log = log.getvalue()
assert 'Removing 5 compensators' in log
raw_pick.save(out_fname, overwrite=True) # should work
raw2 = read_raw_fif(out_fname)
assert (raw_pick.ch_names == raw2.ch_names)
assert_array_equal(raw_pick.times, raw2.times)
assert_allclose(raw2[0:20][0], raw_pick[0:20][0], rtol=1e-6,
atol=1e-20) # atol is very small but > 0
raw2 = read_raw_fif(out_fname, preload=True)
assert (raw_pick.ch_names == raw2.ch_names)
assert_array_equal(raw_pick.times, raw2.times)
assert_allclose(raw2[0:20][0], raw_pick[0:20][0], rtol=1e-6,
atol=1e-20) # atol is very small but > 0
def test_rawctf_clean_names():
"""Test RawCTF _clean_names method."""
# read test data
with pytest.warns(RuntimeWarning, match='ref channel RMSP did not'):
raw = read_raw_ctf(op.join(ctf_dir, ctf_fname_catch))
raw_cleaned = read_raw_ctf(op.join(ctf_dir, ctf_fname_catch),
clean_names=True)
test_channel_names = _clean_names(raw.ch_names)
test_info_comps = copy.deepcopy(raw.info['comps'])
# channel names should not be cleaned by default
assert raw.ch_names != test_channel_names
chs_ch_names = [ch['ch_name'] for ch in raw.info['chs']]
assert chs_ch_names != test_channel_names
for test_comp, comp in zip(test_info_comps, raw.info['comps']):
for key in ('row_names', 'col_names'):
assert not array_equal(_clean_names(test_comp['data'][key]),
comp['data'][key])
# channel names should be cleaned if clean_names=True
assert raw_cleaned.ch_names == test_channel_names
for ch, test_ch_name in zip(raw_cleaned.info['chs'], test_channel_names):
assert ch['ch_name'] == test_ch_name
for test_comp, comp in zip(test_info_comps, raw_cleaned.info['comps']):
for key in ('row_names', 'col_names'):
assert _clean_names(test_comp['data'][key]) == comp['data'][key]
def convert_ds_to_raw_fif(ds_file):
import os
import os.path as op
from nipype.utils.filemanip import split_filename as split_f
from mne.io import read_raw_ctf
subj_path,basename,ext = split_f(ds_file)
print subj_path,basename,ext
raw = read_raw_ctf(ds_file)
#raw_fif_file = os.path.abspath(basename + "_raw.fif")
#raw.save(raw_fif_file)
#return raw_fif_file
raw_fif_file = os.path.join(subj_path,basename + "_raw.fif")
if not op.isfile(raw_fif_file):
raw = read_raw_ctf(ds_file)
raw.save(raw_fif_file)
else:
print '*** RAW FIF file %s exists!!!' % raw_fif_file
return raw_fif_file
def test_cov_ctf():
"""Test basic cov computation on ctf data with/without compensation."""
raw = read_raw_ctf(ctf_fname).crop(0., 2.).load_data()
events = make_fixed_length_events(raw, 99999)
assert len(events) == 2
ch_names = [raw.info['ch_names'][pick]
for pick in pick_types(raw.info, meg=True, eeg=False,
ref_meg=False)]
for comp in [0, 1]:
raw.apply_gradient_compensation(comp)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
with pytest.warns(RuntimeWarning, match='Too few samples'):
noise_cov = compute_covariance(epochs, tmax=0.,
method=['empirical'])
prepare_noise_cov(noise_cov, raw.info, ch_names)
raw.apply_gradient_compensation(0)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
with pytest.warns(RuntimeWarning, match='Too few samples'):
noise_cov = compute_covariance(epochs, tmax=0., method=['empirical'])
raw.apply_gradient_compensation(1)
# TODO This next call in principle should fail.
prepare_noise_cov(noise_cov, raw.info, ch_names)
# make sure comps matrices was not removed from raw
assert raw.info['comps'], 'Comps matrices removed'
def test_check_compensation_consistency():
"""Test check picks compensation."""
raw = read_raw_ctf(ctf_fname, preload=False)
events = make_fixed_length_events(raw, 99999)
picks = pick_types(raw.info, meg=True, exclude=[], ref_meg=True)
pick_ch_names = [raw.info['ch_names'][idx] for idx in picks]
for (comp, expected_result) in zip([0, 1], [False, False]):
raw.apply_gradient_compensation(comp)
ret, missing = _bad_chans_comp(raw.info, pick_ch_names)
assert ret == expected_result
assert len(missing) == 0
Epochs(raw, events, None, -0.2, 0.2, preload=False, picks=picks)
picks = pick_types(raw.info, meg=True, exclude=[], ref_meg=False)
pick_ch_names = [raw.info['ch_names'][idx] for idx in picks]
for (comp, expected_result) in zip([0, 1], [False, True]):
raw.apply_gradient_compensation(comp)
ret, missing = _bad_chans_comp(raw.info, pick_ch_names)
assert ret == expected_result
assert len(missing) == 17
with catch_logging() as log:
Epochs(raw, events, None, -0.2, 0.2, preload=False,
picks=picks, verbose=True)
assert'Removing 5 compensators' in log.getvalue()
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_plot_ref_meg():
"""Test plotting ref_meg."""
import matplotlib.pyplot as plt
raw_ctf = read_raw_ctf(ctf_fname_continuous).crop(0, 1).load_data()
raw_ctf.plot()
plt.close('all')
assert_raises(ValueError, raw_ctf.plot, group_by='selection')
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_check_compensation_consistency():
"""Test check picks compensation."""
raw = read_raw_ctf(ctf_fname, preload=False)
events = make_fixed_length_events(raw, 99999)
picks = pick_types(raw.info, meg=True, exclude=[], ref_meg=True)
pick_ch_names = [raw.info['ch_names'][idx] for idx in picks]
for (comp, expected_result) in zip([0, 1], [False, False]):
raw.apply_gradient_compensation(comp)
ret, missing = _bad_chans_comp(raw.info, pick_ch_names)
assert ret == expected_result
assert len(missing) == 0
Epochs(raw, events, None, -0.2, 0.2, preload=False, picks=picks)
picks = pick_types(raw.info, meg=True, exclude=[], ref_meg=False)
pick_ch_names = [raw.info['ch_names'][idx] for idx in picks]
for (comp, expected_result) in zip([0, 1], [False, True]):
raw.apply_gradient_compensation(comp)
ret, missing = _bad_chans_comp(raw.info, pick_ch_names)
assert ret == expected_result
assert len(missing) == 17
if comp != 0:
with pytest.raises(RuntimeError,
match='Compensation grade 1 has been applied'):
Epochs(raw, events, None, -0.2, 0.2, preload=False,
picks=picks)
else:
Epochs(raw, events, None, -0.2, 0.2, preload=False, picks=picks)
def test_ica_ctf():
"""Test run ICA computation on ctf data with/without compensation."""
method = 'fastica'
raw = read_raw_ctf(ctf_fname, preload=True)
events = make_fixed_length_events(raw, 99999)
for comp in [0, 1]:
raw.apply_gradient_compensation(comp)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
evoked = epochs.average()
# test fit
for inst in [raw, epochs]:
ica = ICA(n_components=2, random_state=0, max_iter=2,
method=method)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(inst)
# test apply and get_sources
for inst in [raw, epochs, evoked]:
ica.apply(inst)
ica.get_sources(inst)
# test mixed compensation case
raw.apply_gradient_compensation(0)
ica = ICA(n_components=2, random_state=0, max_iter=2, method=method)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw)
raw.apply_gradient_compensation(1)
epochs = Epochs(raw, events, None, -0.2, 0.2, preload=True)
evoked = epochs.average()
for inst in [raw, epochs, evoked]:
with pytest.raises(RuntimeError, match='Compensation grade of ICA'):
ica.apply(inst)
with pytest.raises(RuntimeError, match='Compensation grade of ICA'):
ica.get_sources(inst)
def test_ica_eeg():
"""Test ICA on EEG."""
method = 'fastica'
raw_fif = read_raw_fif(fif_fname, preload=True)
with pytest.warns(RuntimeWarning, match='events'):
raw_eeglab = read_raw_eeglab(input_fname=eeglab_fname,
montage=eeglab_montage, preload=True)
for raw in [raw_fif, raw_eeglab]:
events = make_fixed_length_events(raw, 99999, start=0, stop=0.3,
duration=0.1)
picks_meg = pick_types(raw.info, meg=True, eeg=False)[:2]
picks_eeg = pick_types(raw.info, meg=False, eeg=True)[:2]
picks_all = []
picks_all.extend(picks_meg)
picks_all.extend(picks_eeg)
epochs = Epochs(raw, events, None, -0.1, 0.1, preload=True)
evoked = epochs.average()
for picks in [picks_meg, picks_eeg, picks_all]:
if len(picks) == 0:
continue
# test fit
for inst in [raw, epochs]:
ica = ICA(n_components=2, random_state=0, max_iter=2,
method=method)
with pytest.warns(None):
ica.fit(inst, picks=picks)
# test apply and get_sources
for inst in [raw, epochs, evoked]:
ica.apply(inst)
ica.get_sources(inst)
with pytest.warns(RuntimeWarning, match='MISC channel'):
raw = read_raw_ctf(ctf_fname2, preload=True)
events = make_fixed_length_events(raw, 99999, start=0, stop=0.2,
duration=0.1)
picks_meg = pick_types(raw.info, meg=True, eeg=False)[:2]
picks_eeg = pick_types(raw.info, meg=False, eeg=True)[:2]
picks_all = picks_meg + picks_eeg
for comp in [0, 1]:
raw.apply_gradient_compensation(comp)
epochs = Epochs(raw, events, None, -0.1, 0.1, preload=True)
evoked = epochs.average()
for picks in [picks_meg, picks_eeg, picks_all]:
if len(picks) == 0:
continue
# test fit
for inst in [raw, epochs]:
ica = ICA(n_components=2, random_state=0, max_iter=2,
method=method)
with pytest.warns(None):
ica.fit(inst)
# test apply and get_sources
for inst in [raw, epochs, evoked]:
ica.apply(inst)
ica.get_sources(inst)
def test_interpolation_ctf_comp():
"""Test interpolation with compensated CTF data."""
ctf_dir = op.join(testing.data_path(download=False), 'CTF')
raw_fname = op.join(ctf_dir, 'somMDYO-18av.ds')
raw = io.read_raw_ctf(raw_fname, preload=True)
raw.info['bads'] = [raw.ch_names[5], raw.ch_names[-5]]
raw.interpolate_bads(mode='fast')
assert raw.info['bads'] == []
def test_read_spm_ctf():
"""Test CTF reader with omitted samples."""
data_path = spm_face.data_path()
raw_fname = op.join(data_path, 'MEG', 'spm',
'SPM_CTF_MEG_example_faces1_3D.ds')
raw = read_raw_ctf(raw_fname)
extras = raw._raw_extras[0]
assert_equal(extras['n_samp'], raw.n_times)
assert_false(extras['n_samp'] == extras['n_samp_tot'])
def test_calculate_head_pos_ctf():
"""Test extracting of cHPI positions from ctf data."""
raw = read_raw_ctf(ctf_chpi_fname)
quats = _calculate_head_pos_ctf(raw)
mc_quats = read_head_pos(ctf_chpi_pos_fname)
_assert_quats(quats, mc_quats, dist_tol=0.004, angle_tol=2.5)
raw = read_raw_fif(ctf_fname)
pytest.raises(RuntimeError, _calculate_head_pos_ctf, raw)
def test_dipole_fitting_ctf():
"""Test dipole fitting with CTF data."""
raw_ctf = read_raw_ctf(fname_ctf).set_eeg_reference()
events = make_fixed_length_events(raw_ctf, 1)
evoked = Epochs(raw_ctf, events, 1, 0, 0, baseline=None).average()
cov = make_ad_hoc_cov(evoked.info)
sphere = make_sphere_model((0., 0., 0.))
# XXX Eventually we should do some better checks about accuracy, but
# for now our CTF phantom fitting tutorials will have to do
# (otherwise we need to add that to the testing dataset, which is
# a bit too big)
fit_dipole(evoked, cov, sphere)
def test_ica_labels():
"""Test ICA labels."""
# The CTF data are uniquely well suited to testing the ICA.find_bads_
# methods
raw = read_raw_ctf(ctf_fname, preload=True)
# derive reference ICA components and append them to raw
icarf = ICA(n_components=2, random_state=0, max_iter=2, allow_ref_meg=True)
with pytest.warns(UserWarning, match='did not converge'):
icarf.fit(raw.copy().pick_types(meg=False, ref_meg=True))
icacomps = icarf.get_sources(raw)
# rename components so they are auto-detected by find_bads_ref
icacomps.rename_channels({c: 'REF_' + c for c in icacomps.ch_names})
# and add them to raw
raw.add_channels([icacomps])
# set the appropriate EEG channels to EOG and ECG
raw.set_channel_types({'EEG057': 'eog', 'EEG058': 'eog', 'EEG059': 'ecg'})
ica = ICA(n_components=4, random_state=0, max_iter=2, method='fastica')
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw)
ica.find_bads_eog(raw, l_freq=None, h_freq=None)
picks = list(pick_types(raw.info, meg=False, eog=True))
for idx, ch in enumerate(picks):
assert '{}/{}/{}'.format('eog', idx, raw.ch_names[ch]) in ica.labels_
assert 'eog' in ica.labels_
for key in ('ecg', 'ref_meg', 'ecg/ECG-MAG'):
assert key not in ica.labels_
ica.find_bads_ecg(raw, l_freq=None, h_freq=None, method='correlation')
picks = list(pick_types(raw.info, meg=False, ecg=True))
for idx, ch in enumerate(picks):
assert '{}/{}/{}'.format('ecg', idx, raw.ch_names[ch]) in ica.labels_
for key in ('ecg', 'eog'):
assert key in ica.labels_
for key in ('ref_meg', 'ecg/ECG-MAG'):
assert key not in ica.labels_
ica.find_bads_ref(raw, l_freq=None, h_freq=None)
picks = pick_channels_regexp(raw.ch_names, 'REF_ICA*')
for idx, ch in enumerate(picks):
assert '{}/{}/{}'.format('ref_meg', idx,
raw.ch_names[ch]) in ica.labels_
for key in ('ecg', 'eog', 'ref_meg'):
assert key in ica.labels_
assert 'ecg/ECG-MAG' not in ica.labels_
ica.find_bads_ecg(raw, l_freq=None, h_freq=None)
for key in ('ecg', 'eog', 'ref_meg', 'ecg/ECG-MAG'):
assert key in ica.labels_
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_read_spm_ctf():
"""Test CTF reader with omitted samples."""
data_path = spm_face.data_path()
raw_fname = op.join(data_path, 'MEG', 'spm',
'SPM_CTF_MEG_example_faces1_3D.ds')
raw = read_raw_ctf(raw_fname)
extras = raw._raw_extras[0]
assert_equal(extras['n_samp'], raw.n_times)
assert extras['n_samp'] != extras['n_samp_tot']
# Test that LPA, nasion and RPA are correct.
coord_frames = np.array([d['coord_frame'] for d in raw.info['dig']])
assert np.all(coord_frames == FIFF.FIFFV_COORD_HEAD)
cardinals = {d['ident']: d['r'] for d in raw.info['dig']}
assert cardinals[1][0] < cardinals[2][0] < cardinals[3][0] # x coord
assert cardinals[1][1] < cardinals[2][1] # y coord
assert cardinals[3][1] < cardinals[2][1] # y coord
for key in cardinals.keys():
assert_allclose(cardinals[key][2], 0, atol=1e-6) # z coord
def test_saving_picked():
"""Test saving picked CTF instances."""
temp_dir = _TempDir()
out_fname = op.join(temp_dir, 'test_py_raw.fif')
raw = read_raw_ctf(op.join(ctf_dir, ctf_fname_1_trial))
raw.crop(0, 1).load_data()
assert raw.compensation_grade == get_current_comp(raw.info) == 0
assert len(raw.info['comps']) == 5
pick_kwargs = dict(meg=True, ref_meg=False, verbose=True)
with catch_logging() as log:
raw_pick = raw.copy().pick_types(**pick_kwargs)
assert len(raw.info['comps']) == 5
assert len(raw_pick.info['comps']) == 0
log = log.getvalue()
assert 'Removing 5 compensators' in log
raw_pick.save(out_fname) # should work
read_raw_fif(out_fname)
read_raw_fif(out_fname, preload=True)
# If comp is applied, picking should error
raw.apply_gradient_compensation(1)
assert raw.compensation_grade == get_current_comp(raw.info) == 1
with pytest.raises(RuntimeError, match='Compensation grade 1 has been'):
raw.copy().pick_types(**pick_kwargs)
def test_compute_proj_ctf():
"""Test to show that projector code completes on CTF data."""
raw = read_raw_ctf(ctf_fname)
raw.load_data()
# expected channels per projector type
n_mags = len(pick_types(raw.info, meg='mag', ref_meg=False,
exclude='bads'))
n_grads = len(pick_types(raw.info, meg='grad', ref_meg=False,
exclude='bads'))
n_eegs = len(pick_types(raw.info, meg=False, eeg=True, ref_meg=False,
exclude='bads'))
# Test with and without gradient compensation
for c in [0, 1]:
raw.apply_gradient_compensation(c)
for average in [False, True]:
n_projs_init = len(raw.info['projs'])
projs, events = compute_proj_eog(raw, n_mag=2, n_grad=2, n_eeg=2,
average=average,
ch_name='EEG059',
avg_ref=True, no_proj=False,
l_freq=None, h_freq=None,
reject=None, tmax=dur_use,
filter_length=6000)
_check_projs_for_expected_channels(projs, n_mags, n_grads, n_eegs)
assert len(projs) == (5 + n_projs_init)
projs, events = compute_proj_ecg(raw, n_mag=1, n_grad=1, n_eeg=2,
average=average,
ch_name='EEG059',
avg_ref=True, no_proj=False,
l_freq=None, h_freq=None,
reject=None, tmax=dur_use,
filter_length=6000)
_check_projs_for_expected_channels(projs, n_mags, n_grads, n_eegs)
assert len(projs) == (4 + n_projs_init)
# filtered at 600 Hz. Here the data and empty room data files are read to # construct instances of :class:`mne.io.Raw`. data_path = bst_auditory.data_path() subject = 'bst_auditory' subjects_dir = op.join(data_path, 'subjects') raw_fname1 = op.join(data_path, 'MEG', subject, 'S01_AEF_20131218_01.ds') raw_fname2 = op.join(data_path, 'MEG', subject, 'S01_AEF_20131218_02.ds') erm_fname = op.join(data_path, 'MEG', subject, 'S01_Noise_20131218_01.ds') # %% # In the memory saving mode we use ``preload=False`` and use the memory # efficient IO which loads the data on demand. However, filtering and some # other functions require the data to be preloaded into memory. raw = read_raw_ctf(raw_fname1) n_times_run1 = raw.n_times # Here we ignore that these have different device<->head transforms mne.io.concatenate_raws([raw, read_raw_ctf(raw_fname2)], on_mismatch='ignore') raw_erm = read_raw_ctf(erm_fname) # %% # The data array consists of 274 MEG axial gradiometers, 26 MEG reference # sensors and 2 EEG electrodes (Cz and Pz). In addition: # # - 1 stim channel for marking presentation times for the stimuli # - 1 audio channel for the sent signal # - 1 response channel for recording the button presses # - 1 ECG bipolar # - 2 EOG bipolar (vertical and horizontal)
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)
import mne
from mne.datasets.brainstorm import bst_auditory
from mne.io import read_raw_ctf
from mne.preprocessing import annotate_movement, compute_average_dev_head_t
# Load data
data_path = bst_auditory.data_path()
data_path_MEG = op.join(data_path, 'MEG')
subject = 'bst_auditory'
subjects_dir = op.join(data_path, 'subjects')
trans_fname = op.join(data_path, 'MEG', 'bst_auditory',
'bst_auditory-trans.fif')
raw_fname1 = op.join(data_path_MEG, 'bst_auditory', 'S01_AEF_20131218_01.ds')
raw_fname2 = op.join(data_path_MEG, 'bst_auditory', 'S01_AEF_20131218_02.ds')
raw = read_raw_ctf(raw_fname1, preload=False)
mne.io.concatenate_raws([raw, read_raw_ctf(raw_fname2, preload=False)])
raw.crop(350, 410).load_data()
raw.resample(100, npad="auto")
###############################################################################
# Plot continuous head position with respect to the mean recording position
# --------------------------------------------------------------------------
# get cHPI time series and compute average
chpi_locs = mne.chpi.extract_chpi_locs_ctf(raw)
head_pos = mne.chpi.compute_head_pos(raw.info, chpi_locs)
original_head_dev_t = mne.transforms.invert_transform(
raw.info['dev_head_t'])
average_head_dev_t = mne.transforms.invert_transform(
def _run_interface(self, runtime):
raw_filename = self.inputs.raw_filename
cov_fname_in = self.inputs.cov_fname_in
is_epoched = self.inputs.is_epoched
is_evoked = self.inputs.is_evoked
events_id = self.inputs.events_id
t_min = self.inputs.t_min
t_max = self.inputs.t_max
data_path, basename, ext = split_f(raw_filename)
self.cov_fname_out = op.join(data_path, '%s-cov.fif' % basename)
if not op.isfile(cov_fname_in):
if is_epoched and is_evoked:
raw = read_raw_fif(raw_filename)
events = find_events(raw)
if not op.isfile(self.cov_fname_out):
print(('\n*** COMPUTE COV FROM EPOCHS ***\n' +
self.cov_fname_out))
reject = create_reject_dict(raw.info)
picks = pick_types(raw.info,
meg=True,
ref_meg=False,
exclude='bads')
epochs = Epochs(raw,
events,
events_id,
t_min,
t_max,
picks=picks,
baseline=(None, 0),
reject=reject)
# TODO method='auto'? too long!!!
noise_cov = compute_covariance(epochs,
tmax=0,
method='diagonal_fixed')
write_cov(self.cov_fname_out, noise_cov)
else:
print(('\n *** NOISE cov file %s exists!!! \n' %
self.cov_fname_out))
else:
'\n *** RAW DATA \n'
for er_fname in glob.glob(op.join(data_path, cov_fname_in)):
print(('\n found file name %s \n' % er_fname))
try:
if er_fname.rfind('cov.fif') > -1:
print("\n *** NOISE cov file %s exists!! "
"\n" % er_fname)
self.cov_fname_out = er_fname
else:
if er_fname.rfind('.fif') > -1:
er_raw = read_raw_fif(er_fname)
er_fname = er_fname.replace('.fif', '-raw-cov.fif')
elif er_fname.rfind('.ds') > -1:
er_raw = read_raw_ctf(er_fname)
er_fname = er_fname.replace('.ds', '-raw-cov.fif')
self.cov_fname_out = op.join(data_path, er_fname)
if not op.isfile(self.cov_fname_out):
reject = create_reject_dict(er_raw.info)
picks = pick_types(er_raw.info,
meg=True,
ref_meg=False,
exclude='bads')
noise_cov = compute_raw_covariance(er_raw,
picks=picks,
reject=reject)
write_cov(self.cov_fname_out, noise_cov)
else:
print(('\n *** NOISE cov file %s exists!!! \n' %
self.cov_fname_out))
except NameError:
sys.exit("No covariance matrix as input!")
# TODO creare una matrice diagonale?
else:
print(('\n *** NOISE cov file %s exists!!! \n' % cov_fname_in))
self.cov_fname_out = cov_fname_in
return runtime
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)
from mne.datasets.brainstorm import bst_phantom_ctf
from mne.io import read_raw_ctf
print(__doc__)
###############################################################################
# The data were collected with a CTF system at 2400 Hz.
data_path = bst_phantom_ctf.data_path(verbose=True)
# Switch to these to use the higher-SNR data:
# raw_path = op.join(data_path, 'phantom_200uA_20150709_01.ds')
# dip_freq = 7.
raw_path = op.join(data_path, 'phantom_20uA_20150603_03.ds')
dip_freq = 23.
erm_path = op.join(data_path, 'emptyroom_20150709_01.ds')
raw = read_raw_ctf(raw_path, preload=True)
###############################################################################
# The sinusoidal signal is generated on channel HDAC006, so we can use
# that to obtain precise timing.
sinusoid, times = raw[raw.ch_names.index('HDAC006-4408')]
plt.figure()
plt.plot(times[times < 1.], sinusoid.T[times < 1.])
###############################################################################
# Let's create some events using this signal by thresholding the sinusoid.
events = np.where(np.diff(sinusoid > 0.5) > 0)[1] + raw.first_samp
events = np.vstack((events, np.zeros_like(events), np.ones_like(events))).T
def test_plot_trans():
"""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():
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(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',
mri_fiducials=fiducials_path,
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])
from mne import io
from mne.datasets import spm_face
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.cov import compute_covariance
print(__doc__)
##############################################################################
# Get data
data_path = spm_face.data_path()
subjects_dir = data_path + '/subjects'
raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D.ds'
raw = io.read_raw_ctf(raw_fname % 1) # Take first run
# To save time and memory for this demo, we'll just use the first
# 2.5 minutes (all we need to get 30 total events) and heavily
# resample 480->60 Hz (usually you wouldn't do either of these!)
raw = raw.crop(0, 150.).load_data().resample(60, npad='auto')
picks = mne.pick_types(raw.info, meg=True, exclude='bads')
raw.filter(1, None, n_jobs=1)
events = mne.find_events(raw, stim_channel='UPPT001')
event_ids = {"faces": 1, "scrambled": 2}
tmin, tmax = -0.2, 0.5
baseline = None # no baseline as high-pass is applied
reject = dict(mag=3e-12)
# %%
# Neuromag
# --------
kwargs = dict(eeg=False, coord_frame='meg', show_axes=True, verbose=True)
raw = read_raw_fif(sample.data_path() / 'MEG' / 'sample' /
'sample_audvis_raw.fif')
fig = plot_alignment(raw.info, meg=('helmet', 'sensors'), **kwargs)
set_3d_title(figure=fig, title='Neuromag')
# %%
# CTF
# ---
raw = read_raw_ctf(spm_face.data_path() / 'MEG' / 'spm' /
'SPM_CTF_MEG_example_faces1_3D.ds')
fig = plot_alignment(raw.info, meg=('helmet', 'sensors', 'ref'), **kwargs)
set_3d_title(figure=fig, title='CTF 275')
# %%
# BTi
# ---
bti_path = op.abspath(op.dirname(mne.__file__)) + '/io/bti/tests/data/'
raw = read_raw_bti(op.join(bti_path, 'test_pdf_linux'),
op.join(bti_path, 'test_config_linux'),
op.join(bti_path, 'test_hs_linux'))
fig = plot_alignment(raw.info, meg=('helmet', 'sensors', 'ref'), **kwargs)
set_3d_title(figure=fig, title='Magnes 3600wh')
# %%
#! /usr/bin/env python
import os
import numpy as np
import pyctf
import mne
from mne.io import read_raw_ctf
tmin, tmax = -0.1, 0.5
raw_path = os.getenv('ds')
raw = read_raw_ctf(raw_path, clean_names=True, preload=True)
ds = pyctf.dsopen(raw_path)
# pick MEG channels
picks = mne.pick_types(raw.info, meg='mag', eeg=False, stim=True)
# Compute epochs
events = mne.find_events(raw, stim_channel='UPPT001')
m = 'pic1'
srate = raw.info['sfreq']
tlen = ds.getNumberOfSamples()
ev = []
for tr, t in ds.marks[m]:
s = tr * tlen + int(t * srate + .5)
ev.append([s, 0, 1])
epochs = mne.Epochs(raw, ev, {'pic1': 1}, tmin, tmax, picks=picks,
baseline=(None, 0), reject=None, preload=False)
from saflow_utils import array_topoplot, get_ch_pos, create_pval_mask
from saflow_params import IMG_DIR, SUBJ_LIST, RESULTS_PATH, FREQS_NAMES
import itertools
from mlneurotools.stats import compute_pval
RESULTS_PATH = RESULTS_PATH + 'LDA_singlesubj_L1SO_PSD_VTC2575'
MODEL = 'LDA'
NPERM = 1001
CV = '10FOLD'
COND = 2575
if __name__ == "__main__":
##### obtain ch_pos
filename = '/storage/Yann/saflow_DATA/alldata/SA04_SAflow-yharel_20190411_01.ds'
raw = read_raw_ctf(filename, preload=False, verbose=False)
ch_xy = get_ch_pos(raw)
raw.close()
for SUBJ in SUBJ_LIST:
all_acc = []
all_pval = []
all_masks = []
for FREQ in FREQS_NAMES:
freq_acc = []
freq_pval = []
freq_perms_acc = []
for CHAN in range(270):
savepath = '{}/classif_sub-{}_{}_{}.mat'.format(RESULTS_PATH, SUBJ, FREQ, CHAN)
data_acc = loadmat(savepath)['acc_score']
data_pval = loadmat(savepath)['acc_pvalue']
data_perms_acc = loadmat(savepath)['acc_pscores']
def test_ica_eeg():
"""Test ICA on EEG."""
method = 'fastica'
raw_fif = read_raw_fif(fif_fname, preload=True)
raw_eeglab = read_raw_eeglab(input_fname=eeglab_fname,
montage=eeglab_montage,
preload=True)
for raw in [raw_fif, raw_eeglab]:
events = make_fixed_length_events(raw,
99999,
start=0,
stop=0.3,
duration=0.1)
picks_meg = pick_types(raw.info, meg=True, eeg=False)[:2]
picks_eeg = pick_types(raw.info, meg=False, eeg=True)[:2]
picks_all = []
picks_all.extend(picks_meg)
picks_all.extend(picks_eeg)
epochs = Epochs(raw, events, None, -0.1, 0.1, preload=True)
evoked = epochs.average()
for picks in [picks_meg, picks_eeg, picks_all]:
if len(picks) == 0:
continue
# test fit
for inst in [raw, epochs]:
ica = ICA(n_components=2,
random_state=0,
max_iter=2,
method=method)
with pytest.warns(None):
ica.fit(inst, picks=picks)
# test apply and get_sources
for inst in [raw, epochs, evoked]:
ica.apply(inst)
ica.get_sources(inst)
with pytest.warns(RuntimeWarning, match='MISC channel'):
raw = read_raw_ctf(ctf_fname2, preload=True)
events = make_fixed_length_events(raw,
99999,
start=0,
stop=0.2,
duration=0.1)
picks_meg = pick_types(raw.info, meg=True, eeg=False)[:2]
picks_eeg = pick_types(raw.info, meg=False, eeg=True)[:2]
picks_all = picks_meg + picks_eeg
for comp in [0, 1]:
raw.apply_gradient_compensation(comp)
epochs = Epochs(raw, events, None, -0.1, 0.1, preload=True)
evoked = epochs.average()
for picks in [picks_meg, picks_eeg, picks_all]:
if len(picks) == 0:
continue
# test fit
for inst in [raw, epochs]:
ica = ICA(n_components=2,
random_state=0,
max_iter=2,
method=method)
with pytest.warns(None):
ica.fit(inst)
# test apply and get_sources
for inst in [raw, epochs, evoked]:
ica.apply(inst)
ica.get_sources(inst)
def test_channel_name_limit(tmpdir, monkeypatch, fname):
"""Test that our remapping works properly."""
#
# raw
#
if fname.endswith('fif'):
raw = read_raw_fif(fname)
raw.pick_channels(raw.ch_names[:3])
ref_names = []
data_names = raw.ch_names
else:
assert fname.endswith('.ds')
raw = read_raw_ctf(fname)
ref_names = [
raw.ch_names[pick]
for pick in pick_types(raw.info, meg=False, ref_meg=True)
]
data_names = raw.ch_names[32:35]
proj = dict(data=np.ones((1, len(data_names))),
col_names=data_names[:2].copy(),
row_names=None,
nrow=1)
proj = Projection(data=proj,
active=False,
desc='test',
kind=0,
explained_var=0.)
raw.add_proj(proj, remove_existing=True)
raw.info.normalize_proj()
raw.pick_channels(data_names + ref_names).crop(0, 2)
long_names = ['123456789abcdefg' + name for name in raw.ch_names]
fname = tmpdir.join('test-raw.fif')
with catch_logging() as log:
raw.save(fname)
log = log.getvalue()
assert 'truncated' not in log
rename = dict(zip(raw.ch_names, long_names))
long_data_names = [rename[name] for name in data_names]
long_proj_names = long_data_names[:2]
raw.rename_channels(rename)
for comp in raw.info['comps']:
for key in ('row_names', 'col_names'):
for name in comp['data'][key]:
assert name in raw.ch_names
if raw.info['comps']:
assert raw.compensation_grade == 0
raw.apply_gradient_compensation(3)
assert raw.compensation_grade == 3
assert len(raw.info['projs']) == 1
assert raw.info['projs'][0]['data']['col_names'] == long_proj_names
raw.info['bads'] = bads = long_data_names[2:3]
good_long_data_names = [
name for name in long_data_names if name not in bads
]
with catch_logging() as log:
raw.save(fname, overwrite=True, verbose=True)
log = log.getvalue()
assert 'truncated to 15' in log
for name in raw.ch_names:
assert len(name) > 15
# first read the full way
with catch_logging() as log:
raw_read = read_raw_fif(fname, verbose=True)
log = log.getvalue()
assert 'Reading extended channel information' in log
for ra in (raw, raw_read):
assert ra.ch_names == long_names
assert raw_read.info['projs'][0]['data']['col_names'] == long_proj_names
del raw_read
# next read as if no longer names could be read
monkeypatch.setattr(meas_info, '_read_extended_ch_info',
lambda x, y, z: None)
with catch_logging() as log:
raw_read = read_raw_fif(fname, verbose=True)
log = log.getvalue()
assert 'extended' not in log
if raw.info['comps']:
assert raw_read.compensation_grade == 3
raw_read.apply_gradient_compensation(0)
assert raw_read.compensation_grade == 0
monkeypatch.setattr( # restore
meas_info, '_read_extended_ch_info', _read_extended_ch_info)
short_proj_names = [
f'{name[:13 - bool(len(ref_names))]}-{len(ref_names) + ni}'
for ni, name in enumerate(long_data_names[:2])
]
assert raw_read.info['projs'][0]['data']['col_names'] == short_proj_names
#
# epochs
#
epochs = Epochs(raw, make_fixed_length_events(raw))
fname = tmpdir.join('test-epo.fif')
epochs.save(fname)
epochs_read = read_epochs(fname)
for ep in (epochs, epochs_read):
assert ep.info['ch_names'] == long_names
assert ep.ch_names == long_names
del raw, epochs_read
# cov
epochs.info['bads'] = []
cov = compute_covariance(epochs, verbose='error')
fname = tmpdir.join('test-cov.fif')
write_cov(fname, cov)
cov_read = read_cov(fname)
for co in (cov, cov_read):
assert co['names'] == long_data_names
assert co['bads'] == []
del cov_read
#
# evoked
#
evoked = epochs.average()
evoked.info['bads'] = bads
assert evoked.nave == 1
fname = tmpdir.join('test-ave.fif')
evoked.save(fname)
evoked_read = read_evokeds(fname)[0]
for ev in (evoked, evoked_read):
assert ev.ch_names == long_names
assert ev.info['bads'] == bads
del evoked_read, epochs
#
# forward
#
with pytest.warns(None): # not enough points for CTF
sphere = make_sphere_model('auto', 'auto', evoked.info)
src = setup_volume_source_space(
pos=dict(rr=[[0, 0, 0.04]], nn=[[0, 1., 0.]]))
fwd = make_forward_solution(evoked.info, None, src, sphere)
fname = tmpdir.join('temp-fwd.fif')
write_forward_solution(fname, fwd)
fwd_read = read_forward_solution(fname)
for fw in (fwd, fwd_read):
assert fw['sol']['row_names'] == long_data_names
assert fw['info']['ch_names'] == long_data_names
assert fw['info']['bads'] == bads
del fwd_read
#
# inv
#
inv = make_inverse_operator(evoked.info, fwd, cov)
fname = tmpdir.join('test-inv.fif')
write_inverse_operator(fname, inv)
inv_read = read_inverse_operator(fname)
for iv in (inv, inv_read):
assert iv['info']['ch_names'] == good_long_data_names
apply_inverse(evoked, inv) # smoke test
subject = 'bst_auditory'
subjects_dir = op.join(data_path, 'subjects')
raw_fname1 = op.join(data_path, 'MEG', 'bst_auditory',
'S01_AEF_20131218_01.ds')
raw_fname2 = op.join(data_path, 'MEG', 'bst_auditory',
'S01_AEF_20131218_02.ds')
erm_fname = op.join(data_path, 'MEG', 'bst_auditory',
'S01_Noise_20131218_01.ds')
###############################################################################
# In the memory saving mode we use ``preload=False`` and use the memory
# efficient IO which loads the data on demand. However, filtering and some
# other functions require the data to be preloaded in the memory.
raw = read_raw_ctf(raw_fname1)
n_times_run1 = raw.n_times
mne.io.concatenate_raws([raw, read_raw_ctf(raw_fname2)])
raw_erm = read_raw_ctf(erm_fname)
###############################################################################
# Data channel array consisted of 274 MEG axial gradiometers, 26 MEG reference
# sensors and 2 EEG electrodes (Cz and Pz).
# In addition:
#
# - 1 stim channel for marking presentation times for the stimuli
# - 1 audio channel for the sent signal
# - 1 response channel for recording the button presses
# - 1 ECG bipolar
# - 2 EOG bipolar (vertical and horizontal)
# - 12 head tracking channels
import numpy as np
import mne
from mne.datasets.brainstorm import bst_raw
from mne.io import read_raw_ctf
print(__doc__)
tmin, tmax, event_id = -0.1, 0.3, 2 # take right-hand somato
reject = dict(mag=4e-12, eog=250e-6)
data_path = bst_raw.data_path()
raw_path = (data_path + '/MEG/bst_raw/' +
'subj001_somatosensory_20111109_01_AUX-f.ds')
raw = read_raw_ctf(raw_path, preload=True)
raw.plot()
# set EOG channel
raw.set_channel_types({'EEG058': 'eog'})
raw.set_eeg_reference('average', projection=True)
# show power line interference and remove it
raw.plot_psd(tmax=60., average=False)
raw.notch_filter(np.arange(60, 181, 60), fir_design='firwin')
events = mne.find_events(raw, stim_channel='UPPT001')
# pick MEG channels
picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,
exclude='bads')
# Behavioral data path
fname_bhv = path(op.join(path_data, subject, 'behavdata')).glob('*.csv')
# Read behavioral file
for fname_behavior in fname_bhv:
events_behavior = get_events_from_mat(fname_behavior)
# Read all raw to extract MEG event triggers
runs = path(fname_raw).glob('*.ds')
events_meg = list()
run_number = 1
for run in runs:
fname_raw = op.join(path_data, subject, run)
print(fname_raw)
# get raw and events from raw file (need non standard manipulation
# because trigger baseline is 255 and not 0)
raw = read_raw_ctf(fname_raw, preload=True, system_clock='ignore')
channel_trigger = np.where(np.array(raw.ch_names) == 'USPT001')[0][0]
# find where the trigger == 255
trigger_baseline = np.where(raw._data[channel_trigger, :] == 255)[0]
# replace 255 values with 0
raw._data[channel_trigger, trigger_baseline] = 0.
# find correct triggers
events_meg_ = mne.find_events(raw)
# to keep the run from which the event was found
events_meg_[:, 1] = run_number
events_meg.append(events_meg_)
run_number = run_number + 1
events_meg = np.vstack(events_meg) # concatenate all meg events
def test_read_ctf():
"""Test CTF reader"""
temp_dir = _TempDir()
out_fname = op.join(temp_dir, 'test_py_raw.fif')
# Create a dummy .eeg file so we can test our reading/application of it
os.mkdir(op.join(temp_dir, 'randpos'))
ctf_eeg_fname = op.join(temp_dir, 'randpos', ctf_fname_catch)
shutil.copytree(op.join(ctf_dir, ctf_fname_catch), ctf_eeg_fname)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = _test_raw_reader(read_raw_ctf, directory=ctf_eeg_fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
picks = pick_types(raw.info, meg=False, eeg=True)
pos = np.random.RandomState(42).randn(len(picks), 3)
fake_eeg_fname = op.join(ctf_eeg_fname, 'catch-alp-good-f.eeg')
# Create a bad file
with open(fake_eeg_fname, 'wb') as fid:
fid.write('foo\n'.encode('ascii'))
assert_raises(RuntimeError, read_raw_ctf, ctf_eeg_fname)
# Create a good file
with open(fake_eeg_fname, 'wb') as fid:
for ii, ch_num in enumerate(picks):
args = (str(ch_num + 1), raw.ch_names[ch_num],) + tuple(
'%0.5f' % x for x in 100 * pos[ii]) # convert to cm
fid.write(('\t'.join(args) + '\n').encode('ascii'))
pos_read_old = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
with warnings.catch_warnings(record=True) as w: # reclassified channel
raw = read_raw_ctf(ctf_eeg_fname) # read modified data
assert_true(all('MISC channel' in str(ww.message) for ww in w))
pos_read = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
assert_allclose(apply_trans(raw.info['ctf_head_t'], pos), pos_read,
rtol=1e-5, atol=1e-5)
assert_true((pos_read == pos_read_old).mean() < 0.1)
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_randpos_raw.fif'),
op.join(temp_dir, 'randpos', 'catch-alp-good-f.ds_raw.fif'))
# Create a version with no hc, starting out *with* EEG pos (error)
os.mkdir(op.join(temp_dir, 'nohc'))
ctf_no_hc_fname = op.join(temp_dir, 'no_hc', ctf_fname_catch)
shutil.copytree(ctf_eeg_fname, ctf_no_hc_fname)
remove_base = op.join(ctf_no_hc_fname, op.basename(ctf_fname_catch[:-3]))
os.remove(remove_base + '.hc')
with warnings.catch_warnings(record=True): # no coord tr
assert_raises(RuntimeError, read_raw_ctf, ctf_no_hc_fname)
os.remove(remove_base + '.eeg')
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_nohc_raw.fif'),
op.join(temp_dir, 'no_hc', 'catch-alp-good-f.ds_raw.fif'))
# All our files
use_fnames = [op.join(ctf_dir, c) for c in ctf_fnames]
for fname in use_fnames:
raw_c = Raw(fname + '_raw.fif', add_eeg_ref=False, preload=True)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
# check info match
assert_array_equal(raw.ch_names, raw_c.ch_names)
assert_allclose(raw.times, raw_c.times)
assert_allclose(raw._cals, raw_c._cals)
for key in ('version', 'usecs'):
assert_equal(raw.info['meas_id'][key], raw_c.info['meas_id'][key])
py_time = raw.info['meas_id']['secs']
c_time = raw_c.info['meas_id']['secs']
max_offset = 24 * 60 * 60 # probably overkill but covers timezone
assert_true(c_time - max_offset <= py_time <= c_time)
for t in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_allclose(raw.info[t]['trans'], raw_c.info[t]['trans'],
rtol=1e-4, atol=1e-7)
for key in ('acq_pars', 'acq_stim', 'bads',
'ch_names', 'custom_ref_applied', 'description',
'events', 'experimenter', 'highpass', 'line_freq',
'lowpass', 'nchan', 'proj_id', 'proj_name',
'projs', 'sfreq', 'subject_info'):
assert_equal(raw.info[key], raw_c.info[key], key)
if op.basename(fname) not in single_trials:
# We don't force buffer size to be smaller like MNE-C
assert_equal(raw.info['buffer_size_sec'],
raw_c.info['buffer_size_sec'])
assert_equal(len(raw.info['comps']), len(raw_c.info['comps']))
for c1, c2 in zip(raw.info['comps'], raw_c.info['comps']):
for key in ('colcals', 'rowcals'):
assert_allclose(c1[key], c2[key])
assert_equal(c1['save_calibrated'], c2['save_calibrated'])
for key in ('row_names', 'col_names', 'nrow', 'ncol'):
assert_array_equal(c1['data'][key], c2['data'][key])
assert_allclose(c1['data']['data'], c2['data']['data'], atol=1e-7,
rtol=1e-5)
assert_allclose(raw.info['hpi_results'][0]['coord_trans']['trans'],
raw_c.info['hpi_results'][0]['coord_trans']['trans'],
rtol=1e-5, atol=1e-7)
assert_equal(len(raw.info['chs']), len(raw_c.info['chs']))
for ii, (c1, c2) in enumerate(zip(raw.info['chs'], raw_c.info['chs'])):
for key in ('kind', 'scanno', 'unit', 'ch_name', 'unit_mul',
'range', 'coord_frame', 'coil_type', 'logno'):
if c1['ch_name'] == 'RMSP' and \
'catch-alp-good-f' in fname and \
key in ('kind', 'unit', 'coord_frame', 'coil_type',
'logno'):
continue # XXX see below...
assert_equal(c1[key], c2[key], err_msg=key)
for key in ('cal',):
assert_allclose(c1[key], c2[key], atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# XXX 2016/02/24: fixed bug with normal computation that used
# to exist, once mne-C tools are updated we should update our FIF
# conversion files, then the slices can go away (and the check
# can be combined with that for "cal")
for key in ('loc',):
if c1['ch_name'] == 'RMSP' and 'catch-alp-good-f' in fname:
continue
assert_allclose(c1[key][:3], c2[key][:3], atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
assert_allclose(c1[key][9:12], c2[key][9:12], atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
if fname.endswith('catch-alp-good-f.ds'): # omit points from .pos file
raw.info['dig'] = raw.info['dig'][:-10]
assert_dig_allclose(raw.info, raw_c.info)
# check data match
raw_c.save(out_fname, overwrite=True, buffer_size_sec=1.)
raw_read = Raw(out_fname, add_eeg_ref=False)
# so let's check tricky cases based on sample boundaries
rng = np.random.RandomState(0)
pick_ch = rng.permutation(np.arange(len(raw.ch_names)))[:10]
bnd = int(round(raw.info['sfreq'] * raw.info['buffer_size_sec']))
assert_equal(bnd, raw._raw_extras[0]['block_size'])
assert_equal(bnd, block_sizes[op.basename(fname)])
slices = (slice(0, bnd), slice(bnd - 1, bnd), slice(3, bnd),
slice(3, 300), slice(None))
if len(raw.times) >= 2 * bnd: # at least two complete blocks
slices = slices + (slice(bnd, 2 * bnd), slice(bnd, bnd + 1),
slice(0, bnd + 100))
for sl_time in slices:
assert_allclose(raw[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
assert_allclose(raw_read[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
# all data / preload
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname, preload=True)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
assert_allclose(raw[:][0], raw_c[:][0])
assert_raises(TypeError, read_raw_ctf, 1)
assert_raises(ValueError, read_raw_ctf, ctf_fname_continuous + 'foo.ds')
# test ignoring of system clock
read_raw_ctf(op.join(ctf_dir, ctf_fname_continuous), 'ignore')
assert_raises(ValueError, read_raw_ctf,
op.join(ctf_dir, ctf_fname_continuous), 'foo')
def raw_ctf():
"""Get ctf raw data from mne.io.tests.data."""
raw_ctf = read_raw_ctf(fname_ctf_continuous, preload=True)
return raw_ctf
print('*** COMPUTE RAW COV ***')
#locale.setlocale(locale.LC_ALL, "en_US.utf8")
noise_fname_template = 'lyon_Noise*.ds'
for sbj in subject_ids:
print('\nsbj %s \n' %sbj)
ds_path = op.join(noise_path, sbj, '*misc', noise_fname_template)
for noise_ds_fname in glob.glob(ds_path):
print('*** Empty room data file %s found!!!\n' % noise_ds_fname)
if not op.isdir(noise_ds_fname):
raise RuntimeError('*** Empty room data file %s NOT found!!!'
% noise_ds_fname)
raw = read_raw_ctf(noise_ds_fname)
noise_fname = sbj + '_' + noise_fname_template.replace('*.ds', '-raw.fif')
noise_fif_fname = op.join(data_path, sbj, noise_fname)
print('*** Raw fif file name %s ***\n' % noise_fif_fname)
raw.save(noise_fif_fname, overwrite=True)
noise_cov_fname = noise_fif_fname.replace('-raw.fif', '-raw-cov.fif')
print('*** Noise Cov file name %s ***\n' % noise_cov_fname)
reject = create_reject_dict(raw.info)
picks = pick_types(raw.info, meg=True, ref_meg=False, exclude='bads')
noise_cov = compute_raw_covariance(raw, picks=picks, reject=reject)
write_cov(noise_cov_fname, noise_cov)
from mne.datasets import spm_face
from mne.preprocessing import ICA, create_eog_epochs
from mne import io, combine_evoked
from mne.minimum_norm import make_inverse_operator, apply_inverse
print(__doc__)
data_path = spm_face.data_path()
subjects_dir = data_path + '/subjects'
###############################################################################
# Load and filter data, set up epochs
raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D.ds'
raw = io.read_raw_ctf(raw_fname % 1, preload=True) # Take first run
# Here to save memory and time we'll downsample heavily -- this is not
# advised for real data as it can effectively jitter events!
raw.resample(120., npad='auto')
picks = mne.pick_types(raw.info, meg=True, exclude='bads')
raw.filter(1, 30, method='fir', fir_design='firwin')
events = mne.find_events(raw, stim_channel='UPPT001')
# plot the events to get an idea of the paradigm
mne.viz.plot_events(events, raw.info['sfreq'])
event_ids = {"faces": 1, "scrambled": 2}
tmin, tmax = -0.2, 0.6
def test_read_ctf_annotations():
"""Test reading CTF marker file."""
EXPECTED_LATENCIES = np.array([
5640,
7950,
9990,
12253,
14171,
16557,
18896,
20846, # noqa
22702,
24990,
26830,
28974,
30906,
33077,
34985,
36907, # noqa
38922,
40760,
42881,
45222,
47457,
49618,
51802,
54227, # noqa
56171,
58274,
60394,
62375,
64444,
66767,
68827,
71109, # noqa
73499,
75807,
78146,
80415,
82554,
84508,
86403,
88426, # noqa
90746,
92893,
94779,
96822,
98996,
99001,
100949,
103325, # noqa
105322,
107678,
109667,
111844,
113682,
115817,
117691,
119663, # noqa
121966,
123831,
126110,
128490,
130521,
132808,
135204,
137210, # noqa
139130,
141390,
143660,
145748,
147889,
150205,
152528,
154646, # noqa
156897,
159191,
161446,
163722,
166077,
168467,
170624,
172519, # noqa
174719,
176886,
179062,
181405,
183709,
186034,
188454,
190330, # noqa
192660,
194682,
196834,
199161,
201035,
203008,
204999,
207409, # noqa
209661,
211895,
213957,
216005,
218040,
220178,
222137,
224305, # noqa
226297,
228654,
230755,
232909,
235205,
237373,
239723,
241762, # noqa
243748,
245762,
247801,
250055,
251886,
254252,
256441,
258354, # noqa
260680,
263026,
265048,
267073,
269235,
271556,
273927,
276197, # noqa
278436,
280536,
282691,
284933,
287061,
288936,
290941,
293183, # noqa
295369,
297729,
299626,
301546,
303449,
305548,
307882,
310124, # noqa
312374,
314509,
316815,
318789,
320981,
322879,
324878,
326959, # noqa
329341,
331200,
331201,
333469,
335584,
337984,
340143,
342034, # noqa
344360,
346309,
348544,
350970,
353052,
355227,
357449,
359603, # noqa
361725,
363676,
365735,
367799,
369777,
371904,
373856,
376204, # noqa
378391,
380800,
382859,
385161,
387093,
389434,
391624,
393785, # noqa
396093,
398214,
400198,
402166,
404104,
406047,
408372,
410686, # noqa
413029,
414975,
416850,
418797,
420824,
422959,
425026,
427215, # noqa
429278,
431668 # noqa
]) - 1 # Fieldtrip has 1 sample difference with MNE
raw = RawArray(data=np.empty((1, 432000), dtype=np.float64),
info=create_info(ch_names=1, sfreq=1200.0))
raw.set_meas_date(read_raw_ctf(somato_fname).info['meas_date'])
raw.set_annotations(read_annotations(somato_fname))
events, _ = events_from_annotations(raw)
latencies = np.sort(events[:, 0])
assert_allclose(latencies, EXPECTED_LATENCIES, atol=1e-6)
def test_plot_alignment(tmpdir, backends_3d):
"""Test plotting of -trans.fif files and MEG sensor layouts."""
from mne.viz.backends.renderer import _Renderer
backend_name = get_3d_backend()
# 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)
if backend_name == 'mayavi':
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')
fig = plot_alignment(info,
trans_fname,
subject='sample',
subjects_dir=subjects_dir,
meg=meg)
renderer = _Renderer(fig=fig)
renderer.close()
# KIT ref sensor coil def is defined
if backend_name == 'mayavi':
mlab.close(all=True)
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')
if backend_name == 'mayavi':
mlab.close(all=True)
# no-head version
if backend_name == 'mayavi':
mlab.close(all=True)
# 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 = _Renderer(fig=fig)
renderer.close()
# 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)
if backend_name == 'mayavi':
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
# 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)
if backend_name == '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:
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'])
if backend_name == 'mayavi':
mlab.close(all=True)
from mne.datasets.brainstorm import bst_phantom_ctf
from mne.io import read_raw_ctf
print(__doc__)
###############################################################################
# The data were collected with a CTF system at 2400 Hz.
data_path = bst_phantom_ctf.data_path()
# Switch to these to use the higher-SNR data:
# raw_path = op.join(data_path, 'phantom_200uA_20150709_01.ds')
# dip_freq = 7.
raw_path = op.join(data_path, 'phantom_20uA_20150603_03.ds')
dip_freq = 23.
erm_path = op.join(data_path, 'emptyroom_20150709_01.ds')
raw = read_raw_ctf(raw_path, preload=True)
###############################################################################
# The sinusoidal signal is generated on channel HDAC006, so we can use
# that to obtain precise timing.
sinusoid, times = raw[raw.ch_names.index('HDAC006-4408')]
plt.figure()
plt.plot(times[times < 1.], sinusoid.T[times < 1.])
###############################################################################
# Let's create some events using this signal by thresholding the sinusoid.
events = np.where(np.diff(sinusoid > 0.5) > 0)[1] + raw.first_samp
events = np.vstack((events, np.zeros_like(events), np.ones_like(events))).T
def test_read_ctf():
"""Test CTF reader"""
temp_dir = _TempDir()
out_fname = op.join(temp_dir, 'test_py_raw.fif')
# Create a dummy .eeg file so we can test our reading/application of it
os.mkdir(op.join(temp_dir, 'randpos'))
ctf_eeg_fname = op.join(temp_dir, 'randpos', ctf_fname_catch)
shutil.copytree(op.join(ctf_dir, ctf_fname_catch), ctf_eeg_fname)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = _test_raw_reader(read_raw_ctf, directory=ctf_eeg_fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
picks = pick_types(raw.info, meg=False, eeg=True)
pos = np.random.RandomState(42).randn(len(picks), 3)
fake_eeg_fname = op.join(ctf_eeg_fname, 'catch-alp-good-f.eeg')
# Create a bad file
with open(fake_eeg_fname, 'wb') as fid:
fid.write('foo\n'.encode('ascii'))
assert_raises(RuntimeError, read_raw_ctf, ctf_eeg_fname)
# Create a good file
with open(fake_eeg_fname, 'wb') as fid:
for ii, ch_num in enumerate(picks):
args = (
str(ch_num + 1),
raw.ch_names[ch_num],
) + tuple('%0.5f' % x for x in 100 * pos[ii]) # convert to cm
fid.write(('\t'.join(args) + '\n').encode('ascii'))
pos_read_old = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
with warnings.catch_warnings(record=True) as w: # reclassified channel
raw = read_raw_ctf(ctf_eeg_fname) # read modified data
assert_true(all('MISC channel' in str(ww.message) for ww in w))
pos_read = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
assert_allclose(apply_trans(raw.info['ctf_head_t'], pos),
pos_read,
rtol=1e-5,
atol=1e-5)
assert_true((pos_read == pos_read_old).mean() < 0.1)
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_randpos_raw.fif'),
op.join(temp_dir, 'randpos', 'catch-alp-good-f.ds_raw.fif'))
# Create a version with no hc, starting out *with* EEG pos (error)
os.mkdir(op.join(temp_dir, 'nohc'))
ctf_no_hc_fname = op.join(temp_dir, 'no_hc', ctf_fname_catch)
shutil.copytree(ctf_eeg_fname, ctf_no_hc_fname)
remove_base = op.join(ctf_no_hc_fname, op.basename(ctf_fname_catch[:-3]))
os.remove(remove_base + '.hc')
with warnings.catch_warnings(record=True): # no coord tr
assert_raises(RuntimeError, read_raw_ctf, ctf_no_hc_fname)
os.remove(remove_base + '.eeg')
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_nohc_raw.fif'),
op.join(temp_dir, 'no_hc', 'catch-alp-good-f.ds_raw.fif'))
# All our files
use_fnames = [op.join(ctf_dir, c) for c in ctf_fnames]
for fname in use_fnames:
raw_c = Raw(fname + '_raw.fif', add_eeg_ref=False, preload=True)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
# check info match
assert_array_equal(raw.ch_names, raw_c.ch_names)
assert_allclose(raw.times, raw_c.times)
assert_allclose(raw._cals, raw_c._cals)
for key in ('version', 'usecs'):
assert_equal(raw.info['meas_id'][key], raw_c.info['meas_id'][key])
py_time = raw.info['meas_id']['secs']
c_time = raw_c.info['meas_id']['secs']
max_offset = 24 * 60 * 60 # probably overkill but covers timezone
assert_true(c_time - max_offset <= py_time <= c_time)
for t in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_allclose(raw.info[t]['trans'],
raw_c.info[t]['trans'],
rtol=1e-4,
atol=1e-7)
for key in ('acq_pars', 'acq_stim', 'bads', 'ch_names',
'custom_ref_applied', 'description', 'events',
'experimenter', 'highpass', 'line_freq', 'lowpass',
'nchan', 'proj_id', 'proj_name', 'projs', 'sfreq',
'subject_info'):
assert_equal(raw.info[key], raw_c.info[key], key)
if op.basename(fname) not in single_trials:
# We don't force buffer size to be smaller like MNE-C
assert_equal(raw.info['buffer_size_sec'],
raw_c.info['buffer_size_sec'])
assert_equal(len(raw.info['comps']), len(raw_c.info['comps']))
for c1, c2 in zip(raw.info['comps'], raw_c.info['comps']):
for key in ('colcals', 'rowcals'):
assert_allclose(c1[key], c2[key])
assert_equal(c1['save_calibrated'], c2['save_calibrated'])
for key in ('row_names', 'col_names', 'nrow', 'ncol'):
assert_array_equal(c1['data'][key], c2['data'][key])
assert_allclose(c1['data']['data'],
c2['data']['data'],
atol=1e-7,
rtol=1e-5)
assert_allclose(raw.info['hpi_results'][0]['coord_trans']['trans'],
raw_c.info['hpi_results'][0]['coord_trans']['trans'],
rtol=1e-5,
atol=1e-7)
assert_equal(len(raw.info['chs']), len(raw_c.info['chs']))
for ii, (c1, c2) in enumerate(zip(raw.info['chs'], raw_c.info['chs'])):
for key in ('kind', 'scanno', 'unit', 'ch_name', 'unit_mul',
'range', 'coord_frame', 'coil_type', 'logno'):
if c1['ch_name'] == 'RMSP' and \
'catch-alp-good-f' in fname and \
key in ('kind', 'unit', 'coord_frame', 'coil_type',
'logno'):
continue # XXX see below...
assert_equal(c1[key], c2[key], err_msg=key)
for key in ('cal', ):
assert_allclose(c1[key],
c2[key],
atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# XXX 2016/02/24: fixed bug with normal computation that used
# to exist, once mne-C tools are updated we should update our FIF
# conversion files, then the slices can go away (and the check
# can be combined with that for "cal")
for key in ('loc', ):
if c1['ch_name'] == 'RMSP' and 'catch-alp-good-f' in fname:
continue
assert_allclose(c1[key][:3],
c2[key][:3],
atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
assert_allclose(c1[key][9:12],
c2[key][9:12],
atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
if fname.endswith('catch-alp-good-f.ds'): # omit points from .pos file
raw.info['dig'] = raw.info['dig'][:-10]
assert_dig_allclose(raw.info, raw_c.info)
# check data match
raw_c.save(out_fname, overwrite=True, buffer_size_sec=1.)
raw_read = Raw(out_fname, add_eeg_ref=False)
# so let's check tricky cases based on sample boundaries
rng = np.random.RandomState(0)
pick_ch = rng.permutation(np.arange(len(raw.ch_names)))[:10]
bnd = int(round(raw.info['sfreq'] * raw.info['buffer_size_sec']))
assert_equal(bnd, raw._raw_extras[0]['block_size'])
assert_equal(bnd, block_sizes[op.basename(fname)])
slices = (slice(0, bnd), slice(bnd - 1, bnd), slice(3, bnd),
slice(3, 300), slice(None))
if len(raw.times) >= 2 * bnd: # at least two complete blocks
slices = slices + (slice(bnd, 2 * bnd), slice(
bnd, bnd + 1), slice(0, bnd + 100))
for sl_time in slices:
assert_allclose(raw[pick_ch, sl_time][0], raw_c[pick_ch,
sl_time][0])
assert_allclose(raw_read[pick_ch, sl_time][0], raw_c[pick_ch,
sl_time][0])
# all data / preload
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname, preload=True)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
assert_allclose(raw[:][0], raw_c[:][0])
raw.plot(show=False) # Test plotting with ref_meg channels.
assert_raises(TypeError, read_raw_ctf, 1)
assert_raises(ValueError, read_raw_ctf, ctf_fname_continuous + 'foo.ds')
# test ignoring of system clock
read_raw_ctf(op.join(ctf_dir, ctf_fname_continuous), 'ignore')
assert_raises(ValueError, read_raw_ctf,
op.join(ctf_dir, ctf_fname_continuous), 'foo')
# License: BSD-3-Clause # %% import os.path as op import matplotlib.pyplot as plt import numpy as np from mne.datasets.brainstorm import bst_auditory from mne.io import read_raw_ctf from mne.preprocessing import annotate_muscle_zscore # Load data data_path = bst_auditory.data_path() raw_fname = op.join(data_path, 'MEG', 'bst_auditory', 'S01_AEF_20131218_01.ds') raw = read_raw_ctf(raw_fname, preload=False) raw.crop(130, 160).load_data() # just use a fraction of data for speed here raw.resample(300, npad="auto") # %% # Notch filter the data: # # .. note:: # If line noise is present, you should perform notch-filtering *before* # detecting muscle artifacts. See :ref:`tut-section-line-noise` for an # example. raw.notch_filter([50, 100]) # %%
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
plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
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
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_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,
trans_fname,
subject='sample',
meg='helmet',
subjects_dir=subjects_dir,
eeg='projected',
bem=sphere,
surfaces=['head', 'brain', 'inner_skull', 'outer_skull'],
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', 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)
# 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'])
from mne import io
from mne.datasets import spm_face
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.cov import compute_covariance
print(__doc__)
##############################################################################
# Get data
data_path = spm_face.data_path()
subjects_dir = data_path + '/subjects'
raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D.ds'
raw = io.read_raw_ctf(raw_fname % 1) # Take first run
# To save time and memory for this demo, we'll just use the first
# 2.5 minutes (all we need to get 30 total events) and heavily
# resample 480->60 Hz (usually you wouldn't do either of these!)
raw = raw.crop(0, 150.).load_data()
picks = mne.pick_types(raw.info, meg=True, exclude='bads')
raw.filter(1, 20., n_jobs=1, fir_design='firwin')
events = mne.find_events(raw, stim_channel='UPPT001')
event_ids = {"faces": 1, "scrambled": 2}
tmin, tmax = -0.2, 0.5
baseline = None # no baseline as high-pass is applied
reject = dict(mag=3e-12)
def test_read_ctf(tmpdir):
"""Test CTF reader."""
temp_dir = str(tmpdir)
out_fname = op.join(temp_dir, 'test_py_raw.fif')
# Create a dummy .eeg file so we can test our reading/application of it
os.mkdir(op.join(temp_dir, 'randpos'))
ctf_eeg_fname = op.join(temp_dir, 'randpos', ctf_fname_catch)
shutil.copytree(op.join(ctf_dir, ctf_fname_catch), ctf_eeg_fname)
with pytest.warns(RuntimeWarning, match='RMSP .* changed to a MISC ch'):
raw = _test_raw_reader(read_raw_ctf, directory=ctf_eeg_fname)
picks = pick_types(raw.info, meg=False, eeg=True)
pos = np.random.RandomState(42).randn(len(picks), 3)
fake_eeg_fname = op.join(ctf_eeg_fname, 'catch-alp-good-f.eeg')
# Create a bad file
with open(fake_eeg_fname, 'wb') as fid:
fid.write('foo\n'.encode('ascii'))
pytest.raises(RuntimeError, read_raw_ctf, ctf_eeg_fname)
# Create a good file
with open(fake_eeg_fname, 'wb') as fid:
for ii, ch_num in enumerate(picks):
args = (str(ch_num + 1), raw.ch_names[ch_num],) + tuple(
'%0.5f' % x for x in 100 * pos[ii]) # convert to cm
fid.write(('\t'.join(args) + '\n').encode('ascii'))
pos_read_old = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
with pytest.warns(RuntimeWarning, match='RMSP .* changed to a MISC ch'):
raw = read_raw_ctf(ctf_eeg_fname) # read modified data
pos_read = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
assert_allclose(apply_trans(raw.info['ctf_head_t'], pos), pos_read,
rtol=1e-5, atol=1e-5)
assert (pos_read == pos_read_old).mean() < 0.1
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_randpos_raw.fif'),
op.join(temp_dir, 'randpos', 'catch-alp-good-f.ds_raw.fif'))
# Create a version with no hc, starting out *with* EEG pos (error)
os.mkdir(op.join(temp_dir, 'nohc'))
ctf_no_hc_fname = op.join(temp_dir, 'no_hc', ctf_fname_catch)
shutil.copytree(ctf_eeg_fname, ctf_no_hc_fname)
remove_base = op.join(ctf_no_hc_fname, op.basename(ctf_fname_catch[:-3]))
os.remove(remove_base + '.hc')
with pytest.warns(RuntimeWarning, match='MISC channel'):
pytest.raises(RuntimeError, read_raw_ctf, ctf_no_hc_fname)
os.remove(remove_base + '.eeg')
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_nohc_raw.fif'),
op.join(temp_dir, 'no_hc', 'catch-alp-good-f.ds_raw.fif'))
# All our files
use_fnames = [op.join(ctf_dir, c) for c in ctf_fnames]
for fname in use_fnames:
raw_c = read_raw_fif(fname + '_raw.fif', preload=True)
with pytest.warns(None): # sometimes matches "MISC channel"
raw = read_raw_ctf(fname)
# check info match
assert_array_equal(raw.ch_names, raw_c.ch_names)
assert_allclose(raw.times, raw_c.times)
assert_allclose(raw._cals, raw_c._cals)
assert (raw.info['meas_id']['version'] ==
raw_c.info['meas_id']['version'] + 1)
for t in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_allclose(raw.info[t]['trans'], raw_c.info[t]['trans'],
rtol=1e-4, atol=1e-7)
# XXX 2019/11/29 : MNC-C FIF conversion files don't have meas_date set.
# Consider adding meas_date to below checks once this is addressed in
# MNE-C
for key in ('acq_pars', 'acq_stim', 'bads',
'ch_names', 'custom_ref_applied', 'description',
'events', 'experimenter', 'highpass', 'line_freq',
'lowpass', 'nchan', 'proj_id', 'proj_name',
'projs', 'sfreq', 'subject_info'):
assert raw.info[key] == raw_c.info[key], key
if op.basename(fname) not in single_trials:
# We don't force buffer size to be smaller like MNE-C
assert raw.buffer_size_sec == raw_c.buffer_size_sec
assert len(raw.info['comps']) == len(raw_c.info['comps'])
for c1, c2 in zip(raw.info['comps'], raw_c.info['comps']):
for key in ('colcals', 'rowcals'):
assert_allclose(c1[key], c2[key])
assert c1['save_calibrated'] == c2['save_calibrated']
for key in ('row_names', 'col_names', 'nrow', 'ncol'):
assert_array_equal(c1['data'][key], c2['data'][key])
assert_allclose(c1['data']['data'], c2['data']['data'], atol=1e-7,
rtol=1e-5)
assert_allclose(raw.info['hpi_results'][0]['coord_trans']['trans'],
raw_c.info['hpi_results'][0]['coord_trans']['trans'],
rtol=1e-5, atol=1e-7)
assert len(raw.info['chs']) == len(raw_c.info['chs'])
for ii, (c1, c2) in enumerate(zip(raw.info['chs'], raw_c.info['chs'])):
for key in ('kind', 'scanno', 'unit', 'ch_name', 'unit_mul',
'range', 'coord_frame', 'coil_type', 'logno'):
if c1['ch_name'] == 'RMSP' and \
'catch-alp-good-f' in fname and \
key in ('kind', 'unit', 'coord_frame', 'coil_type',
'logno'):
continue # XXX see below...
if key == 'coil_type' and c1[key] == FIFF.FIFFV_COIL_EEG:
# XXX MNE-C bug that this is not set
assert c2[key] == FIFF.FIFFV_COIL_NONE
continue
assert c1[key] == c2[key], key
for key in ('cal',):
assert_allclose(c1[key], c2[key], atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# XXX 2016/02/24: fixed bug with normal computation that used
# to exist, once mne-C tools are updated we should update our FIF
# conversion files, then the slices can go away (and the check
# can be combined with that for "cal")
for key in ('loc',):
if c1['ch_name'] == 'RMSP' and 'catch-alp-good-f' in fname:
continue
if (c2[key][:3] == 0.).all():
check = [np.nan] * 3
else:
check = c2[key][:3]
assert_allclose(c1[key][:3], check, atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
if (c2[key][3:] == 0.).all():
check = [np.nan] * 3
else:
check = c2[key][9:12]
assert_allclose(c1[key][9:12], check, atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# Make sure all digitization points are in the MNE head coord frame
for p in raw.info['dig']:
assert p['coord_frame'] == FIFF.FIFFV_COORD_HEAD, \
'dig points must be in FIFF.FIFFV_COORD_HEAD'
if fname.endswith('catch-alp-good-f.ds'): # omit points from .pos file
raw.info['dig'] = raw.info['dig'][:-10]
# XXX: Next test would fail because c-tools assign the fiducials from
# CTF data as HPI. Should eventually clarify/unify with Matti.
# assert_dig_allclose(raw.info, raw_c.info)
# check data match
raw_c.save(out_fname, overwrite=True, buffer_size_sec=1.)
raw_read = read_raw_fif(out_fname)
# so let's check tricky cases based on sample boundaries
rng = np.random.RandomState(0)
pick_ch = rng.permutation(np.arange(len(raw.ch_names)))[:10]
bnd = int(round(raw.info['sfreq'] * raw.buffer_size_sec))
assert bnd == raw._raw_extras[0]['block_size']
assert bnd == block_sizes[op.basename(fname)]
slices = (slice(0, bnd), slice(bnd - 1, bnd), slice(3, bnd),
slice(3, 300), slice(None))
if len(raw.times) >= 2 * bnd: # at least two complete blocks
slices = slices + (slice(bnd, 2 * bnd), slice(bnd, bnd + 1),
slice(0, bnd + 100))
for sl_time in slices:
assert_allclose(raw[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
assert_allclose(raw_read[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
# all data / preload
raw.load_data()
assert_allclose(raw[:][0], raw_c[:][0], atol=1e-15)
# test bad segment annotations
if 'testdata_ctf_short.ds' in fname:
assert 'bad' in raw.annotations.description[0]
assert_allclose(raw.annotations.onset, [2.15])
assert_allclose(raw.annotations.duration, [0.0225])
pytest.raises(TypeError, read_raw_ctf, 1)
pytest.raises(ValueError, read_raw_ctf, ctf_fname_continuous + 'foo.ds')
# test ignoring of system clock
read_raw_ctf(op.join(ctf_dir, ctf_fname_continuous), 'ignore')
pytest.raises(ValueError, read_raw_ctf,
op.join(ctf_dir, ctf_fname_continuous), 'foo')
def test_compute_proj_ctf():
"""Test to show that projector code completes on CTF data."""
raw = read_raw_ctf(ctf_fname, preload=True)
# expected channels per projector type
mag_picks = pick_types(raw.info, meg='mag', ref_meg=False,
exclude='bads')[::10]
n_mags = len(mag_picks)
grad_picks = pick_types(raw.info,
meg='grad',
ref_meg=False,
exclude='bads')[::10]
n_grads = len(grad_picks)
eeg_picks = pick_types(raw.info,
meg=False,
eeg=True,
ref_meg=False,
exclude='bads')[2::3]
n_eegs = len(eeg_picks)
ref_picks = pick_types(raw.info, meg=False, ref_meg=True)
raw.pick(
np.sort(np.concatenate([mag_picks, grad_picks, eeg_picks, ref_picks])))
del mag_picks, grad_picks, eeg_picks, ref_picks
# Test with and without gradient compensation
raw.apply_gradient_compensation(0)
n_projs_init = len(raw.info['projs'])
with pytest.warns(RuntimeWarning, match='Attenuation'):
projs, _ = compute_proj_eog(raw,
n_mag=2,
n_grad=2,
n_eeg=2,
average=True,
ch_name='EEG059',
avg_ref=True,
no_proj=False,
l_freq=None,
h_freq=None,
reject=None,
tmax=dur_use,
filter_length=1000)
_check_projs_for_expected_channels(projs, n_mags, n_grads, n_eegs)
assert len(projs) == (5 + n_projs_init)
raw.apply_gradient_compensation(1)
with pytest.warns(RuntimeWarning, match='Attenuation'):
projs, _ = compute_proj_ecg(raw,
n_mag=1,
n_grad=1,
n_eeg=2,
average=True,
ch_name='EEG059',
avg_ref=True,
no_proj=False,
l_freq=None,
h_freq=None,
reject=None,
tmax=dur_use,
filter_length=1000)
_check_projs_for_expected_channels(projs, n_mags, n_grads, n_eegs)
assert len(projs) == (4 + n_projs_init)
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,
read_trans(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=Path(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')
# fNIRS
info = read_raw_nirx(nirx_fname).info
with catch_logging() as log:
plot_alignment(info, subject='fsaverage', surfaces=(), verbose=True)
log = log.getvalue()
assert '26 fnirs locations' in log
renderer._close_all()
import numpy as np
import sys
from mne.io import read_raw_ctf
from mne import pick_types
from mne.chpi import _calculate_head_pos_ctf, read_head_pos
sys.path.append(
'/Users/nkozhemi/Documents/Github/mne_chop_tools-master/mne_pipes')
from annotate_artifacts import (annotate_gfp_artifacts,
annotate_motion_artifacts, plot_artifacts,
annotate_muscle_artifacts)
top_dir = '/Users/nkozhemi/Desktop/MEG_repo/MEG_children/18011010C/'
ds_fname = op.join(top_dir, 'MEG_Movie_20190319_09.ds')
raw = read_raw_ctf(ds_fname, preload=True)
#raw.set_channel_types({'EEG057': 'eog', 'EEG058': 'ecg'})
#raw.info['bads'] = []
if raw.compensation_grade != 3:
raw.apply_gradient_compensation(3)
# muscle artifacts
mus_annot, mus_raw = annotate_muscle_artifacts(raw,
art_thresh=2.5,
return_stat_raw=True)
# motion artifacts
pos = _calculate_head_pos_ctf(raw)
mov_annot, raw_hpi = annotate_motion_artifacts(raw, pos, return_stat_raw=True)
from mne.datasets import spm_face
from mne.preprocessing import ICA, create_eog_epochs
from mne import io, combine_evoked
from mne.minimum_norm import make_inverse_operator, apply_inverse
print(__doc__)
data_path = spm_face.data_path()
subjects_dir = data_path + '/subjects'
###############################################################################
# Load and filter data, set up epochs
raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D.ds'
raw = io.read_raw_ctf(raw_fname % 1, preload=True) # Take first run
# Here to save memory and time we'll downsample heavily -- this is not
# advised for real data as it can effectively jitter events!
raw.resample(120., npad='auto')
picks = mne.pick_types(raw.info, meg=True, exclude='bads')
raw.filter(1, 30, method='fir', fir_design='firwin')
events = mne.find_events(raw, stim_channel='UPPT001')
# plot the events to get an idea of the paradigm
mne.viz.plot_events(events, raw.info['sfreq'])
event_ids = {"faces": 1, "scrambled": 2}
tmin, tmax = -0.2, 0.6
def test_plot_ref_meg():
"""Test plotting ref_meg."""
raw_ctf = read_raw_ctf(ctf_fname_continuous).crop(0, 1).load_data()
raw_ctf.plot()
plt.close('all')
pytest.raises(ValueError, raw_ctf.plot, group_by='selection')
import mne
from mne.datasets.brainstorm import bst_raw
from mne.io import read_raw_ctf
print(__doc__)
tmin, tmax, event_id = -0.1, 0.3, 2 # take right-hand somato
reject = dict(mag=4e-12, eog=250e-6)
data_path = bst_raw.data_path()
raw_path = (data_path + '/MEG/bst_raw/' +
'subj001_somatosensory_20111109_01_AUX-f.ds')
# Here we crop to half the length to save memory
raw = read_raw_ctf(raw_path).crop(0, 180).load_data()
raw.plot()
# set EOG channel
raw.set_channel_types({'EEG058': 'eog'})
raw.set_eeg_reference('average', projection=True)
# show power line interference and remove it
raw.plot_psd(tmax=60., average=False)
raw.notch_filter(np.arange(60, 181, 60), fir_design='firwin')
events = mne.find_events(raw, stim_channel='UPPT001')
# pick MEG channels
picks = mne.pick_types(raw.info,
meg=True,
subject = 'bst_auditory'
subjects_dir = op.join(data_path, 'subjects')
raw_fname1 = op.join(data_path, 'MEG', 'bst_auditory',
'S01_AEF_20131218_01.ds')
raw_fname2 = op.join(data_path, 'MEG', 'bst_auditory',
'S01_AEF_20131218_02.ds')
erm_fname = op.join(data_path, 'MEG', 'bst_auditory',
'S01_Noise_20131218_01.ds')
###############################################################################
# In the memory saving mode we use ``preload=False`` and use the memory
# efficient IO which loads the data on demand. However, filtering and some
# other functions require the data to be preloaded in the memory.
preload = not use_precomputed
raw = read_raw_ctf(raw_fname1, preload=preload)
n_times_run1 = raw.n_times
mne.io.concatenate_raws([raw, read_raw_ctf(raw_fname2, preload=preload)])
raw_erm = read_raw_ctf(erm_fname, preload=preload)
###############################################################################
# Data channel array consisted of 274 MEG axial gradiometers, 26 MEG reference
# sensors and 2 EEG electrodes (Cz and Pz).
# In addition:
#
# - 1 stim channel for marking presentation times for the stimuli
# - 1 audio channel for the sent signal
# - 1 response channel for recording the button presses
# - 1 ECG bipolar
# - 2 EOG bipolar (vertical and horizontal)
# - 12 head tracking channels
def test_read_ctf():
"""Test CTF reader."""
temp_dir = _TempDir()
out_fname = op.join(temp_dir, 'test_py_raw.fif')
# Create a dummy .eeg file so we can test our reading/application of it
os.mkdir(op.join(temp_dir, 'randpos'))
ctf_eeg_fname = op.join(temp_dir, 'randpos', ctf_fname_catch)
shutil.copytree(op.join(ctf_dir, ctf_fname_catch), ctf_eeg_fname)
with pytest.warns(RuntimeWarning, match='RMSP .* changed to a MISC ch'):
raw = _test_raw_reader(read_raw_ctf, directory=ctf_eeg_fname)
picks = pick_types(raw.info, meg=False, eeg=True)
pos = np.random.RandomState(42).randn(len(picks), 3)
fake_eeg_fname = op.join(ctf_eeg_fname, 'catch-alp-good-f.eeg')
# Create a bad file
with open(fake_eeg_fname, 'wb') as fid:
fid.write('foo\n'.encode('ascii'))
pytest.raises(RuntimeError, read_raw_ctf, ctf_eeg_fname)
# Create a good file
with open(fake_eeg_fname, 'wb') as fid:
for ii, ch_num in enumerate(picks):
args = (str(ch_num + 1), raw.ch_names[ch_num],) + tuple(
'%0.5f' % x for x in 100 * pos[ii]) # convert to cm
fid.write(('\t'.join(args) + '\n').encode('ascii'))
pos_read_old = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
with pytest.warns(RuntimeWarning, match='RMSP .* changed to a MISC ch'):
raw = read_raw_ctf(ctf_eeg_fname) # read modified data
pos_read = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
assert_allclose(apply_trans(raw.info['ctf_head_t'], pos), pos_read,
rtol=1e-5, atol=1e-5)
assert (pos_read == pos_read_old).mean() < 0.1
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_randpos_raw.fif'),
op.join(temp_dir, 'randpos', 'catch-alp-good-f.ds_raw.fif'))
# Create a version with no hc, starting out *with* EEG pos (error)
os.mkdir(op.join(temp_dir, 'nohc'))
ctf_no_hc_fname = op.join(temp_dir, 'no_hc', ctf_fname_catch)
shutil.copytree(ctf_eeg_fname, ctf_no_hc_fname)
remove_base = op.join(ctf_no_hc_fname, op.basename(ctf_fname_catch[:-3]))
os.remove(remove_base + '.hc')
with pytest.warns(RuntimeWarning, match='MISC channel'):
pytest.raises(RuntimeError, read_raw_ctf, ctf_no_hc_fname)
os.remove(remove_base + '.eeg')
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_nohc_raw.fif'),
op.join(temp_dir, 'no_hc', 'catch-alp-good-f.ds_raw.fif'))
# All our files
use_fnames = [op.join(ctf_dir, c) for c in ctf_fnames]
for fname in use_fnames:
raw_c = read_raw_fif(fname + '_raw.fif', preload=True)
with pytest.warns(None): # sometimes matches "MISC channel"
raw = read_raw_ctf(fname)
# check info match
assert_array_equal(raw.ch_names, raw_c.ch_names)
assert_allclose(raw.times, raw_c.times)
assert_allclose(raw._cals, raw_c._cals)
assert_equal(raw.info['meas_id']['version'],
raw_c.info['meas_id']['version'] + 1)
for t in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_allclose(raw.info[t]['trans'], raw_c.info[t]['trans'],
rtol=1e-4, atol=1e-7)
for key in ('acq_pars', 'acq_stim', 'bads',
'ch_names', 'custom_ref_applied', 'description',
'events', 'experimenter', 'highpass', 'line_freq',
'lowpass', 'nchan', 'proj_id', 'proj_name',
'projs', 'sfreq', 'subject_info'):
assert_equal(raw.info[key], raw_c.info[key], key)
if op.basename(fname) not in single_trials:
# We don't force buffer size to be smaller like MNE-C
assert raw.buffer_size_sec == raw_c.buffer_size_sec
assert_equal(len(raw.info['comps']), len(raw_c.info['comps']))
for c1, c2 in zip(raw.info['comps'], raw_c.info['comps']):
for key in ('colcals', 'rowcals'):
assert_allclose(c1[key], c2[key])
assert_equal(c1['save_calibrated'], c2['save_calibrated'])
for key in ('row_names', 'col_names', 'nrow', 'ncol'):
assert_array_equal(c1['data'][key], c2['data'][key])
assert_allclose(c1['data']['data'], c2['data']['data'], atol=1e-7,
rtol=1e-5)
assert_allclose(raw.info['hpi_results'][0]['coord_trans']['trans'],
raw_c.info['hpi_results'][0]['coord_trans']['trans'],
rtol=1e-5, atol=1e-7)
assert_equal(len(raw.info['chs']), len(raw_c.info['chs']))
for ii, (c1, c2) in enumerate(zip(raw.info['chs'], raw_c.info['chs'])):
for key in ('kind', 'scanno', 'unit', 'ch_name', 'unit_mul',
'range', 'coord_frame', 'coil_type', 'logno'):
if c1['ch_name'] == 'RMSP' and \
'catch-alp-good-f' in fname and \
key in ('kind', 'unit', 'coord_frame', 'coil_type',
'logno'):
continue # XXX see below...
assert_equal(c1[key], c2[key], err_msg=key)
for key in ('cal',):
assert_allclose(c1[key], c2[key], atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# XXX 2016/02/24: fixed bug with normal computation that used
# to exist, once mne-C tools are updated we should update our FIF
# conversion files, then the slices can go away (and the check
# can be combined with that for "cal")
for key in ('loc',):
if c1['ch_name'] == 'RMSP' and 'catch-alp-good-f' in fname:
continue
if (c2[key][:3] == 0.).all():
check = [np.nan] * 3
else:
check = c2[key][:3]
assert_allclose(c1[key][:3], check, atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
if (c2[key][3:] == 0.).all():
check = [np.nan] * 3
else:
check = c2[key][9:12]
assert_allclose(c1[key][9:12], check, atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# Make sure all digitization points are in the MNE head coord frame
for p in raw.info['dig']:
assert_equal(p['coord_frame'], FIFF.FIFFV_COORD_HEAD,
err_msg='dig points must be in FIFF.FIFFV_COORD_HEAD')
if fname.endswith('catch-alp-good-f.ds'): # omit points from .pos file
raw.info['dig'] = raw.info['dig'][:-10]
# XXX: Next test would fail because c-tools assign the fiducials from
# CTF data as HPI. Should eventually clarify/unify with Matti.
# assert_dig_allclose(raw.info, raw_c.info)
# check data match
raw_c.save(out_fname, overwrite=True, buffer_size_sec=1.)
raw_read = read_raw_fif(out_fname)
# so let's check tricky cases based on sample boundaries
rng = np.random.RandomState(0)
pick_ch = rng.permutation(np.arange(len(raw.ch_names)))[:10]
bnd = int(round(raw.info['sfreq'] * raw.buffer_size_sec))
assert_equal(bnd, raw._raw_extras[0]['block_size'])
assert_equal(bnd, block_sizes[op.basename(fname)])
slices = (slice(0, bnd), slice(bnd - 1, bnd), slice(3, bnd),
slice(3, 300), slice(None))
if len(raw.times) >= 2 * bnd: # at least two complete blocks
slices = slices + (slice(bnd, 2 * bnd), slice(bnd, bnd + 1),
slice(0, bnd + 100))
for sl_time in slices:
assert_allclose(raw[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
assert_allclose(raw_read[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
# all data / preload
with pytest.warns(None): # sometimes MISC
raw = read_raw_ctf(fname, preload=True)
assert_allclose(raw[:][0], raw_c[:][0], atol=1e-15)
# test bad segment annotations
if 'testdata_ctf_short.ds' in fname:
assert 'bad' in raw.annotations.description[0]
assert_allclose(raw.annotations.onset, [2.15])
assert_allclose(raw.annotations.duration, [0.0225])
pytest.raises(TypeError, read_raw_ctf, 1)
pytest.raises(ValueError, read_raw_ctf, ctf_fname_continuous + 'foo.ds')
# test ignoring of system clock
read_raw_ctf(op.join(ctf_dir, ctf_fname_continuous), 'ignore')
pytest.raises(ValueError, read_raw_ctf,
op.join(ctf_dir, ctf_fname_continuous), 'foo')
###############################################################################
# Neuromag
# --------
kwargs = dict(eeg=False, coord_frame='meg', show_axes=True, verbose=True)
raw = read_raw_fif(sample.data_path() + '/MEG/sample/sample_audvis_raw.fif')
fig = plot_alignment(raw.info, meg=('helmet', 'sensors'), **kwargs)
set_3d_title(figure=fig, title='Neuromag')
###############################################################################
# CTF
# ---
raw = read_raw_ctf(spm_face.data_path() +
'/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds')
fig = plot_alignment(raw.info, meg=('helmet', 'sensors', 'ref'), **kwargs)
set_3d_title(figure=fig, title='CTF 275')
###############################################################################
# BTi
# ---
bti_path = op.abspath(op.dirname(mne.__file__)) + '/io/bti/tests/data/'
raw = read_raw_bti(op.join(bti_path, 'test_pdf_linux'),
op.join(bti_path, 'test_config_linux'),
op.join(bti_path, 'test_hs_linux'))
fig = plot_alignment(raw.info, meg=('helmet', 'sensors', 'ref'), **kwargs)
set_3d_title(figure=fig, title='Magnes 3600wh')
###############################################################################
fname_bhv = list()
files = os.listdir(op.join(path_data, subject, 'behavdata'))
fname_bhv.extend(([
op.join(fname_raw + '/behavdata/') + f for f in files if 'WorkMem' in f
]))
for fname_behavior in fname_bhv:
events_behavior = get_events_from_mat(fname_behavior)
# Read raw MEG data and extract event triggers
runs = list()
files = os.listdir(fname_raw)
runs.extend(([op.join(fname_raw + '/') + f for f in files if '.ds' in f]))
events_meg = list()
for run_number, this_run in enumerate(runs):
fname_raw = op.join(path_data, subject, this_run)
print(fname_raw)
raw = read_raw_ctf(fname_raw, preload=True, system_clock='ignore')
channel_trigger = np.where(np.array(raw.ch_names) == 'USPT001')[0][0]
# trigger baseline is 255
# Replace 255 values with 0 for easier reading of events
trigger_baseline = np.where(raw._data[channel_trigger, :] == 255)[0]
raw._data[channel_trigger, trigger_baseline] = 0.
# find triggers
events_meg_ = mne.find_events(raw)
# Add 48ms to the trigger events (according to delay with photodiod)
events_meg_ = np.array(events_meg_, float)
events_meg_[:, 0] += round(.048 * raw.info['sfreq'])
events_meg_ = np.array(events_meg_, int)
# to keep the run from which the event was found
events_meg_[:, 1] = run_number
events_meg.append(events_meg_)
# concatenate all meg events
subject = 'bst_auditory'
subjects_dir = op.join(data_path, 'subjects')
raw_fname1 = op.join(data_path, 'MEG', 'bst_auditory',
'S01_AEF_20131218_01.ds')
raw_fname2 = op.join(data_path, 'MEG', 'bst_auditory',
'S01_AEF_20131218_02.ds')
erm_fname = op.join(data_path, 'MEG', 'bst_auditory',
'S01_Noise_20131218_01.ds')
###############################################################################
# In the memory saving mode we use ``preload=False`` and use the memory
# efficient IO which loads the data on demand. However, filtering and some
# other functions require the data to be preloaded in the memory.
preload = not use_precomputed
raw = read_raw_ctf(raw_fname1, preload=preload)
n_times_run1 = raw.n_times
mne.io.concatenate_raws([raw, read_raw_ctf(raw_fname2, preload=preload)])
raw_erm = read_raw_ctf(erm_fname, preload=preload)
###############################################################################
# Data channel array consisted of 274 MEG axial gradiometers, 26 MEG reference
# sensors and 2 EEG electrodes (Cz and Pz).
# In addition:
#
# - 1 stim channel for marking presentation times for the stimuli
# - 1 audio channel for the sent signal
# - 1 response channel for recording the button presses
# - 1 ECG bipolar
# - 2 EOG bipolar (vertical and horizontal)
# - 12 head tracking channels
if len(sys.argv) != 3:
print("usage: {} subject $ds".format(sys.argv[0]))
sys.exit(1)
subject = sys.argv[1]
dsname = sys.argv[2]
try:
FShome = os.environ['FREESURFER_HOME']
except KeyError:
print("You must set the FREESURFER_HOME environment variable!")
sys.exit(1)
try:
Subjdir = os.environ['SUBJECTS_DIR']
except KeyError:
Subjdir = op.join(FShome, "subjects")
print("Note: Using the default SUBJECTS_DIR:", Subjdir)
name = op.join(Subjdir, subject, "bem", "{}-fiducials.fif".format(subject))
fids = read_fiducials(name)
fidc = _fiducial_coords(fids[0])
raw = read_raw_ctf(dsname, clean_names = True, preload = False)
fidd = _fiducial_coords(raw.info['dig'])
xform = fit_matched_points(fidd, fidc, weights = [1, 10, 1])
t = Transform(FIFF.FIFFV_COORD_HEAD, FIFF.FIFFV_COORD_MRI, xform)
name = op.join(Subjdir, subject, "bem", "{}-trans.fif".format(subject))
write_trans(name, t)