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)
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)
def _estimate_rank_meeg_cov(data,
info,
scalings,
tol='auto',
return_singular=False):
"""Estimate rank of M/EEG covariance data, given the covariance.
Parameters
----------
data : np.ndarray of float, shape (n_channels, n_channels)
The M/EEG covariance.
info : Info
The measurement info.
scalings : dict | 'norm' | np.ndarray | None
The rescaling method to be applied. If dict, it will override the
following default dict:
dict(mag=1e12, grad=1e11, eeg=1e5)
If 'norm' data will be scaled by channel-wise norms. If array,
pre-specified norms will be used. If None, no scaling will be applied.
tol : float | str
Tolerance. See ``estimate_rank``.
return_singular : bool
If True, also return the singular values that were used
to determine the rank.
Returns
-------
rank : int
Estimated rank of the data.
s : array
If return_singular is True, the singular values that were
thresholded to determine the rank are also returned.
"""
picks_list = _picks_by_type(info)
scalings = _handle_default('scalings_cov_rank', scalings)
_apply_scaling_cov(data, picks_list, scalings)
if data.shape[1] < data.shape[0]:
ValueError("You've got fewer samples than channels, your "
"rank estimate might be inaccurate.")
out = estimate_rank(data,
tol=tol,
norm=False,
return_singular=return_singular)
rank = out[0] if isinstance(out, tuple) else out
ch_type = ' + '.join(list(zip(*picks_list))[0])
logger.info('estimated rank (%s): %d' % (ch_type, rank))
_undo_scaling_cov(data, picks_list, scalings)
return out
def _estimate_rank_meeg_cov(data, info, scalings, tol='auto',
return_singular=False):
"""Estimate rank of M/EEG covariance data, given the covariance.
Parameters
----------
data : np.ndarray of float, shape (n_channels, n_channels)
The M/EEG covariance.
info : Info
The measurement info.
scalings : dict | 'norm' | np.ndarray | None
The rescaling method to be applied. If dict, it will override the
following default dict:
dict(mag=1e12, grad=1e11, eeg=1e5)
If 'norm' data will be scaled by channel-wise norms. If array,
pre-specified norms will be used. If None, no scaling will be applied.
tol : float | str
Tolerance. See ``estimate_rank``.
return_singular : bool
If True, also return the singular values that were used
to determine the rank.
Returns
-------
rank : int
Estimated rank of the data.
s : array
If return_singular is True, the singular values that were
thresholded to determine the rank are also returned.
"""
picks_list = _picks_by_type(info)
scalings = _handle_default('scalings_cov_rank', scalings)
_apply_scaling_cov(data, picks_list, scalings)
if data.shape[1] < data.shape[0]:
ValueError("You've got fewer samples than channels, your "
"rank estimate might be inaccurate.")
out = estimate_rank(data, tol=tol, norm=False,
return_singular=return_singular)
rank = out[0] if isinstance(out, tuple) else out
ch_type = ' + '.join(list(zip(*picks_list))[0])
logger.info('estimated rank (%s): %d' % (ch_type, rank))
_undo_scaling_cov(data, picks_list, scalings)
return out
