def test_dataobjects():
"Test handing MNE-objects as data-objects"
shift = np.array([0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.1, -0.1])
epochs = datasets.get_mne_epochs()
ds = Dataset(('a', Factor('ab', repeat=8)),
('epochs', epochs))
ds['ets'] = shift_mne_epoch_trigger(epochs, shift, min(shift), max(shift))
# ds operations
sds = ds.sub("a == 'a'")
ads = ds.aggregate('a')
# asndvar
ndvar = asndvar(ds['epochs'])
ndvar = asndvar(ds['ets'])
# connectivity
ds = datasets.get_mne_sample(sub=[0], sns=True)
sensor = ds['meg'].sensor
c = sensor.connectivity()
assert_array_equal(c[:, 0] < c[:, 1], True)
eq_(c.max(), len(sensor) - 1)
def test_dataobjects():
"Test handing MNE-objects as data-objects"
shift = np.array([0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.1, -0.1])
epochs = datasets.get_mne_epochs()
ds = Dataset(('a', Factor('ab', repeat=8)),
('epochs', epochs))
ds['ets'] = shift_mne_epoch_trigger(epochs, shift, min(shift), max(shift))
# ds operations
sds = ds.sub("a == 'a'")
ads = ds.aggregate('a')
# asndvar
ndvar = asndvar(ds['epochs'])
ndvar = asndvar(ds['ets'])
# connectivity
ds = datasets.get_mne_sample(sub=[0], sns=True)
sensor = ds['meg'].sensor
c = sensor.connectivity()
assert_array_equal(c[:, 0] < c[:, 1], True)
eq_(c.max(), len(sensor) - 1)
def test_source_estimate():
"Test SourceSpace dimension"
mne.set_log_level('warning')
ds = datasets.get_mne_sample(src='ico')
dsa = ds.aggregate('side')
# test auto-conversion
asndvar('epochs', ds=ds)
asndvar('epochs', ds=dsa)
asndvar(dsa['epochs'][0])
# source space clustering
res = testnd.ttest_ind('src',
'side',
ds=ds,
samples=0,
pmin=0.05,
tstart=0.05,
mintime=0.02,
minsource=10)
assert res.clusters.n_cases == 52
# test disconnecting parc
src = ds['src']
source = src.source
parc = source.parc
orig_conn = set(map(tuple, source.connectivity()))
disc_conn = set(map(tuple, source.connectivity(True)))
assert len(disc_conn) < len(orig_conn)
for pair in orig_conn:
s, d = pair
if pair in disc_conn:
assert parc[s] == parc[d]
else:
assert parc[s] != parc[d]
# threshold-based test with parc
srcl = src.sub(source='lh')
res = testnd.ttest_ind(srcl,
'side',
ds=ds,
samples=10,
pmin=0.05,
tstart=0.05,
mintime=0.02,
minsource=10,
parc='source')
assert res._cdist.dist.shape[1] == len(srcl.source.parc.cells)
label = 'superiortemporal-lh'
c_all = res.find_clusters(maps=True)
c_label = res.find_clusters(maps=True, source=label)
assert_array_equal(c_label['location'], label)
for case in c_label.itercases():
id_ = case['id']
idx = c_all['id'].index(id_)[0]
assert case['v'] == c_all[idx, 'v']
assert case['tstart'] == c_all[idx, 'tstart']
assert case['tstop'] == c_all[idx, 'tstop']
assert case['p'] <= c_all[idx, 'p']
assert_dataobj_equal(case['cluster'],
c_all[idx, 'cluster'].sub(source=label))
# threshold-free test with parc
res = testnd.ttest_ind(srcl,
'side',
ds=ds,
samples=10,
tstart=0.05,
parc='source')
cl = res.find_clusters(0.05)
assert cl.eval("p.min()") == res.p.min()
mp = res.masked_parameter_map()
assert mp.min() == res.t.min()
assert mp.max() == res.t.max(res.p <= 0.05)
assert mp.max() == pytest.approx(-4.95817732)
# indexing source space
s_sub = src.sub(source='fusiform-lh')
idx = source.index_for_label('fusiform-lh')
s_idx = src[idx]
assert_dataobj_equal(s_sub, s_idx)
# concatenate
src_reconc = concatenate((src.sub(source='lh'), src.sub(source='rh')),
'source')
assert_dataobj_equal(src_reconc, src)
def test_source_estimate():
"Test SourceSpace dimension"
mne.set_log_level('warning')
ds = datasets.get_mne_sample(src='ico')
dsa = ds.aggregate('side')
# test auto-conversion
asndvar('epochs', ds=ds)
asndvar('epochs', ds=dsa)
asndvar(dsa['epochs'][0])
# source space clustering
res = testnd.ttest_ind('src', 'side', ds=ds, samples=0, pmin=0.05,
tstart=0.05, mintime=0.02, minsource=10)
assert_equal(res.clusters.n_cases, 52)
# test disconnecting parc
src = ds['src']
source = src.source
parc = source.parc
orig_conn = set(map(tuple, source.connectivity()))
disc_conn = set(map(tuple, source.connectivity(True)))
assert_true(len(disc_conn) < len(orig_conn))
for pair in orig_conn:
s, d = pair
if pair in disc_conn:
assert_equal(parc[s], parc[d])
else:
assert_not_equal(parc[s], parc[d])
# threshold-based test with parc
srcl = src.sub(source='lh')
res = testnd.ttest_ind(srcl, 'side', ds=ds, samples=10, pmin=0.05,
tstart=0.05, mintime=0.02, minsource=10,
parc='source')
assert_equal(res._cdist.dist.shape[1], len(srcl.source.parc.cells))
label = 'superiortemporal-lh'
c_all = res._clusters(maps=True)
c_label = res._clusters(maps=True, source=label)
assert_array_equal(c_label['location'], label)
for case in c_label.itercases():
id_ = case['id']
idx = c_all['id'].index(id_)[0]
assert_equal(case['v'], c_all[idx, 'v'])
assert_equal(case['tstart'], c_all[idx, 'tstart'])
assert_equal(case['tstop'], c_all[idx, 'tstop'])
assert_less_equal(case['p'], c_all[idx, 'p'])
assert_dataobj_equal(case['cluster'],
c_all[idx, 'cluster'].sub(source=label))
# threshold-free test with parc
res = testnd.ttest_ind(srcl, 'side', ds=ds, samples=10, tstart=0.05,
parc='source')
cl = res._clusters(0.05)
assert_equal(cl.eval("p.min()"), res.p.min())
mp = res.masked_parameter_map()
assert_in(mp.min(), (0, res.t.min()))
assert_in(mp.max(), (0, res.t.max()))
# indexing source space
s_sub = src.sub(source='fusiform-lh')
idx = source.index_for_label('fusiform-lh')
s_idx = src[idx]
assert_dataobj_equal(s_sub, s_idx)