def test_cascade_of_optimizations_and_threading():
cingulum_bundles = two_cingulum_bundles()
cb1 = cingulum_bundles[0]
cb1 = set_number_of_points(cb1, 20)
test_x0 = np.array([10, 4, 3, 0, 20, 10, 1.5, 1.5, 1.5, 0., 0.2, 0])
cb2 = transform_streamlines(cingulum_bundles[0], compose_matrix44(test_x0))
cb2 = set_number_of_points(cb2, 20)
print('first rigid')
slr = StreamlineLinearRegistration(x0=6, num_threads=1)
slm = slr.optimize(cb1, cb2)
print('then similarity')
slr2 = StreamlineLinearRegistration(x0=7, num_threads=2)
slm2 = slr2.optimize(cb1, cb2, slm.matrix)
print('then affine')
slr3 = StreamlineLinearRegistration(x0=12,
options={'maxiter': 50},
num_threads=None)
slm3 = slr3.optimize(cb1, cb2, slm2.matrix)
assert_(slm2.fopt < slm.fopt)
assert_(slm3.fopt < slm2.fopt)
def test_cascade_of_optimizations():
cingulum_bundles = two_cingulum_bundles()
cb1 = cingulum_bundles[0]
cb1 = set_number_of_points(cb1, 20)
test_x0 = np.array([10, 4, 3, 0, 20, 10, 1.5, 1.5, 1.5, 0., 0.2, 0])
cb2 = transform_streamlines(cingulum_bundles[0],
compose_matrix44(test_x0))
cb2 = set_number_of_points(cb2, 20)
print('first rigid')
slr = StreamlineLinearRegistration(x0=6)
slm = slr.optimize(cb1, cb2)
print('then similarity')
slr2 = StreamlineLinearRegistration(x0=7)
slm2 = slr2.optimize(cb1, cb2, slm.matrix)
print('then affine')
slr3 = StreamlineLinearRegistration(x0=12, options={'maxiter': 50})
slm3 = slr3.optimize(cb1, cb2, slm2.matrix)
assert_(slm2.fopt < slm.fopt)
assert_(slm3.fopt < slm2.fopt)
def test_vectorize_streamlines():
cingulum_bundles = two_cingulum_bundles()
cb_subj1 = cingulum_bundles[0]
cb_subj1 = set_number_of_points(cb_subj1, 10)
cb_subj1_pts_no = np.array([s.shape[0] for s in cb_subj1])
assert_equal(np.all(cb_subj1_pts_no == 10), True)
def test_vectorize_streamlines():
cingulum_bundles = two_cingulum_bundles()
cb_subj1 = cingulum_bundles[0]
cb_subj1 = set_number_of_points(cb_subj1, 10)
cb_subj1_pts_no = np.array([s.shape[0] for s in cb_subj1])
assert_equal(np.all(cb_subj1_pts_no == 10), True)
First import the necessary modules. """ import numpy as np from dipy.viz import window, actor from dipy.segment.bundles import bundle_shape_similarity from dipy.segment.bundles import select_random_set_of_streamlines from dipy.data import two_cingulum_bundles """ To show the concept we will use two pre-saved cingulum bundle. Let's start by fetching the data. """ cb_subj1, _ = two_cingulum_bundles() """ Let's create two streamline sets (bundles) from same bundle cb_subj1 by randomly selecting 60 streamlines two times. """ rng = np.random.RandomState() bundle1 = select_random_set_of_streamlines(cb_subj1, 60, rng=None) bundle2 = select_random_set_of_streamlines(cb_subj1, 60, rng=None) """ Now, let's visualize two bundles. """
========================== Direct Bundle Registration ========================== This example explains how you can register two bundles from two different subjects directly in the space of streamlines [Garyfallidis15]_, [Garyfallidis14]_. To show the concept we will use two pre-saved cingulum bundles. The algorithm used here is called Streamline-based Linear Registration (SLR) [Garyfallidis15]_. """ from dipy.viz import fvtk from time import sleep from dipy.data import two_cingulum_bundles cb_subj1, cb_subj2 = two_cingulum_bundles() from dipy.align.streamlinear import StreamlineLinearRegistration from dipy.tracking.streamline import set_number_of_points """ An important step before running the registration is to resample the streamlines so that they both have the same number of points per streamline. Here we will use 20 points. This step is not optional. Inputting streamlines with different number of points will break the theoretical advantages of using the SLR as explained in [Garyfallidis15]_. """ cb_subj1 = set_number_of_points(cb_subj1, 20) cb_subj2 = set_number_of_points(cb_subj2, 20)
========================== Direct Bundle Registration ========================== This example explains how you can register two bundles from two different subjects directly in the space of streamlines [Garyfallidis15]_, [Garyfallidis14]_. To show the concept we will use two pre-saved cingulum bundles. The algorithm used here is called Streamline-based Linear Registration (SLR) [Garyfallidis15]_. """ from dipy.viz import fvtk from time import sleep from dipy.data import two_cingulum_bundles cb_subj1, cb_subj2 = two_cingulum_bundles() from dipy.align.streamlinear import StreamlineLinearRegistration from dipy.tracking.streamline import set_number_of_points """ An important step before running the registration is to resample the streamlines so that they both have the same number of points per streamline. Here we will use 20 points. This step is not optional. Inputting streamlines with different number of points will break the theoretical advantages of using the SLR as explained in [Garyfallidis15]_. """ cb_subj1 = set_number_of_points(cb_subj1, 20) cb_subj2 = set_number_of_points(cb_subj2, 20) """ Let's say now that we want to move the ``cb_subj2`` (moving) so that it can be
