def test_rigid_real_bundles():
bundle_initial = fornix_streamlines()[:20]
bundle, shift = center_streamlines(bundle_initial)
mat = compose_matrix44([0, 0, 20, 45., 0, 0])
bundle2 = transform_streamlines(bundle, mat)
bundle_sum_distance = BundleSumDistanceMatrixMetric()
srr = StreamlineLinearRegistration(bundle_sum_distance,
x0=np.zeros(6),
method='Powell')
new_bundle2 = srr.optimize(bundle, bundle2).transform(bundle2)
evaluate_convergence(bundle, new_bundle2)
bundle_min_distance = BundleMinDistanceMatrixMetric()
srr = StreamlineLinearRegistration(bundle_min_distance,
x0=np.zeros(6),
method='Powell')
new_bundle2 = srr.optimize(bundle, bundle2).transform(bundle2)
evaluate_convergence(bundle, new_bundle2)
assert_raises(ValueError, StreamlineLinearRegistration, method='Whatever')
def test_min_vs_min_fast_precision():
static = fornix_streamlines()[:20]
moving = fornix_streamlines()[:20]
static = [s.astype('f8') for s in static]
moving = [m.astype('f8') for m in moving]
bmd = BundleMinDistanceMatrixMetric()
bmd.setup(static, moving)
bmdf = BundleMinDistanceMetric()
bmdf.setup(static, moving)
x_test = [0.01, 0, 0, 0, 0, 0]
print(bmd.distance(x_test))
print(bmdf.distance(x_test))
assert_equal(bmd.distance(x_test), bmdf.distance(x_test))
def test_affine_real_bundles():
bundle_initial = fornix_streamlines()
bundle_initial, shift = center_streamlines(bundle_initial)
bundle = bundle_initial[:20]
xgold = [0, 4, 2, 0, 10, 10, 1.2, 1.1, 1., 0., 0.2, 0.]
mat = compose_matrix44(xgold)
bundle2 = transform_streamlines(bundle_initial[:20], mat)
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1., 0, 0, 0])
x = 25
bounds = [(-x, x), (-x, x), (-x, x),
(-x, x), (-x, x), (-x, x),
(0.1, 1.5), (0.1, 1.5), (0.1, 1.5),
(-1, 1), (-1, 1), (-1, 1)]
options = {'maxcor': 10, 'ftol': 1e-7, 'gtol': 1e-5, 'eps': 1e-8}
metric = BundleMinDistanceMatrixMetric()
slr = StreamlineLinearRegistration(metric=metric,
x0=x0,
method='L-BFGS-B',
bounds=bounds,
verbose=True,
options=options)
slm = slr.optimize(bundle, bundle2)
new_bundle2 = slm.transform(bundle2)
slr2 = StreamlineLinearRegistration(metric=metric,
x0=x0,
method='Powell',
bounds=None,
verbose=True,
options=None)
slm2 = slr2.optimize(bundle, new_bundle2)
new_bundle2 = slm2.transform(new_bundle2)
evaluate_convergence(bundle, new_bundle2)
def test_similarity_real_bundles():
bundle_initial = fornix_streamlines()
bundle_initial, shift = center_streamlines(bundle_initial)
bundle = bundle_initial[:20]
xgold = [0, 0, 10, 0, 0, 0, 1.5]
mat = compose_matrix44(xgold)
bundle2 = transform_streamlines(bundle_initial[:20], mat)
metric = BundleMinDistanceMatrixMetric()
x0 = np.array([0, 0, 0, 0, 0, 0, 1], 'f8')
slr = StreamlineLinearRegistration(metric=metric,
x0=x0,
method='Powell',
bounds=None,
verbose=False)
slm = slr.optimize(bundle, bundle2)
new_bundle2 = slm.transform(bundle2)
evaluate_convergence(bundle, new_bundle2)
def test_min_vs_min_fast_precision():
static = fornix_streamlines()[:20]
moving = fornix_streamlines()[:20]
static = [s.astype('f8') for s in static]
moving = [m.astype('f8') for m in moving]
bmd = BundleMinDistanceMatrixMetric()
bmd.setup(static, moving)
bmdf = BundleMinDistanceMetric()
bmdf.setup(static, moving)
x_test = [0.01, 0, 0, 0, 0, 0]
print(bmd.distance(x_test))
print(bmdf.distance(x_test))
assert_equal(bmd.distance(x_test), bmdf.distance(x_test))
