def test_mesh_laplacian():
td = get_testdir()
s = Surface(op.join(td, 'in.surf.gii'))
try:
s.mesh_laplacian(-1)
except Exception as e:
assert isinstance(
e, ValueError), 'negative distance weight should give ValueError'
for w in range(4):
lap = s.mesh_laplacian(distance_weight=w)
assert (lap[np.diag_indices(lap.shape[0])] <
0).min(), 'positive diagonal'
outs = Surface(op.join(td, 'out.surf.gii'))
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
hemi = toblerone.Hemisphere(s, outs, 'L')
hemi2 = toblerone.Hemisphere(s, outs, 'R')
proj = toblerone.projection.Projector([hemi, hemi2], spc)
for w in range(4):
lap = proj.mesh_laplacian(w)
n = proj.hemi_dict['L'].n_points
assert not slice_sparse(lap, slice(0, n), slice(n, 2 * n)).nnz
assert not slice_sparse(lap, slice(n, 2 * n), slice(0, n)).nnz
assert not (lap[diag_indices(2 * n)] > 0).any()
def test_structure():
td = get_testdir()
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
ins = Surface(op.join(td, 'in.surf.gii'), name='L')
s2r = np.identity(4)
fracs = pvestimation.structure(surf=op.join(td, 'in.surf.gii'),
ref=spc,
struct2ref=s2r,
cores=1,
flirt=True,
coords='fsl',
struct=op.join(td, 'ref.nii.gz'))
superfactor = 10
spc_high = spc.resize_voxels(1.0 / superfactor)
voxelised = np.zeros(spc_high.size.prod(), dtype=NP_FLOAT)
ins.index_on(spc_high)
ins.indexed.voxelised = ins.voxelise(spc_high, 1)
reindex_in = ins.reindexing_filter(spc_high)
voxelised[reindex_in[1]] = (
ins.indexed.voxelised[reindex_in[0]]).astype(NP_FLOAT)
voxelised = voxelised.reshape(spc_high.size)
truth = sum_array_blocks(voxelised, 3 * [superfactor]) / superfactor**3
np.testing.assert_array_almost_equal(fracs, truth, 2)
def test_discriminated_laplacian():
td = get_testdir()
s = Surface(op.join(td, 'in.surf.gii'))
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
for w in range(4):
lap = s.discriminated_laplacian(spc, distance_weight=w, in_weight=100)
assert (lap[np.diag_indices(lap.shape[0])] <
0).min(), 'positive diagonal'
def test_transform_projector():
td = get_testdir()
ins = op.join(td, 'in.surf.gii')
outs = op.join(td, 'out.surf.gii')
hemi = toblerone.Hemisphere(ins, outs, 'L')
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
proj = toblerone.Projector(hemi, spc)
# proj = toblerone.Projector.load('temp.h5')
trans = np.eye(4)
trans[:3, 3] = 0.4 * np.random.rand(3)
proj2 = proj.transform(trans, spc)
assert (proj.surf2vol_matrix(False) !=
proj2.surf2vol_matrix(False)).A.any()
def test_projector_partial_fov():
td = get_testdir()
ins = op.join(td, 'in.surf.gii')
outs = op.join(td, 'out.surf.gii')
hemi = toblerone.Hemisphere(ins, outs, 'L')
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
spc = spc.resize([1, 1, 1], spc.size - 2)
projector = toblerone.projection.Projector(hemi, spc, cores=8)
sdata = np.ones(hemi.inSurf.n_points, dtype=NP_FLOAT)
vdata = np.ones(spc.size.prod(), dtype=NP_FLOAT)
# ndata = np.concatenate((sdata, vdata))
# proj = projector.surf2vol(sdata, True)
adj = projector.adjacency_matrix()
lap = projector.discriminated_laplacian(1, 100)
def test_projection():
td = get_testdir()
ins = op.join(td, 'in.surf.gii')
outs = op.join(td, 'out.surf.gii')
hemi = toblerone.Hemisphere(ins, outs, 'L')
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
sdata = np.ones(hemi.inSurf.n_points, dtype=NP_FLOAT)
vdata = np.ones(spc.size.prod(), dtype=NP_FLOAT)
ndata = np.concatenate((sdata, vdata))
projector = toblerone.projection.Projector(hemi, spc, 10)
# volume to surface
v2s = projector.vol2surf(vdata, False)
v2s_edge = projector.vol2surf(vdata, True)
assert (v2s <= v2s_edge).all(), "edge correction did not increase signal"
v2s = projector.vol2surf_matrix(False)
v2s_edge = projector.vol2surf_matrix(True)
assert (v2s_edge.data >=
v2s.data).all(), 'egde correction: some weights should increase'
# surface to volume
s2v = projector.surf2vol(sdata, False)
s2v_pv = projector.surf2vol(sdata, True)
assert (s2v_pv <= s2v).all(), "pv weighting did not reduce signal"
s2v = projector.surf2vol_matrix(False)
assert (s2v.sum(1).max() - 1) < 1e-6, 'total voxel weight > 1'
s2v_pv = projector.surf2vol_matrix(False)
assert (s2v_pv.data <=
s2v.data).all(), 'pv weighting should reduce voxel weights'
# volume to node
v2n = projector.vol2node(vdata, False)
v2n_edge = projector.vol2node(vdata, True)
assert (v2n <= v2n_edge).all(), "edge correction did not increase signal"
v2n = projector.vol2node_matrix(False)
assert (v2n.sum(1).max() - 1) < 1e-6, 'total node weight > 1'
v2n_edge = projector.vol2node_matrix(True)
assert (v2n_edge.sum(1).max() -
1) > 1e-6, 'edge correction: node should have weight > 1'
# node to volume
n2v = projector.node2vol(ndata, False)
n2v_pv = projector.node2vol(ndata, True)
assert (n2v_pv <= n2v).all(), "pv weighting did not reduce signal"
n2v = projector.node2vol_matrix(False)
assert (n2v.sum(1).max() - 1) < 1e-6, 'total voxel weight > 1'
n2v_pv = projector.node2vol_matrix(True)
assert (n2v.sum(1).max() - 1) < 1e-6, 'total voxel weight > 1'
def test_indexing():
td = get_testdir()
surf = toblerone.Surface(op.join(td, 'out.surf.gii'))
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
surf.index_on(spc, 1)
surf.indexed.voxelised = surf.voxelise(spc, 1)
truth = pickle.load(open(op.join(td, 'out_indexed.pkl'), 'rb'))
truthspace = truth.indexed.space
space = surf.indexed.space
assocs = surf.indexed.assocs
assert (np.array_equal(truth.indexed.assocs.indices, assocs.indices)
and (np.array_equal(truth.indexed.assocs.data, assocs.data)))
assert np.all(space.bbox_origin == truthspace.bbox_origin)
assert np.all(space.size == truthspace.size)
assert np.all(space.offset == truthspace.offset)
assert np.array_equal(surf.indexed.voxelised, truth.indexed.voxelised)
def test_projector_hdf5():
td = get_testdir()
ins = op.join(td, 'in.surf.gii')
outs = op.join(td, 'out.surf.gii')
hemi = toblerone.Hemisphere(ins, outs, 'L')
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
proj = toblerone.Projector(hemi, spc)
proj.save('proj.h5')
proj2 = toblerone.Projector.load('proj.h5')
assert np.array_equiv(proj.pvs, proj2.pvs)
assert np.array_equiv(proj.spc, proj2.spc)
assert np.array_equiv(proj.vox_tri_mats[0].data,
proj2.vox_tri_mats[0].data)
assert np.array_equiv(proj.vtx_tri_mats[0].data,
proj2.vtx_tri_mats[0].data)
os.remove('proj.h5')
def test_projector_rois():
td = get_testdir()
ins = op.join(td, 'in.surf.gii')
outs = op.join(td, 'out.surf.gii')
hemi = toblerone.Hemisphere(ins, outs, 'L')
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
fracs = nibabel.load(op.join(td, 'sph_fractions.nii.gz')).get_fdata()
hemi.pvs = fracs.reshape(-1, 3)
puta = Surface.manual(0.25 * hemi.inSurf.points, hemi.inSurf.tris,
'L_Puta')
rois = {'L_Puta': puta}
proj = toblerone.projection.Projector(hemi, spc, rois=rois, cores=8)
ndata = np.ones(proj.n_nodes)
ndata[-1] = 2
vdata = proj.node2vol(ndata, True)
spc.save_image(vdata, 'n2v.nii.gz')
ndata = proj.vol2node(vdata, True)
print(ndata)
def test_proj_properties():
td = get_testdir()
ins = Surface(op.join(td, 'in.surf.gii'))
outs = Surface(op.join(td, 'out.surf.gii'))
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
hemi = toblerone.Hemisphere(ins, outs, 'L')
proj = toblerone.projection.Projector(hemi, spc)
assert proj.n_hemis == 1
assert 'L' in proj.hemi_dict
assert hemi.midsurface()
assert proj['LPS']
hemi2 = toblerone.Hemisphere(ins, outs, 'R')
proj = toblerone.projection.Projector([hemi, hemi2], spc)
assert proj.n_hemis == 2
assert proj['RWS']
assert ('L' in proj.hemi_dict) & ('R' in proj.hemi_dict)
for h, s in zip(proj.iter_hemis, ['L', 'R']):
assert h.side == s
assert proj.n_surf_points == 2 * ins.n_points
def test_enclosing_space():
td = get_testdir()
surf = toblerone.Surface(op.join(td, 'out.surf.gii'))
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
surf.index_on(spc, 1)
assert utils.space_encloses_surface(surf.indexed.space,
surf.indexed.points_vox)
spc2 = spc.resize([5, 5, 5], [5, 5, 5])
surf2 = toblerone.Surface(op.join(td, 'out.surf.gii'))
surf2.index_on(spc2, 1)
assert not utils.space_encloses_surface(spc2, surf2.indexed.points_vox)
assert surf.indexed.space == surf2.indexed.space
surf = toblerone.Surface(op.join(td, 'out.surf.gii'))
start = surf.points.min(0)
end = surf.points.max(0)
vsize = (start - end) / 10
spc = toblerone.ImageSpace.create_axis_aligned(start, end, vsize)
surf.index_on(spc, 1)
assert utils.space_encloses_surface(surf.indexed.space,
surf.indexed.points_vox)
def test_cortex():
td = get_testdir()
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
ins = op.join(td, 'in.surf.gii')
outs = op.join(td, 'out.surf.gii')
hemi = Hemisphere(ins, outs, 'L')
s2r = np.identity(4)
supersampler = np.random.randint(3, 6, 3)
fracs = estimators._cortex(hemi, spc, s2r, supersampler, 8, False)
spc.save_image(fracs, f'{td}/fracs.nii.gz')
# REFRESH the surfaces of the hemisphere before starting again - indexing!
hemi = Hemisphere(ins, outs, 'L')
superfactor = 10
spc_high = spc.resize_voxels(1.0 / superfactor)
voxelised = np.zeros(spc_high.size.prod(), dtype=NP_FLOAT)
hemi.inSurf.index_on(spc_high)
hemi.inSurf.indexed.voxelised = hemi.inSurf.voxelise(spc_high, 1)
reindex_in = hemi.inSurf.reindexing_filter(spc_high)
voxelised[reindex_in[1]] = -(
hemi.inSurf.indexed.voxelised[reindex_in[0]]).astype(NP_FLOAT)
hemi.outSurf.index_on(spc_high)
hemi.outSurf.indexed.voxelised = hemi.outSurf.voxelise(spc_high, 1)
reindex_out = hemi.outSurf.reindexing_filter(spc_high)
voxelised[reindex_out[1]] += hemi.outSurf.indexed.voxelised[reindex_out[0]]
voxelised = voxelised.reshape(spc_high.size)
truth = sum_array_blocks(voxelised, 3 * [superfactor]) / superfactor**3
spc.save_image(truth, f'{td}/truth.nii.gz')
# truth = np.squeeze(nibabel.load(op.join(td, 'truth.nii.gz')).get_fdata())
np.testing.assert_array_almost_equal(fracs[..., 0], truth, 2)
def test_adjacency():
td = get_testdir()
s = Surface(op.join(td, 'in.surf.gii'))
for w in range(4):
adj = s.adjacency_matrix(w)
assert not (adj.data < 0).any(), 'negative value in adjacency matrix'
try:
s.adjacency_matrix(-1)
except Exception as e:
assert isinstance(
e, ValueError), 'negative distance weight should give ValueError'
outs = Surface(op.join(td, 'out.surf.gii'))
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
hemi = toblerone.Hemisphere(s, outs, 'L')
hemi2 = toblerone.Hemisphere(s, outs, 'R')
proj = toblerone.projection.Projector([hemi, hemi2], spc)
n = proj.hemi_dict['L'].n_points
for w in range(4):
adj = proj.adjacency_matrix(w)
assert not slice_sparse(adj, slice(0, n), slice(n, 2 * n)).nnz
assert not slice_sparse(adj, slice(n, 2 * n), slice(0, n)).nnz
