def test_morphing():
stc = datasets.get_mne_stc()
y = load.fiff.stc_ndvar(stc,
'sample',
'ico-5',
subjects_dir,
'dSPM',
name='src')
# sample to fsaverage
m = mne.compute_source_morph(stc, 'sample', 'fsaverage', subjects_dir)
stc_fsa = m.apply(stc)
y_fsa = morph_source_space(y, 'fsaverage')
assert_array_equal(y_fsa.x, stc_fsa.data)
stc_fsa_ndvar = load.fiff.stc_ndvar(stc_fsa,
'fsaverage',
'ico-5',
subjects_dir,
'dSPM',
False,
'src',
parc=None)
assert_dataobj_equal(stc_fsa_ndvar, y_fsa)
# scaled to fsaverage
y_scaled = datasets.get_mne_stc(True, subject='fsaverage_scaled')
y_scaled_m = morph_source_space(y_scaled, 'fsaverage')
assert y_scaled_m.source.subject == 'fsaverage'
assert_array_equal(y_scaled_m.x, y_scaled.x)
# scaled to fsaverage [masked]
y_sub = y_scaled.sub(source='superiortemporal-lh')
y_sub_m = morph_source_space(y_sub, 'fsaverage')
assert y_sub_m.source.subject == 'fsaverage'
assert_array_equal(y_sub_m.x, y_sub.x)
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_glassbrain():
ndvar = datasets.get_mne_stc(True, True)
# source space only
p = plot.GlassBrain(ndvar.source)
p.close()
# single time points
ndvar_30 = ndvar.sub(time=0.030)
p = plot.GlassBrain(ndvar_30)
p.close()
# without arrows
p = plot.GlassBrain(ndvar_30, draw_arrows=False)
p.close()
# time series
p = plot.GlassBrain(ndvar)
p.set_time(.03)
p.close()
# masked data
import numpy as np
h = ndvar.sub(time=0.030)
c = 6.15459575929912e-10 # precomputed _fast_abs_percentile(h)
mask = h.norm('space') < c
mask_x = np.repeat(h._ialign(mask), 3, h.get_axis('space'))
mask = NDVar(mask_x, h.dims)
y = h.mask(mask)
p = plot.GlassBrain(y)
p.close()
def test_glassbrain():
ndvar = datasets.get_mne_stc(True, True)
# source space only
p = plot.GlassBrain(ndvar.source)
p.close()
# single time points
ndvar_30 = ndvar.sub(time=0.030)
p = plot.GlassBrain(ndvar_30)
p.close()
# without arrows
p = plot.GlassBrain(ndvar_30, draw_arrows=False)
p.close()
# time series
p = plot.GlassBrain(ndvar)
p.set_time(.03)
p.close()
# masked data
import numpy as np
h = ndvar.sub(time=0.030)
c = 6.15459575929912e-10 # precomputed _fast_abs_percentile(h)
mask = h.norm('space') < c
mask_x = np.repeat(h._ialign(mask), 3, h.get_axis('space'))
mask = NDVar(mask_x, h.dims)
y = h.mask(mask)
p = plot.GlassBrain(y)
p.close()
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)
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)
def test_plot_brain():
"""Test plot.brain plots"""
if sys.platform.startswith('win'):
pytest.xfail("Hangs on Appveyor")
stc = datasets.get_mne_stc(True)
# size
b = plot.brain.brain(stc.source, hemi='rh', w=400, h=300, mask=False)
assert b.screenshot().shape == (300, 400, 3)
if sys.platform == 'linux':
pytest.xfail("Brain.set_size() on Linux/Travis")
b.set_size(200, 150)
assert b.screenshot().shape == (150, 200, 3)
b.close()
# both hemispheres
b = plot.brain.brain(stc.source, w=600, h=300, mask=False)
assert b.screenshot().shape == (300, 600, 3)
b.set_size(400, 150)
assert b.screenshot().shape == (150, 400, 3)
b.close()
# plot shortcuts
p = plot.brain.dspm(stc, mask=False)
cb = p.plot_colorbar()
cb.close()
p.close()
p = plot.brain.dspm(stc, hemi='lh', mask=False)
cb = p.plot_colorbar()
cb.close()
p.close()
p = plot.brain.cluster(stc, hemi='rh', views='parietal', mask=False)
cb = p.plot_colorbar()
cb.close()
p.close()
image = plot.brain.bin_table(stc, tstart=0.05, tstop=0.07, tstep=0.01, surf='white', mask=False)
print(repr(image))
print(image)
# plot p-map
pmap = NDVar(np.random.uniform(0, 1, stc.shape), stc.dims, _info.for_p_map(stc.info))
p = plot.brain.p_map(pmap, stc, mask=False)
cb = p.plot_colorbar()
cb.close()
p.close()
# mask
stcm = stc.mask(stc < 11)
p = plot.brain.brain(stcm, mask=False)
p.close()
def test_morphing():
stc = datasets.get_mne_stc()
y = load.fiff.stc_ndvar(stc, 'sample', 'ico-5', subjects_dir, 'dSPM', name='src')
# sample to fsaverage
m = mne.compute_source_morph(stc, 'sample', 'fsaverage', subjects_dir)
stc_fsa = m.apply(stc)
y_fsa = morph_source_space(y, 'fsaverage')
assert_array_equal(y_fsa.x, stc_fsa.data)
stc_fsa_ndvar = load.fiff.stc_ndvar(stc_fsa, 'fsaverage', 'ico-5', subjects_dir, 'dSPM', False, 'src', parc=None)
assert_dataobj_equal(stc_fsa_ndvar, y_fsa)
# scaled to fsaverage
y_scaled = datasets.get_mne_stc(True, subject='fsaverage_scaled')
y_scaled_m = morph_source_space(y_scaled, 'fsaverage')
assert y_scaled_m.source.subject == 'fsaverage'
assert_array_equal(y_scaled_m.x, y_scaled.x)
# scaled to fsaverage [masked]
y_sub = y_scaled.sub(source='superiortemporal-lh')
y_sub_m = morph_source_space(y_sub, 'fsaverage')
assert y_sub_m.source.subject == 'fsaverage'
assert_array_equal(y_sub_m.x, y_sub.x)
def test_plot_butterfly():
"Test plot.Butterfly"
ds = datasets.get_uts(utsnd=True)
p = plot.Butterfly('utsnd', ds=ds)
p.close()
p = plot.Butterfly('utsnd', 'A%B', ds=ds)
p.close()
# other y-dim
stc = datasets.get_mne_stc(True, subject='fsaverage')
p = plot.Butterfly(stc)
p.close()
# _ax_bfly_epoch (used in GUI, not part of a figure)
fig = Figure(1)
ax = _ax_bfly_epoch(fig._axes[0], ds[0, 'utsnd'])
fig.show()
ax.set_data(ds[1, 'utsnd'])
fig.draw()
# source space
stc = datasets.get_mne_stc(True)
p = plot.Butterfly(stc, '.source.hemi')
p.close()
def test_plot_butterfly():
"Test plot.Butterfly"
ds = datasets.get_uts(utsnd=True)
p = plot.Butterfly('utsnd', ds=ds, show=False)
p.close()
p = plot.Butterfly('utsnd', 'A%B', ds=ds, show=False)
p.close()
# other y-dim
stc = datasets.get_mne_stc(True)
p = plot.Butterfly(stc, show=False)
p.close()
# _ax_bfly_epoch (used in GUI, not part of a figure)
fig = Figure(1, show=False)
ax = _ax_bfly_epoch(fig._axes[0], ds[0, 'utsnd'])
fig.show()
ax.set_data(ds[1, 'utsnd'])
fig.draw()
def test_plot_butterfly():
"Test plot.Butterfly"
ds = datasets.get_uts(utsnd=True)
p = plot.Butterfly('utsnd', ds=ds, show=False)
p.close()
p = plot.Butterfly('utsnd', 'A%B', ds=ds, show=False)
p.close()
# other y-dim
stc = datasets.get_mne_stc(True)
p = plot.Butterfly(stc)
p.close()
# _ax_bfly_epoch
fig = Figure(1, show=False)
ax = _ax_bfly_epoch(fig._axes[0], ds[0, 'utsnd'])
fig.show()
ax.set_data(ds[1, 'utsnd'])
fig.draw()
def test_ndvar_indexing():
ds = datasets.get_uts(utsnd=True)
x = ds['utsnd']
# case
test_ndvar_index(x, 'case', 1, 1)
test_ndvar_index(x, 'case', [0, 3], [0, 3])
test_ndvar_index(x, 'case', slice(0, 10, 2), slice(0, 10, 2))
# sensor
test_ndvar_index(x, 'sensor', '0', 0)
test_ndvar_index(x, 'sensor', ['0', '2'], [0, 2])
test_ndvar_index(x, 'sensor', slice('0', '2'), slice(0, 2))
test_ndvar_index(x, 'sensor', 0, 0, False)
test_ndvar_index(x, 'sensor', [0, 2], [0, 2], False)
test_ndvar_index(x, 'sensor', slice(0, 2), slice(0, 2), False)
# time
test_ndvar_index(x, 'time', 0, 20)
test_ndvar_index(x, 'time', 0.1, 30)
test_ndvar_index(x, 'time', 0.102, 30, False)
test_ndvar_index(x, 'time', [0, 0.1, 0.2], [20, 30, 40])
test_ndvar_index(x, 'time', slice(0.1, None), slice(30, None))
test_ndvar_index(x, 'time', slice(0.2), slice(40))
test_ndvar_index(x, 'time', slice(0.202), slice(41), False)
test_ndvar_index(x, 'time', slice(0.1, 0.2), slice(30, 40))
test_ndvar_index(x, 'time', slice(0.102, 0.2), slice(31, 40), False)
test_ndvar_index(x, 'time', slice(0.1, None, 0.1), slice(30, None, 10))
test_ndvar_index(x, 'time', slice(0.1, None, 1), slice(30, None, 100))
# Ordered
x = cwt_morlet(ds['uts'], [8, 10, 13, 17])
assert_raises(IndexError, x.__getitem__, (full_slice, 9))
assert_raises(IndexError, x.__getitem__, (full_slice, 6))
test_ndvar_index(x, 'frequency', 10, 1)
test_ndvar_index(x, 'frequency', 10.1, 1, False)
test_ndvar_index(x, 'frequency', 9.9, 1, False)
test_ndvar_index(x, 'frequency', [8.1, 10.1], [0, 1], False)
test_ndvar_index(x, 'frequency', slice(8, 13), slice(0, 2))
test_ndvar_index(x, 'frequency', slice(8, 13.1), slice(0, 3), False)
test_ndvar_index(x, 'frequency', slice(8, 13.1, 2), slice(0, 3, 2), False)
# Categorial
x = NDVar(x.x, ('case', Categorial('cat', ['8', '10', '13', '17']), x.time))
assert_raises(TypeError, x.__getitem__, (full_slice, 9))
assert_raises(IndexError, x.__getitem__, (full_slice, '9'))
test_ndvar_index(x, 'cat', '13', 2)
test_ndvar_index(x, 'cat', ['8', '13'], [0, 2])
test_ndvar_index(x, 'cat', slice('8', '13'), slice(0, 2))
test_ndvar_index(x, 'cat', slice('8', None, 2), slice(0, None, 2))
# SourceSpace
x = datasets.get_mne_stc(True)
assert_raises(TypeError, x.__getitem__, slice('insula-rh'))
assert_raises(TypeError, x.__getitem__, slice('insula-lh', 'insula-rh'))
assert_raises(TypeError, x.__getitem__, ('insula-lh', 'insula-rh'))
test_ndvar_index(x, 'source', 'L90', 90)
test_ndvar_index(x, 'source', 'R90', 642 + 90)
test_ndvar_index(x, 'source', ['L90', 'R90'], [90, 642 + 90])
test_ndvar_index(x, 'source', slice('L90', 'R90'), slice(90, 642 + 90))
test_ndvar_index(x, 'source', 90, 90, False)
test_ndvar_index(x, 'source', [90, 95], [90, 95], False)
test_ndvar_index(x, 'source', slice(90, 95), slice(90, 95), False)
test_ndvar_index(x, 'source', 'insula-lh', x.source.parc == 'insula-lh', False)
test_ndvar_index(x, 'source', ('insula-lh', 'insula-rh'),
x.source.parc.isin(('insula-lh', 'insula-rh')), False)
n_lh = x.source.parc.endswith('lh').sum()
test_ndvar_index(x, 'source', 'lh', slice(n_lh), False)
test_ndvar_index(x, 'source', 'rh', slice(n_lh, None), False)
# argmax
x.x[10, 10] = 20
eq_(x.argmax(), ('L10', 0.1))
eq_(x[('L10', 0.1)], 20)
eq_(x.sub(source='L10').argmax(), 0.1)
eq_(x.sub(time=0.1).argmax(), 'L10')