def test_whole_brain_slr():
streams, hdr = nib.trackvis.read(get_fnames('fornix'))
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f.copy()
# check translation
f2._data += np.array([50, 0, 0])
moved, transform, qb_centroids1, qb_centroids2 = whole_brain_slr(
f1, f2, x0='affine', verbose=True, rm_small_clusters=2,
greater_than=0, less_than=np.inf,
qbx_thr=[5, 2, 1], progressive=False)
# we can check the quality of registration by comparing the matrices
# MAM streamline distances before and after SLR
D12 = bundles_distances_mam(f1, f2)
D1M = bundles_distances_mam(f1, moved)
d12_minsum = np.sum(np.min(D12, axis=0))
d1m_minsum = np.sum(np.min(D1M, axis=0))
print("distances= ", d12_minsum, " ", d1m_minsum)
assert_equal(d1m_minsum < d12_minsum, True)
assert_array_almost_equal(transform[:3, 3], [-50, -0, -0], 2)
# check rotation
mat = compose_matrix44([0, 0, 0, 15, 0, 0])
f3 = f.copy()
f3 = transform_streamlines(f3, mat)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1, f3, verbose=False, rm_small_clusters=1, greater_than=20,
less_than=np.inf, qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1, f3, verbose=False, rm_small_clusters=1, select_random=400,
greater_than=20, less_than=np.inf, qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
def test_compose_decompose_matrix44():
for i in range(20):
x0 = np.random.rand(12)
mat = compose_matrix44(x0[:6])
assert_array_almost_equal(x0[:6], decompose_matrix44(mat, size=6))
mat = compose_matrix44(x0[:7])
assert_array_almost_equal(x0[:7], decompose_matrix44(mat, size=7))
mat = compose_matrix44(x0[:12])
assert_array_almost_equal(x0[:12], decompose_matrix44(mat, size=12))
assert_raises(ValueError, decompose_matrix44, mat, 20)
def test_compose_decompose_matrix44():
for i in range(20):
x0 = np.random.rand(12)
mat = compose_matrix44(x0[:6])
assert_array_almost_equal(x0[:6], decompose_matrix44(mat, size=6))
mat = compose_matrix44(x0[:7])
assert_array_almost_equal(x0[:7], decompose_matrix44(mat, size=7))
mat = compose_matrix44(x0[:12])
assert_array_almost_equal(x0[:12], decompose_matrix44(mat, size=12))
assert_raises(ValueError, decompose_matrix44, mat, 20)
def test_from_to_rigid():
t = np.array([10, 2, 3, 0.1, 20., 30.])
mat = compose_matrix44(t)
vec = decompose_matrix44(mat, 6)
assert_array_almost_equal(t, vec)
t = np.array([0, 0, 0, 180, 0., 0.])
mat = np.eye(4)
mat[0, 0] = -1
vec = decompose_matrix44(mat, 6)
assert_array_almost_equal(-t, vec)
def test_from_to_rigid():
t = np.array([10, 2, 3, 0.1, 20., 30.])
mat = compose_matrix44(t)
vec = decompose_matrix44(mat, 6)
assert_array_almost_equal(t, vec)
t = np.array([0, 0, 0, 180, 0., 0.])
mat = np.eye(4)
mat[0, 0] = -1
vec = decompose_matrix44(mat, 6)
assert_array_almost_equal(-t, vec)
def test_whole_brain_slr():
streams, hdr = nib.trackvis.read(get_data('fornix'))
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f.copy()
# check translation
f2._data += np.array([50, 0, 0])
moved, transform, qb_centroids1, qb_centroids2 = whole_brain_slr(
f1, f2, verbose=True, rm_small_clusters=2, greater_than=0,
less_than=np.inf, qb_thr=5, progressive=False)
# we can check the quality of registration by comparing the matrices
# MAM streamline distances before and after SLR
D12 = bundles_distances_mam(f1, f2)
D1M = bundles_distances_mam(f1, moved)
d12_minsum = np.sum(np.min(D12, axis=0))
d1m_minsum = np.sum(np.min(D1M, axis=0))
assert_equal(d1m_minsum < d12_minsum, True)
assert_array_almost_equal(transform[:3, 3], [-50, -0, -0], 3)
# check rotation
mat = compose_matrix44([0, 0, 0, 15, 0, 0])
f3 = f.copy()
f3 = transform_streamlines(f3, mat)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qb(
f1, f3, verbose=False, rm_small_clusters=1, greater_than=20,
less_than=np.inf, qb_thr=2, progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qb(
f1, f3, verbose=False, rm_small_clusters=1, select_random=400,
greater_than=20,
less_than=np.inf, qb_thr=2, progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
def test_whole_brain_slr():
fname = get_fnames('fornix')
fornix = load_tractogram(fname, 'same', bbox_valid_check=False).streamlines
f = Streamlines(fornix)
f1 = f.copy()
f2 = f.copy()
# check translation
f2._data += np.array([50, 0, 0])
moved, transform, qb_centroids1, qb_centroids2 = whole_brain_slr(
f1,
f2,
x0='affine',
verbose=True,
rm_small_clusters=2,
greater_than=0,
less_than=np.inf,
qbx_thr=[5, 2, 1],
progressive=False)
# we can check the quality of registration by comparing the matrices
# MAM streamline distances before and after SLR
D12 = bundles_distances_mam(f1, f2)
D1M = bundles_distances_mam(f1, moved)
d12_minsum = np.sum(np.min(D12, axis=0))
d1m_minsum = np.sum(np.min(D1M, axis=0))
print("distances= ", d12_minsum, " ", d1m_minsum)
assert_equal(d1m_minsum < d12_minsum, True)
assert_array_almost_equal(transform[:3, 3], [-50, -0, -0], 2)
# check rotation
mat = compose_matrix44([0, 0, 0, 15, 0, 0])
f3 = f.copy()
f3 = transform_streamlines(f3, mat)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1,
f3,
verbose=False,
rm_small_clusters=1,
greater_than=20,
less_than=np.inf,
qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1,
f3,
verbose=False,
rm_small_clusters=1,
select_random=400,
greater_than=20,
less_than=np.inf,
qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)