def assignment_map(target_bundle, model_bundle, no_disks):
"""
Calculates assignment maps of the target bundle with reference to
model bundle centroids.
Parameters
----------
target_bundle : streamlines
target bundle extracted from subject data in common space
model_bundle : streamlines
atlas bundle used as reference
no_disks : integer, optional
Number of disks used for dividing bundle into disks. (Default 100)
References
----------
.. [Chandio2020] Chandio, B.Q., Risacher, S.L., Pestilli, F., Bullock, D.,
Yeh, FC., Koudoro, S., Rokem, A., Harezlak, J., and Garyfallidis, E.
Bundle analytics, a computational framework for investigating the
shapes and profiles of brain pathways across populations.
Sci Rep 10, 17149 (2020)
"""
mbundle_streamlines = set_number_of_points(model_bundle,
nb_points=no_disks)
metric = AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=85., metric=metric)
clusters = qb.cluster(mbundle_streamlines)
centroids = Streamlines(clusters.centroids)
_, indx = cKDTree(centroids.get_data(), 1,
copy_data=True).query(target_bundle.get_data(), k=1)
return indx
def assignment_map(target_bundle, model_bundle, no_disks):
"""
Calculates assignment maps of the target bundle with reference to
model bundle centroids.
Parameters
----------
target_bundle : streamlines
target bundle extracted from subject data in common space
model_bundle : streamlines
atlas bundle used as reference
no_disks : integer, optional
Number of disks used for dividing bundle into disks. (Default 100)
References
----------
.. [Chandio19] Chandio, B.Q., S. Koudoro, D. Reagan, J. Harezlak,
E. Garyfallidis, Bundle Analytics: a computational and statistical
analyses framework for tractometric studies, Proceedings of:
International Society of Magnetic Resonance in Medicine (ISMRM),
Montreal, Canada, 2019.
"""
mbundle_streamlines = set_number_of_points(model_bundle,
nb_points=no_disks)
metric = AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=85., metric=metric)
clusters = qb.cluster(mbundle_streamlines)
centroids = Streamlines(clusters.centroids)
_, indx = cKDTree(centroids.get_data(), 1,
copy_data=True).query(target_bundle.get_data(), k=1)
return indx
def test_horizon_flow():
s1 = 10 * np.array([[0, 0, 0], [1, 0, 0], [2, 0, 0], [3, 0, 0], [4, 0, 0]],
dtype='f8')
s2 = 10 * np.array([[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0], [0, 4, 0]],
dtype='f8')
s3 = 10 * np.array(
[[0, 0, 0], [1, 0.2, 0], [2, 0.2, 0], [3, 0.2, 0], [4, 0.2, 0]],
dtype='f8')
affine = np.array([[1., 0., 0., -98.], [0., 1., 0., -134.],
[0., 0., 1., -72.], [0., 0., 0., 1.]])
data = 255 * np.random.rand(197, 233, 189)
vox_size = (1., 1., 1.)
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
header = create_nifti_header(affine, data.shape, vox_size)
sft = StatefulTractogram(streamlines, header, Space.RASMM)
tractograms = [sft]
images = None
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=False)
buan_colors = np.ones(streamlines.get_data().shape)
horizon(tractograms,
buan=True,
buan_colors=buan_colors,
world_coords=True,
interactive=False)
data = 255 * np.random.rand(197, 233, 189)
images = [(data, affine)]
horizon(tractograms,
images=images,
cluster=True,
cluster_thr=5,
random_colors=False,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=False)
with TemporaryDirectory() as out_dir:
fimg = os.path.join(out_dir, 'test.nii.gz')
ftrk = os.path.join(out_dir, 'test.trk')
fnpy = os.path.join(out_dir, 'test.npy')
save_nifti(fimg, data, affine)
dimensions = data.shape
nii_header = create_nifti_header(affine, dimensions, vox_size)
sft = StatefulTractogram(streamlines, nii_header, space=Space.RASMM)
save_tractogram(sft, ftrk, bbox_valid_check=False)
pvalues = np.random.uniform(low=0, high=1, size=(10, ))
np.save(fnpy, pvalues)
input_files = [ftrk, fimg]
npt.assert_equal(len(input_files), 2)
hz_flow = HorizonFlow()
hz_flow.run(input_files=input_files,
stealth=True,
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
npt.assert_raises(ValueError,
hz_flow.run,
input_files=input_files,
bg_color=(0.2, 0.2))
hz_flow.run(input_files=input_files,
stealth=True,
bg_color=[
0.5,
],
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
input_files = [ftrk, fnpy]
npt.assert_equal(len(input_files), 2)
hz_flow.run(input_files=input_files,
stealth=True,
bg_color=[
0.5,
],
buan=True,
buan_thr=0.5,
buan_highlight=(1, 1, 0),
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
npt.assert_raises(ValueError,
hz_flow.run,
input_files=input_files,
roi_colors=(0.2, 0.2))
hz_flow.run(input_files=input_files,
stealth=True,
roi_colors=[
0.5,
],
out_dir=out_dir,
out_stealth_png='tmp_x.png')
npt.assert_equal(os.path.exists(os.path.join(out_dir, 'tmp_x.png')),
True)
