def new_window():
active_streamlines = Streamlines()
for bundle in self.cla.keys():
if bundle.GetVisibility():
t = self.cla[bundle]['tractogram']
c = self.cla[bundle]['cluster']
indices = self.tractogram_clusters[t][c]
active_streamlines.extend(Streamlines(indices))
# Using the header of the first of the tractograms
active_sft = StatefulTractogram(active_streamlines,
self.tractograms[0], Space.RASMM)
hz2 = Horizon([active_sft],
self.images,
cluster=True,
cluster_thr=self.cluster_thr / 2.,
random_colors=self.random_colors,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=True)
ren2 = hz2.build_scene()
hz2.build_show(ren2)
def test_rb_no_neighb():
# what if no neighbors are found? No recognition
b = Streamlines(fornix)
b1 = b.copy()
b2 = b1[:20].copy()
b2._data += np.array([100, 0, 0])
b3 = b1[:20].copy()
b3._data += np.array([300, 0, 0])
b.extend(b3)
rb = RecoBundles(b, greater_than=0, clust_thr=10)
rec_trans, rec_labels = rb.recognize(model_bundle=b2,
model_clust_thr=5.,
reduction_thr=10)
if len(rec_trans) > 0:
refine_trans, refine_labels = rb.refine(model_bundle=b2,
pruned_streamlines=rec_trans,
model_clust_thr=5.,
reduction_thr=10)
assert_equal(len(refine_labels), 0)
assert_equal(len(refine_trans), 0)
else:
assert_equal(len(rec_labels), 0)
assert_equal(len(rec_trans), 0)
def test_rb_no_neighb():
# what if no neighbors are found? No recognition
b = Streamlines(fornix)
b1 = b.copy()
b2 = b1[:20].copy()
b2._data += np.array([100, 0, 0])
b3 = b1[:20].copy()
b3._data += np.array([300, 0, 0])
b.extend(b3)
rb = RecoBundles(b, greater_than=0, clust_thr=10)
rec_trans, rec_labels = rb.recognize(model_bundle=b2,
model_clust_thr=5.,
reduction_thr=10)
if len(rec_trans) > 0:
refine_trans, refine_labels = rb.refine(model_bundle=b2,
pruned_streamlines=rec_trans,
model_clust_thr=5.,
reduction_thr=10)
assert_equal(len(refine_labels), 0)
assert_equal(len(refine_trans), 0)
else:
assert_equal(len(rec_labels), 0)
assert_equal(len(rec_trans), 0)
def test_recobundles_flow():
with TemporaryDirectory() as out_dir:
data_path = get_fnames('fornix')
fornix = load_tractogram(data_path, 'same',
bbox_valid_check=False).streamlines
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:15].copy()
f2._data += np.array([40, 0, 0])
f.extend(f2)
f2_path = pjoin(out_dir, "f2.trk")
sft = StatefulTractogram(f2, data_path, Space.RASMM)
save_tractogram(sft, f2_path, bbox_valid_check=False)
f1_path = pjoin(out_dir, "f1.trk")
sft = StatefulTractogram(f, data_path, Space.RASMM)
save_tractogram(sft, f1_path, bbox_valid_check=False)
rb_flow = RecoBundlesFlow(force=True)
rb_flow.run(f1_path,
f2_path,
greater_than=0,
clust_thr=10,
model_clust_thr=5.,
reduction_thr=10,
out_dir=out_dir)
labels = rb_flow.last_generated_outputs['out_recognized_labels']
recog_trk = rb_flow.last_generated_outputs['out_recognized_transf']
rec_bundle = load_tractogram(recog_trk, 'same',
bbox_valid_check=False).streamlines
npt.assert_equal(len(rec_bundle) == len(f2), True)
label_flow = LabelsBundlesFlow(force=True)
label_flow.run(f1_path, labels)
recog_bundle = label_flow.last_generated_outputs['out_bundle']
rec_bundle_org = load_tractogram(recog_bundle,
'same',
bbox_valid_check=False).streamlines
BMD = BundleMinDistanceMetric()
nb_pts = 20
static = set_number_of_points(f2, nb_pts)
moving = set_number_of_points(rec_bundle_org, nb_pts)
BMD.setup(static, moving)
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine
bmd_value = BMD.distance(x0.tolist())
npt.assert_equal(bmd_value < 1, True)
def test_afq_profile():
data = np.ones((10, 10, 10))
bundle = Streamlines()
bundle.extend(np.array([[[0, 0., 0],
[1, 0., 0.],
[2, 0., 0.]]]))
bundle.extend(np.array([[[0, 0., 0.],
[1, 0., 0],
[2, 0, 0.]]]))
profile = afq_profile(data, bundle)
npt.assert_equal(profile, np.ones(100))
profile = afq_profile(data, bundle, affine=None, n_points=10,
weights=None)
npt.assert_equal(profile, np.ones(10))
profile = afq_profile(data, bundle, affine=None,
weights=gaussian_weights, stat=np.median)
npt.assert_equal(profile, np.ones(100))
profile = afq_profile(data, bundle, affine=None, orient_by=bundle[0],
weights=gaussian_weights, stat=np.median)
npt.assert_equal(profile, np.ones(100))
profile = afq_profile(data, bundle, affine=None, n_points=10,
weights=None)
npt.assert_equal(profile, np.ones(10))
profile = afq_profile(data, bundle, affine=None, n_points=10,
weights=np.ones((2, 10)) * 0.5)
npt.assert_equal(profile, np.ones(10))
# Disallow setting weights that don't sum to 1 across fibers/nodes:
npt.assert_raises(ValueError, afq_profile,
data, bundle, affine=None,
n_points=10, weights=np.ones((2, 10)) * 0.6)
# Test using an affine:
affine = np.eye(4)
affine[:, 3] = [-1, 100, -20, 1]
# Transform the streamlines:
bundle._data = bundle._data + affine[:3, 3]
profile = afq_profile(data,
bundle,
affine=affine,
n_points=10,
weights=None)
npt.assert_equal(profile, np.ones(10))
# Test for error-handling:
empty_bundle = Streamlines([])
npt.assert_raises(ValueError, afq_profile, data, empty_bundle)
def test_afq_profile():
data = np.ones((10, 10, 10))
bundle = Streamlines()
bundle.extend(np.array([[[0, 0., 0],
[1, 0., 0.],
[2, 0., 0.]]]))
bundle.extend(np.array([[[0, 0., 0.],
[1, 0., 0],
[2, 0, 0.]]]))
profile = afq_profile(data, bundle)
npt.assert_equal(profile, np.ones(100))
profile = afq_profile(data, bundle, affine=None, n_points=10,
weights=None)
npt.assert_equal(profile, np.ones(10))
profile = afq_profile(data, bundle, affine=None,
weights=gaussian_weights, stat=np.median)
npt.assert_equal(profile, np.ones(100))
profile = afq_profile(data, bundle, affine=None, orient_by=bundle[0],
weights=gaussian_weights, stat=np.median)
npt.assert_equal(profile, np.ones(100))
profile = afq_profile(data, bundle, affine=None, n_points=10,
weights=None)
npt.assert_equal(profile, np.ones(10))
profile = afq_profile(data, bundle, affine=None, n_points=10,
weights=np.ones((2, 10)) * 0.5)
npt.assert_equal(profile, np.ones(10))
# Disallow setting weights that don't sum to 1 across fibers/nodes:
npt.assert_raises(ValueError, afq_profile,
data, bundle, affine=None,
n_points=10, weights=np.ones((2, 10)) * 0.6)
# Test using an affine:
affine = np.eye(4)
affine[:, 3] = [-1, 100, -20, 1]
# Transform the streamlines:
bundle._data = bundle._data + affine[:3, 3]
profile = afq_profile(data,
bundle,
affine=affine,
n_points=10,
weights=None)
npt.assert_equal(profile, np.ones(10))
# Test for error-handling:
empty_bundle = Streamlines([])
npt.assert_raises(ValueError, afq_profile, data, empty_bundle)
def test_recobundles_flow():
with TemporaryDirectory() as out_dir:
data_path = get_fnames('fornix')
streams, hdr = nib.trackvis.read(data_path)
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:15].copy()
f2._data += np.array([40, 0, 0])
f.extend(f2)
f2_path = pjoin(out_dir, "f2.trk")
save_trk(f2_path, f2, affine=np.eye(4))
f1_path = pjoin(out_dir, "f1.trk")
save_trk(f1_path, f, affine=np.eye(4))
rb_flow = RecoBundlesFlow(force=True)
rb_flow.run(f1_path,
f2_path,
greater_than=0,
clust_thr=10,
model_clust_thr=5.,
reduction_thr=10,
out_dir=out_dir)
labels = rb_flow.last_generated_outputs['out_recognized_labels']
recog_trk = rb_flow.last_generated_outputs['out_recognized_transf']
rec_bundle, _ = load_trk(recog_trk)
npt.assert_equal(len(rec_bundle) == len(f2), True)
label_flow = LabelsBundlesFlow(force=True)
label_flow.run(f1_path, labels)
recog_bundle = label_flow.last_generated_outputs['out_bundle']
rec_bundle_org, _ = load_trk(recog_bundle)
BMD = BundleMinDistanceMetric()
nb_pts = 20
static = set_number_of_points(f2, nb_pts)
moving = set_number_of_points(rec_bundle_org, nb_pts)
BMD.setup(static, moving)
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine
bmd_value = BMD.distance(x0.tolist())
npt.assert_equal(bmd_value < 1, True)
def save():
saving_streamlines = Streamlines()
for bundle in self.cla.keys():
if bundle.GetVisibility():
t = self.cla[bundle]['tractogram']
c = self.cla[bundle]['cluster']
indices = self.tractogram_clusters[t][c]
saving_streamlines.extend(Streamlines(indices))
print('Saving result in tmp.trk')
# Using the header of the first of the tractograms
sft_new = StatefulTractogram(saving_streamlines,
self.tractograms[0], Space.RASMM)
save_tractogram(sft_new, 'tmp.trk', bbox_valid_check=False)
print('Saved!')
def setup_module():
global f, f1, f2, f3, fornix
fname = get_fnames('fornix')
fornix = load_tractogram(fname, 'same', bbox_valid_check=False).streamlines
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:20].copy()
f2._data += np.array([50, 0, 0])
f3 = f1[200:].copy()
f3._data += np.array([100, 0, 0])
f.extend(f2)
f.extend(f3)
def test_recobundles_flow():
with TemporaryDirectory() as out_dir:
data_path = get_fnames('fornix')
streams, hdr = nib.trackvis.read(data_path)
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:15].copy()
f2._data += np.array([40, 0, 0])
f.extend(f2)
f2_path = pjoin(out_dir, "f2.trk")
save_trk(f2_path, f2, affine=np.eye(4))
f1_path = pjoin(out_dir, "f1.trk")
save_trk(f1_path, f, affine=np.eye(4))
rb_flow = RecoBundlesFlow(force=True)
rb_flow.run(f1_path, f2_path, greater_than=0, clust_thr=10,
model_clust_thr=5., reduction_thr=10, out_dir=out_dir)
labels = rb_flow.last_generated_outputs['out_recognized_labels']
recog_trk = rb_flow.last_generated_outputs['out_recognized_transf']
rec_bundle, _ = load_trk(recog_trk)
npt.assert_equal(len(rec_bundle) == len(f2), True)
label_flow = LabelsBundlesFlow(force=True)
label_flow.run(f1_path, labels)
recog_bundle = label_flow.last_generated_outputs['out_bundle']
rec_bundle_org, _ = load_trk(recog_bundle)
BMD = BundleMinDistanceMetric()
nb_pts = 20
static = set_number_of_points(f2, nb_pts)
moving = set_number_of_points(rec_bundle_org, nb_pts)
BMD.setup(static, moving)
x0 = np.array([0, 0, 0, 0, 0, 0, 1., 1., 1, 0, 0, 0]) # affine
bmd_value = BMD.distance(x0.tolist())
npt.assert_equal(bmd_value < 1, True)
fetch_bundles_2_subjects()
"""
The following function outputs a dictionary with the required bundles e.g. ``af
left`` (left arcuate fasciculus) and maps, e.g. FA for a specific subject.
"""
res = read_bundles_2_subjects('subj_1', ['t1', 'fa'],
['af.left', 'cst.right', 'cc_1'])
"""
We will use 3 bundles, FA and the affine transformation that brings the voxel
coordinates to world coordinates (RAS 1mm).
"""
streamlines = Streamlines(res['af.left'])
streamlines.extend(res['cst.right'])
streamlines.extend(res['cc_1'])
data = res['fa']
shape = data.shape
affine = res['affine']
"""
With our current design it is easy to decide in which space you want the
streamlines and slices to appear. The default we have here is to appear in
world coordinates (RAS 1mm).
"""
world_coords = True
"""
If we want to see the objects in native space we need to make sure that all
objects which are currently in world coordinates are transformed back to
def key_press(obj, event):
key = obj.GetKeySym()
if self.cluster:
# hide on/off unselected centroids
if key == 'h' or key == 'H':
if self.hide_centroids:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min
or self.cea[ca]['size'] >= self.size_min):
if self.cea[ca]['selected'] == 0:
ca.VisibilityOff()
else:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min
and self.cea[ca]['size'] >= self.size_min):
if self.cea[ca]['selected'] == 0:
ca.VisibilityOn()
self.hide_centroids = not self.hide_centroids
show_m.render()
# invert selection
if key == 'i' or key == 'I':
for ca in self.cea:
if (self.cea[ca]['length'] >= self.self.length_min
and self.cea[ca]['size'] >= self.size_min):
self.cea[ca]['selected'] = \
not self.cea[ca]['selected']
cas = self.cea[ca]['cluster_actor']
self.cla[cas]['selected'] = \
self.cea[ca]['selected']
show_m.render()
# save current result
if key == 's' or key == 'S':
saving_streamlines = Streamlines()
for bundle in self.cla.keys():
if bundle.GetVisibility():
t = self.cla[bundle]['tractogram']
c = self.cla[bundle]['cluster']
indices = self.tractogram_clusters[t][c]
saving_streamlines.extend(Streamlines(indices))
print('Saving result in tmp.trk')
save_trk('tmp.trk', saving_streamlines, np.eye(4))
if key == 'y' or key == 'Y':
active_streamlines = Streamlines()
for bundle in self.cla.keys():
if bundle.GetVisibility():
t = self.cla[bundle]['tractogram']
c = self.cla[bundle]['cluster']
indices = self.tractogram_clusters[t][c]
active_streamlines.extend(Streamlines(indices))
# self.tractograms = [active_streamlines]
hz2 = horizon([active_streamlines],
self.images,
cluster=True,
cluster_thr=5,
random_colors=self.random_colors,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=True)
ren2 = hz2.build_scene()
hz2.build_show(ren2)
if key == 'a' or key == 'A':
if self.select_all is False:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min
and self.cea[ca]['size'] >= self.size_min):
self.cea[ca]['selected'] = 1
cas = self.cea[ca]['cluster_actor']
self.cla[cas]['selected'] = \
self.cea[ca]['selected']
show_m.render()
self.select_all = True
else:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min
and self.cea[ca]['size'] >= self.size_min):
self.cea[ca]['selected'] = 0
cas = self.cea[ca]['cluster_actor']
self.cla[cas]['selected'] = \
self.cea[ca]['selected']
show_m.render()
self.select_all = False
if key == 'e' or key == 'E':
for c in self.cea:
if self.cea[c]['selected']:
if not self.cea[c]['expanded']:
len_ = self.cea[c]['length']
sz_ = self.cea[c]['size']
if (len_ >= self.length_min
and sz_ >= self.size_min):
self.cea[c]['cluster_actor']. \
VisibilityOn()
c.VisibilityOff()
self.cea[c]['expanded'] = 1
show_m.render()
if key == 'r' or key == 'R':
for c in self.cea:
if (self.cea[c]['length'] >= self.length_min
and self.cea[c]['size'] >= self.size_min):
self.cea[c]['cluster_actor'].VisibilityOff()
c.VisibilityOn()
self.cea[c]['expanded'] = 0
show_m.render()
from dipy.segment.clustering import qbx_and_merge
streams, hdr = nib.trackvis.read(get_fnames('fornix'))
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:20].copy()
f2._data += np.array([50, 0, 0])
f3 = f1[200:].copy()
f3._data += np.array([100, 0, 0])
f.extend(f2)
f.extend(f3)
def test_rb_check_defaults():
rb = RecoBundles(f, greater_than=0, clust_thr=10)
rec_trans, rec_labels = rb.recognize(model_bundle=f2,
model_clust_thr=5.,
reduction_thr=10)
D = bundles_distances_mam(f2, f[rec_labels])
# check if the bundle is recognized correctly
if len(f2) == len(rec_labels):
from dipy.tracking.streamline import Streamlines
from dipy.segment.clustering import qbx_and_merge
streams, hdr = nib.trackvis.read(get_fnames('fornix'))
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f1[:20].copy()
f2._data += np.array([50, 0, 0])
f3 = f1[200:].copy()
f3._data += np.array([100, 0, 0])
f.extend(f2)
f.extend(f3)
def test_rb_check_defaults():
rb = RecoBundles(f, greater_than=0, clust_thr=10)
rec_trans, rec_labels = rb.recognize(model_bundle=f2,
model_clust_thr=5.,
reduction_thr=10)
D = bundles_distances_mam(f2, f[rec_labels])
# check if the bundle is recognized correctly
if len(f2) == len(rec_labels):
def key_press(obj, event):
key = obj.GetKeySym()
if self.cluster:
# hide on/off unselected centroids
if key == 'h' or key == 'H':
if self.hide_centroids:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min or
self.cea[ca]['size'] >= self.size_min):
if self.cea[ca]['selected'] == 0:
ca.VisibilityOff()
else:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min and
self.cea[ca]['size'] >= self.size_min):
if self.cea[ca]['selected'] == 0:
ca.VisibilityOn()
self.hide_centroids = not self.hide_centroids
show_m.render()
# invert selection
if key == 'i' or key == 'I':
for ca in self.cea:
if (self.cea[ca]['length'] >= self.self.length_min and
self.cea[ca]['size'] >= self.size_min):
self.cea[ca]['selected'] = \
not self.cea[ca]['selected']
cas = self.cea[ca]['cluster_actor']
self.cla[cas]['selected'] = \
self.cea[ca]['selected']
show_m.render()
# save current result
if key == 's' or key == 'S':
saving_streamlines = Streamlines()
for bundle in self.cla.keys():
if bundle.GetVisibility():
t = self.cla[bundle]['tractogram']
c = self.cla[bundle]['cluster']
indices = self.tractogram_clusters[t][c]
saving_streamlines.extend(Streamlines(indices))
print('Saving result in tmp.trk')
save_trk('tmp.trk', saving_streamlines, np.eye(4))
if key == 'y' or key == 'Y':
active_streamlines = Streamlines()
for bundle in self.cla.keys():
if bundle.GetVisibility():
t = self.cla[bundle]['tractogram']
c = self.cla[bundle]['cluster']
indices = self.tractogram_clusters[t][c]
active_streamlines.extend(Streamlines(indices))
# self.tractograms = [active_streamlines]
hz2 = horizon([active_streamlines],
self.images, cluster=True, cluster_thr=5,
random_colors=self.random_colors,
length_lt=np.inf,
length_gt=0, clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=True)
ren2 = hz2.build_scene()
hz2.build_show(ren2)
if key == 'a' or key == 'A':
if self.select_all is False:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min and
self.cea[ca]['size'] >= self.size_min):
self.cea[ca]['selected'] = 1
cas = self.cea[ca]['cluster_actor']
self.cla[cas]['selected'] = \
self.cea[ca]['selected']
show_m.render()
self.select_all = True
else:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min and
self.cea[ca]['size'] >= self.size_min):
self.cea[ca]['selected'] = 0
cas = self.cea[ca]['cluster_actor']
self.cla[cas]['selected'] = \
self.cea[ca]['selected']
show_m.render()
self.select_all = False
if key == 'e' or key == 'E':
for c in self.cea:
if self.cea[c]['selected']:
if not self.cea[c]['expanded']:
len_ = self.cea[c]['length']
sz_ = self.cea[c]['size']
if (len_ >= self.length_min and
sz_ >= self.size_min):
self.cea[c]['cluster_actor']. \
VisibilityOn()
c.VisibilityOff()
self.cea[c]['expanded'] = 1
show_m.render()
if key == 'r' or key == 'R':
for c in self.cea:
if (self.cea[c]['length'] >= self.length_min and
self.cea[c]['size'] >= self.size_min):
self.cea[c]['cluster_actor'].VisibilityOff()
c.VisibilityOn()
self.cea[c]['expanded'] = 0
show_m.render()
