def test_optimize(self, intensity, max_el_c, max_tot_c, max_ac,
sphere_surf, fn_surf, leadfield_surf):
target_img = np.random.rand(100, 100, 100)
affine = np.eye(4)
affine[:3, 3] = -100
affine[:3, :3] *= 2
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_surf,
max_individual_current=max_el_c,
max_total_current=max_tot_c,
max_active_electrodes=max_ac,
target_image=(target_img,
affine),
intensity=intensity,
min_img_value=0,
mni_space=False)
if max_el_c is None and max_tot_c is None:
pass
else:
currents = p.optimize()
assert np.isclose(np.sum(currents), 0, atol=1e-6)
if max_el_c is not None:
assert np.max(np.abs(currents)) < max_el_c * 1.05
if max_tot_c is not None:
assert np.linalg.norm(currents, 1) < 2 * max_tot_c * 1.05
if max_ac is not None:
assert np.linalg.norm(currents, 0) <= max_ac
def test_prepare_set_mesh_vol(self, fn_vol, sphere_surf):
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_vol)
p.mesh = sphere_surf
assert np.all(
np.isclose(p.mesh.nodes.node_coord, sphere_surf.nodes.node_coord))
assert np.all(
p.mesh.elm.node_number_list == sphere_surf.elm.node_number_list)
def test_target_distribution_subject(self, sphere_surf, fn_surf):
target_image = np.meshgrid(np.arange(-100, 100, 2),
np.arange(-100, 100, 2),
np.arange(-100, 100, 2),
indexing='ij')[0].astype(np.float)
affine = np.eye(4)
affine[:3, 3] = -100
affine[:3, :3] *= 2
min_img_value = 10
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_surf,
target_image=(target_image,
affine),
mni_space=False,
min_img_value=min_img_value,
intensity=2)
field, W = p._target_distribution()
assert np.allclose(field[np.abs(field) > min_img_value],
2 *
sphere_surf.nodes[np.abs(field) > min_img_value, 0],
atol=1e-2)
assert np.allclose(
field[np.abs(sphere_surf.nodes[:, 0]) < min_img_value], 0)
assert np.allclose(
W[np.abs(sphere_surf.nodes[:, 0]) > min_img_value],
np.abs(
sphere_surf.nodes[np.abs(sphere_surf.nodes[:,
0]) > min_img_value,
0]),
atol=1e-2)
assert np.allclose(W[np.abs(sphere_surf.nodes[:, 0]) < min_img_value],
min_img_value)
def test_field_msh_node(self, leadfield_surf, fn_surf, sphere_surf):
target_distribution = np.meshgrid(np.arange(-100, 100, 2),
np.arange(-100, 100, 2),
np.arange(-100, 100, 2),
indexing='ij')[0].astype(np.float)
affine = np.eye(4)
affine[:3, 3] = -100
affine[:3, :3] *= 2
p = opt_struct.TDCSDistributedOptimize(
leadfield_hdf=fn_surf,
target_image=(target_distribution, affine),
mni_space=False,
min_img_value=0.,
intensity=1,
)
c = [1., -1., 0, 0., 0.]
m = p.field_mesh(c)
normals = sphere_surf.nodes_normals()[:]
assert isinstance(m.field['Field'], mesh_io.NodeData)
assert np.allclose(m.field['Field'][:], -leadfield_surf[0])
assert np.allclose(m.field['normField'][:],
np.linalg.norm(leadfield_surf[0], axis=1))
assert np.allclose(m.field['normalField'][:],
np.sum(leadfield_surf[0] * normals, axis=1))
assert np.allclose(m.field['target_map'][:],
sphere_surf.nodes[:, 0],
atol=1e-3)
def test_terget_field_mni(self, s2mni_coords_mock, sphere_surf, fn_surf):
target_image = np.meshgrid(np.arange(-100, 100, 2),
np.arange(-100, 100, 2),
np.arange(-100, 100, 2),
indexing='ij')[0].astype(np.float)
affine = np.eye(4)
affine[:3, 3] = -100
affine[:3, :3] *= 2
s2mni_coords_mock.return_value = -sphere_surf.nodes[:]
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_surf,
target_image=(target_image,
affine),
subpath='',
min_img_value=0,
intensity=1)
field, _ = p._target_distribution()
assert np.allclose(field, -sphere_surf.nodes[:, 0], atol=1e-2)
def test_target_distribution_subject_elm(self, sphere_vol, fn_vol):
target_image = np.meshgrid(np.arange(-100, 100, 2),
np.arange(-100, 100, 2),
np.arange(-100, 100, 2),
indexing='ij')[0].astype(np.float)
affine = np.eye(4)
affine[:3, 3] = -100
affine[:3, :3] *= 2
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_vol,
target_image=(target_image,
affine),
mni_space=False,
min_img_value=0,
intensity=1)
field, _ = p._target_distribution()
assert np.allclose(field,
sphere_vol.elements_baricenters()[:, 0],
atol=1e-1)
def test_target_distribution_subject_file(self, sphere_surf, fn_surf):
target_image = np.meshgrid(np.arange(-100, 100, 2),
np.arange(-100, 100, 2),
np.arange(-100, 100, 2),
indexing='ij')[0].astype(np.float)
affine = np.eye(4)
affine[:3, 3] = -100
affine[:3, :3] *= 2
fn_nii = 'tmp.nii.gz'
nibabel.save(nibabel.Nifti1Image(target_image, affine), fn_nii)
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_surf,
target_image=fn_nii,
mni_space=False,
min_img_value=0,
intensity=1)
field, W = p._target_distribution()
os.remove(fn_nii)
assert np.allclose(field, sphere_surf.nodes[:, 0], atol=1e-3)
assert np.allclose(W, np.abs(sphere_surf.nodes[:, 0]), atol=1e-3)
def test_set_lf(self, fn_surf, leadfield_vol):
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_surf)
p.leadfield = leadfield_vol
assert np.all(np.isclose(p.leadfield, leadfield_vol))
def test_read_lf(self, fn_surf, leadfield_surf):
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_surf)
assert np.all(np.isclose(p.leadfield, leadfield_surf))
p.leadfield
def test_field_node(self, leadfield_surf, fn_surf):
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_surf)
c = [1., -1., 0, 0., 0.]
E = p.field(c)
assert isinstance(E, mesh_io.NodeData)
assert np.allclose(E.value, -leadfield_surf[0])
def test_normals_vol(self, fn_vol, sphere_vol):
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_vol)
with pytest.raises(ValueError):
p.normal_directions()
def test_normals_surf(self, fn_surf, sphere_surf):
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_surf)
n = p.normal_directions()
assert np.allclose(n, -sphere_surf.nodes_normals()[:])
def test_read_lftype_wrong(self, fn_vol, sphere_surf):
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_vol)
p.mesh = sphere_surf
with pytest.raises(ValueError):
p.lf_type
def test_read_lftype_elm(self, fn_vol):
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_vol)
assert p.lf_type == 'element'
def test_read_lftype(self, fn_surf):
p = opt_struct.TDCSDistributedOptimize(leadfield_hdf=fn_surf)
assert p.lf_type == 'node'