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_evoked_baseline():
"""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])
# Mean baseline correction is applied, since the data is equal to its mean
# the resulting data should be a matrix of zeroes.
evoked.apply_baseline((None, None))
assert_allclose(evoked.data, np.zeros_like(evoked.data))
def test_evoked_baseline():
"""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])
# Mean baseline correction is applied, since the data is equal to its mean
# the resulting data should be a matrix of zeroes.
evoked.apply_baseline((None, None))
assert_allclose(evoked.data, np.zeros_like(evoked.data))