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_read_selection():
"""Test reading of selections."""
# test one channel for each selection
ch_names = ['MEG 2211', 'MEG 0223', 'MEG 1312', 'MEG 0412', 'MEG 1043',
'MEG 2042', 'MEG 2032', 'MEG 0522', 'MEG 1031']
sel_names = ['Vertex', 'Left-temporal', 'Right-temporal', 'Left-parietal',
'Right-parietal', 'Left-occipital', 'Right-occipital',
'Left-frontal', 'Right-frontal']
raw = read_raw_fif(raw_fname)
for i, name in enumerate(sel_names):
sel = read_selection(name)
assert_true(ch_names[i] in sel)
sel_info = read_selection(name, info=raw.info)
assert_equal(sel, sel_info)
# test some combinations
all_ch = read_selection(['L', 'R'])
left = read_selection('L')
right = read_selection('R')
assert_true(len(all_ch) == len(left) + len(right))
assert_true(len(set(left).intersection(set(right))) == 0)
frontal = read_selection('frontal')
occipital = read_selection('Right-occipital')
assert_true(len(set(frontal).intersection(set(occipital))) == 0)
ch_names_new = [ch.replace(' ', '') for ch in ch_names]
raw_new = read_raw_fif(raw_new_fname)
for i, name in enumerate(sel_names):
sel = read_selection(name, info=raw_new.info)
assert_true(ch_names_new[i] in sel)
assert_raises(TypeError, read_selection, name, info='foo')
def test_chpi_subtraction():
"""Test subtraction of cHPI signals."""
raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes', preload=True)
raw.info['bads'] = ['MEG0111']
raw.del_proj()
with catch_logging() as log:
filter_chpi(raw, include_line=False, verbose=True)
assert 'No average EEG' not in log.getvalue()
assert '5 cHPI' in log.getvalue()
# MaxFilter doesn't do quite as well as our algorithm with the last bit
raw.crop(0, 16)
# remove cHPI status chans
raw_c = read_raw_fif(sss_hpisubt_fname).crop(0, 16).load_data()
raw_c.pick_types(
meg=True, eeg=True, eog=True, ecg=True, stim=True, misc=True)
assert_meg_snr(raw, raw_c, 143, 624)
# Degenerate cases
raw_nohpi = read_raw_fif(test_fif_fname, preload=True)
pytest.raises(RuntimeError, filter_chpi, raw_nohpi)
# When MaxFliter downsamples, like::
# $ maxfilter -nosss -ds 2 -f test_move_anon_raw.fif \
# -o test_move_anon_ds2_raw.fif
# it can strip out some values of info, which we emulate here:
raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes')
raw = raw.crop(0, 1).load_data().resample(600., npad='auto')
raw.info['lowpass'] = 200.
del raw.info['maxshield']
del raw.info['hpi_results'][0]['moments']
del raw.info['hpi_subsystem']['event_channel']
with catch_logging() as log:
filter_chpi(raw, verbose=True)
pytest.raises(ValueError, filter_chpi, raw, t_window=-1)
assert '2 cHPI' in log.getvalue()
def test_calculate_chpi_positions():
"""Test calculation of cHPI positions."""
trans, rot, t = head_pos_to_trans_rot_t(read_head_pos(pos_fname))
raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes', preload=True,
add_eeg_ref=False)
t -= raw.first_samp / raw.info['sfreq']
quats = _calculate_chpi_positions(raw, verbose='debug')
trans_est, rot_est, t_est = head_pos_to_trans_rot_t(quats)
_compare_positions((trans, rot, t), (trans_est, rot_est, t_est), 0.003)
# degenerate conditions
raw_no_chpi = read_raw_fif(test_fif_fname, add_eeg_ref=False)
assert_raises(RuntimeError, _calculate_chpi_positions, raw_no_chpi)
raw_bad = raw.copy()
for d in raw_bad.info['dig']:
if d['kind'] == FIFF.FIFFV_POINT_HPI:
d['coord_frame'] = 999
break
assert_raises(RuntimeError, _calculate_chpi_positions, raw_bad)
raw_bad = raw.copy()
for d in raw_bad.info['dig']:
if d['kind'] == FIFF.FIFFV_POINT_HPI:
d['r'] = np.ones(3)
raw_bad.crop(0, 1., copy=False)
with warnings.catch_warnings(record=True): # bad pos
with catch_logging() as log_file:
_calculate_chpi_positions(raw_bad, verbose=True)
# ignore HPI info header and [done] footer
assert_true('0/5 good' in log_file.getvalue().strip().split('\n')[-2])
# half the rate cuts off cHPI coils
with warnings.catch_warnings(record=True): # uint cast suggestion
raw.resample(300., npad='auto')
assert_raises_regex(RuntimeError, 'above the',
_calculate_chpi_positions, raw)
def test_mne_c_design():
"""Test MNE-C filter design."""
tempdir = _TempDir()
temp_fname = op.join(tempdir, 'test_raw.fif')
out_fname = op.join(tempdir, 'test_c_raw.fif')
x = np.zeros((1, 10001))
x[0, 5000] = 1.
time_sl = slice(5000 - 4096, 5000 + 4097)
sfreq = 1000.
RawArray(x, create_info(1, sfreq, 'eeg')).save(temp_fname)
tols = dict(rtol=1e-4, atol=1e-4)
cmd = ('mne_process_raw', '--projoff', '--raw', temp_fname,
'--save', out_fname)
run_subprocess(cmd)
h = design_mne_c_filter(sfreq, None, 40)
h_c = read_raw_fif(out_fname)[0][0][0][time_sl]
assert_allclose(h, h_c, **tols)
run_subprocess(cmd + ('--highpass', '5', '--highpassw', '2.5'))
h = design_mne_c_filter(sfreq, 5, 40, 2.5)
h_c = read_raw_fif(out_fname)[0][0][0][time_sl]
assert_allclose(h, h_c, **tols)
run_subprocess(cmd + ('--lowpass', '1000', '--highpass', '10'))
h = design_mne_c_filter(sfreq, 10, None, verbose=True)
h_c = read_raw_fif(out_fname)[0][0][0][time_sl]
assert_allclose(h, h_c, **tols)
def test_bad_proj():
"""Test dealing with bad projection application."""
raw = read_raw_fif(raw_fname, preload=True)
events = read_events(event_fname)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[2:9:3]
_check_warnings(raw, events, picks)
# still bad
raw.pick_channels([raw.ch_names[ii] for ii in picks])
_check_warnings(raw, events)
# "fixed"
raw.info.normalize_proj() # avoid projection warnings
_check_warnings(raw, events, count=0)
# eeg avg ref is okay
raw = read_raw_fif(raw_fname, preload=True).pick_types(meg=False, eeg=True)
raw.set_eeg_reference()
_check_warnings(raw, events, count=0)
raw.info['bads'] = raw.ch_names[:10]
_check_warnings(raw, events, count=0)
raw = read_raw_fif(raw_fname)
assert_raises(ValueError, raw.del_proj, 'foo')
n_proj = len(raw.info['projs'])
raw.del_proj(0)
assert_equal(len(raw.info['projs']), n_proj - 1)
raw.del_proj()
assert_equal(len(raw.info['projs']), 0)
def test_set_eeg_reference():
"""Test rereference eeg data."""
raw = read_raw_fif(fif_fname, preload=True)
raw.info['projs'] = []
# Test setting an average reference
assert_true(not _has_eeg_average_ref_proj(raw.info['projs']))
reref, ref_data = set_eeg_reference(raw)
assert_true(_has_eeg_average_ref_proj(reref.info['projs']))
assert_true(not reref.info['projs'][0]['active'])
assert_true(ref_data is None)
reref.apply_proj()
eeg_chans = [raw.ch_names[ch]
for ch in pick_types(raw.info, meg=False, eeg=True)]
_test_reference(raw, reref, ref_data,
[ch for ch in eeg_chans if ch not in raw.info['bads']])
# Test setting an average reference when one was already present
with warnings.catch_warnings(record=True):
reref, ref_data = set_eeg_reference(raw, copy=False)
assert_true(ref_data is None)
# Test setting an average reference on non-preloaded data
raw_nopreload = read_raw_fif(fif_fname, preload=False)
raw_nopreload.info['projs'] = []
reref, ref_data = set_eeg_reference(raw_nopreload)
assert_true(_has_eeg_average_ref_proj(reref.info['projs']))
assert_true(not reref.info['projs'][0]['active'])
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'], copy=True)
assert_true(reref.info['custom_ref_applied'])
_test_reference(raw, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert_true(raw is reref)
# Test moving from custom to average reference
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'])
reref, _ = set_eeg_reference(reref)
assert_true(_has_eeg_average_ref_proj(reref.info['projs']))
assert_equal(reref.info['custom_ref_applied'], False)
# When creating an average reference fails, make sure the
# custom_ref_applied flag remains untouched.
reref = raw.copy()
reref.info['custom_ref_applied'] = True
reref.pick_types(eeg=False) # Cause making average ref fail
assert_raises(ValueError, set_eeg_reference, reref)
assert_true(reref.info['custom_ref_applied'])
# Test moving from average to custom reference
reref, ref_data = set_eeg_reference(raw)
reref, _ = set_eeg_reference(reref, ['EEG 001', 'EEG 002'])
assert_true(not _has_eeg_average_ref_proj(reref.info['projs']))
assert_equal(reref.info['custom_ref_applied'], True)
def test_set_channel_types():
"""Test set_channel_types"""
raw = read_raw_fif(raw_fname)
# Error Tests
# Test channel name exists in ch_names
mapping = {'EEG 160': 'EEG060'}
assert_raises(ValueError, raw.set_channel_types, mapping)
# Test change to illegal channel type
mapping = {'EOG 061': 'xxx'}
assert_raises(ValueError, raw.set_channel_types, mapping)
# Test changing type if in proj (avg eeg ref here)
mapping = {'EEG 058': 'ecog', 'EEG 059': 'ecg', 'EEG 060': 'eog',
'EOG 061': 'seeg', 'MEG 2441': 'eeg', 'MEG 2443': 'eeg',
'MEG 2442': 'hbo'}
assert_raises(RuntimeError, raw.set_channel_types, mapping)
# Test type change
raw2 = read_raw_fif(raw_fname)
raw2.info['bads'] = ['EEG 059', 'EEG 060', 'EOG 061']
with warnings.catch_warnings(record=True): # MEG channel change
assert_raises(RuntimeError, raw2.set_channel_types, mapping) # has prj
raw2.add_proj([], remove_existing=True)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw2.set_channel_types(mapping)
assert_true(len(w) >= 1, msg=[str(ww.message) for ww in w])
assert_true(all('The unit for channel' in str(ww.message) for ww in w))
info = raw2.info
assert_true(info['chs'][372]['ch_name'] == 'EEG 058')
assert_true(info['chs'][372]['kind'] == FIFF.FIFFV_ECOG_CH)
assert_true(info['chs'][372]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][372]['coil_type'] == FIFF.FIFFV_COIL_EEG)
assert_true(info['chs'][373]['ch_name'] == 'EEG 059')
assert_true(info['chs'][373]['kind'] == FIFF.FIFFV_ECG_CH)
assert_true(info['chs'][373]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][373]['coil_type'] == FIFF.FIFFV_COIL_NONE)
assert_true(info['chs'][374]['ch_name'] == 'EEG 060')
assert_true(info['chs'][374]['kind'] == FIFF.FIFFV_EOG_CH)
assert_true(info['chs'][374]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][374]['coil_type'] == FIFF.FIFFV_COIL_NONE)
assert_true(info['chs'][375]['ch_name'] == 'EOG 061')
assert_true(info['chs'][375]['kind'] == FIFF.FIFFV_SEEG_CH)
assert_true(info['chs'][375]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][375]['coil_type'] == FIFF.FIFFV_COIL_EEG)
for idx in pick_channels(raw.ch_names, ['MEG 2441', 'MEG 2443']):
assert_true(info['chs'][idx]['kind'] == FIFF.FIFFV_EEG_CH)
assert_true(info['chs'][idx]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][idx]['coil_type'] == FIFF.FIFFV_COIL_EEG)
idx = pick_channels(raw.ch_names, ['MEG 2442'])[0]
assert_true(info['chs'][idx]['kind'] == FIFF.FIFFV_FNIRS_CH)
assert_true(info['chs'][idx]['unit'] == FIFF.FIFF_UNIT_MOL)
assert_true(info['chs'][idx]['coil_type'] == FIFF.FIFFV_COIL_FNIRS_HBO)
# Test meaningful error when setting channel type with unknown unit
raw.info['chs'][0]['unit'] = 0.
ch_types = {raw.ch_names[0]: 'misc'}
assert_raises(ValueError, raw.set_channel_types, ch_types)
def test_raw():
"""Test bti conversion to Raw object."""
for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
exported_fnames):
# rx = 2 if 'linux' in pdf else 0
pytest.raises(ValueError, read_raw_bti, pdf, 'eggs', preload=False)
pytest.raises(ValueError, read_raw_bti, pdf, config, 'spam',
preload=False)
if op.exists(tmp_raw_fname):
os.remove(tmp_raw_fname)
ex = read_raw_fif(exported, preload=True)
ra = read_raw_bti(pdf, config, hs, preload=False)
assert ('RawBTi' in repr(ra))
assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
assert_array_almost_equal(ex.info['dev_head_t']['trans'],
ra.info['dev_head_t']['trans'], 7)
assert len(ex.info['dig']) in (3563, 5154)
assert_dig_allclose(ex.info, ra.info, limit=100)
coil1, coil2 = [np.concatenate([d['loc'].flatten()
for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)]
assert_array_almost_equal(coil1, coil2, 7)
loc1, loc2 = [np.concatenate([d['loc'].flatten()
for d in r_.info['chs'][:NCH]])
for r_ in (ra, ex)]
assert_allclose(loc1, loc2)
assert_allclose(ra[:NCH][0], ex[:NCH][0])
assert_array_equal([c['range'] for c in ra.info['chs'][:NCH]],
[c['range'] for c in ex.info['chs'][:NCH]])
assert_array_equal([c['cal'] for c in ra.info['chs'][:NCH]],
[c['cal'] for c in ex.info['chs'][:NCH]])
assert_array_equal(ra._cals[:NCH], ex._cals[:NCH])
# check our transforms
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
if ex.info[key] is None:
pass
else:
assert (ra.info[key] is not None)
for ent in ('to', 'from', 'trans'):
assert_allclose(ex.info[key][ent],
ra.info[key][ent])
ra.save(tmp_raw_fname)
re = read_raw_fif(tmp_raw_fname)
print(re)
for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert (isinstance(re.info[key], dict))
this_t = re.info[key]['trans']
assert_equal(this_t.shape, (4, 4))
# check that matrix by is not identity
assert (not np.allclose(this_t, np.eye(4)))
os.remove(tmp_raw_fname)
def test_hpi_info():
"""Test getting HPI info"""
tempdir = _TempDir()
temp_name = op.join(tempdir, 'temp_raw.fif')
for fname in (chpi_fif_fname, sss_fif_fname):
raw = read_raw_fif(fname, allow_maxshield='yes')
assert_true(len(raw.info['hpi_subsystem']) > 0)
raw.save(temp_name, overwrite=True)
raw_2 = read_raw_fif(temp_name, allow_maxshield='yes')
assert_equal(len(raw_2.info['hpi_subsystem']),
len(raw.info['hpi_subsystem']))
def test_calculate_chpi_positions():
"""Test calculation of cHPI positions."""
# Check to make sure our fits match MF decently
mf_quats = read_head_pos(pos_fname)
raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes', preload=True)
# This is a little hack (aliasing while decimating) to make it much faster
# for testing purposes only. We can relax this later if we find it breaks
# something.
raw_dec = _decimate_chpi(raw, 15)
with catch_logging() as log:
py_quats = _calculate_chpi_positions(raw_dec, t_step_max=1.,
verbose='debug')
assert_true(log.getvalue().startswith('HPIFIT'))
_assert_quats(py_quats, mf_quats, dist_tol=0.004, angle_tol=2.5)
# degenerate conditions
raw_no_chpi = read_raw_fif(test_fif_fname)
assert_raises(RuntimeError, _calculate_chpi_positions, raw_no_chpi)
raw_bad = raw.copy()
del raw_bad.info['hpi_meas'][0]['hpi_coils'][0]['coil_freq']
assert_raises(RuntimeError, _calculate_chpi_positions, raw_bad)
raw_bad = raw.copy()
for d in raw_bad.info['dig']:
if d['kind'] == FIFF.FIFFV_POINT_HPI:
d['coord_frame'] = FIFF.FIFFV_COORD_UNKNOWN
break
assert_raises(RuntimeError, _calculate_chpi_positions, raw_bad)
for d in raw_bad.info['dig']:
if d['kind'] == FIFF.FIFFV_POINT_HPI:
d['coord_frame'] = FIFF.FIFFV_COORD_HEAD
d['r'] = np.ones(3)
raw_bad.crop(0, 1.)
picks = np.concatenate([np.arange(306, len(raw_bad.ch_names)),
pick_types(raw_bad.info, meg=True)[::16]])
raw_bad.pick_channels([raw_bad.ch_names[pick] for pick in picks])
with warnings.catch_warnings(record=True): # bad pos
with catch_logging() as log_file:
_calculate_chpi_positions(raw_bad, t_step_min=1., verbose=True)
# ignore HPI info header and [done] footer
assert_true('0/5 good' in log_file.getvalue().strip().split('\n')[-2])
# half the rate cuts off cHPI coils
raw.info['lowpass'] /= 2.
assert_raises_regex(RuntimeError, 'above the',
_calculate_chpi_positions, raw)
# test on 5k artemis data
raw = read_raw_artemis123(art_fname, preload=True)
mf_quats = read_head_pos(art_mc_fname)
with catch_logging() as log:
py_quats = _calculate_chpi_positions(raw, t_step_min=2.,
verbose='debug')
_assert_quats(py_quats, mf_quats, dist_tol=0.004, angle_tol=2.5)
def test_set_channel_types():
"""Test set_channel_types."""
raw = read_raw_fif(raw_fname)
# Error Tests
# Test channel name exists in ch_names
mapping = {'EEG 160': 'EEG060'}
pytest.raises(ValueError, raw.set_channel_types, mapping)
# Test change to illegal channel type
mapping = {'EOG 061': 'xxx'}
pytest.raises(ValueError, raw.set_channel_types, mapping)
# Test changing type if in proj (avg eeg ref here)
mapping = {'EEG 058': 'ecog', 'EEG 059': 'ecg', 'EEG 060': 'eog',
'EOG 061': 'seeg', 'MEG 2441': 'eeg', 'MEG 2443': 'eeg',
'MEG 2442': 'hbo'}
pytest.raises(RuntimeError, raw.set_channel_types, mapping)
# Test type change
raw2 = read_raw_fif(raw_fname)
raw2.info['bads'] = ['EEG 059', 'EEG 060', 'EOG 061']
pytest.raises(RuntimeError, raw2.set_channel_types, mapping) # has prj
raw2.add_proj([], remove_existing=True)
with pytest.warns(RuntimeWarning, match='The unit for channel'):
raw2.set_channel_types(mapping)
info = raw2.info
assert info['chs'][372]['ch_name'] == 'EEG 058'
assert info['chs'][372]['kind'] == FIFF.FIFFV_ECOG_CH
assert info['chs'][372]['unit'] == FIFF.FIFF_UNIT_V
assert info['chs'][372]['coil_type'] == FIFF.FIFFV_COIL_EEG
assert info['chs'][373]['ch_name'] == 'EEG 059'
assert info['chs'][373]['kind'] == FIFF.FIFFV_ECG_CH
assert info['chs'][373]['unit'] == FIFF.FIFF_UNIT_V
assert info['chs'][373]['coil_type'] == FIFF.FIFFV_COIL_NONE
assert info['chs'][374]['ch_name'] == 'EEG 060'
assert info['chs'][374]['kind'] == FIFF.FIFFV_EOG_CH
assert info['chs'][374]['unit'] == FIFF.FIFF_UNIT_V
assert info['chs'][374]['coil_type'] == FIFF.FIFFV_COIL_NONE
assert info['chs'][375]['ch_name'] == 'EOG 061'
assert info['chs'][375]['kind'] == FIFF.FIFFV_SEEG_CH
assert info['chs'][375]['unit'] == FIFF.FIFF_UNIT_V
assert info['chs'][375]['coil_type'] == FIFF.FIFFV_COIL_EEG
for idx in pick_channels(raw.ch_names, ['MEG 2441', 'MEG 2443']):
assert info['chs'][idx]['kind'] == FIFF.FIFFV_EEG_CH
assert info['chs'][idx]['unit'] == FIFF.FIFF_UNIT_V
assert info['chs'][idx]['coil_type'] == FIFF.FIFFV_COIL_EEG
idx = pick_channels(raw.ch_names, ['MEG 2442'])[0]
assert info['chs'][idx]['kind'] == FIFF.FIFFV_FNIRS_CH
assert info['chs'][idx]['unit'] == FIFF.FIFF_UNIT_MOL
assert info['chs'][idx]['coil_type'] == FIFF.FIFFV_COIL_FNIRS_HBO
# Test meaningful error when setting channel type with unknown unit
raw.info['chs'][0]['unit'] = 0.
ch_types = {raw.ch_names[0]: 'misc'}
pytest.raises(ValueError, raw.set_channel_types, ch_types)
def test_read_selection():
"""Test reading of selections"""
# test one channel for each selection
ch_names = [
"MEG 2211",
"MEG 0223",
"MEG 1312",
"MEG 0412",
"MEG 1043",
"MEG 2042",
"MEG 2032",
"MEG 0522",
"MEG 1031",
]
sel_names = [
"Vertex",
"Left-temporal",
"Right-temporal",
"Left-parietal",
"Right-parietal",
"Left-occipital",
"Right-occipital",
"Left-frontal",
"Right-frontal",
]
raw = read_raw_fif(raw_fname)
for i, name in enumerate(sel_names):
sel = read_selection(name)
assert_true(ch_names[i] in sel)
sel_info = read_selection(name, info=raw.info)
assert_equal(sel, sel_info)
# test some combinations
all_ch = read_selection(["L", "R"])
left = read_selection("L")
right = read_selection("R")
assert_true(len(all_ch) == len(left) + len(right))
assert_true(len(set(left).intersection(set(right))) == 0)
frontal = read_selection("frontal")
occipital = read_selection("Right-occipital")
assert_true(len(set(frontal).intersection(set(occipital))) == 0)
ch_names_new = [ch.replace(" ", "") for ch in ch_names]
raw_new = read_raw_fif(raw_new_fname)
for i, name in enumerate(sel_names):
sel = read_selection(name, info=raw_new.info)
assert_true(ch_names_new[i] in sel)
assert_raises(TypeError, read_selection, name, info="foo")
def test_cov_estimation_on_raw():
"""Test estimation from raw (typically empty room)"""
tempdir = _TempDir()
raw = read_raw_fif(raw_fname, preload=True)
cov_mne = read_cov(erm_cov_fname)
# The pure-string uses the more efficient numpy-based method, the
# the list gets triaged to compute_covariance (should be equivalent
# but use more memory)
for method in (None, ['empirical']): # None is cast to 'empirical'
cov = compute_raw_covariance(raw, tstep=None, method=method)
assert_equal(cov.ch_names, cov_mne.ch_names)
assert_equal(cov.nfree, cov_mne.nfree)
assert_snr(cov.data, cov_mne.data, 1e4)
cov = compute_raw_covariance(raw, method=method) # tstep=0.2 (default)
assert_equal(cov.nfree, cov_mne.nfree - 119) # cutoff some samples
assert_snr(cov.data, cov_mne.data, 1e2)
# test IO when computation done in Python
cov.save(op.join(tempdir, 'test-cov.fif')) # test saving
cov_read = read_cov(op.join(tempdir, 'test-cov.fif'))
assert_true(cov_read.ch_names == cov.ch_names)
assert_true(cov_read.nfree == cov.nfree)
assert_array_almost_equal(cov.data, cov_read.data)
# test with a subset of channels
picks = pick_channels(raw.ch_names, include=raw.ch_names[:5])
raw_pick = raw.copy().pick_channels(
[raw.ch_names[pick] for pick in picks])
raw_pick.info.normalize_proj()
cov = compute_raw_covariance(raw_pick, picks=picks, tstep=None,
method=method)
assert_true(cov_mne.ch_names[:5] == cov.ch_names)
assert_snr(cov.data, cov_mne.data[picks][:, picks], 1e4)
cov = compute_raw_covariance(raw_pick, picks=picks, method=method)
assert_snr(cov.data, cov_mne.data[picks][:, picks], 90) # cutoff samps
# make sure we get a warning with too short a segment
raw_2 = read_raw_fif(raw_fname).crop(0, 1, copy=False)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
cov = compute_raw_covariance(raw_2, method=method)
assert_true(any('Too few samples' in str(ww.message) for ww in w))
# no epochs found due to rejection
assert_raises(ValueError, compute_raw_covariance, raw, tstep=None,
method='empirical', reject=dict(eog=200e-6))
# but this should work
cov = compute_raw_covariance(raw.copy().crop(0, 10., copy=False),
tstep=None, method=method,
reject=dict(eog=1000e-6))
def test_cov_estimation_on_raw(method, tmpdir):
"""Test estimation from raw (typically empty room)."""
tempdir = str(tmpdir)
raw = read_raw_fif(raw_fname, preload=True)
cov_mne = read_cov(erm_cov_fname)
# The pure-string uses the more efficient numpy-based method, the
# the list gets triaged to compute_covariance (should be equivalent
# but use more memory)
with pytest.warns(None): # can warn about EEG ref
cov = compute_raw_covariance(raw, tstep=None, method=method,
rank='full')
assert_equal(cov.ch_names, cov_mne.ch_names)
assert_equal(cov.nfree, cov_mne.nfree)
assert_snr(cov.data, cov_mne.data, 1e4)
# tstep=0.2 (default)
with pytest.warns(None): # can warn about EEG ref
cov = compute_raw_covariance(raw, method=method, rank='full')
assert_equal(cov.nfree, cov_mne.nfree - 119) # cutoff some samples
assert_snr(cov.data, cov_mne.data, 1e2)
# test IO when computation done in Python
cov.save(op.join(tempdir, 'test-cov.fif')) # test saving
cov_read = read_cov(op.join(tempdir, 'test-cov.fif'))
assert cov_read.ch_names == cov.ch_names
assert cov_read.nfree == cov.nfree
assert_array_almost_equal(cov.data, cov_read.data)
# test with a subset of channels
raw_pick = raw.copy().pick_channels(raw.ch_names[:5])
raw_pick.info.normalize_proj()
cov = compute_raw_covariance(raw_pick, tstep=None, method=method,
rank='full')
assert cov_mne.ch_names[:5] == cov.ch_names
assert_snr(cov.data, cov_mne.data[:5, :5], 1e4)
cov = compute_raw_covariance(raw_pick, method=method, rank='full')
assert_snr(cov.data, cov_mne.data[:5, :5], 90) # cutoff samps
# make sure we get a warning with too short a segment
raw_2 = read_raw_fif(raw_fname).crop(0, 1)
with pytest.warns(RuntimeWarning, match='Too few samples'):
cov = compute_raw_covariance(raw_2, method=method)
# no epochs found due to rejection
pytest.raises(ValueError, compute_raw_covariance, raw, tstep=None,
method='empirical', reject=dict(eog=200e-6))
# but this should work
cov = compute_raw_covariance(raw.copy().crop(0, 10.),
tstep=None, method=method,
reject=dict(eog=1000e-6),
verbose='error')
def test_get_set_sensor_positions():
"""Test get/set functions for sensor positions."""
raw1 = read_raw_fif(raw_fname)
picks = pick_types(raw1.info, meg=False, eeg=True)
pos = np.array([ch['loc'][:3] for ch in raw1.info['chs']])[picks]
raw_pos = raw1._get_channel_positions(picks=picks)
assert_array_equal(raw_pos, pos)
ch_name = raw1.info['ch_names'][13]
pytest.raises(ValueError, raw1._set_channel_positions, [1, 2], ['name'])
raw2 = read_raw_fif(raw_fname)
raw2.info['chs'][13]['loc'][:3] = np.array([1, 2, 3])
raw1._set_channel_positions([[1, 2, 3]], [ch_name])
assert_array_equal(raw1.info['chs'][13]['loc'],
raw2.info['chs'][13]['loc'])
def test_apply_mne_inverse_raw():
"""Test MNE with precomputed inverse operator on Raw."""
start = 3
stop = 10
raw = read_raw_fif(fname_raw)
label_lh = read_label(fname_label % 'Aud-lh')
_, times = raw[0, start:stop]
inverse_operator = read_inverse_operator(fname_full)
inverse_operator = prepare_inverse_operator(inverse_operator, nave=1,
lambda2=lambda2, method="dSPM")
for pick_ori in [None, "normal", "vector"]:
stc = apply_inverse_raw(raw, inverse_operator, lambda2, "dSPM",
label=label_lh, start=start, stop=stop, nave=1,
pick_ori=pick_ori, buffer_size=None,
prepared=True)
stc2 = apply_inverse_raw(raw, inverse_operator, lambda2, "dSPM",
label=label_lh, start=start, stop=stop,
nave=1, pick_ori=pick_ori,
buffer_size=3, prepared=True)
if pick_ori is None:
assert_true(np.all(stc.data > 0))
assert_true(np.all(stc2.data > 0))
assert_true(stc.subject == 'sample')
assert_true(stc2.subject == 'sample')
assert_array_almost_equal(stc.times, times)
assert_array_almost_equal(stc2.times, times)
assert_array_almost_equal(stc.data, stc2.data)
def test_cov_mismatch():
"""Test estimation with MEG<->Head mismatch."""
raw = read_raw_fif(raw_fname).crop(0, 5).load_data()
events = find_events(raw, stim_channel='STI 014')
raw.pick_channels(raw.ch_names[:5])
raw.add_proj([], remove_existing=True)
epochs = Epochs(raw, events, None, tmin=-0.2, tmax=0., preload=True)
for kind in ('shift', 'None'):
epochs_2 = epochs.copy()
# This should be fine
with warnings.catch_warnings(record=True) as w:
compute_covariance([epochs, epochs_2])
assert_equal(len(w), 0)
if kind == 'shift':
epochs_2.info['dev_head_t']['trans'][:3, 3] += 0.001
else: # None
epochs_2.info['dev_head_t'] = None
assert_raises(ValueError, compute_covariance, [epochs, epochs_2])
assert_equal(len(w), 0)
compute_covariance([epochs, epochs_2], on_mismatch='ignore')
assert_equal(len(w), 0)
compute_covariance([epochs, epochs_2], on_mismatch='warn')
assert_raises(ValueError, compute_covariance, epochs,
on_mismatch='x')
assert_true(any('transform mismatch' in str(ww.message) for ww in w))
# This should work
epochs.info['dev_head_t'] = None
epochs_2.info['dev_head_t'] = None
compute_covariance([epochs, epochs_2], method=None)
def test_calculate_chpi_coil_locs():
"""Test computing just cHPI locations."""
raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes', preload=True)
# This is a little hack (aliasing while decimating) to make it much faster
# for testing purposes only. We can relax this later if we find it breaks
# something.
raw_dec = _decimate_chpi(raw, 15)
times, cHPI_digs = _calculate_chpi_coil_locs(raw_dec, verbose='debug')
# spot check
assert_allclose(times[9], 9.9, atol=1e-3)
assert_allclose(cHPI_digs[9][2]['r'],
[-0.01937833, 0.00346804, 0.06331209], atol=1e-3)
assert_allclose(cHPI_digs[9][2]['gof'], 0.9957976, atol=1e-3)
assert_allclose(cHPI_digs[9][4]['r'],
[0.05442122, 0.00997692, 0.03721696], atol=1e-3)
assert_allclose(cHPI_digs[9][4]['gof'], 0.075700080794629199, atol=1e-3)
# test on 5k artemis data
raw = read_raw_artemis123(art_fname, preload=True)
times, cHPI_digs = _calculate_chpi_coil_locs(raw, verbose='debug')
assert_allclose(times[2], 2.9, atol=1e-3)
assert_allclose(cHPI_digs[2][0]['gof'], 0.9980471794552791, atol=1e-3)
assert_allclose(cHPI_digs[2][0]['r'],
[-0.0157762, 0.06655744, 0.00545172], atol=1e-3)
def test_unsupervised_spatial_filter():
"""Test unsupervised spatial filter."""
from sklearn.decomposition import PCA
from sklearn.kernel_ridge import KernelRidge
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
preload=True, baseline=None, verbose=False)
# Test estimator
assert_raises(ValueError, UnsupervisedSpatialFilter, KernelRidge(2))
# Test fit
X = epochs.get_data()
n_components = 4
usf = UnsupervisedSpatialFilter(PCA(n_components))
usf.fit(X)
usf1 = UnsupervisedSpatialFilter(PCA(n_components))
# test transform
assert_equal(usf.transform(X).ndim, 3)
# test fit_transform
assert_array_almost_equal(usf.transform(X), usf1.fit_transform(X))
# assert shape
assert_equal(usf.transform(X).shape[1], n_components)
# Test with average param
usf = UnsupervisedSpatialFilter(PCA(4), average=True)
usf.fit_transform(X)
assert_raises(ValueError, UnsupervisedSpatialFilter, PCA(4), 2)
def test_define_events():
"""Test defining response events."""
events = read_events(fname)
raw = read_raw_fif(raw_fname)
events_, _ = define_target_events(events, 5, 32, raw.info['sfreq'],
.2, 0.7, 42, 99)
n_target = events[events[:, 2] == 5].shape[0]
n_miss = events_[events_[:, 2] == 99].shape[0]
n_target_ = events_[events_[:, 2] == 42].shape[0]
assert_true(n_target_ == (n_target - n_miss))
events = np.array([[0, 0, 1],
[375, 0, 2],
[500, 0, 1],
[875, 0, 3],
[1000, 0, 1],
[1375, 0, 3],
[1100, 0, 1],
[1475, 0, 2],
[1500, 0, 1],
[1875, 0, 2]])
true_lag_nofill = [1500., 1500., 1500.]
true_lag_fill = [1500., np.nan, np.nan, 1500., 1500.]
n, lag_nofill = define_target_events(events, 1, 2, 250., 1.4, 1.6, 5)
n, lag_fill = define_target_events(events, 1, 2, 250., 1.4, 1.6, 5, 99)
assert_array_equal(true_lag_fill, lag_fill)
assert_array_equal(true_lag_nofill, lag_nofill)
def test_plot_ica_overlay():
"""Test plotting of ICA cleaning."""
import matplotlib.pyplot as plt
raw = _get_raw(preload=True)
picks = _get_picks(raw)
ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
max_pca_components=3, n_pca_components=3)
# can't use info.normalize_proj here because of how and when ICA and Epochs
# objects do picking of Raw data
with pytest.warns(RuntimeWarning, match='projection'):
ica.fit(raw, picks=picks)
# don't test raw, needs preload ...
with pytest.warns(RuntimeWarning, match='projection'):
ecg_epochs = create_ecg_epochs(raw, picks=picks)
ica.plot_overlay(ecg_epochs.average())
with pytest.warns(RuntimeWarning, match='projection'):
eog_epochs = create_eog_epochs(raw, picks=picks)
ica.plot_overlay(eog_epochs.average())
pytest.raises(TypeError, ica.plot_overlay, raw[:2, :3][0])
ica.plot_overlay(raw)
plt.close('all')
# smoke test for CTF
raw = read_raw_fif(raw_ctf_fname)
raw.apply_gradient_compensation(3)
picks = pick_types(raw.info, meg=True, ref_meg=False)
ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
ica.fit(raw, picks=picks)
with pytest.warns(RuntimeWarning, match='longer than'):
ecg_epochs = create_ecg_epochs(raw)
ica.plot_overlay(ecg_epochs.average())
plt.close('all')
def test_eog_channel(method):
"""Test that EOG channel is included when performing ICA."""
_skip_check_picard(method)
raw = read_raw_fif(raw_fname, preload=True)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=True, ecg=False,
eog=True, exclude='bads')
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
n_components = 0.9
ica = ICA(n_components=n_components, method=method)
# Test case for MEG and EOG data. Should have EOG channel
for inst in [raw, epochs]:
picks1a = pick_types(inst.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[:4]
picks1b = pick_types(inst.info, meg=False, stim=False, ecg=False,
eog=True, exclude='bads')
picks1 = np.append(picks1a, picks1b)
ica.fit(inst, picks=picks1)
assert (any('EOG' in ch for ch in ica.ch_names))
# Test case for MEG data. Should have no EOG channel
for inst in [raw, epochs]:
picks1 = pick_types(inst.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[:5]
ica.fit(inst, picks=picks1)
assert not any('EOG' in ch for ch in ica.ch_names)
def test_n_components_and_max_pca_components_none(method):
"""Test n_components and max_pca_components=None."""
_skip_check_picard(method)
raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
events = read_events(event_name)
picks = pick_types(raw.info, eeg=True, meg=False)
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
max_pca_components = None
n_components = None
random_state = 12345
tempdir = _TempDir()
output_fname = op.join(tempdir, 'test_ica-ica.fif')
ica = ICA(max_pca_components=max_pca_components, method=method,
n_components=n_components, random_state=random_state)
with pytest.warns(None): # convergence
ica.fit(epochs)
ica.save(output_fname)
ica = read_ica(output_fname)
# ICA.fit() replaced max_pca_components, which was previously None,
# with the appropriate integer value.
assert_equal(ica.max_pca_components, epochs.info['nchan'])
assert ica.n_components is None
def test_ica_rank_reduction(method):
"""Test recovery ICA rank reduction."""
_skip_check_picard(method)
# Most basic recovery
raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data()
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[:10]
n_components = 5
max_pca_components = len(picks)
for n_pca_components in [6, 10]:
with pytest.warns(UserWarning, match='did not converge'):
ica = ICA(n_components=n_components,
max_pca_components=max_pca_components,
n_pca_components=n_pca_components,
method=method, max_iter=1).fit(raw, picks=picks)
rank_before = _compute_rank_int(raw.copy().pick(picks), proj=False)
assert_equal(rank_before, len(picks))
raw_clean = ica.apply(raw.copy())
rank_after = _compute_rank_int(raw_clean.copy().pick(picks),
proj=False)
# interaction between ICA rejection and PCA components difficult
# to preduct. Rank_after often seems to be 1 higher then
# n_pca_components
assert (n_components < n_pca_components <= rank_after <=
rank_before)
def test_ica_reset(method):
"""Test ICA resetting."""
_skip_check_picard(method)
raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data()
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[:10]
run_time_attrs = (
'pre_whitener_',
'unmixing_matrix_',
'mixing_matrix_',
'n_components_',
'n_samples_',
'pca_components_',
'pca_explained_variance_',
'pca_mean_'
)
with pytest.warns(UserWarning, match='did not converge'):
ica = ICA(
n_components=3, max_pca_components=3, n_pca_components=3,
method=method, max_iter=1).fit(raw, picks=picks)
assert (all(hasattr(ica, attr) for attr in run_time_attrs))
assert ica.labels_ is not None
ica._reset()
assert (not any(hasattr(ica, attr) for attr in run_time_attrs))
assert ica.labels_ is not None
def test_min_distance_fit_dipole():
"""Test dipole min_dist to inner_skull."""
subject = 'sample'
raw = read_raw_fif(fname_raw, preload=True)
# select eeg data
picks = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
info = pick_info(raw.info, picks)
# Let's use cov = Identity
cov = read_cov(fname_cov)
cov['data'] = np.eye(cov['data'].shape[0])
# Simulated scal map
simulated_scalp_map = np.zeros(picks.shape[0])
simulated_scalp_map[27:34] = 1
simulated_scalp_map = simulated_scalp_map[:, None]
evoked = EvokedArray(simulated_scalp_map, info, tmin=0)
min_dist = 5. # distance in mm
bem = read_bem_solution(fname_bem)
dip, residual = fit_dipole(evoked, cov, bem, fname_trans,
min_dist=min_dist)
dist = _compute_depth(dip, fname_bem, fname_trans, subject, subjects_dir)
# Constraints are not exact, so bump the minimum slightly
assert_true(min_dist - 0.1 < (dist[0] * 1000.) < (min_dist + 1.))
assert_raises(ValueError, fit_dipole, evoked, cov, fname_bem, fname_trans,
-1.)
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_ch_loc():
"""Test raw kit loc."""
raw_py = read_raw_kit(sqd_path, mrk_path, elp_txt_path, hsp_txt_path,
stim='<')
raw_bin = read_raw_fif(op.join(data_dir, 'test_bin_raw.fif'))
ch_py = np.array([ch['loc'] for ch in
raw_py._raw_extras[0]['channels'][:160]])
# ch locs stored as m, not mm
ch_py[:, :3] *= 1e3
ch_sns = read_sns(op.join(data_dir, 'sns.txt'))
assert_array_almost_equal(ch_py, ch_sns, 2)
assert_array_almost_equal(raw_py.info['dev_head_t']['trans'],
raw_bin.info['dev_head_t']['trans'], 4)
for py_ch, bin_ch in zip(raw_py.info['chs'], raw_bin.info['chs']):
if bin_ch['ch_name'].startswith('MEG'):
# the stored ch locs have more precision than the sns.txt
assert_array_almost_equal(py_ch['loc'], bin_ch['loc'], decimal=2)
# test when more than one marker file provided
mrks = [mrk_path, mrk2_path, mrk3_path]
read_raw_kit(sqd_path, mrks, elp_txt_path, hsp_txt_path, preload=False)
# this dataset does not have the equivalent set of points :(
raw_bin.info['dig'] = raw_bin.info['dig'][:8]
raw_py.info['dig'] = raw_py.info['dig'][:8]
assert_dig_allclose(raw_py.info, raw_bin.info)
def test_drop_channels():
"""Test if dropping channels works with various arguments."""
raw = read_raw_fif(raw_fname, preload=True).crop(0, 0.1)
raw.drop_channels(["MEG 0111"]) # list argument
raw.drop_channels("MEG 0112") # str argument
pytest.raises(ValueError, raw.drop_channels, ["MEG 0111", 5])
pytest.raises(ValueError, raw.drop_channels, 5) # must be list or str
def test_find_events():
"""Test find events in raw file."""
events = read_events(fname)
raw = read_raw_fif(raw_fname, preload=True)
# let's test the defaulting behavior while we're at it
extra_ends = ['', '_1']
orig_envs = [os.getenv('MNE_STIM_CHANNEL%s' % s) for s in extra_ends]
os.environ['MNE_STIM_CHANNEL'] = 'STI 014'
if 'MNE_STIM_CHANNEL_1' in os.environ:
del os.environ['MNE_STIM_CHANNEL_1']
events2 = find_events(raw)
assert_array_almost_equal(events, events2)
# now test with mask
events11 = find_events(raw, mask=3, mask_type='not_and')
with pytest.warns(RuntimeWarning, match='events masked'):
events22 = read_events(fname, mask=3, mask_type='not_and')
assert_array_equal(events11, events22)
# Reset some data for ease of comparison
raw._first_samps[0] = 0
raw.info['sfreq'] = 1000
stim_channel = 'STI 014'
stim_channel_idx = pick_channels(raw.info['ch_names'],
include=[stim_channel])
# test digital masking
raw._data[stim_channel_idx, :5] = np.arange(5)
raw._data[stim_channel_idx, 5:] = 0
# 1 == '0b1', 2 == '0b10', 3 == '0b11', 4 == '0b100'
pytest.raises(TypeError, find_events, raw, mask="0", mask_type='and')
pytest.raises(ValueError, find_events, raw, mask=0, mask_type='blah')
# testing mask_type. default = 'not_and'
assert_array_equal(
find_events(raw, shortest_event=1, mask=1, mask_type='not_and'),
[[2, 0, 2], [4, 2, 4]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=2, mask_type='not_and'),
[[1, 0, 1], [3, 0, 1], [4, 1, 4]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=3, mask_type='not_and'),
[[4, 0, 4]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=4, mask_type='not_and'),
[[1, 0, 1], [2, 1, 2], [3, 2, 3]])
# testing with mask_type = 'and'
assert_array_equal(
find_events(raw, shortest_event=1, mask=1, mask_type='and'),
[[1, 0, 1], [3, 0, 1]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=2, mask_type='and'),
[[2, 0, 2]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=3, mask_type='and'),
[[1, 0, 1], [2, 1, 2], [3, 2, 3]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=4, mask_type='and'),
[[4, 0, 4]])
# test empty events channel
raw._data[stim_channel_idx, :] = 0
assert_array_equal(find_events(raw), np.empty((0, 3), dtype='int32'))
raw._data[stim_channel_idx, :4] = 1
assert_array_equal(find_events(raw), np.empty((0, 3), dtype='int32'))
raw._data[stim_channel_idx, -1:] = 9
assert_array_equal(find_events(raw), [[14399, 0, 9]])
# Test that we can handle consecutive events with no gap
raw._data[stim_channel_idx, 10:20] = 5
raw._data[stim_channel_idx, 20:30] = 6
raw._data[stim_channel_idx, 30:32] = 5
raw._data[stim_channel_idx, 40] = 6
assert_array_equal(find_events(raw, consecutive=False),
[[10, 0, 5], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(
find_events(raw, consecutive=True),
[[10, 0, 5], [20, 5, 6], [30, 6, 5], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(find_events(raw),
[[10, 0, 5], [20, 5, 6], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(find_events(raw, output='offset', consecutive=False),
[[31, 0, 5], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(
find_events(raw, output='offset', consecutive=True),
[[19, 6, 5], [29, 5, 6], [31, 0, 5], [40, 0, 6], [14399, 0, 9]])
pytest.raises(ValueError,
find_events,
raw,
output='step',
consecutive=True)
assert_array_equal(
find_events(raw, output='step', consecutive=True, shortest_event=1),
[[10, 0, 5], [20, 5, 6], [30, 6, 5], [32, 5, 0], [40, 0, 6],
[41, 6, 0], [14399, 0, 9], [14400, 9, 0]])
assert_array_equal(find_events(raw, output='offset'),
[[19, 6, 5], [31, 0, 6], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(find_events(raw, consecutive=False, min_duration=0.002),
[[10, 0, 5]])
assert_array_equal(find_events(raw, consecutive=True, min_duration=0.002),
[[10, 0, 5], [20, 5, 6], [30, 6, 5]])
assert_array_equal(
find_events(raw,
output='offset',
consecutive=False,
min_duration=0.002), [[31, 0, 5]])
assert_array_equal(
find_events(raw, output='offset', consecutive=True,
min_duration=0.002), [[19, 6, 5], [29, 5, 6], [31, 0, 5]])
assert_array_equal(find_events(raw, consecutive=True, min_duration=0.003),
[[10, 0, 5], [20, 5, 6]])
# test find_stim_steps merge parameter
raw._data[stim_channel_idx, :] = 0
raw._data[stim_channel_idx, 0] = 1
raw._data[stim_channel_idx, 10] = 4
raw._data[stim_channel_idx, 11:20] = 5
assert_array_equal(
find_stim_steps(raw, pad_start=0, merge=0, stim_channel=stim_channel),
[[0, 0, 1], [1, 1, 0], [10, 0, 4], [11, 4, 5], [20, 5, 0]])
assert_array_equal(
find_stim_steps(raw, merge=-1, stim_channel=stim_channel),
[[1, 1, 0], [10, 0, 5], [20, 5, 0]])
assert_array_equal(
find_stim_steps(raw, merge=1, stim_channel=stim_channel),
[[1, 1, 0], [11, 0, 5], [20, 5, 0]])
# put back the env vars we trampled on
for s, o in zip(extra_ends, orig_envs):
if o is not None:
os.environ['MNE_STIM_CHANNEL%s' % s] = o
# Test with list of stim channels
raw._data[stim_channel_idx, 1:101] = np.zeros(100)
raw._data[stim_channel_idx, 10:11] = 1
raw._data[stim_channel_idx, 30:31] = 3
stim_channel2 = 'STI 015'
stim_channel2_idx = pick_channels(raw.info['ch_names'],
include=[stim_channel2])
raw._data[stim_channel2_idx, :] = 0
raw._data[stim_channel2_idx, :100] = raw._data[stim_channel_idx, 5:105]
events1 = find_events(raw, stim_channel='STI 014')
events2 = events1.copy()
events2[:, 0] -= 5
events = find_events(raw, stim_channel=['STI 014', stim_channel2])
assert_array_equal(events[::2], events2)
assert_array_equal(events[1::2], events1)
# test initial_event argument
info = create_info(['MYSTI'], 1000, 'stim')
data = np.zeros((1, 1000))
raw = RawArray(data, info)
data[0, :10] = 100
data[0, 30:40] = 200
assert_array_equal(find_events(raw, 'MYSTI'), [[30, 0, 200]])
assert_array_equal(find_events(raw, 'MYSTI', initial_event=True),
[[0, 0, 100], [30, 0, 200]])
# test error message for raw without stim channels
raw = read_raw_fif(raw_fname, preload=True)
raw.pick_types(meg=True, stim=False)
# raw does not have annotations
with pytest.raises(ValueError, match="'stim_channel'"):
find_events(raw)
# if raw has annotations, we show a different error message
raw.set_annotations(Annotations(0, 2, "test"))
with pytest.raises(ValueError, match="mne.events_from_annotations"):
find_events(raw)
def test_getitem_epochsTFR():
"""Test GetEpochsMixin in the context of EpochsTFR."""
from pandas import DataFrame
# Setup for reading the raw data and select a few trials
raw = read_raw_fif(raw_fname)
events = read_events(event_fname)
# create fake data, test with and without dropping epochs
for n_drop_epochs in [0, 2]:
n_events = 12
# create fake metadata
rng = np.random.RandomState(42)
rt = rng.uniform(size=(n_events, ))
trialtypes = np.array(['face', 'place'])
trial = trialtypes[(rng.uniform(size=(n_events, )) > .5).astype(int)]
meta = DataFrame(dict(RT=rt, Trial=trial))
event_id = dict(a=1, b=2, c=3, d=4)
epochs = Epochs(raw,
events[:n_events],
event_id=event_id,
metadata=meta,
decim=1)
epochs.drop(np.arange(n_drop_epochs))
n_events -= n_drop_epochs
freqs = np.arange(12., 17., 2.) # define frequencies of interest
n_cycles = freqs / 2. # 0.5 second time windows for all frequencies
# Choose time x (full) bandwidth product
time_bandwidth = 4.0
# With 0.5 s time windows, this gives 8 Hz smoothing
kwargs = dict(freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
time_bandwidth=time_bandwidth,
return_itc=False,
average=False,
n_jobs=1)
power = tfr_multitaper(epochs, **kwargs)
# Check that power and epochs metadata is the same
assert_metadata_equal(epochs.metadata, power.metadata)
assert_metadata_equal(epochs[::2].metadata, power[::2].metadata)
assert_metadata_equal(epochs['RT < .5'].metadata,
power['RT < .5'].metadata)
assert_array_equal(epochs.selection, power.selection)
assert epochs.drop_log == power.drop_log
# Check that get power is functioning
assert_array_equal(power[3:6].data, power.data[3:6])
assert_array_equal(power[3:6].events, power.events[3:6])
assert_array_equal(epochs.selection[3:6], power.selection[3:6])
indx_check = (power.metadata['Trial'] == 'face')
try:
indx_check = indx_check.to_numpy()
except Exception:
pass # older Pandas
indx_check = indx_check.nonzero()
assert_array_equal(power['Trial == "face"'].events,
power.events[indx_check])
assert_array_equal(power['Trial == "face"'].data,
power.data[indx_check])
# Check that the wrong Key generates a Key Error for Metadata search
with pytest.raises(KeyError):
power['Trialz == "place"']
# Test length function
assert len(power) == n_events
assert len(power[3:6]) == 3
# Test iteration function
for ind, power_ep in enumerate(power):
assert_array_equal(power_ep, power.data[ind])
if ind == 5:
break
# Test that current state is maintained
assert_array_equal(power.next(), power.data[ind + 1])
# Check decim affects sfreq
power_decim = tfr_multitaper(epochs, decim=2, **kwargs)
assert power.info['sfreq'] / 2. == power_decim.info['sfreq']
def test_time_frequency():
"""Test time-frequency transform (PSD and ITC)."""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.498 # Allows exhaustive decimation testing
# Setup for reading the raw data
raw = read_raw_fif(raw_fname)
events = read_events(event_fname)
include = []
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info,
meg='grad',
eeg=False,
stim=False,
include=include,
exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks)
data = epochs.get_data()
times = epochs.times
nave = len(data)
epochs_nopicks = Epochs(raw, events, event_id, tmin, tmax)
freqs = np.arange(6, 20, 5) # define frequencies of interest
n_cycles = freqs / 4.
# Test first with a single epoch
power, itc = tfr_morlet(epochs[0],
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=True)
# Now compute evoked
evoked = epochs.average()
pytest.raises(ValueError, tfr_morlet, evoked, freqs, 1., return_itc=True)
power, itc = tfr_morlet(epochs,
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=True)
power_, itc_ = tfr_morlet(epochs,
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=True,
decim=slice(0, 2))
# Test picks argument and average parameter
pytest.raises(ValueError,
tfr_morlet,
epochs,
freqs=freqs,
n_cycles=n_cycles,
return_itc=True,
average=False)
power_picks, itc_picks = \
tfr_morlet(epochs_nopicks,
freqs=freqs, n_cycles=n_cycles, use_fft=True,
return_itc=True, picks=picks, average=True)
epochs_power_picks = \
tfr_morlet(epochs_nopicks,
freqs=freqs, n_cycles=n_cycles, use_fft=True,
return_itc=False, picks=picks, average=False)
power_picks_avg = epochs_power_picks.average()
# the actual data arrays here are equivalent, too...
assert_allclose(power.data, power_picks.data)
assert_allclose(power.data, power_picks_avg.data)
assert_allclose(itc.data, itc_picks.data)
# test on evoked
power_evoked = tfr_morlet(evoked,
freqs,
n_cycles,
use_fft=True,
return_itc=False)
# one is squared magnitude of the average (evoked) and
# the other is average of the squared magnitudes (epochs PSD)
# so values shouldn't match, but shapes should
assert_array_equal(power.data.shape, power_evoked.data.shape)
pytest.raises(AssertionError, assert_allclose, power.data,
power_evoked.data)
# complex output
pytest.raises(ValueError,
tfr_morlet,
epochs,
freqs,
n_cycles,
return_itc=False,
average=True,
output="complex")
pytest.raises(ValueError,
tfr_morlet,
epochs,
freqs,
n_cycles,
output="complex",
average=False,
return_itc=True)
epochs_power_complex = tfr_morlet(epochs,
freqs,
n_cycles,
output="complex",
average=False,
return_itc=False)
epochs_amplitude_2 = abs(epochs_power_complex)
epochs_amplitude_3 = epochs_amplitude_2.copy()
epochs_amplitude_3.data[:] = np.inf # test that it's actually copied
# test that the power computed via `complex` is equivalent to power
# computed within the method.
assert_allclose(epochs_amplitude_2.data**2, epochs_power_picks.data)
print(itc) # test repr
print(itc.ch_names) # test property
itc += power # test add
itc -= power # test sub
ret = itc * 23 # test mult
itc = ret / 23 # test dic
power = power.apply_baseline(baseline=(-0.1, 0), mode='logratio')
assert 'meg' in power
assert 'grad' in power
assert 'mag' not in power
assert 'eeg' not in power
assert power.nave == nave
assert itc.nave == nave
assert (power.data.shape == (len(picks), len(freqs), len(times)))
assert (power.data.shape == itc.data.shape)
assert (power_.data.shape == (len(picks), len(freqs), 2))
assert (power_.data.shape == itc_.data.shape)
assert (np.sum(itc.data >= 1) == 0)
assert (np.sum(itc.data <= 0) == 0)
# grand average
itc2 = itc.copy()
itc2.info['bads'] = [itc2.ch_names[0]] # test channel drop
gave = grand_average([itc2, itc])
assert gave.data.shape == (itc2.data.shape[0] - 1, itc2.data.shape[1],
itc2.data.shape[2])
assert itc2.ch_names[1:] == gave.ch_names
assert gave.nave == 2
itc2.drop_channels(itc2.info["bads"])
assert_allclose(gave.data, itc2.data)
itc2.data = np.ones(itc2.data.shape)
itc.data = np.zeros(itc.data.shape)
itc2.nave = 2
itc.nave = 1
itc.drop_channels([itc.ch_names[0]])
combined_itc = combine_tfr([itc2, itc])
assert_allclose(combined_itc.data,
np.ones(combined_itc.data.shape) * 2 / 3)
# more tests
power, itc = tfr_morlet(epochs,
freqs=freqs,
n_cycles=2,
use_fft=False,
return_itc=True)
assert (power.data.shape == (len(picks), len(freqs), len(times)))
assert (power.data.shape == itc.data.shape)
assert (np.sum(itc.data >= 1) == 0)
assert (np.sum(itc.data <= 0) == 0)
tfr = tfr_morlet(epochs[0],
freqs,
use_fft=True,
n_cycles=2,
average=False,
return_itc=False)
tfr_data = tfr.data[0]
assert (tfr_data.shape == (len(picks), len(freqs), len(times)))
tfr2 = tfr_morlet(epochs[0],
freqs,
use_fft=True,
n_cycles=2,
decim=slice(0, 2),
average=False,
return_itc=False).data[0]
assert (tfr2.shape == (len(picks), len(freqs), 2))
single_power = tfr_morlet(epochs,
freqs,
2,
average=False,
return_itc=False).data
single_power2 = tfr_morlet(epochs,
freqs,
2,
decim=slice(0, 2),
average=False,
return_itc=False).data
single_power3 = tfr_morlet(epochs,
freqs,
2,
decim=slice(1, 3),
average=False,
return_itc=False).data
single_power4 = tfr_morlet(epochs,
freqs,
2,
decim=slice(2, 4),
average=False,
return_itc=False).data
assert_allclose(np.mean(single_power, axis=0), power.data)
assert_allclose(np.mean(single_power2, axis=0), power.data[:, :, :2])
assert_allclose(np.mean(single_power3, axis=0), power.data[:, :, 1:3])
assert_allclose(np.mean(single_power4, axis=0), power.data[:, :, 2:4])
power_pick = power.pick_channels(power.ch_names[:10:2])
assert_equal(len(power_pick.ch_names), len(power.ch_names[:10:2]))
assert_equal(power_pick.data.shape[0], len(power.ch_names[:10:2]))
power_drop = power.drop_channels(power.ch_names[1:10:2])
assert_equal(power_drop.ch_names, power_pick.ch_names)
assert_equal(power_pick.data.shape[0], len(power_drop.ch_names))
power_pick, power_drop = mne.equalize_channels([power_pick, power_drop])
assert_equal(power_pick.ch_names, power_drop.ch_names)
assert_equal(power_pick.data.shape, power_drop.data.shape)
# Test decimation:
# 2: multiple of len(times) even
# 3: multiple odd
# 8: not multiple, even
# 9: not multiple, odd
for decim in [2, 3, 8, 9]:
for use_fft in [True, False]:
power, itc = tfr_morlet(epochs,
freqs=freqs,
n_cycles=2,
use_fft=use_fft,
return_itc=True,
decim=decim)
assert_equal(power.data.shape[2],
np.ceil(float(len(times)) / decim))
freqs = list(range(50, 55))
decim = 2
_, n_chan, n_time = data.shape
tfr = tfr_morlet(epochs[0],
freqs,
2.,
decim=decim,
average=False,
return_itc=False).data[0]
assert_equal(tfr.shape, (n_chan, len(freqs), n_time // decim))
# Test cwt modes
Ws = morlet(512, [10, 20], n_cycles=2)
pytest.raises(ValueError, cwt, data[0, :, :], Ws, mode='foo')
for use_fft in [True, False]:
for mode in ['same', 'valid', 'full']:
cwt(data[0], Ws, use_fft=use_fft, mode=mode)
# Test invalid frequency arguments
with pytest.raises(ValueError, match=" 'freqs' must be greater than 0"):
tfr_morlet(epochs, freqs=np.arange(0, 3), n_cycles=7)
with pytest.raises(ValueError, match=" 'freqs' must be greater than 0"):
tfr_morlet(epochs, freqs=np.arange(-4, -1), n_cycles=7)
# Test decim parameter checks
pytest.raises(TypeError,
tfr_morlet,
epochs,
freqs=freqs,
n_cycles=n_cycles,
use_fft=True,
return_itc=True,
decim='decim')
# When convolving in time, wavelets must not be longer than the data
pytest.raises(ValueError,
cwt,
data[0, :, :Ws[0].size - 1],
Ws,
use_fft=False)
with pytest.warns(UserWarning, match='one of the wavelets.*is longer'):
cwt(data[0, :, :Ws[0].size - 1], Ws, use_fft=True)
# Check for off-by-one errors when using wavelets with an even number of
# samples
psd = cwt(data[0], [Ws[0][:-1]], use_fft=False, mode='full')
assert_equal(psd.shape, (2, 1, 420))
def test_set_eeg_reference():
"""Test rereference eeg data."""
raw = read_raw_fif(fif_fname, preload=True)
raw.info['projs'] = []
# Test setting an average reference projection
assert (not _has_eeg_average_ref_proj(raw.info['projs']))
reref, ref_data = set_eeg_reference(raw, projection=True)
assert (_has_eeg_average_ref_proj(reref.info['projs']))
assert (not reref.info['projs'][0]['active'])
assert (ref_data is None)
reref.apply_proj()
eeg_chans = [raw.ch_names[ch]
for ch in pick_types(raw.info, meg=False, eeg=True)]
_test_reference(raw, reref, ref_data,
[ch for ch in eeg_chans if ch not in raw.info['bads']])
# Test setting an average reference when one was already present
with pytest.warns(RuntimeWarning, match='untouched'):
reref, ref_data = set_eeg_reference(raw, copy=False, projection=True)
assert ref_data is None
# Test setting an average reference on non-preloaded data
raw_nopreload = read_raw_fif(fif_fname, preload=False)
raw_nopreload.info['projs'] = []
reref, ref_data = set_eeg_reference(raw_nopreload, projection=True)
assert (_has_eeg_average_ref_proj(reref.info['projs']))
assert (not reref.info['projs'][0]['active'])
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'], copy=True)
assert (reref.info['custom_ref_applied'])
_test_reference(raw, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert (raw is reref)
# Test moving from custom to average reference
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'])
reref, _ = set_eeg_reference(reref, projection=True)
assert (_has_eeg_average_ref_proj(reref.info['projs']))
assert_equal(reref.info['custom_ref_applied'], False)
# When creating an average reference fails, make sure the
# custom_ref_applied flag remains untouched.
reref = raw.copy()
reref.info['custom_ref_applied'] = True
reref.pick_types(eeg=False) # Cause making average ref fail
pytest.raises(ValueError, set_eeg_reference, reref, projection=True)
assert (reref.info['custom_ref_applied'])
# Test moving from average to custom reference
reref, ref_data = set_eeg_reference(raw, projection=True)
reref, _ = set_eeg_reference(reref, ['EEG 001', 'EEG 002'])
assert not _has_eeg_average_ref_proj(reref.info['projs'])
assert len(reref.info['projs']) == 0
assert_equal(reref.info['custom_ref_applied'], True)
# Test that disabling the reference does not change the data
assert _has_eeg_average_ref_proj(raw.info['projs'])
reref, _ = set_eeg_reference(raw, [])
assert_array_equal(raw._data, reref._data)
assert not _has_eeg_average_ref_proj(reref.info['projs'])
# make sure ref_channels=[] removes average reference projectors
assert _has_eeg_average_ref_proj(raw.info['projs'])
reref, _ = set_eeg_reference(raw, [])
assert (not _has_eeg_average_ref_proj(reref.info['projs']))
# Test that average reference gives identical results when calculated
# via SSP projection (projection=True) or directly (projection=False)
raw.info['projs'] = []
reref_1, _ = set_eeg_reference(raw.copy(), projection=True)
reref_1.apply_proj()
reref_2, _ = set_eeg_reference(raw.copy(), projection=False)
assert_allclose(reref_1._data, reref_2._data, rtol=1e-6, atol=1e-15)
# Test average reference without projection
reref, ref_data = set_eeg_reference(raw.copy(), ref_channels="average",
projection=False)
_test_reference(raw, reref, ref_data, eeg_chans)
with pytest.raises(ValueError, match='supported for ref_channels="averag'):
set_eeg_reference(raw, [], True, True)
with pytest.raises(ValueError, match='supported for ref_channels="averag'):
set_eeg_reference(raw, ['EEG 001'], True, True)
# gh-6454
rng = np.random.RandomState(0)
data = rng.randn(3, 1000)
raw = RawArray(data, create_info(3, 1000., ['ecog'] * 2 + ['misc']))
reref, ref_data = set_eeg_reference(raw.copy())
_test_reference(raw, reref, ref_data, ['0', '1'])
def test_set_channel_types():
"""Test set_channel_types"""
raw = read_raw_fif(raw_fname)
# Error Tests
# Test channel name exists in ch_names
mapping = {'EEG 160': 'EEG060'}
assert_raises(ValueError, raw.set_channel_types, mapping)
# Test change to illegal channel type
mapping = {'EOG 061': 'xxx'}
assert_raises(ValueError, raw.set_channel_types, mapping)
# Test changing type if in proj (avg eeg ref here)
mapping = {
'EEG 058': 'ecog',
'EEG 059': 'ecg',
'EEG 060': 'eog',
'EOG 061': 'seeg',
'MEG 2441': 'eeg',
'MEG 2443': 'eeg',
'MEG 2442': 'hbo'
}
assert_raises(RuntimeError, raw.set_channel_types, mapping)
# Test type change
raw2 = read_raw_fif(raw_fname)
raw2.info['bads'] = ['EEG 059', 'EEG 060', 'EOG 061']
with warnings.catch_warnings(record=True): # MEG channel change
assert_raises(RuntimeError, raw2.set_channel_types, mapping) # has prj
raw2.add_proj([], remove_existing=True)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw2.set_channel_types(mapping)
assert_true(len(w) >= 1, msg=[str(ww.message) for ww in w])
assert_true(all('The unit for channel' in str(ww.message) for ww in w))
info = raw2.info
assert_true(info['chs'][372]['ch_name'] == 'EEG 058')
assert_true(info['chs'][372]['kind'] == FIFF.FIFFV_ECOG_CH)
assert_true(info['chs'][372]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][372]['coil_type'] == FIFF.FIFFV_COIL_EEG)
assert_true(info['chs'][373]['ch_name'] == 'EEG 059')
assert_true(info['chs'][373]['kind'] == FIFF.FIFFV_ECG_CH)
assert_true(info['chs'][373]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][373]['coil_type'] == FIFF.FIFFV_COIL_NONE)
assert_true(info['chs'][374]['ch_name'] == 'EEG 060')
assert_true(info['chs'][374]['kind'] == FIFF.FIFFV_EOG_CH)
assert_true(info['chs'][374]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][374]['coil_type'] == FIFF.FIFFV_COIL_NONE)
assert_true(info['chs'][375]['ch_name'] == 'EOG 061')
assert_true(info['chs'][375]['kind'] == FIFF.FIFFV_SEEG_CH)
assert_true(info['chs'][375]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][375]['coil_type'] == FIFF.FIFFV_COIL_EEG)
for idx in pick_channels(raw.ch_names, ['MEG 2441', 'MEG 2443']):
assert_true(info['chs'][idx]['kind'] == FIFF.FIFFV_EEG_CH)
assert_true(info['chs'][idx]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][idx]['coil_type'] == FIFF.FIFFV_COIL_EEG)
idx = pick_channels(raw.ch_names, ['MEG 2442'])[0]
assert_true(info['chs'][idx]['kind'] == FIFF.FIFFV_FNIRS_CH)
assert_true(info['chs'][idx]['unit'] == FIFF.FIFF_UNIT_MOL)
assert_true(info['chs'][idx]['coil_type'] == FIFF.FIFFV_COIL_FNIRS_HBO)
# Test meaningful error when setting channel type with unknown unit
raw.info['chs'][0]['unit'] = 0.
ch_types = {raw.ch_names[0]: 'misc'}
assert_raises(ValueError, raw.set_channel_types, ch_types)
def test_psd():
"""Tests the welch and multitaper PSD
"""
raw = io.read_raw_fif(raw_fname)
picks_psd = [0, 1]
# Populate raw with sinusoids
rng = np.random.RandomState(40)
data = 0.1 * rng.randn(len(raw.ch_names), raw.n_times)
freqs_sig = [8., 50.]
for ix, freq in zip(picks_psd, freqs_sig):
data[ix, :] += 2 * np.sin(np.pi * 2. * freq * raw.times)
first_samp = raw._first_samps[0]
raw = RawArray(data, raw.info)
tmin, tmax = 0, 20 # use a few seconds of data
fmin, fmax = 2, 70 # look at frequencies between 2 and 70Hz
n_fft = 128
# -- Raw --
kws_psd = dict(tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,
picks=picks_psd) # Common to all
kws_welch = dict(n_fft=n_fft)
kws_mt = dict(low_bias=True)
funcs = [(psd_welch, kws_welch), (psd_multitaper, kws_mt)]
for func, kws in funcs:
kws = kws.copy()
kws.update(kws_psd)
psds, freqs = func(raw, proj=False, **kws)
psds_proj, freqs_proj = func(raw, proj=True, **kws)
assert_true(psds.shape == (len(kws['picks']), len(freqs)))
assert_true(np.sum(freqs < 0) == 0)
assert_true(np.sum(psds < 0) == 0)
# Is power found where it should be
ixs_max = np.argmax(psds, axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs))
assert_true(np.abs(ixmax - ixtrue) < 2)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds, psds_proj)
# Array input shouldn't work
assert_raises(ValueError, func, raw[:3, :20][0])
# -- Epochs/Evoked --
events = read_events(event_fname)
events[:, 0] -= first_samp
tmin, tmax, event_id = -0.5, 0.5, 1
epochs = Epochs(raw,
events[:10],
event_id,
tmin,
tmax,
picks=picks_psd,
proj=False,
preload=True,
baseline=None)
evoked = epochs.average()
tmin_full, tmax_full = -1, 1
epochs_full = Epochs(raw,
events[:10],
event_id,
tmin_full,
tmax_full,
picks=picks_psd,
proj=False,
preload=True,
baseline=None)
kws_psd = dict(tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,
picks=picks_psd) # Common to all
funcs = [(psd_welch, kws_welch), (psd_multitaper, kws_mt)]
for func, kws in funcs:
kws = kws.copy()
kws.update(kws_psd)
psds, freqs = func(epochs[:1], proj=False, **kws)
psds_proj, freqs_proj = func(epochs[:1], proj=True, **kws)
psds_f, freqs_f = func(epochs_full[:1], proj=False, **kws)
# this one will fail if you add for example 0.1 to tmin
assert_array_almost_equal(psds, psds_f, 27)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds, psds_proj, 27)
# Is power found where it should be
ixs_max = np.argmax(psds.mean(0), axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs))
assert_true(np.abs(ixmax - ixtrue) < 2)
assert_true(psds.shape == (1, len(kws['picks']), len(freqs)))
assert_true(np.sum(freqs < 0) == 0)
assert_true(np.sum(psds < 0) == 0)
# Array input shouldn't work
assert_raises(ValueError, func, epochs.get_data())
# Testing evoked (doesn't work w/ compute_epochs_psd)
psds_ev, freqs_ev = func(evoked, proj=False, **kws)
psds_ev_proj, freqs_ev_proj = func(evoked, proj=True, **kws)
# Is power found where it should be
ixs_max = np.argmax(psds_ev, axis=1)
for ixmax, ifreq in zip(ixs_max, freqs_sig):
# Find nearest frequency to the "true" freq
ixtrue = np.argmin(np.abs(ifreq - freqs_ev))
assert_true(np.abs(ixmax - ixtrue) < 2)
# Make sure the projection doesn't change channels it shouldn't
assert_array_almost_equal(psds_ev, psds_ev_proj, 27)
assert_true(psds_ev.shape == (len(kws['picks']), len(freqs)))
def test_basics():
"""Test annotation class."""
raw = read_raw_fif(fif_fname)
assert raw.annotations is not None # XXX to be fixed in #5416
assert len(raw.annotations.onset) == 0 # XXX to be fixed in #5416
pytest.raises(IOError, read_annotations, fif_fname)
onset = np.array(range(10))
duration = np.ones(10)
description = np.repeat('test', 10)
dt = datetime.utcnow()
meas_date = raw.info['meas_date']
# Test time shifts.
for orig_time in [None, dt, meas_date[0], meas_date]:
annot = Annotations(onset, duration, description, orig_time)
pytest.raises(ValueError, Annotations, onset, duration, description[:9])
pytest.raises(ValueError, Annotations, [onset, 1], duration, description)
pytest.raises(ValueError, Annotations, onset, [duration, 1], description)
# Test combining annotations with concatenate_raws
raw2 = raw.copy()
delta = raw.times[-1] + 1. / raw.info['sfreq']
orig_time = (meas_date[0] + meas_date[1] * 1e-6 + raw2._first_time)
offset = orig_time - _handle_meas_date(raw2.info['meas_date'])
annot = Annotations(onset, duration, description, orig_time)
assert ' segments' in repr(annot)
raw2.set_annotations(annot)
assert_array_equal(raw2.annotations.onset, onset + offset)
assert id(raw2.annotations) != id(annot)
concatenate_raws([raw, raw2])
raw.annotations.delete(-1) # remove boundary annotations
raw.annotations.delete(-1)
assert_allclose(onset + offset + delta, raw.annotations.onset, rtol=1e-5)
assert_array_equal(annot.duration, raw.annotations.duration)
assert_array_equal(raw.annotations.description, np.repeat('test', 10))
# Test combining with RawArray and orig_times
data = np.random.randn(2, 1000) * 10e-12
sfreq = 100.
info = create_info(ch_names=['MEG1', 'MEG2'],
ch_types=['grad'] * 2,
sfreq=sfreq)
info['meas_date'] = (np.pi, 0)
raws = []
for first_samp in [12300, 100, 12]:
raw = RawArray(data.copy(), info, first_samp=first_samp)
ants = Annotations([1., 2.], [.5, .5], 'x', np.pi + first_samp / sfreq)
raw.set_annotations(ants)
raws.append(raw)
raw = RawArray(data.copy(), info)
raw.set_annotations(Annotations([1.], [.5], 'x', None))
raws.append(raw)
raw = concatenate_raws(raws, verbose='debug')
boundary_idx = np.where(raw.annotations.description == 'BAD boundary')[0]
assert len(boundary_idx) == 3
raw.annotations.delete(boundary_idx)
boundary_idx = np.where(raw.annotations.description == 'EDGE boundary')[0]
assert len(boundary_idx) == 3
raw.annotations.delete(boundary_idx)
assert_array_equal(raw.annotations.onset,
[124., 125., 134., 135., 144., 145., 154.])
raw.annotations.delete(2)
assert_array_equal(raw.annotations.onset,
[124., 125., 135., 144., 145., 154.])
raw.annotations.append(5, 1.5, 'y')
assert_array_equal(raw.annotations.onset,
[124., 125., 135., 144., 145., 154., 5.])
assert_array_equal(raw.annotations.duration, [.5, .5, .5, .5, .5, .5, 1.5])
assert_array_equal(raw.annotations.description,
['x', 'x', 'x', 'x', 'x', 'x', 'y'])
def test_events_from_annot_in_raw_objects():
"""Test basic functionality of events_fron_annot for raw objects."""
raw = read_raw_fif(fif_fname)
events = mne.find_events(raw)
event_id = {
'Auditory/Left': 1,
'Auditory/Right': 2,
'Visual/Left': 3,
'Visual/Right': 4,
'Visual/Smiley': 32,
'Motor/Button': 5
}
event_map = {v: k for k, v in event_id.items()}
annot = Annotations(onset=raw.times[events[:, 0] - raw.first_samp],
duration=np.zeros(len(events)),
description=[event_map[vv] for vv in events[:, 2]],
orig_time=None)
raw.set_annotations(annot)
events2, event_id2 = \
events_from_annotations(raw, event_id=event_id, regexp=None)
assert_array_equal(events, events2)
assert_equal(event_id, event_id2)
events3, event_id3 = \
events_from_annotations(raw, event_id=None, regexp=None)
assert_array_equal(events[:, 0], events3[:, 0])
assert set(event_id.keys()) == set(event_id3.keys())
first = np.unique(events3[:, 2])
second = np.arange(1, len(event_id) + 1, 1).astype(first.dtype)
assert_array_equal(first, second)
first = np.unique(list(event_id3.values()))
second = np.arange(1, len(event_id) + 1, 1).astype(first.dtype)
assert_array_equal(first, second)
events4, event_id4 =\
events_from_annotations(raw, event_id=None, regexp='.*Left')
expected_event_id4 = {k: v for k, v in event_id.items() if 'Left' in k}
assert_equal(event_id4.keys(), expected_event_id4.keys())
expected_events4 = events[(events[:, 2] == 1) | (events[:, 2] == 3)]
assert_array_equal(expected_events4[:, 0], events4[:, 0])
events5, event_id5 = \
events_from_annotations(raw, event_id=event_id, regexp='.*Left')
expected_event_id5 = {k: v for k, v in event_id.items() if 'Left' in k}
assert_equal(event_id5, expected_event_id5)
expected_events5 = events[(events[:, 2] == 1) | (events[:, 2] == 3)]
assert_array_equal(expected_events5, events5)
with pytest.raises(ValueError, match='not find any of the events'):
events_from_annotations(raw, regexp='not_there')
raw.set_annotations(None)
events7, _ = events_from_annotations(raw)
assert_array_equal(events7, np.empty((0, 3), dtype=int))
def test_compute_proj_epochs(tmpdir):
"""Test SSP computation on epochs."""
tempdir = str(tmpdir)
event_id, tmin, tmax = 1, -0.2, 0.3
raw = read_raw_fif(raw_fname, preload=True)
events = read_events(event_fname)
bad_ch = 'MEG 2443'
picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False,
exclude=[])
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=None, proj=False)
evoked = epochs.average()
projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1)
write_proj(op.join(tempdir, 'test-proj.fif.gz'), projs)
for p_fname in [proj_fname, proj_gz_fname,
op.join(tempdir, 'test-proj.fif.gz')]:
projs2 = read_proj(p_fname)
assert len(projs) == len(projs2)
for p1, p2 in zip(projs, projs2):
assert p1['desc'] == p2['desc']
assert p1['data']['col_names'] == p2['data']['col_names']
assert p1['active'] == p2['active']
# compare with sign invariance
p1_data = p1['data']['data'] * np.sign(p1['data']['data'][0, 0])
p2_data = p2['data']['data'] * np.sign(p2['data']['data'][0, 0])
if bad_ch in p1['data']['col_names']:
bad = p1['data']['col_names'].index('MEG 2443')
mask = np.ones(p1_data.size, dtype=bool)
mask[bad] = False
p1_data = p1_data[:, mask]
p2_data = p2_data[:, mask]
corr = np.corrcoef(p1_data, p2_data)[0, 1]
assert_array_almost_equal(corr, 1.0, 5)
if p2['explained_var']:
assert_array_almost_equal(p1['explained_var'],
p2['explained_var'])
# test that you can compute the projection matrix
projs = activate_proj(projs)
proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[])
assert nproj == 2
assert U.shape[1] == 2
# test that you can save them
epochs.info['projs'] += projs
evoked = epochs.average()
evoked.save(op.join(tempdir, 'foo-ave.fif'))
projs = read_proj(proj_fname)
projs_evoked = compute_proj_evoked(evoked, n_grad=1, n_mag=1, n_eeg=0)
assert len(projs_evoked) == 2
# XXX : test something
# test parallelization
projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1,
desc_prefix='foobar')
assert all('foobar' in x['desc'] for x in projs)
projs = activate_proj(projs)
proj_par, _, _ = make_projector(projs, epochs.ch_names, bads=[])
assert_allclose(proj, proj_par, rtol=1e-8, atol=1e-16)
# test warnings on bad filenames
proj_badname = op.join(tempdir, 'test-bad-name.fif.gz')
with pytest.warns(RuntimeWarning, match='-proj.fif'):
write_proj(proj_badname, projs)
with pytest.warns(RuntimeWarning, match='-proj.fif'):
read_proj(proj_badname)
# bad inputs
fname = op.join(tempdir, 'out-proj.fif')
with pytest.raises(TypeError, match='projs'):
write_proj(fname, 'foo')
with pytest.raises(TypeError, match=r'projs\[0\] must be .*'):
write_proj(fname, ['foo'])
def raw_orig():
"""Get raw data without any change to it from mne.io.tests.data."""
raw = read_raw_fif(fname_raw_io, preload=True)
return raw
from mne import find_events, fit_dipole from mne.datasets.brainstorm import bst_phantom_elekta from mne.io import read_raw_fif from mayavi import mlab print(__doc__) ############################################################################### # The data were collected with an Elekta Neuromag VectorView system at 1000 Hz # and low-pass filtered at 330 Hz. Here the medium-amplitude (200 nAm) data # are read to construct instances of :class:`mne.io.Raw`. data_path = bst_phantom_elekta.data_path(verbose=True) raw_fname = op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif') raw = read_raw_fif(raw_fname) ############################################################################### # Data channel array consisted of 204 MEG planor gradiometers, # 102 axial magnetometers, and 3 stimulus channels. Let's get the events # for the phantom, where each dipole (1-32) gets its own event: events = find_events(raw, 'STI201') raw.plot(events=events) raw.info['bads'] = ['MEG2421'] ############################################################################### # The data have strong line frequency (60 Hz and harmonics) and cHPI coil # noise (five peaks around 300 Hz). Here we plot only out to 60 seconds # to save memory:
def test_chpi_subtraction_filter_chpi():
"""Test subtraction of cHPI signals."""
raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes', preload=True)
raw.info['bads'] = ['MEG0111']
raw.del_proj()
raw_orig = raw.copy().crop(0, 16)
with catch_logging() as log:
filter_chpi(raw, include_line=False, t_window=0.2, verbose=True)
log = log.getvalue()
assert 'No average EEG' not in log
assert '5 cHPI' in log
# MaxFilter doesn't do quite as well as our algorithm with the last bit
raw.crop(0, 16)
# remove cHPI status chans
raw_c = read_raw_fif(sss_hpisubt_fname).crop(0, 16).load_data()
raw_c.pick_types(
meg=True, eeg=True, eog=True, ecg=True, stim=True, misc=True)
assert_meg_snr(raw, raw_c, 143, 624)
# cHPI suppressed but not line freqs (or others)
assert_suppressed(raw, raw_orig, np.arange(83, 324, 60), [30, 60, 150])
raw = raw_orig.copy()
with catch_logging() as log:
filter_chpi(raw, include_line=True, t_window=0.2, verbose=True)
log = log.getvalue()
assert '5 cHPI' in log
assert '6 line' in log
# cHPI and line freqs suppressed
suppressed = np.sort(np.concatenate([
np.arange(83, 324, 60), np.arange(60, 301, 60),
]))
assert_suppressed(raw, raw_orig, suppressed, [30, 150])
# No HPI information
raw = read_raw_fif(sample_fname, preload=True)
raw_orig = raw.copy()
assert raw.info['line_freq'] is None
with pytest.raises(RuntimeError, match='line_freq.*consider setting it'):
filter_chpi(raw, t_window=0.2)
with raw.info._unlock():
raw.info['line_freq'] = 60.
with pytest.raises(ValueError, match='No appropriate cHPI information'):
filter_chpi(raw, t_window=0.2)
# but this is allowed
with catch_logging() as log:
filter_chpi(raw, t_window='auto', allow_line_only=True, verbose=True)
log = log.getvalue()
assert '0 cHPI' in log
assert '1 line' in log
# Our one line freq suppressed but not others
assert_suppressed(raw, raw_orig, [60], [30, 45, 75])
# When MaxFliter downsamples, like::
# $ maxfilter -nosss -ds 2 -f test_move_anon_raw.fif \
# -o test_move_anon_ds2_raw.fif
# it can strip out some values of info, which we emulate here:
raw = read_raw_fif(chpi_fif_fname, allow_maxshield='yes')
raw = raw.crop(0, 1).load_data().resample(600., npad='auto')
with raw.info._unlock():
raw.info['lowpass'] = 200.
del raw.info['maxshield']
del raw.info['hpi_results'][0]['moments']
del raw.info['hpi_subsystem']['event_channel']
with catch_logging() as log:
filter_chpi(raw, t_window='auto', verbose=True)
with pytest.raises(ValueError, match='must be > 0'):
filter_chpi(raw, t_window=-1)
assert '2 cHPI' in log.getvalue()
from mne.datasets import sample
print(__doc__)
# %%
# Set parameters
data_path = sample.data_path()
meg_path = data_path / 'MEG' / 'sample'
raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif'
event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif'
event_id = 1
tmin = -0.2
tmax = 0.5
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = mne.read_events(event_fname)
# pick MEG Gradiometers
picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=True,
exclude='bads')
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6))
data = epochs.get_data()
times = epochs.times
temporal_mask = np.logical_and(0.04 <= times, times <= 0.06)
data = np.mean(data[:, :, temporal_mask], axis=2)
n_permutations = 50000
T0, p_values, H0 = permutation_t_test(data, n_permutations, n_jobs=1)
def test_add_reference():
"""Test adding a reference."""
raw = read_raw_fif(fif_fname, preload=True)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
# check if channel already exists
assert_raises(ValueError, add_reference_channels, raw,
raw.info['ch_names'][0])
# add reference channel to Raw
raw_ref = add_reference_channels(raw, 'Ref', copy=True)
assert_equal(raw_ref._data.shape[0], raw._data.shape[0] + 1)
assert_array_equal(raw._data[picks_eeg, :], raw_ref._data[picks_eeg, :])
_check_channel_names(raw_ref, 'Ref')
orig_nchan = raw.info['nchan']
raw = add_reference_channels(raw, 'Ref', copy=False)
assert_array_equal(raw._data, raw_ref._data)
assert_equal(raw.info['nchan'], orig_nchan + 1)
_check_channel_names(raw, 'Ref')
# for Neuromag fif's, the reference electrode location is placed in
# elements [3:6] of each "data" electrode location
assert_allclose(raw.info['chs'][-1]['loc'][:3],
raw.info['chs'][picks_eeg[0]]['loc'][3:6], 1e-6)
ref_idx = raw.ch_names.index('Ref')
ref_data, _ = raw[ref_idx]
assert_array_equal(ref_data, 0)
# add reference channel to Raw when no digitization points exist
raw = read_raw_fif(fif_fname).crop(0, 1).load_data()
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
del raw.info['dig']
raw_ref = add_reference_channels(raw, 'Ref', copy=True)
assert_equal(raw_ref._data.shape[0], raw._data.shape[0] + 1)
assert_array_equal(raw._data[picks_eeg, :], raw_ref._data[picks_eeg, :])
_check_channel_names(raw_ref, 'Ref')
orig_nchan = raw.info['nchan']
raw = add_reference_channels(raw, 'Ref', copy=False)
assert_array_equal(raw._data, raw_ref._data)
assert_equal(raw.info['nchan'], orig_nchan + 1)
_check_channel_names(raw, 'Ref')
# Test adding an existing channel as reference channel
assert_raises(ValueError, add_reference_channels, raw,
raw.info['ch_names'][0])
# add two reference channels to Raw
raw_ref = add_reference_channels(raw, ['M1', 'M2'], copy=True)
_check_channel_names(raw_ref, ['M1', 'M2'])
assert_equal(raw_ref._data.shape[0], raw._data.shape[0] + 2)
assert_array_equal(raw._data[picks_eeg, :], raw_ref._data[picks_eeg, :])
assert_array_equal(raw_ref._data[-2:, :], 0)
raw = add_reference_channels(raw, ['M1', 'M2'], copy=False)
_check_channel_names(raw, ['M1', 'M2'])
ref_idx = raw.ch_names.index('M1')
ref_idy = raw.ch_names.index('M2')
ref_data, _ = raw[[ref_idx, ref_idy]]
assert_array_equal(ref_data, 0)
# add reference channel to epochs
raw = read_raw_fif(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True)
# default: proj=True, after which adding a Ref channel is prohibited
assert_raises(RuntimeError, add_reference_channels, epochs, 'Ref')
# create epochs in delayed mode, allowing removal of CAR when re-reffing
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True,
proj='delayed')
epochs_ref = add_reference_channels(epochs, 'Ref', copy=True)
assert_equal(epochs_ref._data.shape[1], epochs._data.shape[1] + 1)
_check_channel_names(epochs_ref, 'Ref')
ref_idx = epochs_ref.ch_names.index('Ref')
ref_data = epochs_ref.get_data()[:, ref_idx, :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(epochs.info, meg=False, eeg=True)
assert_array_equal(epochs.get_data()[:, picks_eeg, :],
epochs_ref.get_data()[:, picks_eeg, :])
# add two reference channels to epochs
raw = read_raw_fif(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
# create epochs in delayed mode, allowing removal of CAR when re-reffing
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True,
proj='delayed')
with warnings.catch_warnings(record=True): # multiple set zero
epochs_ref = add_reference_channels(epochs, ['M1', 'M2'], copy=True)
assert_equal(epochs_ref._data.shape[1], epochs._data.shape[1] + 2)
_check_channel_names(epochs_ref, ['M1', 'M2'])
ref_idx = epochs_ref.ch_names.index('M1')
ref_idy = epochs_ref.ch_names.index('M2')
assert_equal(epochs_ref.info['chs'][ref_idx]['ch_name'], 'M1')
assert_equal(epochs_ref.info['chs'][ref_idy]['ch_name'], 'M2')
ref_data = epochs_ref.get_data()[:, [ref_idx, ref_idy], :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(epochs.info, meg=False, eeg=True)
assert_array_equal(epochs.get_data()[:, picks_eeg, :],
epochs_ref.get_data()[:, picks_eeg, :])
# add reference channel to evoked
raw = read_raw_fif(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
# create epochs in delayed mode, allowing removal of CAR when re-reffing
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True,
proj='delayed')
evoked = epochs.average()
evoked_ref = add_reference_channels(evoked, 'Ref', copy=True)
assert_equal(evoked_ref.data.shape[0], evoked.data.shape[0] + 1)
_check_channel_names(evoked_ref, 'Ref')
ref_idx = evoked_ref.ch_names.index('Ref')
ref_data = evoked_ref.data[ref_idx, :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(evoked.info, meg=False, eeg=True)
assert_array_equal(evoked.data[picks_eeg, :],
evoked_ref.data[picks_eeg, :])
# add two reference channels to evoked
raw = read_raw_fif(fif_fname, preload=True)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
# create epochs in delayed mode, allowing removal of CAR when re-reffing
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True,
proj='delayed')
evoked = epochs.average()
with warnings.catch_warnings(record=True): # multiple set zero
evoked_ref = add_reference_channels(evoked, ['M1', 'M2'], copy=True)
assert_equal(evoked_ref.data.shape[0], evoked.data.shape[0] + 2)
_check_channel_names(evoked_ref, ['M1', 'M2'])
ref_idx = evoked_ref.ch_names.index('M1')
ref_idy = evoked_ref.ch_names.index('M2')
ref_data = evoked_ref.data[[ref_idx, ref_idy], :]
assert_array_equal(ref_data, 0)
picks_eeg = pick_types(evoked.info, meg=False, eeg=True)
assert_array_equal(evoked.data[picks_eeg, :],
evoked_ref.data[picks_eeg, :])
# Test invalid inputs
raw_np = read_raw_fif(fif_fname, preload=False)
assert_raises(RuntimeError, add_reference_channels, raw_np, ['Ref'])
assert_raises(ValueError, add_reference_channels, raw, 1)
def test_set_eeg_reference():
"""Test rereference eeg data."""
raw = read_raw_fif(fif_fname, preload=True)
raw.info['projs'] = []
# Test setting an average reference projection
assert_true(not _has_eeg_average_ref_proj(raw.info['projs']))
reref, ref_data = set_eeg_reference(raw, projection=True)
assert_true(_has_eeg_average_ref_proj(reref.info['projs']))
assert_true(not reref.info['projs'][0]['active'])
assert_true(ref_data is None)
reref.apply_proj()
eeg_chans = [
raw.ch_names[ch] for ch in pick_types(raw.info, meg=False, eeg=True)
]
_test_reference(raw, reref, ref_data,
[ch for ch in eeg_chans if ch not in raw.info['bads']])
# Test setting an average reference when one was already present
with warnings.catch_warnings(record=True):
reref, ref_data = set_eeg_reference(raw, copy=False, projection=True)
assert_true(ref_data is None)
# Test setting an average reference on non-preloaded data
raw_nopreload = read_raw_fif(fif_fname, preload=False)
raw_nopreload.info['projs'] = []
reref, ref_data = set_eeg_reference(raw_nopreload, projection=True)
assert_true(_has_eeg_average_ref_proj(reref.info['projs']))
assert_true(not reref.info['projs'][0]['active'])
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'], copy=True)
assert_true(reref.info['custom_ref_applied'])
_test_reference(raw, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert_true(raw is reref)
# Test moving from custom to average reference
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'])
reref, _ = set_eeg_reference(reref, projection=True)
assert_true(_has_eeg_average_ref_proj(reref.info['projs']))
assert_equal(reref.info['custom_ref_applied'], False)
# When creating an average reference fails, make sure the
# custom_ref_applied flag remains untouched.
reref = raw.copy()
reref.info['custom_ref_applied'] = True
reref.pick_types(eeg=False) # Cause making average ref fail
assert_raises(ValueError, set_eeg_reference, reref, projection=True)
assert_true(reref.info['custom_ref_applied'])
# Test moving from average to custom reference
reref, ref_data = set_eeg_reference(raw, projection=True)
reref, _ = set_eeg_reference(reref, ['EEG 001', 'EEG 002'])
assert_true(not _has_eeg_average_ref_proj(reref.info['projs']))
assert_equal(reref.info['custom_ref_applied'], True)
# Test that disabling the reference does not change anything
reref, ref_data = set_eeg_reference(raw, [])
assert_array_equal(raw._data, reref._data)
# Test that average reference gives identical results when calculated
# via SSP projection (projection=True) or directly (projection=False)
raw.info['projs'] = []
reref_1, _ = set_eeg_reference(raw.copy(), projection=True)
reref_1.apply_proj()
reref_2, _ = set_eeg_reference(raw.copy(), projection=False)
assert_allclose(reref_1._data, reref_2._data, rtol=1e-6, atol=1e-15)
# Test average reference without projection
reref, ref_data = set_eeg_reference(raw.copy(),
ref_channels="average",
projection=False)
_test_reference(raw, reref, ref_data, eeg_chans)
# projection=True only works for ref_channels='average'
assert_raises(ValueError, set_eeg_reference, raw, [], True, True)
assert_raises(ValueError, set_eeg_reference, raw, ['EEG 001'], True, True)
def test_apply_reference():
"""Test base function for rereferencing."""
raw = read_raw_fif(fif_fname, preload=True)
# Rereference raw data by creating a copy of original data
reref, ref_data = _apply_reference(raw.copy(),
ref_from=['EEG 001', 'EEG 002'])
assert_true(reref.info['custom_ref_applied'])
_test_reference(raw, reref, ref_data, ['EEG 001', 'EEG 002'])
# The CAR reference projection should have been removed by the function
assert_true(not _has_eeg_average_ref_proj(reref.info['projs']))
# Test that data is modified in place when copy=False
reref, ref_data = _apply_reference(raw, ['EEG 001', 'EEG 002'])
assert_true(raw is reref)
# Test that disabling the reference does not change anything
reref, ref_data = _apply_reference(raw.copy(), [])
assert_array_equal(raw._data, reref._data)
# Test re-referencing Epochs object
raw = read_raw_fif(fif_fname, preload=False)
events = read_events(eve_fname)
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
epochs = Epochs(raw,
events=events,
event_id=1,
tmin=-0.2,
tmax=0.5,
picks=picks_eeg,
preload=True)
reref, ref_data = _apply_reference(epochs.copy(),
ref_from=['EEG 001', 'EEG 002'])
assert_true(reref.info['custom_ref_applied'])
_test_reference(epochs, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test re-referencing Evoked object
evoked = epochs.average()
reref, ref_data = _apply_reference(evoked.copy(),
ref_from=['EEG 001', 'EEG 002'])
assert_true(reref.info['custom_ref_applied'])
_test_reference(evoked, reref, ref_data, ['EEG 001', 'EEG 002'])
# Referencing needs data to be preloaded
raw_np = read_raw_fif(fif_fname, preload=False)
assert_raises(RuntimeError, _apply_reference, raw_np, ['EEG 001'])
# Test having inactive SSP projections that deal with channels involved
# during re-referencing
raw = read_raw_fif(fif_fname, preload=True)
raw.add_proj(
Projection(
active=False,
data=dict(col_names=['EEG 001', 'EEG 002'],
row_names=None,
data=np.array([[1, 1]]),
ncol=2,
nrow=1),
desc='test',
kind=1,
))
# Projection concerns channels mentioned in projector
assert_raises(RuntimeError, _apply_reference, raw, ['EEG 001'])
# Projection does not concern channels mentioned in projector, no error
_apply_reference(raw, ['EEG 003'], ['EEG 004'])
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, compute_source_psd
print(__doc__)
# %%
# Set parameters
data_path = sample.data_path()
meg_path = data_path / 'MEG' / 'sample'
raw_fname = meg_path / 'sample_audvis_raw.fif'
fname_inv = meg_path / 'sample_audvis-meg-oct-6-meg-inv.fif'
fname_label = meg_path / 'labels' / 'Aud-lh.label'
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname, verbose=False)
events = mne.find_events(raw, stim_channel='STI 014')
inverse_operator = read_inverse_operator(fname_inv)
raw.info['bads'] = ['MEG 2443', 'EEG 053']
# picks MEG gradiometers
picks = mne.pick_types(raw.info,
meg=True,
eeg=False,
eog=True,
stim=False,
exclude='bads')
tmin, tmax = 0, 120 # use the first 120s of data
fmin, fmax = 4, 100 # look at frequencies between 4 and 100Hz
n_fft = 2048 # the FFT size (n_fft). Ideally a power of 2
def test_set_bipolar_reference():
"""Test bipolar referencing."""
raw = read_raw_fif(fif_fname, preload=True)
raw.apply_proj()
reref = set_bipolar_reference(raw, 'EEG 001', 'EEG 002', 'bipolar', {
'kind': FIFF.FIFFV_EOG_CH,
'extra': 'some extra value'
})
assert_true(reref.info['custom_ref_applied'])
# Compare result to a manual calculation
a = raw.copy().pick_channels(['EEG 001', 'EEG 002'])
a = a._data[0, :] - a._data[1, :]
b = reref.copy().pick_channels(['bipolar'])._data[0, :]
assert_allclose(a, b)
# Original channels should be replaced by a virtual one
assert_true('EEG 001' not in reref.ch_names)
assert_true('EEG 002' not in reref.ch_names)
assert_true('bipolar' in reref.ch_names)
# Check channel information
bp_info = reref.info['chs'][reref.ch_names.index('bipolar')]
an_info = reref.info['chs'][raw.ch_names.index('EEG 001')]
for key in bp_info:
if key == 'loc':
assert_array_equal(bp_info[key], 0)
elif key == 'coil_type':
assert_equal(bp_info[key], FIFF.FIFFV_COIL_EEG_BIPOLAR)
elif key == 'kind':
assert_equal(bp_info[key], FIFF.FIFFV_EOG_CH)
else:
assert_equal(bp_info[key], an_info[key])
assert_equal(bp_info['extra'], 'some extra value')
# Minimalist call
reref = set_bipolar_reference(raw, 'EEG 001', 'EEG 002')
assert_true('EEG 001-EEG 002' in reref.ch_names)
# Minimalist call with twice the same anode
reref = set_bipolar_reference(raw, ['EEG 001', 'EEG 001', 'EEG 002'],
['EEG 002', 'EEG 003', 'EEG 003'])
assert_true('EEG 001-EEG 002' in reref.ch_names)
assert_true('EEG 001-EEG 003' in reref.ch_names)
# Set multiple references at once
reref = set_bipolar_reference(
raw,
['EEG 001', 'EEG 003'],
['EEG 002', 'EEG 004'],
['bipolar1', 'bipolar2'],
[{
'kind': FIFF.FIFFV_EOG_CH,
'extra': 'some extra value'
}, {
'kind': FIFF.FIFFV_EOG_CH,
'extra': 'some extra value'
}],
)
a = raw.copy().pick_channels(['EEG 001', 'EEG 002', 'EEG 003', 'EEG 004'])
a = np.array(
[a._data[0, :] - a._data[1, :], a._data[2, :] - a._data[3, :]])
b = reref.copy().pick_channels(['bipolar1', 'bipolar2'])._data
assert_allclose(a, b)
# Test creating a bipolar reference that doesn't involve EEG channels:
# it should not set the custom_ref_applied flag
reref = set_bipolar_reference(raw,
'MEG 0111',
'MEG 0112',
ch_info={'kind': FIFF.FIFFV_MEG_CH})
assert_true(not reref.info['custom_ref_applied'])
assert_true('MEG 0111-MEG 0112' in reref.ch_names)
# Test a battery of invalid inputs
assert_raises(ValueError, set_bipolar_reference, raw, 'EEG 001',
['EEG 002', 'EEG 003'], 'bipolar')
assert_raises(ValueError, set_bipolar_reference, raw,
['EEG 001', 'EEG 002'], 'EEG 003', 'bipolar')
assert_raises(ValueError, set_bipolar_reference, raw, 'EEG 001', 'EEG 002',
['bipolar1', 'bipolar2'])
assert_raises(ValueError,
set_bipolar_reference,
raw,
'EEG 001',
'EEG 002',
'bipolar',
ch_info=[{
'foo': 'bar'
}, {
'foo': 'bar'
}])
assert_raises(ValueError,
set_bipolar_reference,
raw,
'EEG 001',
'EEG 002',
ch_name='EEG 003')
def test_ica_core():
"""Test ICA on raw and epochs."""
raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
# XXX. The None cases helped revealing bugs but are time consuming.
test_cov = read_cov(test_cov_name)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
noise_cov = [None, test_cov]
# removed None cases to speed up...
n_components = [2, 1.0] # for future dbg add cases
max_pca_components = [3]
picks_ = [picks]
methods = ['fastica']
iter_ica_params = product(noise_cov, n_components, max_pca_components,
picks_, methods)
# # test init catchers
assert_raises(ValueError, ICA, n_components=3, max_pca_components=2)
assert_raises(ValueError, ICA, n_components=2.3, max_pca_components=2)
# test essential core functionality
for n_cov, n_comp, max_n, pcks, method in iter_ica_params:
# Test ICA raw
ica = ICA(noise_cov=n_cov, n_components=n_comp,
max_pca_components=max_n, n_pca_components=max_n,
random_state=0, method=method, max_iter=1)
assert_raises(ValueError, ica.__contains__, 'mag')
print(ica) # to test repr
# test fit checker
assert_raises(RuntimeError, ica.get_sources, raw)
assert_raises(RuntimeError, ica.get_sources, epochs)
# test decomposition
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=pcks, start=start, stop=stop)
repr(ica) # to test repr
assert_true('mag' in ica) # should now work without error
# test re-fit
unmixing1 = ica.unmixing_matrix_
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=pcks, start=start, stop=stop)
assert_array_almost_equal(unmixing1, ica.unmixing_matrix_)
raw_sources = ica.get_sources(raw)
# test for #3804
assert_equal(raw_sources._filenames, [None])
print(raw_sources)
sources = raw_sources[:, :][0]
assert_true(sources.shape[0] == ica.n_components_)
# test preload filter
raw3 = raw.copy()
raw3.preload = False
assert_raises(ValueError, ica.apply, raw3,
include=[1, 2])
#######################################################################
# test epochs decomposition
ica = ICA(noise_cov=n_cov, n_components=n_comp,
max_pca_components=max_n, n_pca_components=max_n,
random_state=0)
with warnings.catch_warnings(record=True):
ica.fit(epochs, picks=picks)
data = epochs.get_data()[:, 0, :]
n_samples = np.prod(data.shape)
assert_equal(ica.n_samples_, n_samples)
print(ica) # to test repr
sources = ica.get_sources(epochs).get_data()
assert_true(sources.shape[1] == ica.n_components_)
assert_raises(ValueError, ica.score_sources, epochs,
target=np.arange(1))
# test preload filter
epochs3 = epochs.copy()
epochs3.preload = False
assert_raises(ValueError, ica.apply, epochs3,
include=[1, 2])
# test for bug with whitener updating
_pre_whitener = ica._pre_whitener.copy()
epochs._data[:, 0, 10:15] *= 1e12
ica.apply(epochs.copy())
assert_array_equal(_pre_whitener, ica._pre_whitener)
# test expl. var threshold leading to empty sel
ica.n_components = 0.1
assert_raises(RuntimeError, ica.fit, epochs)
offender = 1, 2, 3,
assert_raises(ValueError, ica.get_sources, offender)
assert_raises(ValueError, ica.fit, offender)
assert_raises(ValueError, ica.apply, offender)
def _get_raw(preload=False):
"""Get raw data."""
return read_raw_fif(raw_fname, preload=preload)
def test_kit2fiff_model():
"""Test Kit2Fiff model."""
from mne.gui._kit2fiff_gui import Kit2FiffModel
tempdir = _TempDir()
tgt_fname = os.path.join(tempdir, 'test-raw.fif')
model = Kit2FiffModel()
assert not model.can_save
assert model.misc_chs_desc == "No SQD file selected..."
assert model.stim_chs_comment == ""
model.markers.mrk1.file = mrk_pre_path
model.markers.mrk2.file = mrk_post_path
model.sqd_file = sqd_path
assert model.misc_chs_desc == "160:192"
model.hsp_file = hsp_path
assert not model.can_save
model.fid_file = fid_path
assert model.can_save
# events
model.stim_slope = '+'
assert model.get_event_info() == {1: 2}
model.stim_slope = '-'
assert model.get_event_info() == {254: 2, 255: 2}
# stim channels
model.stim_chs = "181:184, 186"
assert_array_equal(model.stim_chs_array, [181, 182, 183, 186])
assert model.stim_chs_ok
assert model.get_event_info() == {}
model.stim_chs = "181:184, bad"
assert not model.stim_chs_ok
assert not model.can_save
model.stim_chs = ""
assert model.can_save
# export raw
raw_out = model.get_raw()
raw_out.save(tgt_fname)
raw = read_raw_fif(tgt_fname)
# Compare exported raw with the original binary conversion
raw_bin = read_raw_fif(fif_path)
trans_bin = raw.info['dev_head_t']['trans']
want_keys = list(raw_bin.info.keys())
assert sorted(want_keys) == sorted(list(raw.info.keys()))
trans_transform = raw_bin.info['dev_head_t']['trans']
assert_allclose(trans_transform, trans_bin, 0.1)
# Averaging markers
model.markers.mrk3.method = "Average"
trans_avg = model.dev_head_trans
assert not np.all(trans_avg == trans_transform)
assert_allclose(trans_avg, trans_bin, 0.1)
# Test exclusion of one marker
model.markers.mrk3.method = "Transform"
model.use_mrk = [1, 2, 3, 4]
assert not np.all(model.dev_head_trans == trans_transform)
assert not np.all(model.dev_head_trans == trans_avg)
assert not np.all(model.dev_head_trans == np.eye(4))
# test setting stim channels
model.stim_slope = '+'
events_bin = mne.find_events(raw_bin, stim_channel='STI 014')
model.stim_coding = '<'
raw = model.get_raw()
events = mne.find_events(raw, stim_channel='STI 014')
assert_array_equal(events, events_bin)
events_rev = events_bin.copy()
events_rev[:, 2] = 1
model.stim_coding = '>'
raw = model.get_raw()
events = mne.find_events(raw, stim_channel='STI 014')
assert_array_equal(events, events_rev)
model.stim_coding = 'channel'
model.stim_chs = "160:161"
raw = model.get_raw()
events = mne.find_events(raw, stim_channel='STI 014')
assert_array_equal(events, events_bin + [0, 0, 32])
# test reset
model.clear_all()
assert model.use_mrk == [0, 1, 2, 3, 4]
assert model.sqd_file == ""
def test_ica_additional():
"""Test additional ICA functionality."""
import matplotlib.pyplot as plt
tempdir = _TempDir()
stop2 = 500
raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
# XXX This breaks the tests :(
# raw.info['bads'] = [raw.ch_names[1]]
test_cov = read_cov(test_cov_name)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
# test if n_components=None works
with warnings.catch_warnings(record=True):
ica = ICA(n_components=None,
max_pca_components=None,
n_pca_components=None, random_state=0)
ica.fit(epochs, picks=picks, decim=3)
# for testing eog functionality
picks2 = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=True, exclude='bads')
epochs_eog = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks2,
baseline=(None, 0), preload=True)
test_cov2 = test_cov.copy()
ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4,
n_pca_components=4)
assert_true(ica.info is None)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks[:5])
assert_true(isinstance(ica.info, Info))
assert_true(ica.n_components_ < 5)
ica = ICA(n_components=3, max_pca_components=4,
n_pca_components=4)
assert_raises(RuntimeError, ica.save, '')
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2)
# test corrmap
ica2 = ica.copy()
ica3 = ica.copy()
corrmap([ica, ica2], (0, 0), threshold='auto', label='blinks', plot=True,
ch_type="mag")
corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False)
assert_true(ica.labels_["blinks"] == ica2.labels_["blinks"])
assert_true(0 in ica.labels_["blinks"])
# test retrieval of component maps as arrays
components = ica.get_components()
template = components[:, 0]
EvokedArray(components, ica.info, tmin=0.).plot_topomap([0])
corrmap([ica, ica3], template, threshold='auto', label='blinks', plot=True,
ch_type="mag")
assert_true(ica2.labels_["blinks"] == ica3.labels_["blinks"])
plt.close('all')
# test warnings on bad filenames
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz')
ica.save(ica_badname)
read_ica(ica_badname)
assert_naming(w, 'test_ica.py', 2)
# test decim
ica = ICA(n_components=3, max_pca_components=4,
n_pca_components=4)
raw_ = raw.copy()
for _ in range(3):
raw_.append(raw_)
n_samples = raw_._data.shape[1]
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=None, decim=3)
assert_true(raw_._data.shape[1], n_samples)
# test expl var
ica = ICA(n_components=1.0, max_pca_components=4,
n_pca_components=4)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=None, decim=3)
assert_true(ica.n_components_ == 4)
ica_var = _ica_explained_variance(ica, raw, normalize=True)
assert_true(np.all(ica_var[:-1] >= ica_var[1:]))
# test ica sorting
ica.exclude = [0]
ica.labels_ = dict(blink=[0], think=[1])
ica_sorted = _sort_components(ica, [3, 2, 1, 0], copy=True)
assert_equal(ica_sorted.exclude, [3])
assert_equal(ica_sorted.labels_, dict(blink=[3], think=[2]))
# epochs extraction from raw fit
assert_raises(RuntimeError, ica.get_sources, epochs)
# test reading and writing
test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif')
for cov in (None, test_cov):
ica = ICA(noise_cov=cov, n_components=2, max_pca_components=4,
n_pca_components=4)
with warnings.catch_warnings(record=True): # ICA does not converge
ica.fit(raw, picks=picks, start=start, stop=stop2)
sources = ica.get_sources(epochs).get_data()
assert_true(ica.mixing_matrix_.shape == (2, 2))
assert_true(ica.unmixing_matrix_.shape == (2, 2))
assert_true(ica.pca_components_.shape == (4, len(picks)))
assert_true(sources.shape[1] == ica.n_components_)
for exclude in [[], [0]]:
ica.exclude = exclude
ica.labels_ = {'foo': [0]}
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert_true(ica.exclude == ica_read.exclude)
assert_equal(ica.labels_, ica_read.labels_)
ica.exclude = []
ica.apply(raw, exclude=[1])
assert_true(ica.exclude == [])
ica.exclude = [0, 1]
ica.apply(raw, exclude=[1])
assert_true(ica.exclude == [0, 1])
ica_raw = ica.get_sources(raw)
assert_true(ica.exclude == [ica_raw.ch_names.index(e) for e in
ica_raw.info['bads']])
# test filtering
d1 = ica_raw._data[0].copy()
ica_raw.filter(4, 20, l_trans_bandwidth='auto',
h_trans_bandwidth='auto', filter_length='auto',
phase='zero', fir_window='hamming')
assert_equal(ica_raw.info['lowpass'], 20.)
assert_equal(ica_raw.info['highpass'], 4.)
assert_true((d1 != ica_raw._data[0]).any())
d1 = ica_raw._data[0].copy()
ica_raw.notch_filter([10], filter_length='auto', trans_bandwidth=10,
phase='zero', fir_window='hamming')
assert_true((d1 != ica_raw._data[0]).any())
ica.n_pca_components = 2
ica.method = 'fake'
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert_true(ica.n_pca_components == ica_read.n_pca_components)
assert_equal(ica.method, ica_read.method)
assert_equal(ica.labels_, ica_read.labels_)
# check type consistency
attrs = ('mixing_matrix_ unmixing_matrix_ pca_components_ '
'pca_explained_variance_ _pre_whitener')
def f(x, y):
return getattr(x, y).dtype
for attr in attrs.split():
assert_equal(f(ica_read, attr), f(ica, attr))
ica.n_pca_components = 4
ica_read.n_pca_components = 4
ica.exclude = []
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
for attr in ['mixing_matrix_', 'unmixing_matrix_', 'pca_components_',
'pca_mean_', 'pca_explained_variance_',
'_pre_whitener']:
assert_array_almost_equal(getattr(ica, attr),
getattr(ica_read, attr))
assert_true(ica.ch_names == ica_read.ch_names)
assert_true(isinstance(ica_read.info, Info))
sources = ica.get_sources(raw)[:, :][0]
sources2 = ica_read.get_sources(raw)[:, :][0]
assert_array_almost_equal(sources, sources2)
_raw1 = ica.apply(raw, exclude=[1])
_raw2 = ica_read.apply(raw, exclude=[1])
assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])
os.remove(test_ica_fname)
# check scrore funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(raw, target='EOG 061', score_func=func,
start=0, stop=10)
assert_true(ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(raw, score_func=stats.skew)
# check exception handling
assert_raises(ValueError, ica.score_sources, raw,
target=np.arange(1))
params = []
params += [(None, -1, slice(2), [0, 1])] # varicance, kurtosis idx params
params += [(None, 'MEG 1531')] # ECG / EOG channel params
for idx, ch_name in product(*params):
ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=ch_name,
eog_ch=ch_name, skew_criterion=idx,
var_criterion=idx, kurt_criterion=idx)
evoked = epochs.average()
evoked_data = evoked.data.copy()
raw_data = raw[:][0].copy()
epochs_data = epochs.get_data().copy()
with warnings.catch_warnings(record=True):
idx, scores = ica.find_bads_ecg(raw, method='ctps')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(raw, method='correlation')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(epochs, method='ctps')
assert_equal(len(scores), ica.n_components_)
assert_raises(ValueError, ica.find_bads_ecg, epochs.average(),
method='ctps')
assert_raises(ValueError, ica.find_bads_ecg, raw,
method='crazy-coupling')
raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202
idx, scores = ica.find_bads_eog(raw)
assert_true(isinstance(scores, list))
assert_equal(len(scores[0]), ica.n_components_)
idx, scores = ica.find_bads_eog(evoked, ch_name='MEG 1441')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(evoked, method='correlation')
assert_equal(len(scores), ica.n_components_)
assert_array_equal(raw_data, raw[:][0])
assert_array_equal(epochs_data, epochs.get_data())
assert_array_equal(evoked_data, evoked.data)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(epochs_eog, target='EOG 061',
score_func=func)
assert_true(ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(epochs, score_func=stats.skew)
# check exception handling
assert_raises(ValueError, ica.score_sources, epochs,
target=np.arange(1))
# ecg functionality
ecg_scores = ica.score_sources(raw, target='MEG 1531',
score_func='pearsonr')
with warnings.catch_warnings(record=True): # filter attenuation warning
ecg_events = ica_find_ecg_events(raw,
sources[np.abs(ecg_scores).argmax()])
assert_true(ecg_events.ndim == 2)
# eog functionality
eog_scores = ica.score_sources(raw, target='EOG 061',
score_func='pearsonr')
with warnings.catch_warnings(record=True): # filter attenuation warning
eog_events = ica_find_eog_events(raw,
sources[np.abs(eog_scores).argmax()])
assert_true(eog_events.ndim == 2)
# Test ica fiff export
ica_raw = ica.get_sources(raw, start=0, stop=100)
assert_true(ica_raw.last_samp - ica_raw.first_samp == 100)
assert_equal(len(ica_raw._filenames), 1) # API consistency
ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch]
assert_true(ica.n_components_ == len(ica_chans))
test_ica_fname = op.join(op.abspath(op.curdir), 'test-ica_raw.fif')
ica.n_components = np.int32(ica.n_components)
ica_raw.save(test_ica_fname, overwrite=True)
ica_raw2 = read_raw_fif(test_ica_fname, preload=True)
assert_allclose(ica_raw._data, ica_raw2._data, rtol=1e-5, atol=1e-4)
ica_raw2.close()
os.remove(test_ica_fname)
# Test ica epochs export
ica_epochs = ica.get_sources(epochs)
assert_true(ica_epochs.events.shape == epochs.events.shape)
ica_chans = [ch for ch in ica_epochs.ch_names if 'ICA' in ch]
assert_true(ica.n_components_ == len(ica_chans))
assert_true(ica.n_components_ == ica_epochs.get_data().shape[1])
assert_true(ica_epochs._raw is None)
assert_true(ica_epochs.preload is True)
# test float n pca components
ica.pca_explained_variance_ = np.array([0.2] * 5)
ica.n_components_ = 0
for ncomps, expected in [[0.3, 1], [0.9, 4], [1, 1]]:
ncomps_ = ica._check_n_pca_components(ncomps)
assert_true(ncomps_ == expected)
ica = ICA()
ica.fit(raw, picks=picks[:5])
ica.find_bads_ecg(raw)
ica.find_bads_eog(epochs, ch_name='MEG 0121')
assert_array_equal(raw_data, raw[:][0])
raw.drop_channels(['MEG 0122'])
assert_raises(RuntimeError, ica.find_bads_eog, raw)
assert_raises(RuntimeError, ica.find_bads_ecg, raw)
def test_crop():
"""Test cropping with annotations."""
raw = read_raw_fif(fif_fname)
events = mne.find_events(raw)
onset = events[events[:, 2] == 1, 0] / raw.info['sfreq']
duration = np.full_like(onset, 0.5)
description = ['bad %d' % k for k in range(len(onset))]
annot = mne.Annotations(onset,
duration,
description,
orig_time=raw.info['meas_date'])
raw.set_annotations(annot)
split_time = raw.times[-1] / 2. + 2.
split_idx = len(onset) // 2 + 1
raw_cropped_left = raw.copy().crop(0., split_time - 1. / raw.info['sfreq'])
assert_array_equal(raw_cropped_left.annotations.description,
raw.annotations.description[:split_idx])
assert_allclose(raw_cropped_left.annotations.duration,
raw.annotations.duration[:split_idx])
assert_allclose(raw_cropped_left.annotations.onset,
raw.annotations.onset[:split_idx])
raw_cropped_right = raw.copy().crop(split_time, None)
assert_array_equal(raw_cropped_right.annotations.description,
raw.annotations.description[split_idx:])
assert_allclose(raw_cropped_right.annotations.duration,
raw.annotations.duration[split_idx:])
assert_allclose(raw_cropped_right.annotations.onset,
raw.annotations.onset[split_idx:])
raw_concat = mne.concatenate_raws([raw_cropped_left, raw_cropped_right],
verbose='debug')
assert_allclose(raw_concat.times, raw.times)
assert_allclose(raw_concat[:][0], raw[:][0], atol=1e-20)
# Get rid of the boundary events
raw_concat.annotations.delete(-1)
raw_concat.annotations.delete(-1)
# Ensure we annotations survive round-trip crop->concat
assert_array_equal(raw_concat.annotations.description,
raw.annotations.description)
for attr in ('onset', 'duration'):
assert_allclose(getattr(raw_concat.annotations, attr),
getattr(raw.annotations, attr),
err_msg='Failed for %s:' % (attr, ))
raw.set_annotations(None) # undo
# Test concatenating annotations with and without orig_time.
raw2 = raw.copy()
raw.set_annotations(Annotations([45.], [3], 'test', raw.info['meas_date']))
raw2.set_annotations(Annotations([2.], [3], 'BAD', None))
expected_onset = [45., 2. + raw._last_time]
raw = concatenate_raws([raw, raw2])
raw.annotations.delete(-1) # remove boundary annotations
raw.annotations.delete(-1)
assert_array_almost_equal(raw.annotations.onset, expected_onset, decimal=2)
# Test IO
tempdir = _TempDir()
fname = op.join(tempdir, 'test-annot.fif')
raw.annotations.save(fname)
annot_read = read_annotations(fname)
for attr in ('onset', 'duration', 'orig_time'):
assert_allclose(getattr(annot_read, attr),
getattr(raw.annotations, attr))
assert_array_equal(annot_read.description, raw.annotations.description)
annot = Annotations((), (), ())
annot.save(fname)
pytest.raises(IOError, read_annotations, fif_fname) # none in old raw
annot = read_annotations(fname)
assert isinstance(annot, Annotations)
assert len(annot) == 0
# Test that empty annotations can be saved with an object
fname = op.join(tempdir, 'test_raw.fif')
raw.set_annotations(annot)
raw.save(fname)
raw_read = read_raw_fif(fname)
assert isinstance(raw_read.annotations, Annotations)
assert len(raw_read.annotations) == 0
raw.set_annotations(None)
raw.save(fname, overwrite=True)
raw_read = read_raw_fif(fname)
assert raw_read.annotations is not None # XXX to be fixed in #5416
assert len(raw_read.annotations.onset) == 0 # XXX to be fixed in #5416
def test_cov_rank_estimation(rank_method, proj, meg):
"""Test cov rank estimation."""
# Test that our rank estimation works properly on a simple case
evoked = read_evokeds(ave_fname, condition=0, baseline=(None, 0),
proj=False)
cov = read_cov(cov_fname)
ch_names = [ch for ch in evoked.info['ch_names'] if '053' not in ch and
ch.startswith('EEG')]
cov = prepare_noise_cov(cov, evoked.info, ch_names, None)
assert cov['eig'][0] <= 1e-25 # avg projector should set this to zero
assert (cov['eig'][1:] > 1e-16).all() # all else should be > 0
# Now do some more comprehensive tests
raw_sample = read_raw_fif(raw_fname)
assert not _has_eeg_average_ref_proj(raw_sample.info['projs'])
raw_sss = read_raw_fif(hp_fif_fname)
assert not _has_eeg_average_ref_proj(raw_sss.info['projs'])
raw_sss.add_proj(compute_proj_raw(raw_sss, meg=meg))
cov_sample = compute_raw_covariance(raw_sample)
cov_sample_proj = compute_raw_covariance(raw_sample.copy().apply_proj())
cov_sss = compute_raw_covariance(raw_sss)
cov_sss_proj = compute_raw_covariance(raw_sss.copy().apply_proj())
picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)
info_sample = pick_info(raw_sample.info, picks_all_sample)
picks_stack_sample = [('eeg', pick_types(info_sample, meg=False,
eeg=True))]
picks_stack_sample += [('meg', pick_types(info_sample, meg=True))]
picks_stack_sample += [('all',
pick_types(info_sample, meg=True, eeg=True))]
info_sss = pick_info(raw_sss.info, picks_all_sss)
picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))]
picks_stack_somato += [('meg', pick_types(info_sss, meg=True))]
picks_stack_somato += [('all',
pick_types(info_sss, meg=True, eeg=True))]
iter_tests = list(itt.product(
[(cov_sample, picks_stack_sample, info_sample),
(cov_sample_proj, picks_stack_sample, info_sample),
(cov_sss, picks_stack_somato, info_sss),
(cov_sss_proj, picks_stack_somato, info_sss)], # sss
[dict(mag=1e15, grad=1e13, eeg=1e6)],
))
for (cov, picks_list, iter_info), scalings in iter_tests:
rank = compute_rank(cov, rank_method, scalings, iter_info,
proj=proj)
rank['all'] = sum(rank.values())
for ch_type, picks in picks_list:
this_info = pick_info(iter_info, picks)
# compute subset of projs, active and inactive
n_projs_applied = sum(proj['active'] and
len(set(proj['data']['col_names']) &
set(this_info['ch_names'])) > 0
for proj in cov['projs'])
n_projs_info = sum(len(set(proj['data']['col_names']) &
set(this_info['ch_names'])) > 0
for proj in this_info['projs'])
# count channel types
ch_types = [channel_type(this_info, idx)
for idx in range(len(picks))]
n_eeg, n_mag, n_grad = [ch_types.count(k) for k in
['eeg', 'mag', 'grad']]
n_meg = n_mag + n_grad
has_sss = (n_meg > 0 and len(this_info['proc_history']) > 0)
if has_sss:
n_meg = _get_rank_sss(this_info)
expected_rank = n_meg + n_eeg
if rank_method is None:
if meg == 'combined' or not has_sss:
if proj:
expected_rank -= n_projs_info
else:
expected_rank -= n_projs_applied
else:
# XXX for now it just uses the total count
assert rank_method == 'info'
if proj:
expected_rank -= n_projs_info
assert rank[ch_type] == expected_rank
def test_maxfilter_get_rank(n_proj, fname, rank_orig, meg, tol_kind, tol):
"""Test maxfilter rank lookup."""
raw = read_raw_fif(fname).crop(0, 5).load_data().pick_types(meg=True)
assert raw.info['projs'] == []
mf = raw.info['proc_history'][0]['max_info']
assert mf['sss_info']['nfree'] == rank_orig
assert compute_rank(raw, 'info')['meg'] == rank_orig
assert compute_rank(raw.copy().pick('grad'), 'info')['grad'] == rank_orig
assert compute_rank(raw.copy().pick('mag'), 'info')['mag'] == rank_orig
mult = 1 + (meg == 'separate')
rank = rank_orig - mult * n_proj
if n_proj > 0:
# Let's do some projection
raw.add_proj(compute_proj_raw(raw, n_mag=n_proj, n_grad=n_proj,
meg=meg, verbose=True))
raw.apply_proj()
data_orig = raw[:][0]
# degenerate cases
with pytest.raises(ValueError, match='tol must be'):
_estimate_rank_raw(raw, tol='foo')
with pytest.raises(TypeError, match='must be a string or a'):
_estimate_rank_raw(raw, tol=None)
allowed_rank = [rank_orig if meg == 'separate' else rank]
if fname == mf_fif_fname:
# Here we permit a -1 because for mf_fif_fname we miss by 1, which is
# probably acceptable. If we use the entire duration instead of 5 sec
# this problem goes away, but the test is much slower.
allowed_rank.append(allowed_rank[0] - 1)
# multiple ways of hopefully getting the same thing
# default tol=1e-4, scalings='norm'
rank_new = _estimate_rank_raw(raw, tol_kind=tol_kind)
assert rank_new in allowed_rank
rank_new = _estimate_rank_raw(
raw, tol=tol, tol_kind=tol_kind)
if fname == mf_fif_fname and tol_kind == 'relative' and tol != 'auto':
pass # does not play nicely with row norms of _estimate_rank_raw
else:
assert rank_new in allowed_rank
rank_new = _estimate_rank_raw(
raw, scalings=dict(), tol=tol, tol_kind=tol_kind)
assert rank_new in allowed_rank
scalings = dict(grad=1e13, mag=1e15)
rank_new = _compute_rank_int(
raw, None, scalings=scalings, tol=tol, tol_kind=tol_kind,
verbose='debug')
assert rank_new in allowed_rank
# XXX default scalings mis-estimate sometimes :(
if fname == hp_fif_fname:
allowed_rank.append(allowed_rank[0] - 2)
rank_new = _compute_rank_int(
raw, None, tol=tol, tol_kind=tol_kind, verbose='debug')
assert rank_new in allowed_rank
del allowed_rank
rank_new = _compute_rank_int(raw, 'info')
assert rank_new == rank
assert_array_equal(raw[:][0], data_orig)
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
###############################################################################
# Set parameters and read data
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
tmin, tmax = -0.2, 0.5
event_id = dict(aud_l=1, vis_l=3)
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname, preload=True)
raw.filter(2, None, method='iir') # replace baselining with high-pass
events = mne.read_events(event_fname)
raw.info['bads'] = ['MEG 2443'] # set bad channels
picks = mne.pick_types(raw.info,
meg='grad',
eeg=False,
stim=False,
eog=False,
exclude='bads')
# Read epochs
epochs = mne.Epochs(raw,
events,
event_id,
def test_scaler(info, method):
"""Test methods of Scaler."""
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True)
epochs_data = epochs.get_data()
y = epochs.events[:, -1]
epochs_data_t = epochs_data.transpose([1, 0, 2])
if method in ('mean', 'median'):
if not check_version('sklearn'):
with pytest.raises(ImportError, match='No module'):
Scaler(info, method)
return
if info:
info = epochs.info
scaler = Scaler(info, method)
X = scaler.fit_transform(epochs_data, y)
assert_equal(X.shape, epochs_data.shape)
if method is None or isinstance(method, dict):
sd = DEFAULTS['scalings'] if method is None else method
stds = np.zeros(len(picks))
for key in ('mag', 'grad'):
stds[pick_types(epochs.info, meg=key)] = 1. / sd[key]
stds[pick_types(epochs.info, meg=False, eeg=True)] = 1. / sd['eeg']
means = np.zeros(len(epochs.ch_names))
elif method == 'mean':
stds = np.array([np.std(ch_data) for ch_data in epochs_data_t])
means = np.array([np.mean(ch_data) for ch_data in epochs_data_t])
else: # median
percs = np.array([
np.percentile(ch_data, [25, 50, 75]) for ch_data in epochs_data_t
])
stds = percs[:, 2] - percs[:, 0]
means = percs[:, 1]
assert_allclose(X * stds[:, np.newaxis] + means[:, np.newaxis],
epochs_data,
rtol=1e-12,
atol=1e-20,
err_msg=method)
X2 = scaler.fit(epochs_data, y).transform(epochs_data)
assert_array_equal(X, X2)
# inverse_transform
Xi = scaler.inverse_transform(X)
assert_array_almost_equal(epochs_data, Xi)
# Test init exception
pytest.raises(ValueError, Scaler, None, None)
pytest.raises(TypeError, scaler.fit, epochs, y)
pytest.raises(TypeError, scaler.transform, epochs)
epochs_bad = Epochs(raw,
events,
event_id,
0,
0.01,
baseline=None,
picks=np.arange(len(raw.ch_names))) # non-data chs
scaler = Scaler(epochs_bad.info, None)
pytest.raises(ValueError, scaler.fit, epochs_bad.get_data(), y)
def test_compute_tfr():
"""Test _compute_tfr function."""
# Set parameters
event_id = 1
tmin = -0.2
tmax = 0.498 # Allows exhaustive decimation testing
# Setup for reading the raw data
raw = read_raw_fif(raw_fname)
events = read_events(event_fname)
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info,
meg='grad',
eeg=False,
stim=False,
include=[],
exclude=exclude)
picks = picks[:2]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks)
data = epochs.get_data()
sfreq = epochs.info['sfreq']
freqs = np.arange(10, 20, 3).astype(float)
# Check all combination of options
for func, use_fft, zero_mean, output in product(
(tfr_array_multitaper, tfr_array_morlet), (False, True), (False, True),
('complex', 'power', 'phase', 'avg_power_itc', 'avg_power', 'itc')):
# Check exception
if (func == tfr_array_multitaper) and (output == 'phase'):
pytest.raises(NotImplementedError,
func,
data,
sfreq=sfreq,
freqs=freqs,
output=output)
continue
# Check runs
out = func(data,
sfreq=sfreq,
freqs=freqs,
use_fft=use_fft,
zero_mean=zero_mean,
n_cycles=2.,
output=output)
# Check shapes
shape = np.r_[data.shape[:2], len(freqs), data.shape[2]]
if ('avg' in output) or ('itc' in output):
assert_array_equal(shape[1:], out.shape)
else:
assert_array_equal(shape, out.shape)
# Check types
if output in ('complex', 'avg_power_itc'):
assert_equal(np.complex128, out.dtype)
else:
assert_equal(np.float64, out.dtype)
assert (np.all(np.isfinite(out)))
# Check errors params
for _data in (None, 'foo', data[0]):
pytest.raises(ValueError, _compute_tfr, _data, freqs, sfreq)
for _freqs in (None, 'foo', [[0]]):
pytest.raises(ValueError, _compute_tfr, data, _freqs, sfreq)
for _sfreq in (None, 'foo'):
pytest.raises(ValueError, _compute_tfr, data, freqs, _sfreq)
for key in ('output', 'method', 'use_fft', 'decim', 'n_jobs'):
for value in (None, 'foo'):
kwargs = {key: value} # FIXME pep8
pytest.raises(ValueError, _compute_tfr, data, freqs, sfreq,
**kwargs)
with pytest.raises(ValueError, match='above Nyquist'):
_compute_tfr(data, [sfreq], sfreq)
# No time_bandwidth param in morlet
pytest.raises(ValueError,
_compute_tfr,
data,
freqs,
sfreq,
method='morlet',
time_bandwidth=1)
# No phase in multitaper XXX Check ?
pytest.raises(NotImplementedError,
_compute_tfr,
data,
freqs,
sfreq,
method='multitaper',
output='phase')
# Inter-trial coherence tests
out = _compute_tfr(data, freqs, sfreq, output='itc', n_cycles=2.)
assert np.sum(out >= 1) == 0
assert np.sum(out <= 0) == 0
# Check decim shapes
# 2: multiple of len(times) even
# 3: multiple odd
# 8: not multiple, even
# 9: not multiple, odd
for decim in (2, 3, 8, 9, slice(0, 2), slice(1, 3), slice(2, 4)):
_decim = slice(None, None, decim) if isinstance(decim, int) else decim
n_time = len(np.arange(data.shape[2])[_decim])
shape = np.r_[data.shape[:2], len(freqs), n_time]
for method in ('multitaper', 'morlet'):
# Single trials
out = _compute_tfr(data,
freqs,
sfreq,
method=method,
decim=decim,
n_cycles=2.)
assert_array_equal(shape, out.shape)
# Averages
out = _compute_tfr(data,
freqs,
sfreq,
method=method,
decim=decim,
output='avg_power',
n_cycles=2.)
assert_array_equal(shape[1:], out.shape)
# to speed up this example; we recommend oct6 in actual analyses)
src = setup_source_space(subject, subjects_dir=subjects_dir,
spacing='oct5', add_dist=False)
# Setup a volume source space
# set pos=10.0 for speed, not very accurate; we recommend something smaller
# like 5.0 in actual analyses:
vol_src = setup_volume_source_space(
subject, mri=fname_aseg, pos=10.0, bem=fname_model,
add_interpolator=False, # just for speed, usually use True
volume_label=labels_vol, subjects_dir=subjects_dir)
# Generate the mixed source space
src += vol_src
# Load data
raw = read_raw_fif(fname_raw, preload=True)
events = mne.find_events(raw)
raw.pick_types(meg=True, eeg=False, eog=True)
noise_cov = mne.read_cov(fname_cov)
# compute the fwd matrix
fwd = make_forward_solution(raw.info, fname_trans, src, fname_bem,
mindist=5.0) # ignore sources<=5mm from innerskull
del src
# Define epochs for left-auditory condition
event_id, tmin, tmax = 1, -0.2, 0.5
reject = dict(mag=4e-12, grad=4000e-13, eog=150e-6)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
reject=reject, preload=False)
del raw
os.mkdir(os.path.join(output_path))
# files to be analysed
files = sorted(glob.glob(os.path.join(data_path, '*-raw.fif')))
# === LOOP THROUGH FILES AND EXTRACT EPOCHS =========================
for file in files:
# --- 1) set up paths and file names -----------------------
filepath, filename = os.path.split(file)
# subject in question
subj = re.findall(r'\d+', filename)[0]
# --- 2) Read in the data ----------------------------------
raw = read_raw_fif(file, preload=True)
# sampling freq
sfreq = raw.info['sfreq']
# --- 3) RECODE EVENTS -----------------------------------------
# Get events
evs = mne.find_events(raw,
stim_channel='Status',
output='onset',
min_duration=0.002)
# Copy of events
new_evs = evs.copy()
broken = []
trial = 0
rt = np.zeros((1248, 4))