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import os.path as op
import numpy as np
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_equal)
import pytest
from mne.datasets import testing
from mne import (read_label, read_forward_solution, pick_types_forward,
convert_forward_solution)
from mne.label import Label
from mne.simulation.source import simulate_stc, simulate_sparse_stc
from mne.utils import run_tests_if_main
data_path = testing.data_path(download=False)
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-6-fwd.fif')
label_names = ['Aud-lh', 'Aud-rh', 'Vis-rh']
label_names_single_hemi = ['Aud-rh', 'Vis-rh']
subjects_dir = op.join(data_path, 'subjects')
def read_forward_solution_meg(*args, **kwargs):
"""Read forward MEG."""
fwd = read_forward_solution(*args)
fwd = convert_forward_solution(fwd, **kwargs)
fwd = pick_types_forward(fwd, meg=True, eeg=False)
return fwd
@testing.requires_testing_data
def test_simulate_stc():
"""Test generation of source estimate."""
fwd = read_forward_solution_meg(fname_fwd, force_fixed=True, use_cps=True)
labels = [read_label(op.join(data_path, 'MEG', 'sample', 'labels',
'%s.label' % label)) for label in label_names]
mylabels = []
for i, label in enumerate(labels):
new_label = Label(vertices=label.vertices,
pos=label.pos,
values=2 * i * np.ones(len(label.values)),
hemi=label.hemi,
comment=label.comment)
mylabels.append(new_label)
n_times = 10
tmin = 0
tstep = 1e-3
stc_data = np.ones((len(labels), n_times))
stc = simulate_stc(fwd['src'], mylabels, stc_data, tmin, tstep)
assert_equal(stc.subject, 'sample')
for label in labels:
if label.hemi == 'lh':
hemi_idx = 0
else:
hemi_idx = 1
idx = np.intersect1d(stc.vertices[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertices[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertices[0])
assert (np.all(stc.data[idx] == 1.0))
assert (stc.data[idx].shape[1] == n_times)
# test with function
def fun(x):
return x ** 2
stc = simulate_stc(fwd['src'], mylabels, stc_data, tmin, tstep, fun)
# the first label has value 0, the second value 2, the third value 6
for i, label in enumerate(labels):
if label.hemi == 'lh':
hemi_idx = 0
else:
hemi_idx = 1
idx = np.intersect1d(stc.vertices[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertices[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertices[0])
res = ((2. * i) ** 2.) * np.ones((len(idx), n_times))
assert_array_almost_equal(stc.data[idx], res)
# degenerate conditions
label_subset = mylabels[:2]
data_subset = stc_data[:2]
stc = simulate_stc(fwd['src'], label_subset, data_subset, tmin, tstep, fun)
pytest.raises(ValueError, simulate_stc, fwd['src'],
label_subset, data_subset[:-1], tmin, tstep, fun)
pytest.raises(RuntimeError, simulate_stc, fwd['src'], label_subset * 2,
np.concatenate([data_subset] * 2, axis=0), tmin, tstep, fun)
@testing.requires_testing_data
def test_simulate_sparse_stc():
"""Test generation of sparse source estimate."""
fwd = read_forward_solution_meg(fname_fwd, force_fixed=True, use_cps=True)
labels = [read_label(op.join(data_path, 'MEG', 'sample', 'labels',
'%s.label' % label)) for label in label_names]
n_times = 10
tmin = 0
tstep = 1e-3
times = np.arange(n_times, dtype=np.float) * tstep + tmin
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(labels),
times, labels=labels, location='center', subject='sample',
subjects_dir=subjects_dir) # no non-zero values
for label in labels:
label.values.fill(1.)
for location in ('random', 'center'):
random_state = 0 if location == 'random' else None
stc_1 = simulate_sparse_stc(fwd['src'], len(labels), times,
labels=labels, random_state=random_state,
location=location,
subjects_dir=subjects_dir)
assert_equal(stc_1.subject, 'sample')
assert (stc_1.data.shape[0] == len(labels))
assert (stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = simulate_sparse_stc(fwd['src'], len(labels), times,
labels=labels, random_state=random_state,
location=location,
subjects_dir=subjects_dir)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
# Degenerate cases
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(labels),
times, labels=labels, location='center', subject='foo',
subjects_dir=subjects_dir) # wrong subject
del fwd['src'][0]['subject_his_id']
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(labels),
times, labels=labels, location='center',
subjects_dir=subjects_dir) # no subject
pytest.raises(ValueError, simulate_sparse_stc, fwd['src'], len(labels),
times, labels=labels, location='foo') # bad location
@testing.requires_testing_data
def test_generate_stc_single_hemi():
"""Test generation of source estimate, single hemi."""
fwd = read_forward_solution_meg(fname_fwd, force_fixed=True, use_cps=True)
labels_single_hemi = [read_label(op.join(data_path, 'MEG', 'sample',
'labels', '%s.label' % label))
for label in label_names_single_hemi]
mylabels = []
for i, label in enumerate(labels_single_hemi):
new_label = Label(vertices=label.vertices,
pos=label.pos,
values=2 * i * np.ones(len(label.values)),
hemi=label.hemi,
comment=label.comment)
mylabels.append(new_label)
n_times = 10
tmin = 0
tstep = 1e-3
stc_data = np.ones((len(labels_single_hemi), n_times))
stc = simulate_stc(fwd['src'], mylabels, stc_data, tmin, tstep)
for label in labels_single_hemi:
if label.hemi == 'lh':
hemi_idx = 0
else:
hemi_idx = 1
idx = np.intersect1d(stc.vertices[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertices[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertices[0])
assert (np.all(stc.data[idx] == 1.0))
assert (stc.data[idx].shape[1] == n_times)
# test with function
def fun(x):
return x ** 2
stc = simulate_stc(fwd['src'], mylabels, stc_data, tmin, tstep, fun)
# the first label has value 0, the second value 2, the third value 6
for i, label in enumerate(labels_single_hemi):
if label.hemi == 'lh':
hemi_idx = 0
else:
hemi_idx = 1
idx = np.intersect1d(stc.vertices[hemi_idx], label.vertices)
idx = np.searchsorted(stc.vertices[hemi_idx], idx)
if hemi_idx == 1:
idx += len(stc.vertices[0])
res = ((2. * i) ** 2.) * np.ones((len(idx), n_times))
assert_array_almost_equal(stc.data[idx], res)
@testing.requires_testing_data
def test_simulate_sparse_stc_single_hemi():
"""Test generation of sparse source estimate."""
fwd = read_forward_solution_meg(fname_fwd, force_fixed=True, use_cps=True)
n_times = 10
tmin = 0
tstep = 1e-3
times = np.arange(n_times, dtype=np.float) * tstep + tmin
labels_single_hemi = [read_label(op.join(data_path, 'MEG', 'sample',
'labels', '%s.label' % label))
for label in label_names_single_hemi]
stc_1 = simulate_sparse_stc(fwd['src'], len(labels_single_hemi), times,
labels=labels_single_hemi, random_state=0)
assert (stc_1.data.shape[0] == len(labels_single_hemi))
assert (stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = simulate_sparse_stc(fwd['src'], len(labels_single_hemi), times,
labels=labels_single_hemi, random_state=0)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
run_tests_if_main()
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