def test_cov_scaling():
"""Test rescaling covs"""
evoked = read_evokeds(ave_fname, condition=0, baseline=(None, 0),
proj=True)
cov = read_cov(cov_fname)['data']
cov2 = read_cov(cov_fname)['data']
assert_array_equal(cov, cov2)
evoked.pick_channels([evoked.ch_names[k] for k in pick_types(
evoked.info, meg=True, eeg=True
)])
picks_list = _picks_by_type(evoked.info)
scalings = dict(mag=1e15, grad=1e13, eeg=1e6)
_apply_scaling_cov(cov2, picks_list, scalings=scalings)
_apply_scaling_cov(cov, picks_list, scalings=scalings)
assert_array_equal(cov, cov2)
assert_true(cov.max() > 1)
_undo_scaling_cov(cov2, picks_list, scalings=scalings)
_undo_scaling_cov(cov, picks_list, scalings=scalings)
assert_array_equal(cov, cov2)
assert_true(cov.max() < 1)
data = evoked.data.copy()
_apply_scaling_array(data, picks_list, scalings=scalings)
_undo_scaling_array(data, picks_list, scalings=scalings)
assert_allclose(data, evoked.data, atol=1e-20)
def test_cov_scaling():
"""Test rescaling covs"""
evoked = read_evokeds(ave_fname,
condition=0,
baseline=(None, 0),
proj=True)
cov = read_cov(cov_fname)['data']
cov2 = read_cov(cov_fname)['data']
assert_array_equal(cov, cov2)
evoked.pick_channels([
evoked.ch_names[k] for k in pick_types(evoked.info, meg=True, eeg=True)
])
picks_list = _picks_by_type(evoked.info)
scalings = dict(mag=1e15, grad=1e13, eeg=1e6)
_apply_scaling_cov(cov2, picks_list, scalings=scalings)
_apply_scaling_cov(cov, picks_list, scalings=scalings)
assert_array_equal(cov, cov2)
assert_true(cov.max() > 1)
_undo_scaling_cov(cov2, picks_list, scalings=scalings)
_undo_scaling_cov(cov, picks_list, scalings=scalings)
assert_array_equal(cov, cov2)
assert_true(cov.max() < 1)
data = evoked.data.copy()
_apply_scaling_array(data, picks_list, scalings=scalings)
_undo_scaling_array(data, picks_list, scalings=scalings)
assert_allclose(data, evoked.data, atol=1e-20)
def test_cov_scaling():
"""Test rescaling covs."""
evoked = read_evokeds(ave_fname, condition=0, baseline=(None, 0),
proj=True)
cov = read_cov(cov_fname)['data']
cov2 = read_cov(cov_fname)['data']
assert_array_equal(cov, cov2)
evoked.pick_channels([evoked.ch_names[k] for k in pick_types(
evoked.info, meg=True, eeg=True
)])
picks_list = _picks_by_type(evoked.info)
scalings = dict(mag=1e15, grad=1e13, eeg=1e6)
_apply_scaling_cov(cov2, picks_list, scalings=scalings)
_apply_scaling_cov(cov, picks_list, scalings=scalings)
assert_array_equal(cov, cov2)
assert cov.max() > 1
_undo_scaling_cov(cov2, picks_list, scalings=scalings)
_undo_scaling_cov(cov, picks_list, scalings=scalings)
assert_array_equal(cov, cov2)
assert cov.max() < 1
data = evoked.data.copy()
_apply_scaling_array(data, picks_list, scalings=scalings)
_undo_scaling_array(data, picks_list, scalings=scalings)
assert_allclose(data, evoked.data, atol=1e-20)
# check that input data remain unchanged. gh-5698
_regularized_covariance(data)
assert_array_almost_equal(data, evoked.data)
