def test_raw_rank_estimation(fname, ref_meg, scalings):
"""Test raw rank estimation."""
if ref_meg and scalings != 'norm':
# Adjust for CTF data (scale factors are quite different)
scalings = dict(mag=1e31, grad=1e11)
raw = read_raw_fif(fname)
raw.crop(0, min(4., raw.times[-1])).load_data()
out = _picks_by_type(raw.info, ref_meg=ref_meg, meg_combined=True)
has_eeg = 'eeg' in raw
if has_eeg:
(_, picks_meg), (_, picks_eeg) = out
else:
(_, picks_meg), = out
picks_eeg = []
n_meg = len(picks_meg)
n_eeg = len(picks_eeg)
if len(raw.info['proc_history']) == 0:
expected_rank = n_meg + n_eeg
else:
expected_rank = _get_rank_sss(raw.info) + n_eeg
got_rank = _estimate_rank_raw(raw, scalings=scalings, with_ref_meg=ref_meg)
assert got_rank == expected_rank
if 'sss' in fname:
raw.add_proj(compute_proj_raw(raw))
raw.apply_proj()
n_proj = len(raw.info['projs'])
want_rank = expected_rank - (0 if 'sss' in fname else n_proj)
got_rank = _estimate_rank_raw(raw, scalings=scalings, with_ref_meg=ref_meg)
assert got_rank == want_rank
def test_raw_rank_estimation(fname, ref_meg, scalings):
"""Test raw rank estimation."""
if ref_meg and scalings != 'norm':
# Adjust for CTF data (scale factors are quite different)
scalings = dict(mag=1e31, grad=1e11)
raw = read_raw_fif(fname)
raw.crop(0, min(4., raw.times[-1])).load_data()
out = _picks_by_type(raw.info, ref_meg=ref_meg, meg_combined=True)
has_eeg = 'eeg' in raw
if has_eeg:
(_, picks_meg), (_, picks_eeg) = out
else:
(_, picks_meg), = out
picks_eeg = []
n_meg = len(picks_meg)
n_eeg = len(picks_eeg)
if len(raw.info['proc_history']) == 0:
expected_rank = n_meg + n_eeg
else:
expected_rank = _get_rank_sss(raw.info) + n_eeg
got_rank = _estimate_rank_raw(raw, scalings=scalings, with_ref_meg=ref_meg)
assert got_rank == expected_rank
if has_eeg:
with pytest.deprecated_call():
assert raw.estimate_rank(picks=picks_eeg,
scalings=scalings) == n_eeg
if 'sss' in fname:
raw.add_proj(compute_proj_raw(raw))
raw.apply_proj()
n_proj = len(raw.info['projs'])
want_rank = expected_rank - (0 if 'sss' in fname else n_proj)
got_rank = _estimate_rank_raw(raw, scalings=scalings, with_ref_meg=ref_meg)
assert got_rank == want_rank
def test_rank_estimation():
"""Test raw rank estimation."""
iter_tests = itt.product(
[fif_fname, hp_fif_fname], # sss
['norm', dict(mag=1e11, grad=1e9, eeg=1e5)])
for fname, scalings in iter_tests:
raw = read_raw_fif(fname).crop(0, 4.).load_data()
(_, picks_meg), (_, picks_eeg) = _picks_by_type(raw.info,
meg_combined=True)
n_meg = len(picks_meg)
n_eeg = len(picks_eeg)
if len(raw.info['proc_history']) == 0:
expected_rank = n_meg + n_eeg
else:
expected_rank = _get_rank_sss(raw.info) + n_eeg
assert _estimate_rank_raw(raw, scalings=scalings) == expected_rank
with pytest.deprecated_call():
assert raw.estimate_rank(picks=picks_eeg,
scalings=scalings) == n_eeg
if 'sss' in fname:
raw.add_proj(compute_proj_raw(raw))
raw.apply_proj()
n_proj = len(raw.info['projs'])
want_rank = expected_rank - (0 if 'sss' in fname else n_proj)
assert _estimate_rank_raw(raw, scalings=scalings) == want_rank
def test_maxfilter_get_rank(n_proj, fname, rank_orig, meg):
"""Test maxfilter rank lookup."""
raw = read_raw_fif(fname).crop(0, 5).load_data().pick_types()
assert raw.info['projs'] == []
mf = raw.info['proc_history'][0]['max_info']
assert mf['sss_info']['nfree'] == rank_orig
assert _get_rank_sss(raw) == rank_orig
mult = 1 + (meg == 'separate')
rank = rank_orig - mult * n_proj
if n_proj > 0:
# Let's do some projection
raw.add_proj(
compute_proj_raw(raw,
n_mag=n_proj,
n_grad=n_proj,
meg=meg,
verbose=True))
raw.apply_proj()
data_orig = raw[:][0]
# degenerate cases
with pytest.raises(ValueError, match='tol must be'):
_estimate_rank_raw(raw, tol='foo')
with pytest.raises(TypeError, match='must be a string or a number'):
_estimate_rank_raw(raw, tol=None)
allowed_rank = [rank_orig if meg == 'separate' else rank]
if fname == mf_fif_fname:
# Here we permit a -1 because for mf_fif_fname we miss by 1, which is
# probably acceptable. If we use the entire duration instead of 5 sec
# this problem goes away, but the test is much slower.
allowed_rank.append(allowed_rank[0] - 1)
# multiple ways of hopefully getting the same thing
# default tol=1e-4, scalings='norm'
rank_new = _estimate_rank_raw(raw)
assert rank_new in allowed_rank
tol = 'float32' # temporary option until we can fix things
rank_new = _estimate_rank_raw(raw, tol=tol)
assert rank_new in allowed_rank
rank_new = _estimate_rank_raw(raw, scalings=dict(), tol=tol)
assert rank_new in allowed_rank
scalings = dict(grad=1e13, mag=1e15)
rank_new = _compute_rank_int(raw,
None,
scalings=scalings,
tol=tol,
verbose='debug')
assert rank_new in allowed_rank
# XXX default scalings mis-estimate sometimes :(
if fname == hp_fif_fname:
allowed_rank.append(allowed_rank[0] - 2)
rank_new = _compute_rank_int(raw, None, tol=tol, verbose='debug')
assert rank_new in allowed_rank
del allowed_rank
rank_new = _compute_rank_int(raw, 'info')
assert rank_new == rank
assert_array_equal(raw[:][0], data_orig)
def test_maxfilter_get_rank(n_proj, fname, rank_orig, meg):
"""Test maxfilter rank lookup."""
raw = read_raw_fif(fname).crop(0, 5).load_data().pick_types()
assert raw.info['projs'] == []
mf = raw.info['proc_history'][0]['max_info']
assert mf['sss_info']['nfree'] == rank_orig
assert _get_rank_sss(raw) == rank_orig
mult = 1 + (meg == 'separate')
rank = rank_orig - mult * n_proj
if n_proj > 0:
# Let's do some projection
raw.add_proj(compute_proj_raw(raw, n_mag=n_proj, n_grad=n_proj,
meg=meg, verbose=True))
raw.apply_proj()
data_orig = raw[:][0]
# degenerate cases
with pytest.raises(ValueError, match='tol must be'):
_estimate_rank_raw(raw, tol='foo')
with pytest.raises(TypeError, match='must be a string or a number'):
_estimate_rank_raw(raw, tol=None)
allowed_rank = [rank_orig if meg == 'separate' else rank]
if fname == mf_fif_fname:
# Here we permit a -1 because for mf_fif_fname we miss by 1, which is
# probably acceptable. If we use the entire duration instead of 5 sec
# this problem goes away, but the test is much slower.
allowed_rank.append(allowed_rank[0] - 1)
# multiple ways of hopefully getting the same thing
# default tol=1e-4, scalings='norm'
rank_new = _estimate_rank_raw(raw)
assert rank_new in allowed_rank
tol = 'float32' # temporary option until we can fix things
rank_new = _estimate_rank_raw(raw, tol=tol)
assert rank_new in allowed_rank
rank_new = _estimate_rank_raw(raw, scalings=dict(), tol=tol)
assert rank_new in allowed_rank
scalings = dict(grad=1e13, mag=1e15)
rank_new = _compute_rank_int(raw, None, scalings=scalings, tol=tol,
verbose='debug')
assert rank_new in allowed_rank
# XXX default scalings mis-estimate sometimes :(
if fname == hp_fif_fname:
allowed_rank.append(allowed_rank[0] - 2)
rank_new = _compute_rank_int(raw, None, tol=tol, verbose='debug')
assert rank_new in allowed_rank
del allowed_rank
rank_new = _compute_rank_int(raw, 'info')
assert rank_new == rank
assert_array_equal(raw[:][0], data_orig)