def test_evoked_baseline(tmp_path):
"""Test evoked baseline."""
evoked = read_evokeds(fname, condition=0, baseline=None)
# Here we create a data_set with constant data.
evoked = EvokedArray(np.ones_like(evoked.data), evoked.info,
evoked.times[0])
assert evoked.baseline is None
evoked_baselined = EvokedArray(np.ones_like(evoked.data),
evoked.info,
evoked.times[0],
baseline=(None, 0))
assert_allclose(evoked_baselined.baseline, (evoked_baselined.tmin, 0))
del evoked_baselined
# Mean baseline correction is applied, since the data is equal to its mean
# the resulting data should be a matrix of zeroes.
baseline = (None, None)
evoked.apply_baseline(baseline)
assert_allclose(evoked.baseline, (evoked.tmin, evoked.tmax))
assert_allclose(evoked.data, np.zeros_like(evoked.data))
# Test that the .baseline attribute changes if we apply a different
# baseline now.
baseline = (None, 0)
evoked.apply_baseline(baseline)
assert_allclose(evoked.baseline, (evoked.tmin, 0))
# By default for our test file, no baseline should be set upon reading
evoked = read_evokeds(fname, condition=0)
assert evoked.baseline is None
# Test that the .baseline attribute is set when we call read_evokeds()
# with a `baseline` parameter.
baseline = (-0.2, -0.1)
evoked = read_evokeds(fname, condition=0, baseline=baseline)
assert_allclose(evoked.baseline, baseline)
# Test that the .baseline attribute survives an I/O roundtrip.
evoked = read_evokeds(fname, condition=0)
baseline = (-0.2, -0.1)
evoked.apply_baseline(baseline)
assert_allclose(evoked.baseline, baseline)
tmp_fname = tmp_path / 'test-ave.fif'
evoked.save(tmp_fname)
evoked_read = read_evokeds(tmp_fname, condition=0)
assert_allclose(evoked_read.baseline, evoked.baseline)
# We shouldn't be able to remove a baseline correction after it has been
# applied.
evoked = read_evokeds(fname, condition=0)
baseline = (-0.2, -0.1)
evoked.apply_baseline(baseline)
with pytest.raises(ValueError, match='already been baseline-corrected'):
evoked.apply_baseline(None)
def test_array_epochs():
"""Test creating evoked from array."""
tempdir = _TempDir()
# creating
rng = np.random.RandomState(42)
data1 = rng.randn(20, 60)
sfreq = 1e3
ch_names = ['EEG %03d' % (i + 1) for i in range(20)]
types = ['eeg'] * 20
info = create_info(ch_names, sfreq, types)
evoked1 = EvokedArray(data1, info, tmin=-0.01)
# save, read, and compare evokeds
tmp_fname = op.join(tempdir, 'evkdary-ave.fif')
evoked1.save(tmp_fname)
evoked2 = read_evokeds(tmp_fname)[0]
data2 = evoked2.data
assert_allclose(data1, data2)
assert_array_almost_equal(evoked1.times, evoked2.times, 8)
assert_equal(evoked1.first, evoked2.first)
assert_equal(evoked1.last, evoked2.last)
assert_equal(evoked1.kind, evoked2.kind)
assert_equal(evoked1.nave, evoked2.nave)
# now compare with EpochsArray (with single epoch)
data3 = data1[np.newaxis, :, :]
events = np.c_[10, 0, 1]
evoked3 = EpochsArray(data3, info, events=events, tmin=-0.01).average()
assert_allclose(evoked1.data, evoked3.data)
assert_allclose(evoked1.times, evoked3.times)
assert_equal(evoked1.first, evoked3.first)
assert_equal(evoked1.last, evoked3.last)
assert_equal(evoked1.kind, evoked3.kind)
assert_equal(evoked1.nave, evoked3.nave)
# test kind check
with pytest.raises(ValueError, match='Invalid value'):
EvokedArray(data1, info, tmin=0, kind=1)
with pytest.raises(ValueError, match='Invalid value'):
EvokedArray(data1, info, kind='mean')
# test match between channels info and data
ch_names = ['EEG %03d' % (i + 1) for i in range(19)]
types = ['eeg'] * 19
info = create_info(ch_names, sfreq, types)
pytest.raises(ValueError, EvokedArray, data1, info, tmin=-0.01)
def test_array_epochs():
"""Test creating evoked from array."""
tempdir = _TempDir()
# creating
rng = np.random.RandomState(42)
data1 = rng.randn(20, 60)
sfreq = 1e3
ch_names = ['EEG %03d' % (i + 1) for i in range(20)]
types = ['eeg'] * 20
info = create_info(ch_names, sfreq, types)
evoked1 = EvokedArray(data1, info, tmin=-0.01)
# save, read, and compare evokeds
tmp_fname = op.join(tempdir, 'evkdary-ave.fif')
evoked1.save(tmp_fname)
evoked2 = read_evokeds(tmp_fname)[0]
data2 = evoked2.data
assert_allclose(data1, data2)
assert_allclose(evoked1.times, evoked2.times)
assert_equal(evoked1.first, evoked2.first)
assert_equal(evoked1.last, evoked2.last)
assert_equal(evoked1.kind, evoked2.kind)
assert_equal(evoked1.nave, evoked2.nave)
# now compare with EpochsArray (with single epoch)
data3 = data1[np.newaxis, :, :]
events = np.c_[10, 0, 1]
evoked3 = EpochsArray(data3, info, events=events, tmin=-0.01).average()
assert_allclose(evoked1.data, evoked3.data)
assert_allclose(evoked1.times, evoked3.times)
assert_equal(evoked1.first, evoked3.first)
assert_equal(evoked1.last, evoked3.last)
assert_equal(evoked1.kind, evoked3.kind)
assert_equal(evoked1.nave, evoked3.nave)
# test kind check
with pytest.raises(ValueError, match='Invalid value'):
EvokedArray(data1, info, tmin=0, kind=1)
with pytest.raises(ValueError, match='Invalid value'):
EvokedArray(data1, info, kind='mean')
# test match between channels info and data
ch_names = ['EEG %03d' % (i + 1) for i in range(19)]
types = ['eeg'] * 19
info = create_info(ch_names, sfreq, types)
pytest.raises(ValueError, EvokedArray, data1, info, tmin=-0.01)
