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test_reference.py
521 lines (439 loc) · 21.9 KB
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test_reference.py
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# Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Teon Brooks <teon.brooks@gmail.com>
#
# License: BSD (3-clause)
import itertools
import os.path as op
import numpy as np
from numpy.testing import assert_array_equal, assert_allclose, assert_equal
import pytest
from mne import (pick_channels, pick_types, Epochs, read_events,
set_eeg_reference, set_bipolar_reference,
add_reference_channels, create_info)
from mne.epochs import BaseEpochs
from mne.io import RawArray, read_raw_fif
from mne.io.constants import FIFF
from mne.io.proj import _has_eeg_average_ref_proj, Projection
from mne.io.reference import _apply_reference
from mne.datasets import testing
from mne.utils import run_tests_if_main
data_dir = op.join(testing.data_path(download=False), 'MEG', 'sample')
fif_fname = op.join(data_dir, 'sample_audvis_trunc_raw.fif')
eve_fname = op.join(data_dir, 'sample_audvis_trunc_raw-eve.fif')
ave_fname = op.join(data_dir, 'sample_audvis_trunc-ave.fif')
def _test_reference(raw, reref, ref_data, ref_from):
"""Test whether a reference has been correctly applied."""
# Separate EEG channels from other channel types
picks_eeg = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
picks_other = pick_types(raw.info, meg=True, eeg=False, eog=True,
stim=True, exclude='bads')
# Calculate indices of reference channesl
picks_ref = [raw.ch_names.index(ch) for ch in ref_from]
# Get data
_data = raw._data
_reref = reref._data
# Check that the ref has been properly computed
if ref_data is not None:
assert_array_equal(ref_data, _data[..., picks_ref, :].mean(-2))
# Get the raw EEG data and other channel data
raw_eeg_data = _data[..., picks_eeg, :]
raw_other_data = _data[..., picks_other, :]
# Get the rereferenced EEG data
reref_eeg_data = _reref[..., picks_eeg, :]
reref_other_data = _reref[..., picks_other, :]
# Check that non-EEG channels are untouched
assert_allclose(raw_other_data, reref_other_data, 1e-6, atol=1e-15)
# Undo rereferencing of EEG channels if possible
if ref_data is not None:
if isinstance(raw, BaseEpochs):
unref_eeg_data = reref_eeg_data + ref_data[:, np.newaxis, :]
else:
unref_eeg_data = reref_eeg_data + ref_data
assert_allclose(raw_eeg_data, unref_eeg_data, 1e-6, atol=1e-15)
@testing.requires_testing_data
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'])
@testing.requires_testing_data
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)
@testing.requires_testing_data
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 (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 ('EEG 001' not in reref.ch_names)
assert ('EEG 002' not in reref.ch_names)
assert ('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 ('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 ('EEG 001-EEG 002' in reref.ch_names)
assert ('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},
verbose='error')
assert (not reref.info['custom_ref_applied'])
assert ('MEG 0111-MEG 0112'[:15] in reref.ch_names)
# Test a battery of invalid inputs
pytest.raises(ValueError, set_bipolar_reference, raw,
'EEG 001', ['EEG 002', 'EEG 003'], 'bipolar')
pytest.raises(ValueError, set_bipolar_reference, raw,
['EEG 001', 'EEG 002'], 'EEG 003', 'bipolar')
pytest.raises(ValueError, set_bipolar_reference, raw,
'EEG 001', 'EEG 002', ['bipolar1', 'bipolar2'])
pytest.raises(ValueError, set_bipolar_reference, raw,
'EEG 001', 'EEG 002', 'bipolar',
ch_info=[{'foo': 'bar'}, {'foo': 'bar'}])
pytest.raises(ValueError, set_bipolar_reference, raw,
'EEG 001', 'EEG 002', ch_name='EEG 003')
def _check_channel_names(inst, ref_names):
"""Check channel names."""
if isinstance(ref_names, str):
ref_names = [ref_names]
# Test that the names of the reference channels are present in `ch_names`
ref_idx = pick_channels(inst.info['ch_names'], ref_names)
assert len(ref_idx) == len(ref_names)
# Test that the names of the reference channels are present in the `chs`
# list
inst.info._check_consistency() # Should raise no exceptions
@testing.requires_testing_data
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
pytest.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
pytest.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
pytest.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 pytest.warns(RuntimeWarning, match='ignored .set to 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 pytest.warns(RuntimeWarning, match='ignored .set to 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)
pytest.raises(RuntimeError, add_reference_channels, raw_np, ['Ref'])
pytest.raises(ValueError, add_reference_channels, raw, 1)
def test_bipolar_combinations():
"""Test bipolar channel generation."""
ch_names = ['CH' + str(ni + 1) for ni in range(10)]
info = create_info(
ch_names=ch_names, sfreq=1000., ch_types=['eeg'] * len(ch_names))
raw_data = np.random.randn(len(ch_names), 1000)
raw = RawArray(raw_data, info)
def _check_bipolar(raw_test, ch_a, ch_b):
picks = [raw_test.ch_names.index(ch_a + '-' + ch_b)]
get_data_res = raw_test.get_data(picks=picks)[0, :]
manual_a = raw_data[ch_names.index(ch_a), :]
manual_b = raw_data[ch_names.index(ch_b), :]
assert_array_equal(get_data_res, manual_a - manual_b)
# test classic EOG/ECG bipolar reference (only two channels per pair).
raw_test = set_bipolar_reference(raw, ['CH2'], ['CH1'], copy=True)
_check_bipolar(raw_test, 'CH2', 'CH1')
# test all combinations.
a_channels, b_channels = zip(*itertools.combinations(ch_names, 2))
a_channels, b_channels = list(a_channels), list(b_channels)
raw_test = set_bipolar_reference(raw, a_channels, b_channels, copy=True)
for ch_a, ch_b in zip(a_channels, b_channels):
_check_bipolar(raw_test, ch_a, ch_b)
# check if reference channels have been dropped.
assert (len(raw_test.ch_names) == len(a_channels))
raw_test = set_bipolar_reference(
raw, a_channels, b_channels, drop_refs=False, copy=True)
# check if reference channels have been kept correctly.
assert (len(raw_test.ch_names) == len(a_channels) + len(ch_names))
for idx, ch_label in enumerate(ch_names):
manual_ch = raw_data[idx, :]
assert_array_equal(
raw_test._data[raw_test.ch_names.index(ch_label), :], manual_ch)
# test bipolars with a channel in both list (anode & cathode).
raw_test = set_bipolar_reference(
raw, ['CH2', 'CH1'], ['CH1', 'CH2'], copy=True)
_check_bipolar(raw_test, 'CH2', 'CH1')
_check_bipolar(raw_test, 'CH1', 'CH2')
run_tests_if_main()