def test_plot_topomap_mne():
"Test plot.Topomap with MNE data"
ds = datasets.get_mne_sample(sub=[0, 1], sns=True)
p = plot.Topomap(ds['meg'].summary(time=(.1, .12)), proj='left', show=False)
p.close()
# grad
ds = datasets.get_mne_sample(sub=[0], sns='grad')
assert_raises(NotImplementedError, plot.Topomap, 'meg.sub(time=.1)', ds=ds, show=False)
def test_plot_topomap_mne():
"Test plot.Topomap with MNE data"
ds = datasets.get_mne_sample(sub=[0, 1], sns=True)
p = plot.Topomap(ds['meg'].summary(time=(.1, .12)), proj='left')
p.close()
# grad
ds = datasets.get_mne_sample(sub=[0], sns='grad')
with pytest.raises(NotImplementedError), pytest.warns(RuntimeWarning):
plot.Topomap('meg.sub(time=.1)', ds=ds)
def test_plot_topomap_mne():
"Test plot.Topomap with MNE data"
ds = datasets.get_mne_sample(sub=[0, 1], sns=True)
p = plot.Topomap(ds['meg'].summary(time=(.1, .12)), proj='left')
p.close()
# grad
ds = datasets.get_mne_sample(sub=[0], sns='grad')
with pytest.raises(NotImplementedError), pytest.warns(RuntimeWarning):
plot.Topomap('meg.sub(time=.1)', ds=ds)
def test_plot_topomap_mne():
"Test plot.Topomap with MNE data"
ds = datasets.get_mne_sample(sub=[0, 1], sns=True)
p = plot.Topomap(ds['meg'].summary(time=(.1, .12)),
proj='left',
show=False)
p.close()
def test_epoch_trigger_shift():
"Test the shift_mne_epoch_trigger() function"
epochs = datasets.get_mne_sample(sns=True, sub="[1,2,3]")['epochs']
n_lost_start = np.sum(epochs.times < epochs.tmin + 0.05)
n_lost_end = np.sum(epochs.times > epochs.tmax - 0.05)
data = epochs.get_data()
epochs_s = shift_mne_epoch_trigger(epochs, [0, 0, 0])
assert_array_equal(epochs_s.get_data(), data)
epochs_s = shift_mne_epoch_trigger(epochs, [-0.05, 0., 0.05])
data_s = epochs_s.get_data()
assert_array_equal(data_s[0], data[0, :, : -(n_lost_end + n_lost_start)])
assert_array_equal(data_s[1], data[1, :, n_lost_start: -n_lost_end])
assert_array_equal(data_s[2], data[2, :, n_lost_end + n_lost_start:])
assert_allclose(epochs_s.times, epochs.times[n_lost_start: -n_lost_end],
rtol=1e-1, atol=1e-3) # ms accuracy
epochs_s = shift_mne_epoch_trigger(epochs, [0.05, 0., 0.05])
data_s = epochs_s.get_data()
assert_array_equal(data_s[0], data[0, :, n_lost_end:])
assert_array_equal(data_s[1], data[1, :, :-n_lost_end])
assert_array_equal(data_s[2], data[2, :, n_lost_end:])
assert_allclose(epochs_s.times, epochs.times[:-n_lost_end],
rtol=1e-1, atol=1e-3) # ms accuracy
def test_epoch_trigger_shift():
"Test the shift_mne_epoch_trigger() function"
epochs = datasets.get_mne_sample(sns=True, sub="[1,2,3]")['epochs']
epochs.info['projs'] = []
n_lost_start = np.sum(epochs.times < epochs.tmin + 0.05)
n_lost_end = np.sum(epochs.times > epochs.tmax - 0.05)
data = epochs.get_data()
# don't shift
epochs_s = shift_mne_epoch_trigger(epochs, [0, 0, 0])
assert_array_equal(epochs_s.get_data(), data)
epochs_s = shift_mne_epoch_trigger(epochs, [-0.05, 0., 0.05])
data_s = epochs_s.get_data()
assert_array_equal(data_s[0], data[0, :, :-(n_lost_end + n_lost_start)])
assert_array_equal(data_s[1], data[1, :, n_lost_start:-n_lost_end])
assert_array_equal(data_s[2], data[2, :, n_lost_end + n_lost_start:])
assert_allclose(epochs_s.times,
epochs.times[n_lost_start:-n_lost_end],
rtol=1e-1,
atol=1e-3) # ms accuracy
epochs_s = shift_mne_epoch_trigger(epochs, [0.05, 0., 0.05])
data_s = epochs_s.get_data()
assert_array_equal(data_s[0], data[0, :, n_lost_end:])
assert_array_equal(data_s[1], data[1, :, :-n_lost_end])
assert_array_equal(data_s[2], data[2, :, n_lost_end:])
assert_allclose(epochs_s.times,
epochs.times[:-n_lost_end],
rtol=1e-1,
atol=1e-3) # ms accuracy
def test_dataobjects():
"Test handing MNE-objects as data-objects"
ds = datasets.get_mne_sample(sns=True)
ds['C'] = Factor(ds['index'] > 155, labels={False: 'a', True: 'b'})
sds = ds.sub("side % C != ('L', 'b')")
ads = sds.aggregate('side % C')
eq_(ads.n_cases, 3)
def test_xhemi():
y = datasets.get_mne_stc(ndvar=True)
data_dir = mne.datasets.sample.data_path()
subjects_dir = os.path.join(data_dir, 'subjects')
load.update_subjects_dir(y, subjects_dir)
lh, rh = xhemi(y, mask=False)
assert lh.source.rh_n == 0
assert rh.source.rh_n == 0
assert lh.max() == pytest.approx(10.80, abs=1e-2)
assert rh.max() == pytest.approx(7.91, abs=1e-2)
# volume source space
ds = datasets.get_mne_sample(src='vol', ori='vector', hpf=1)
y = ds[0, 'src']
with pytest.raises(NotImplementedError):
xhemi(y)
# make symmetric
coords = list(map(tuple, y.source.coordinates))
index = [r == 0 or (-r, a, s) in coords for r, a, s in coords]
y = y.sub(source=np.array(index))
# test xhemi
yl, yr = xhemi(y)
assert yl.source == yr.source
# test vector mirroring
r, a, s = coords[10]
assert r # make sure it's not on midline
for i_orig, coords in enumerate(y.source.coordinates):
if tuple(coords) == (r, a, s):
break
for i_flipped, coords in enumerate(yr.source.coordinates):
if tuple(coords) == (-r, a, s):
break
assert_array_equal(yr.x[i_flipped], y.x[i_orig] * [[-1], [1], [1]])
def test_vec_source():
"Test vector source space"
ds = datasets.get_mne_sample(0, 0.1, src='vol', sub="(modality=='A') & (side == 'L')", ori='vector', stc=True)
# conversion: vector
stc = ds[0, 'stc']
stc2 = load.fiff.stc_ndvar([stc, stc], ds.info['subject'], 'vol-10', ds.info['subjects_dir'])
assert_dataobj_equal(stc2[1], ds[0, 'src'], name=False)
# non-vector
if hasattr(stc, 'magnitude'): # added in mne 0.18
stc = stc.magnitude()
ndvar = load.fiff.stc_ndvar(stc, ds.info['subject'], 'vol-10', ds.info['subjects_dir'])
assert_dataobj_equal(ndvar, ds[0, 'src'].norm('space'), name=False)
# test
res = testnd.Vector('src', ds=ds, samples=2)
clusters = res.find_clusters()
assert_array_equal(clusters['n_sources'], [799, 1, 7, 1, 2, 1])
# NDVar
v = ds['src']
assert v.sub(source='lh', time=0).shape == (72, 712, 3)
# parc
v = ds[0, 'src']
v = set_parc(v, Factor('abcdefg', repeat=227))
v1 = v.sub(source='a')
assert len(v1.source) == 227
v2 = v.sub(source=('b', 'c'))
assert len(v2.source) == 454
assert 'b' in v2.source.parc
assert 'd' not in v2.source.parc
with pytest.raises(IndexError):
v.sub(source='ab')
with pytest.raises(IndexError):
v.sub(source=['a', 'bc'])
def test_source_ndvar():
"Test NDVar with source dimension"
ds = datasets.get_mne_sample(-0.1, 0.1, src='ico', sub='index<=1')
v = ds['src', 0]
assert v.source.parc.name == 'aparc'
v_2009 = set_parc(v, 'aparc.a2009s')
assert v_2009.source.parc.name == 'aparc.a2009s'
conn = v_2009.source.connectivity()
assert np.sum(v.source.parc == v_2009.source.parc) < len(v.source)
v_back = set_parc(v_2009, 'aparc')
assert v_back.source.parc.name == 'aparc'
assert_array_equal(v.source.parc, v_back.source.parc)
assert v.x is v_back.x
assert_array_equal(v_back.source.connectivity(), conn)
# labels_from_cluster
v1, v2 = ds['src']
v1 = v1 * (v1 > 15)
labels1 = labels_from_clusters(v1)
assert len(labels1) == 1
labels1s = labels_from_clusters(v1.sum('time'))
assert len(labels1s) == 1
assert_label_equal(labels1s[0], labels1[0])
v2 = v2 * (v2 > 2)
labels2 = labels_from_clusters(concatenate((v1, v2), 'case'))
assert len(labels2) == 2
assert_label_equal(labels1[0], labels2[0])
def test_morphing():
mne.set_log_level('warning')
data_dir = mne.datasets.sample.data_path()
subjects_dir = os.path.join(data_dir, 'subjects')
sss = datasets._mne_source_space('fsaverage', 'ico-4', subjects_dir)
vertices_to = [sss[0]['vertno'], sss[1]['vertno']]
ds = datasets.get_mne_sample(-0.1,
0.1,
src='ico',
sub='index==0',
stc=True)
stc = ds['stc', 0]
morph_mat = mne.compute_morph_matrix('sample', 'fsaverage', stc.vertices,
vertices_to, None, subjects_dir)
ndvar = ds['src']
morphed_ndvar = morph_source_space(ndvar, 'fsaverage')
morphed_stc = mne.morph_data_precomputed('sample', 'fsaverage', stc,
vertices_to, morph_mat)
assert_array_equal(morphed_ndvar.x[0], morphed_stc.data)
morphed_stc_ndvar = load.fiff.stc_ndvar([morphed_stc],
'fsaverage',
'ico-4',
subjects_dir,
'dSPM',
False,
'src',
parc=None)
assert_dataobj_equal(morphed_ndvar, morphed_stc_ndvar)
def test_plot_brain():
"""Test plot.brain plots"""
src = datasets.get_mne_sample(src='ico', sub=[0])['src']
p = plot.brain.dspm(src)
cb = p.plot_colorbar(show=False)
cb.close()
p.close()
# not closing figures leads to weird interactions with the QT backend
p = plot.brain.dspm(src, hemi='lh')
cb = p.plot_colorbar(show=False)
cb.close()
p.close()
p = plot.brain.cluster(src, hemi='rh', views='parietal')
cb = p.plot_colorbar(show=False)
cb.close()
p.close()
image = plot.brain.bin_table(src, tstart=0.1, tstop=0.3, tstep=0.1)
print(repr(image))
print(image)
# plot p-map
pmap = src.abs()
pmap /= src.max()
p = plot.brain.p_map(pmap, src)
cb = p.plot_colorbar(show=False)
cb.close()
p.close()
def test_source_ndvar():
"Test NDVar with source dimension"
ds = datasets.get_mne_sample(-0.1, 0.1, src='ico', sub='index<=1')
v = ds['src', 0]
assert v.source.parc.name == 'aparc'
v_2009 = set_parc(v, 'aparc.a2009s')
assert v_2009.source.parc.name == 'aparc.a2009s'
conn = v_2009.source.connectivity()
assert np.sum(v.source.parc == v_2009.source.parc) < len(v.source)
v_back = set_parc(v_2009, 'aparc')
assert v_back.source.parc.name == 'aparc'
assert_array_equal(v.source.parc, v_back.source.parc)
assert v.x is v_back.x
assert_array_equal(v_back.source.connectivity(), conn)
# labels_from_cluster
v1, v2 = ds['src']
v1 = v1 * (v1 > 15)
labels1 = labels_from_clusters(v1)
assert len(labels1) == 1
labels1s = labels_from_clusters(v1.sum('time'))
assert len(labels1s) == 1
assert_label_equal(labels1s[0], labels1[0])
v2 = v2 * (v2 > 2)
labels2 = labels_from_clusters(concatenate((v1, v2), 'case'))
assert len(labels2) == 2
assert_label_equal(labels1[0], labels2[0])
def test_dataobjects():
"Test handing MNE-objects as data-objects"
ds = datasets.get_mne_sample(sns=True)
ds['C'] = Factor(ds['index'] > 155, labels={False: 'a', True: 'b'})
sds = ds.sub("side % C != ('L', 'b')")
ads = sds.aggregate('side % C')
eq_(ads.n_cases, 3)
def test_anova_parc():
"Test ANOVA with parc argument and source space data"
set_log_level('warning', 'mne')
ds = datasets.get_mne_sample(src='ico', sub="side.isin(('L', 'R'))")
y = ds['src'].sub(source=('lateraloccipital-lh', 'cuneus-lh'))
y1 = y.sub(source='lateraloccipital-lh')
y2 = y.sub(source='cuneus-lh')
kwa = dict(ds=ds, tstart=0.2, tstop=0.3, samples=100)
resp = testnd.anova(y, "side*modality", pmin=0.05, parc='source', **kwa)
c1p = resp.find_clusters(source='lateraloccipital-lh')
c2p = resp.find_clusters(source='cuneus-lh')
del c1p['p_parc', 'id']
del c2p['p_parc', 'id']
res1 = testnd.anova(y1, "side*modality", pmin=0.05, **kwa)
c1 = res1.find_clusters()
del c1['id']
res2 = testnd.anova(y2, "side*modality", pmin=0.05, **kwa)
c2 = res2.find_clusters()
del c2['id']
assert_dataset_equal(c1p, c1)
assert_dataset_equal(c2p, c2)
assert_array_equal(c2['p'], [
0.85, 0.88, 0.97, 0.75, 0.99, 0.99, 0.98, 0.0, 0.12, 0.88, 0.25, 0.97,
0.34, 0.96
])
# without multiprocessing
configure(n_workers=0)
ress = testnd.anova(y, "side*modality", pmin=0.05, parc='source', **kwa)
c1s = ress.find_clusters(source='lateraloccipital-lh')
c2s = ress.find_clusters(source='cuneus-lh')
del c1s['p_parc', 'id']
del c2s['p_parc', 'id']
assert_dataset_equal(c1s, c1)
assert_dataset_equal(c2s, c2)
configure(n_workers=True)
# parc but single label
resp2 = testnd.anova(y2, "side*modality", pmin=0.05, parc='source', **kwa)
c2sp = resp2.find_clusters(source='cuneus-lh')
del c2sp['p_parc', 'id']
assert_dataset_equal(c2sp, c2)
# not defined
assert_raises(NotImplementedError,
testnd.anova,
y,
"side*modality",
tfce=True,
parc='source',
**kwa)
def test_dataobjects():
"Test handing MNE-objects as data-objects"
ds = datasets.get_mne_sample(sns=True)
ds['C'] = Factor(ds['index'] > 155, labels={False: 'a', True: 'b'})
sds = ds.sub("side % C != ('L', 'b')")
ads = sds.aggregate('side % C')
eq_(ads.n_cases, 3)
# connectivity
sensor = ds['sns'].sensor
c = sensor.connectivity()
assert_array_equal(c[:, 0] < c[:, 1], True)
eq_(c.max(), len(sensor) - 1)
def test_morphing():
mne.set_log_level('warning')
sss = datasets._mne_source_space('fsaverage', 'ico-4', subjects_dir)
vertices_to = [sss[0]['vertno'], sss[1]['vertno']]
ds = datasets.get_mne_sample(-0.1, 0.1, src='ico', sub='index==0', stc=True)
stc = ds['stc', 0]
morph_mat = mne.compute_morph_matrix('sample', 'fsaverage', stc.vertno,
vertices_to, None, subjects_dir)
ndvar = ds['src']
morphed_ndvar = morph_source_space(ndvar, 'fsaverage')
morphed_stc = mne.morph_data_precomputed('sample', 'fsaverage', stc,
vertices_to, morph_mat)
assert_array_equal(morphed_ndvar.x[0], morphed_stc.data)
morphed_stc_ndvar = load.fiff.stc_ndvar([morphed_stc], 'fsaverage', 'ico-4',
subjects_dir, 'src', parc=None)
assert_dataobj_equal(morphed_ndvar, morphed_stc_ndvar)
def test_anova_parc():
"Test ANOVA with parc argument and source space data"
set_log_level('warning', 'mne')
ds = datasets.get_mne_sample(src='ico', sub="side.isin(('L', 'R'))")
y = ds['src'].sub(source=('lateraloccipital-lh', 'cuneus-lh'))
y1 = y.sub(source='lateraloccipital-lh')
y2 = y.sub(source='cuneus-lh')
kwa = dict(ds=ds, tstart=0.2, tstop=0.3, samples=100)
resp = testnd.anova(y, "side*modality", pmin=0.05, parc='source', **kwa)
c1p = resp.find_clusters(source='lateraloccipital-lh')
c2p = resp.find_clusters(source='cuneus-lh')
del c1p['p_parc', 'id']
del c2p['p_parc', 'id']
res1 = testnd.anova(y1, "side*modality", pmin=0.05, **kwa)
c1 = res1.find_clusters()
del c1['id']
res2 = testnd.anova(y2, "side*modality", pmin=0.05, **kwa)
c2 = res2.find_clusters()
del c2['id']
assert_dataset_equal(c1p, c1)
assert_dataset_equal(c2p, c2)
assert_array_equal(c2['p'], [0.85, 0.88, 0.97, 0.75, 0.99, 0.99, 0.98, 0.0,
0.12, 0.88, 0.25, 0.97, 0.34, 0.96])
# without multiprocessing
testnd.configure(0)
ress = testnd.anova(y, "side*modality", pmin=0.05, parc='source', **kwa)
c1s = ress.find_clusters(source='lateraloccipital-lh')
c2s = ress.find_clusters(source='cuneus-lh')
del c1s['p_parc', 'id']
del c2s['p_parc', 'id']
assert_dataset_equal(c1s, c1)
assert_dataset_equal(c2s, c2)
testnd.configure(-1)
# parc but single label
resp2 = testnd.anova(y2, "side*modality", pmin=0.05, parc='source', **kwa)
c2sp = resp2.find_clusters(source='cuneus-lh')
del c2sp['p_parc', 'id']
assert_dataset_equal(c2sp, c2)
# not defined
assert_raises(NotImplementedError, testnd.anova, y, "side*modality",
tfce=True, parc='source', **kwa)
def test_plot_topomap():
"Test plot.Topomap"
ds = datasets.get_uts(utsnd=True)
topo = ds.eval('utsnd.summary(time=(0.075, 0.125))')
p = plot.Topomap(topo, ds=ds, show=False)
p.close()
p = plot.Topomap(topo, ds=ds, vmax=0.2, w=2, show=False)
p.close()
p = plot.Topomap(topo, 'A%B', ds=ds, axw=2, show=False)
p.close()
p = plot.Topomap(topo, ds=ds, sensorlabels=None, show=False)
p.close()
# MNE data
ds = datasets.get_mne_sample(sub=[0, 1], sns=True)
p = plot.Topomap(ds['sns'].summary(time=(.1, .12)), proj='left', show=False)
p.close()
def test_plot_brain():
"""Test plot.brain plots"""
src = datasets.get_mne_sample(src='ico', sub=[0])['src']
# size
b = plot.brain.brain(src.source, hemi='rh', w=400, h=300, mask=False)
eq_(b.screenshot().shape, (300, 400, 3))
b.set_size(200, 150)
eq_(b.screenshot().shape, (150, 200, 3))
b.close()
# both hemispheres
b = plot.brain.brain(src.source, w=600, h=300, mask=False)
eq_(b.screenshot().shape, (300, 600, 3))
b.set_size(400, 150)
eq_(b.screenshot().shape, (150, 400, 3))
b.close()
# plot shortcuts
p = plot.brain.dspm(src)
cb = p.plot_colorbar(show=False)
cb.close()
p.close()
p = plot.brain.dspm(src, hemi='lh')
cb = p.plot_colorbar(show=False)
cb.close()
p.close()
p = plot.brain.cluster(src, hemi='rh', views='parietal')
cb = p.plot_colorbar(show=False)
cb.close()
p.close()
image = plot.brain.bin_table(src, tstart=0.1, tstop=0.3, tstep=0.1)
print(repr(image))
print(image)
# plot p-map
pmap = src.abs()
pmap /= src.max()
p = plot.brain.p_map(pmap, src)
cb = p.plot_colorbar(show=False)
cb.close()
p.close()
def test_interpolation():
"Test MNE channel interpolation by epoch"
ds = datasets.get_mne_sample(sub=[0, 1, 2, 3])
bads1 = ['MEG 0531', 'MEG 1321']
bads3 = ['MEG 0531', 'MEG 2231']
bads_list = [[], bads1, [], bads3]
test_epochs = ds['epochs']
epochs1 = test_epochs.copy()
epochs3 = test_epochs.copy()
_interpolate_bads_meg(test_epochs, bads_list, {})
assert_array_equal(test_epochs._data[0], epochs1._data[0])
assert_array_equal(test_epochs._data[2], epochs1._data[2])
epochs1.info['bads'] = bads1
epochs1.interpolate_bads(mode='accurate')
assert_array_almost_equal(test_epochs._data[1], epochs1._data[1], 25)
epochs3.info['bads'] = bads3
epochs3.interpolate_bads(mode='accurate')
assert_array_almost_equal(test_epochs._data[3], epochs3._data[3], 25)
def test_plot_brain():
"""Test plot.brain plots"""
src = datasets.get_mne_sample(src='ico', sub=[0])['src']
p = plot.brain.dspm(src)
cb = p.plot_colorbar(show=False)
cb.close()
p = plot.brain.dspm(src, hemi='lh')
cb = p.plot_colorbar(show=False)
cb.close()
p = plot.brain.cluster(src, hemi='rh', views='parietal')
cb = p.plot_colorbar(show=False)
cb.close()
image = plot.brain.bin_table(src, tstart=0.1, tstop=0.3, tstep=0.1)
print repr(image)
print image
def test_vec_source():
"Test vector source space"
ds = datasets.get_mne_sample(0,
0.1, (0, 0),
src='vol',
sub="(modality=='A') & (side == 'L')",
ori='vector',
stc=True)
# conversion: vector
stc = ds[0, 'stc']
stc2 = load.fiff.stc_ndvar([stc, stc], ds.info['subject'], 'vol-10',
ds.info['subjects_dir'])
assert_dataobj_equal(stc2[1], ds[0, 'src'], name=False)
# non-vector
if hasattr(stc, 'magnitude'): # added in mne 0.18
stc = stc.magnitude()
ndvar = load.fiff.stc_ndvar(stc, ds.info['subject'], 'vol-10',
ds.info['subjects_dir'])
assert_dataobj_equal(ndvar, ds[0, 'src'].norm('space'), name=False)
# test
res = testnd.Vector('src', ds=ds, samples=2)
clusters = res.find_clusters()
assert_array_equal(clusters['n_sources'], [799, 1, 7, 1, 2, 1])
# NDVar
v = ds['src']
assert v.sub(source='lh', time=0).shape == (72, 712, 3)
# parc
v = ds[0, 'src']
v = set_parc(v, Factor('abcdefg', repeat=227))
v1 = v.sub(source='a')
assert len(v1.source) == 227
v2 = v.sub(source=('b', 'c'))
assert len(v2.source) == 454
assert 'b' in v2.source.parc
assert 'd' not in v2.source.parc
with pytest.raises(IndexError):
v.sub(source='ab')
with pytest.raises(IndexError):
v.sub(source=['a', 'bc'])
def test_select_components():
"Test Select-Epochs GUI Document"
tempdir = TempDir()
PATH = join(tempdir, 'test-ica.fif')
ds = datasets.get_mne_sample()
ds['epochs'] = ds['epochs'].pick_types('mag')
ica = mne.preprocessing.ICA(0.95)
ica.fit(ds['epochs'])
ica.save(PATH)
frame = gui.select_components(PATH, ds)
frame.model.toggle(1)
frame.OnSave(None)
ica = mne.preprocessing.read_ica(PATH)
assert ica.exclude == [1]
frame.OnUndo(None)
frame.OnSave(None)
ica = mne.preprocessing.read_ica(PATH)
assert ica.exclude == []
frame.Close()
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_select_epochs():
"Test Select-Epochs GUI Document"
set_log_level('warning', 'mne')
ds = datasets.get_mne_sample(sns=True)
tempdir = TempDir()
path = os.path.join(tempdir, 'rej.pickled')
# create a file
doc = Document(ds, 'meg')
doc.set_path(path)
doc.set_case(1, False, 'tag', None)
doc.set_case(2, None, None, ['2'])
doc.set_bad_channels([1])
# check modifications
eq_(doc.accept[1], False)
eq_(doc.tag[1], 'tag')
eq_(doc.interpolate[1], [])
eq_(doc.interpolate[2], ['2'])
eq_(doc.bad_channels, [1])
assert_array_equal(doc.accept[2:], True)
# save
doc.save()
# check the file
ds_ = load.unpickle(path)
eq_(doc.epochs.sensor.dimindex(ds_.info['bad_channels']), [1])
# load the file
ds = datasets.get_mne_sample(sns=True)
doc = Document(ds, 'meg', path=path)
# modification checks
eq_(doc.accept[1], False)
eq_(doc.tag[1], 'tag')
eq_(doc.interpolate[1], [])
eq_(doc.interpolate[2], ['2'])
eq_(doc.bad_channels, [1])
assert_array_equal(doc.accept[2:], True)
# Test model
# ==========
ds = datasets.get_mne_sample(sns=True)
doc = Document(ds, 'meg')
model = Model(doc)
# accept
model.set_case(0, False, None, None)
eq_(doc.accept[0], False)
model.history.undo()
eq_(doc.accept[0], True)
model.history.redo()
eq_(doc.accept[0], False)
# interpolate
model.toggle_interpolation(2, '3')
eq_(doc.interpolate[2], ['3'])
model.toggle_interpolation(2, '4')
eq_(doc.interpolate[2], ['3', '4'])
model.toggle_interpolation(2, '3')
eq_(doc.interpolate[2], ['4'])
model.toggle_interpolation(3, '3')
eq_(doc.interpolate[2], ['4'])
eq_(doc.interpolate[3], ['3'])
model.history.undo()
model.history.undo()
eq_(doc.interpolate[2], ['3', '4'])
eq_(doc.interpolate[3], [])
model.history.redo()
eq_(doc.interpolate[2], ['4'])
# bad channels
model.set_bad_channels([1])
model.set_bad_channels([1, 10])
eq_(doc.bad_channels, [1, 10])
model.history.undo()
eq_(doc.bad_channels, [1])
model.history.redo()
eq_(doc.bad_channels, [1, 10])
# reload to reset
model.load(path)
# tests
eq_(doc.accept[1], False)
eq_(doc.tag[1], 'tag')
eq_(doc.interpolate[1], [])
eq_(doc.interpolate[2], ['2'])
eq_(doc.bad_channels, [1])
assert_array_equal(doc.accept[2:], True)
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_plot_topomap_mne():
"Test plot.Topomap with MNE data"
ds = datasets.get_mne_sample(sub=[0, 1], sns=True)
p = plot.Topomap(ds["meg"].summary(time=(0.1, 0.12)), proj="left", show=False)
p.close()
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)
def test_select_epochs():
"Test Select-Epochs GUI Document"
set_log_level('warning', 'mne')
ds = datasets.get_mne_sample(sns=True)
tempdir = TempDir()
path = os.path.join(tempdir, 'rej.pickled')
# Test Document
# =============
# create a file
doc = Document(ds, 'meg')
doc.set_path(path)
doc.set_case(1, False, 'tag', None)
doc.set_case(2, None, None, ['2'])
doc.set_bad_channels([1])
# check modifications
eq_(doc.accept[1], False)
eq_(doc.tag[1], 'tag')
eq_(doc.interpolate[1], [])
eq_(doc.interpolate[2], ['2'])
eq_(doc.bad_channels, [1])
assert_array_equal(doc.accept[2:], True)
# save
doc.save()
# check the file
ds_ = load.unpickle(path)
eq_(doc.epochs.sensor._array_index(ds_.info['bad_channels']), [1])
# load the file
ds = datasets.get_mne_sample(sns=True)
doc = Document(ds, 'meg', path=path)
# modification checks
eq_(doc.accept[1], False)
eq_(doc.tag[1], 'tag')
eq_(doc.interpolate[1], [])
eq_(doc.interpolate[2], ['2'])
eq_(doc.bad_channels, [1])
assert_array_equal(doc.accept[2:], True)
# Test Model
# ==========
ds = datasets.get_mne_sample(sns=True)
doc = Document(ds, 'meg')
model = Model(doc)
# accept
model.set_case(0, False, None, None)
eq_(doc.accept[0], False)
model.history.undo()
eq_(doc.accept[0], True)
model.history.redo()
eq_(doc.accept[0], False)
# interpolate
model.toggle_interpolation(2, '3')
eq_(doc.interpolate[2], ['3'])
model.toggle_interpolation(2, '4')
eq_(doc.interpolate[2], ['3', '4'])
model.toggle_interpolation(2, '3')
eq_(doc.interpolate[2], ['4'])
model.toggle_interpolation(3, '3')
eq_(doc.interpolate[2], ['4'])
eq_(doc.interpolate[3], ['3'])
model.history.undo()
model.history.undo()
eq_(doc.interpolate[2], ['3', '4'])
eq_(doc.interpolate[3], [])
model.history.redo()
eq_(doc.interpolate[2], ['4'])
# bad channels
model.set_bad_channels([1])
model.set_bad_channels([1, 10])
eq_(doc.bad_channels, [1, 10])
model.history.undo()
eq_(doc.bad_channels, [1])
model.history.redo()
eq_(doc.bad_channels, [1, 10])
# reload to reset
model.load(path)
# tests
eq_(doc.accept[1], False)
eq_(doc.tag[1], 'tag')
eq_(doc.interpolate[1], [])
eq_(doc.interpolate[2], ['2'])
eq_(doc.bad_channels, [1])
assert_array_equal(doc.accept[2:], True)
# Test GUI
# ========
frame = gui.select_epochs(ds)
assert_false(frame.CanBackward())
ok_(frame.CanForward())
frame.OnForward(None)
import os
import timeit
import mne
from eelbrain import datasets, save
mne.set_log_level("warning")
fname = "temp.pickled"
if not os.path.exists(fname):
ds = datasets.get_mne_sample(-0.1, 0.2, src="ico", sub="modality == 'A'")
source = ds["src"].source
y = ds["src"][0].x
save.pickle((y, source), fname)
setup = (
"""
from itertools import izip
import numpy as np
import scipy as sp
from eelbrain.lab import stats, load
y, source = load.unpickle(%r)
out = np.empty(y.shape, np.uint32)
bin_buff = np.empty(y.shape, np.bool_)
int_buff = np.empty(y.shape, np.uint32)
threshold = 1
tail = 0
struct = sp.ndimage.generate_binary_structure(y.ndim, 1)
import os
import timeit
import mne
from eelbrain import datasets, save
mne.set_log_level('warning')
fname = 'temp.pickled'
if not os.path.exists(fname):
ds = datasets.get_mne_sample(-0.1, 0.2, src='ico', sub="modality == 'A'")
source = ds['src'].source
y = ds['src'][0].x
save.pickle((y, source), fname)
setup = '''
from itertools import izip
import numpy as np
import scipy as sp
from eelbrain.lab import stats, load
y, source = load.unpickle(%r)
out = np.empty(y.shape, np.uint32)
bin_buff = np.empty(y.shape, np.bool_)
int_buff = np.empty(y.shape, np.uint32)
threshold = 1
tail = 0
struct = sp.ndimage.generate_binary_structure(y.ndim, 1)
struct[::2] = False
