def test_epochs_proj():
"""Test handling projection (apply proj in Raw or in Epochs)
"""
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
this_picks = pick_types(raw.info, meg=True, eeg=False, stim=True,
eog=True, exclude=exclude)
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
baseline=(None, 0), proj=True)
assert_true(all(p['active'] is True for p in epochs.info['projs']))
evoked = epochs.average()
assert_true(all(p['active'] is True for p in evoked.info['projs']))
data = epochs.get_data()
raw_proj = io.Raw(raw_fname, proj=True)
epochs_no_proj = Epochs(raw_proj, events[:4], event_id, tmin, tmax,
picks=this_picks, baseline=(None, 0), proj=False)
data_no_proj = epochs_no_proj.get_data()
assert_true(all(p['active'] is True for p in epochs_no_proj.info['projs']))
evoked_no_proj = epochs_no_proj.average()
assert_true(all(p['active'] is True for p in evoked_no_proj.info['projs']))
assert_true(epochs_no_proj.proj is True) # as projs are active from Raw
assert_array_almost_equal(data, data_no_proj, decimal=8)
# make sure we can exclude avg ref
this_picks = pick_types(raw.info, meg=True, eeg=True, stim=True,
eog=True, exclude=exclude)
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
baseline=(None, 0), proj=True, add_eeg_ref=True)
assert_true(_has_eeg_average_ref_proj(epochs.info['projs']))
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
baseline=(None, 0), proj=True, add_eeg_ref=False)
assert_true(not _has_eeg_average_ref_proj(epochs.info['projs']))
def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(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(ref_data is None)
# Test setting an average reference when one was already present
with warnings.catch_warnings(record=True): # weight tables
reref, ref_data = set_eeg_reference(raw, copy=False)
assert_true(ref_data is None)
# 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)
def test_epochs_proj():
"""Test handling projection (apply proj in Raw or in Epochs)
"""
exclude = raw.info["bads"] + ["MEG 2443", "EEG 053"] # bads + 2 more
this_picks = pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True, exclude=exclude)
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks, baseline=(None, 0), proj=True)
assert_true(all(p["active"] is True for p in epochs.info["projs"]))
evoked = epochs.average()
assert_true(all(p["active"] is True for p in evoked.info["projs"]))
data = epochs.get_data()
raw_proj = io.Raw(raw_fname, proj=True)
epochs_no_proj = Epochs(
raw_proj, events[:4], event_id, tmin, tmax, picks=this_picks, baseline=(None, 0), proj=False
)
data_no_proj = epochs_no_proj.get_data()
assert_true(all(p["active"] is True for p in epochs_no_proj.info["projs"]))
evoked_no_proj = epochs_no_proj.average()
assert_true(all(p["active"] is True for p in evoked_no_proj.info["projs"]))
assert_true(epochs_no_proj.proj is True) # as projs are active from Raw
assert_array_almost_equal(data, data_no_proj, decimal=8)
# make sure we can exclude avg ref
this_picks = pick_types(raw.info, meg=True, eeg=True, stim=True, eog=True, exclude=exclude)
epochs = Epochs(
raw, events[:4], event_id, tmin, tmax, picks=this_picks, baseline=(None, 0), proj=True, add_eeg_ref=True
)
assert_true(_has_eeg_average_ref_proj(epochs.info["projs"]))
epochs = Epochs(
raw, events[:4], event_id, tmin, tmax, picks=this_picks, baseline=(None, 0), proj=True, add_eeg_ref=False
)
assert_true(not _has_eeg_average_ref_proj(epochs.info["projs"]))
def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(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(ref_data is None)
# Test setting an average reference when one was already present
with warnings.catch_warnings(record=True): # weight tables
reref, ref_data = set_eeg_reference(raw, copy=False)
assert_true(ref_data is None)
# 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)
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_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_epochs_proj():
"""Test handling projection (apply proj in Raw or in Epochs)
"""
raw, events, picks = _get_data()
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
this_picks = pick_types(raw.info, meg=True, eeg=False, stim=True,
eog=True, exclude=exclude)
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
baseline=(None, 0), proj=True)
assert_true(all(p['active'] is True for p in epochs.info['projs']))
evoked = epochs.average()
assert_true(all(p['active'] is True for p in evoked.info['projs']))
data = epochs.get_data()
raw_proj = io.Raw(raw_fname, proj=True)
epochs_no_proj = Epochs(raw_proj, events[:4], event_id, tmin, tmax,
picks=this_picks, baseline=(None, 0), proj=False)
data_no_proj = epochs_no_proj.get_data()
assert_true(all(p['active'] is True for p in epochs_no_proj.info['projs']))
evoked_no_proj = epochs_no_proj.average()
assert_true(all(p['active'] is True for p in evoked_no_proj.info['projs']))
assert_true(epochs_no_proj.proj is True) # as projs are active from Raw
assert_array_almost_equal(data, data_no_proj, decimal=8)
# make sure we can exclude avg ref
this_picks = pick_types(raw.info, meg=True, eeg=True, stim=True,
eog=True, exclude=exclude)
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
baseline=(None, 0), proj=True, add_eeg_ref=True)
assert_true(_has_eeg_average_ref_proj(epochs.info['projs']))
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
baseline=(None, 0), proj=True, add_eeg_ref=False)
assert_true(not _has_eeg_average_ref_proj(epochs.info['projs']))
# make sure we don't add avg ref when a custom ref has been applied
raw.info['custom_ref_applied'] = True
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks,
baseline=(None, 0), proj=True)
assert_true(not _has_eeg_average_ref_proj(epochs.info['projs']))
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(ref_data is None)
# Test setting an average reference when one was already present
with warnings.catch_warnings(record=True): # weight tables
reref, ref_data = set_eeg_reference(raw, copy=False)
assert_true(ref_data is None)
# 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'])
def test_apply_reference():
"""Test base function for rereferencing"""
raw = Raw(fif_fname, preload=True)
# Rereference raw data by creating a copy of original data
reref, ref_data = _apply_reference(raw,
ref_from=['EEG 001', 'EEG 002'],
copy=True)
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 disabling the reference does not break anything
reref, ref_data = _apply_reference(raw, [])
assert_array_equal(raw._data, reref._data)
# Test that data is modified in place when copy=False
reref, ref_data = _apply_reference(raw, ['EEG 001', 'EEG 002'], copy=False)
assert_true(raw is reref)
# Test re-referencing Epochs object
raw = Raw(fif_fname, preload=False, add_eeg_ref=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,
ref_from=['EEG 001', 'EEG 002'],
copy=True)
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,
ref_from=['EEG 001', 'EEG 002'],
copy=True)
assert_true(reref.info['custom_ref_applied'])
_test_reference(evoked, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test invalid input
raw_np = Raw(fif_fname, preload=False)
assert_raises(RuntimeError, _apply_reference, raw_np, ['EEG 001'])
def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(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(ref_data is None)
# Test setting an average reference when one was already present
reref, ref_data = set_eeg_reference(raw, copy=False)
assert_true(ref_data is None)
# 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)
def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(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(ref_data is None)
# 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)
# Setting an average reference on a dataset that already contains a custom
# reference should fail
assert_raises(RuntimeError, set_eeg_reference, reref)
def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(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(ref_data is None)
# 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)
# Setting an average reference on a dataset that already contains a custom
# reference should fail
assert_raises(RuntimeError, set_eeg_reference, reref)
def test_apply_reference():
"""Test base function for rereferencing"""
raw = Raw(fif_fname, preload=True)
# Rereference raw data by creating a copy of original data
reref, ref_data = _apply_reference(raw, ref_from=['EEG 001', 'EEG 002'],
copy=True)
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 disabling the reference does not break anything
reref, ref_data = _apply_reference(raw, [])
assert_array_equal(raw._data, reref._data)
# Test that data is modified in place when copy=False
reref, ref_data = _apply_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert_true(raw is reref)
# Test re-referencing Epochs object
raw = Raw(fif_fname, preload=False, add_eeg_ref=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, ref_from=['EEG 001', 'EEG 002'],
copy=True)
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, ref_from=['EEG 001', 'EEG 002'],
copy=True)
assert_true(reref.info['custom_ref_applied'])
_test_reference(evoked, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test invalid input
raw_np = Raw(fif_fname, preload=False)
assert_raises(RuntimeError, _apply_reference, raw_np, ['EEG 001'])
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 (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 (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 (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 (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 (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)
pytest.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
with pytest.raises(RuntimeError, match='Inactive signal space'):
_apply_reference(raw, ['EEG 001'])
# Projection does not concern channels mentioned in projector, no error
_apply_reference(raw, ['EEG 003'], ['EEG 004'])
# CSD cannot be rereferenced
raw.info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_CSD
with pytest.raises(RuntimeError, match="Cannot set.* type 'CSD'"):
raw.set_eeg_reference()
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 not reref.info['custom_ref_applied']
# When creating an average reference fails, make sure the
# custom_ref_applied flag remains untouched.
reref = raw.copy()
reref.info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_ON
reref.pick_types(meg=True, eeg=False) # Cause making average ref fail
pytest.raises(ValueError, set_eeg_reference, reref, projection=True)
assert reref.info['custom_ref_applied'] == FIFF.FIFFV_MNE_CUSTOM_REF_ON
# 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 reref.info['custom_ref_applied'] == FIFF.FIFFV_MNE_CUSTOM_REF_ON
# 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)
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 = _get_channel_types(this_info)
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_rank():
"""Test cov rank estimation."""
# Test that our rank estimation works properly on a simple case
evoked = read_evokeds(ave_fname, condition=0, baseline=(None, 0),
proj=False)
cov = read_cov(cov_fname)
ch_names = [ch for ch in evoked.info['ch_names'] if '053' not in ch and
ch.startswith('EEG')]
cov = prepare_noise_cov(cov, evoked.info, ch_names, None)
assert_equal(cov['eig'][0], 0.) # avg projector should set this to zero
assert (cov['eig'][1:] > 0).all() # all else should be > 0
# Now do some more comprehensive tests
raw_sample = read_raw_fif(raw_fname)
assert not _has_eeg_average_ref_proj(raw_sample.info['projs'])
raw_sss = read_raw_fif(hp_fif_fname)
assert not _has_eeg_average_ref_proj(raw_sss.info['projs'])
raw_sss.add_proj(compute_proj_raw(raw_sss))
cov_sample = compute_raw_covariance(raw_sample)
cov_sample_proj = compute_raw_covariance(
raw_sample.copy().apply_proj())
cov_sss = compute_raw_covariance(raw_sss)
cov_sss_proj = compute_raw_covariance(
raw_sss.copy().apply_proj())
picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)
info_sample = pick_info(raw_sample.info, picks_all_sample)
picks_stack_sample = [('eeg', pick_types(info_sample, meg=False,
eeg=True))]
picks_stack_sample += [('meg', pick_types(info_sample, meg=True))]
picks_stack_sample += [('all',
pick_types(info_sample, meg=True, eeg=True))]
info_sss = pick_info(raw_sss.info, picks_all_sss)
picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))]
picks_stack_somato += [('meg', pick_types(info_sss, meg=True))]
picks_stack_somato += [('all',
pick_types(info_sss, meg=True, eeg=True))]
iter_tests = list(itt.product(
[(cov_sample, picks_stack_sample, info_sample),
(cov_sample_proj, picks_stack_sample, info_sample),
(cov_sss, picks_stack_somato, info_sss),
(cov_sss_proj, picks_stack_somato, info_sss)], # sss
[dict(mag=1e15, grad=1e13, eeg=1e6)]
))
for (cov, picks_list, this_info), scalings in iter_tests:
for ch_type, picks in picks_list:
this_very_info = pick_info(this_info, picks)
# compute subset of projs
this_projs = [c['active'] and
len(set(c['data']['col_names'])
.intersection(set(this_very_info['ch_names']))) >
0 for c in cov['projs']]
n_projs = sum(this_projs)
# count channel types
ch_types = [channel_type(this_very_info, idx)
for idx in range(len(picks))]
n_eeg, n_mag, n_grad = [ch_types.count(k) for k in
['eeg', 'mag', 'grad']]
n_meg = n_mag + n_grad
# check sss
if len(this_very_info['proc_history']) > 0:
mf = this_very_info['proc_history'][0]['max_info']
n_free = _get_sss_rank(mf)
if 'mag' not in ch_types and 'grad' not in ch_types:
n_free = 0
# - n_projs XXX clarify
expected_rank = n_free + n_eeg
else:
expected_rank = n_meg + n_eeg - n_projs
C = cov['data'][np.ix_(picks, picks)]
est_rank = _estimate_rank_meeg_cov(C, this_very_info,
scalings=scalings)
assert_equal(expected_rank, est_rank)
def test_interpolation_eeg(offset, avg_proj, ctol, atol, method):
"""Test interpolation of EEG channels."""
raw, epochs_eeg = _load_data('eeg')
epochs_eeg = epochs_eeg.copy()
assert not _has_eeg_average_ref_proj(epochs_eeg.info['projs'])
# Offsetting the coordinate frame should have no effect on the output
for inst in (raw, epochs_eeg):
for ch in inst.info['chs']:
if ch['kind'] == io.constants.FIFF.FIFFV_EEG_CH:
ch['loc'][:3] += offset
ch['loc'][3:6] += offset
for d in inst.info['dig']:
d['r'] += offset
# check that interpolation does nothing if no bads are marked
epochs_eeg.info['bads'] = []
evoked_eeg = epochs_eeg.average()
kw = dict(method=method)
with pytest.warns(RuntimeWarning, match='Doing nothing'):
evoked_eeg.interpolate_bads(**kw)
# create good and bad channels for EEG
epochs_eeg.info['bads'] = []
goods_idx = np.ones(len(epochs_eeg.ch_names), dtype=bool)
goods_idx[epochs_eeg.ch_names.index('EEG 012')] = False
bads_idx = ~goods_idx
pos = epochs_eeg._get_channel_positions()
evoked_eeg = epochs_eeg.average()
if avg_proj:
evoked_eeg.set_eeg_reference(projection=True).apply_proj()
assert_allclose(evoked_eeg.data.mean(0), 0., atol=1e-20)
ave_before = evoked_eeg.data[bads_idx]
# interpolate bad channels for EEG
epochs_eeg.info['bads'] = ['EEG 012']
evoked_eeg = epochs_eeg.average()
if avg_proj:
evoked_eeg.set_eeg_reference(projection=True).apply_proj()
good_picks = pick_types(evoked_eeg.info, meg=False, eeg=True)
assert_allclose(evoked_eeg.data[good_picks].mean(0), 0., atol=1e-20)
evoked_eeg_bad = evoked_eeg.copy()
bads_picks = pick_channels(epochs_eeg.ch_names,
include=epochs_eeg.info['bads'],
ordered=True)
evoked_eeg_bad.data[bads_picks, :] = 1e10
# Test first the exclude parameter
evoked_eeg_2_bads = evoked_eeg_bad.copy()
evoked_eeg_2_bads.info['bads'] = ['EEG 004', 'EEG 012']
evoked_eeg_2_bads.data[pick_channels(evoked_eeg_bad.ch_names,
['EEG 004', 'EEG 012'])] = 1e10
evoked_eeg_interp = evoked_eeg_2_bads.interpolate_bads(origin=(0., 0., 0.),
exclude=['EEG 004'],
**kw)
assert evoked_eeg_interp.info['bads'] == ['EEG 004']
assert np.all(evoked_eeg_interp.get_data('EEG 004') == 1e10)
assert np.all(evoked_eeg_interp.get_data('EEG 012') != 1e10)
# Now test without exclude parameter
evoked_eeg_bad.info['bads'] = ['EEG 012']
evoked_eeg_interp = evoked_eeg_bad.copy().interpolate_bads(origin=(0., 0.,
0.),
**kw)
if avg_proj:
assert_allclose(evoked_eeg_interp.data.mean(0), 0., atol=1e-6)
interp_zero = evoked_eeg_interp.data[bads_idx]
if method is None: # using
pos_good = pos[goods_idx]
pos_bad = pos[bads_idx]
interpolation = _make_interpolation_matrix(pos_good, pos_bad)
assert interpolation.shape == (1, len(epochs_eeg.ch_names) - 1)
interp_manual = np.dot(interpolation, evoked_eeg_bad.data[goods_idx])
assert_array_equal(interp_manual, interp_zero)
del interp_manual, interpolation, pos, pos_good, pos_bad
assert_allclose(ave_before, interp_zero, atol=atol)
assert ctol[0] < np.corrcoef(ave_before, interp_zero)[0, 1] < ctol[1]
interp_fit = evoked_eeg_bad.copy().interpolate_bads(**kw).data[bads_idx]
assert_allclose(ave_before, interp_fit, atol=2.5e-6)
assert ctol[1] < np.corrcoef(ave_before, interp_fit)[0, 1] # better
# check that interpolation fails when preload is False
epochs_eeg.preload = False
with pytest.raises(RuntimeError, match='requires epochs data to be loade'):
epochs_eeg.interpolate_bads(**kw)
epochs_eeg.preload = True
# check that interpolation changes the data in raw
raw_eeg = io.RawArray(data=epochs_eeg._data[0], info=epochs_eeg.info)
raw_before = raw_eeg._data[bads_idx]
raw_after = raw_eeg.interpolate_bads(**kw)._data[bads_idx]
assert not np.all(raw_before == raw_after)
# check that interpolation fails when preload is False
for inst in [raw, epochs_eeg]:
assert hasattr(inst, 'preload')
inst.preload = False
inst.info['bads'] = [inst.ch_names[1]]
with pytest.raises(RuntimeError, match='requires.*data to be loaded'):
inst.interpolate_bads(**kw)
# check that interpolation works with few channels
raw_few = raw.copy().crop(0, 0.1).load_data()
raw_few.pick_channels(raw_few.ch_names[:1] + raw_few.ch_names[3:4])
assert len(raw_few.ch_names) == 2
raw_few.del_proj()
raw_few.info['bads'] = [raw_few.ch_names[-1]]
orig_data = raw_few[1][0]
with _record_warnings() as w:
raw_few.interpolate_bads(reset_bads=False, **kw)
assert len([ww for ww in w if 'more than' not in str(ww.message)]) == 0
new_data = raw_few[1][0]
assert (new_data == 0).mean() < 0.5
assert np.corrcoef(new_data, orig_data)[0, 1] > 0.2
def test_rank():
"""Test cov rank estimation."""
# Test that our rank estimation works properly on a simple case
evoked = read_evokeds(ave_fname,
condition=0,
baseline=(None, 0),
proj=False)
cov = read_cov(cov_fname)
ch_names = [
ch for ch in evoked.info['ch_names']
if '053' not in ch and ch.startswith('EEG')
]
cov = prepare_noise_cov(cov, evoked.info, ch_names, None)
assert_equal(cov['eig'][0], 0.) # avg projector should set this to zero
assert (cov['eig'][1:] > 0).all() # all else should be > 0
# Now do some more comprehensive tests
raw_sample = read_raw_fif(raw_fname)
assert not _has_eeg_average_ref_proj(raw_sample.info['projs'])
raw_sss = read_raw_fif(hp_fif_fname)
assert not _has_eeg_average_ref_proj(raw_sss.info['projs'])
raw_sss.add_proj(compute_proj_raw(raw_sss))
cov_sample = compute_raw_covariance(raw_sample)
cov_sample_proj = compute_raw_covariance(raw_sample.copy().apply_proj())
cov_sss = compute_raw_covariance(raw_sss)
cov_sss_proj = compute_raw_covariance(raw_sss.copy().apply_proj())
picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)
info_sample = pick_info(raw_sample.info, picks_all_sample)
picks_stack_sample = [('eeg', pick_types(info_sample, meg=False,
eeg=True))]
picks_stack_sample += [('meg', pick_types(info_sample, meg=True))]
picks_stack_sample += [('all', pick_types(info_sample, meg=True,
eeg=True))]
info_sss = pick_info(raw_sss.info, picks_all_sss)
picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))]
picks_stack_somato += [('meg', pick_types(info_sss, meg=True))]
picks_stack_somato += [('all', pick_types(info_sss, meg=True, eeg=True))]
iter_tests = list(
itt.product(
[(cov_sample, picks_stack_sample, info_sample),
(cov_sample_proj, picks_stack_sample, info_sample),
(cov_sss, picks_stack_somato, info_sss),
(cov_sss_proj, picks_stack_somato, info_sss)], # sss
[dict(mag=1e15, grad=1e13, eeg=1e6)]))
for (cov, picks_list, this_info), scalings in iter_tests:
for ch_type, picks in picks_list:
this_very_info = pick_info(this_info, picks)
# compute subset of projs
this_projs = [
c['active'] and len(
set(c['data']['col_names']).intersection(
set(this_very_info['ch_names']))) > 0
for c in cov['projs']
]
n_projs = sum(this_projs)
# count channel types
ch_types = [
channel_type(this_very_info, idx) for idx in range(len(picks))
]
n_eeg, n_mag, n_grad = [
ch_types.count(k) for k in ['eeg', 'mag', 'grad']
]
n_meg = n_mag + n_grad
# check sss
if len(this_very_info['proc_history']) > 0:
mf = this_very_info['proc_history'][0]['max_info']
n_free = _get_sss_rank(mf)
if 'mag' not in ch_types and 'grad' not in ch_types:
n_free = 0
# - n_projs XXX clarify
expected_rank = n_free + n_eeg
else:
expected_rank = n_meg + n_eeg - n_projs
C = cov['data'][np.ix_(picks, picks)]
est_rank = _estimate_rank_meeg_cov(C,
this_very_info,
scalings=scalings)
assert_equal(expected_rank, est_rank)
def test_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_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 (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 (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 (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 (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 (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)
pytest.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
pytest.raises(RuntimeError, _apply_reference, raw, ['EEG 001'])
# Projection does not concern channels mentioned in projector, no error
_apply_reference(raw, ['EEG 003'], ['EEG 004'])
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)
# projection=True only works for ref_channels='average'
pytest.raises(ValueError, set_eeg_reference, raw, [], True, True)
pytest.raises(ValueError, set_eeg_reference, raw, ['EEG 001'], True, True)
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)
