def test_slr_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()
f1_path = pjoin(out_dir, "f1.trk")
save_trk(f1_path, Streamlines(f1), affine=np.eye(4))
f2 = f1.copy()
f2._data += np.array([50, 0, 0])
f2_path = pjoin(out_dir, "f2.trk")
save_trk(f2_path, Streamlines(f2), affine=np.eye(4))
slr_flow = SlrWithQbxFlow(force=True)
slr_flow.run(f1_path, f2_path)
out_path = slr_flow.last_generated_outputs['out_moved']
npt.assert_equal(os.path.isfile(out_path), True)
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 remove_clusters_by_size(clusters, min_size=0):
ob = filter(lambda c: len(c) >= min_size, clusters)
centroids = Streamlines()
for cluster in ob:
centroids.append(cluster.centroid)
return centroids
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_horizon():
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')
print(s1.shape)
print(s2.shape)
print(s3.shape)
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
tractograms = [streamlines]
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=False, interactive=False)
affine = np.diag([2., 1, 1, 1]).astype('f8')
data = 255 * np.random.rand(150, 150, 150)
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)
tractograms = []
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)
def test_streamlines_generator():
# Test generator
streamlines_generator = Streamlines(generate_sl(streamlines))
npt.assert_equal(len(streamlines_generator), len(streamlines))
# Nothing should change
streamlines_generator.append(np.array([]))
npt.assert_equal(len(streamlines_generator), len(streamlines))
# Test append error
npt.assert_raises(ValueError, streamlines_generator.append, streamlines)
# Test empty streamlines
streamlines_generator = Streamlines(np.array([]))
npt.assert_equal(len(streamlines_generator), 0)
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 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)
from dipy.direction import ProbabilisticDirectionGetter
from dipy.data import small_sphere
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.io.streamline import save_trk
fod = csd_fit.odf(small_sphere)
pmf = fod.clip(min=0)
prob_dg = ProbabilisticDirectionGetter.from_pmf(pmf,
max_angle=30.,
sphere=small_sphere)
streamline_generator = LocalTracking(prob_dg,
stopping_criterion,
seeds,
affine,
step_size=.5)
streamlines = Streamlines(streamline_generator)
sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
save_trk(sft, "tractogram_probabilistic_dg_pmf.trk")
if has_fury:
scene = window.Scene()
scene.add(actor.line(streamlines, colormap.line_colors(streamlines)))
window.record(scene,
out_path='tractogram_probabilistic_dg_pmf.png',
size=(800, 800))
if interactive:
window.show(scene)
"""
.. figure:: tractogram_probabilistic_dg_pmf.png
:align: center
fig.savefig('threshold_fa.png')
"""
.. figure:: threshold_fa.png
:align: center
**Thresholded fractional anisotropy map.**
"""
all_streamline_threshold_tc_generator = LocalTracking(dg,
threshold_classifier,
seeds,
affine,
step_size=.5,
return_all=True)
streamlines = Streamlines(all_streamline_threshold_tc_generator)
save_trk("all_streamlines_threshold_classifier.trk",
streamlines,
affine,
labels.shape)
if have_fury:
window.clear(ren)
ren.add(actor.line(streamlines, cmap.line_colors(streamlines)))
window.record(ren, out_path='all_streamlines_threshold_classifier.png',
size=(600, 600))
if interactive:
window.show(ren)
"""
.. figure:: all_streamlines_threshold_classifier.png
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()
def run(context):
####################################################
# Get the path to input files and other parameter #
####################################################
analysis_data = context.fetch_analysis_data()
settings = analysis_data['settings']
postprocessing = settings['postprocessing']
hcpl_dwi_file_handle = context.get_files('input', modality='HARDI')[0]
hcpl_dwi_file_path = hcpl_dwi_file_handle.download('/root/')
hcpl_bvalues_file_handle = context.get_files(
'input', reg_expression='.*prep.bvalues.hcpl.txt')[0]
hcpl_bvalues_file_path = hcpl_bvalues_file_handle.download('/root/')
hcpl_bvecs_file_handle = context.get_files(
'input', reg_expression='.*prep.gradients.hcpl.txt')[0]
hcpl_bvecs_file_path = hcpl_bvecs_file_handle.download('/root/')
dwi_file_handle = context.get_files('input', modality='DSI')[0]
dwi_file_path = dwi_file_handle.download('/root/')
bvalues_file_handle = context.get_files(
'input', reg_expression='.*prep.bvalues.txt')[0]
bvalues_file_path = bvalues_file_handle.download('/root/')
bvecs_file_handle = context.get_files(
'input', reg_expression='.*prep.gradients.txt')[0]
bvecs_file_path = bvecs_file_handle.download('/root/')
inject_file_handle = context.get_files(
'input', reg_expression='.*prep.inject.nii.gz')[0]
inject_file_path = inject_file_handle.download('/root/')
VUMC_ROIs_file_handle = context.get_files(
'input', reg_expression='.*VUMC_ROIs.nii.gz')[0]
VUMC_ROIs_file_path = VUMC_ROIs_file_handle.download('/root/')
###############################
# _____ _____ _______ __ #
# | __ \_ _| __ \ \ / / #
# | | | || | | |__) \ \_/ / #
# | | | || | | ___/ \ / #
# | |__| || |_| | | | #
# |_____/_____|_| |_| #
# #
# dipy.org/documentation #
###############################
# IronTract Team #
# TrackyMcTrackface #
###############################
#################
# Load the data #
#################
dwi_img = nib.load(hcpl_dwi_file_path)
bvals, bvecs = read_bvals_bvecs(hcpl_bvalues_file_path,
hcpl_bvecs_file_path)
gtab = gradient_table(bvals, bvecs)
############################################
# Extract the brain mask from the b0 image #
############################################
_, brain_mask = median_otsu(dwi_img.get_data()[:, :, :, 0],
median_radius=2,
numpass=1)
##################################################################
# Fit the tensor model and compute the fractional anisotropy map #
##################################################################
context.set_progress(message='Processing voxel-wise DTI metrics.')
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(dwi_img.get_data(), mask=brain_mask)
FA = fractional_anisotropy(tenfit.evals)
# fa_file_path = "/root/fa.nii.gz"
# nib.Nifti1Image(FA,dwi_img.affine).to_filename(fa_file_path)
################################################
# Compute Fiber Orientation Distribution (CSD) #
################################################
context.set_progress(message='Processing voxel-wise FOD estimation.')
response, _ = auto_response_ssst(gtab,
dwi_img.get_data(),
roi_radii=10,
fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
csd_fit = csd_model.fit(dwi_img.get_data(), mask=brain_mask)
# fod_file_path = "/root/fod.nii.gz"
# nib.Nifti1Image(csd_fit.shm_coeff,dwi_img.affine).to_filename(fod_file_path)
###########################################
# Compute DIPY Probabilistic Tractography #
###########################################
context.set_progress(message='Processing tractography.')
sphere = get_sphere("repulsion724")
seed_mask_img = nib.load(inject_file_path)
affine = seed_mask_img.affine
seeds = utils.seeds_from_mask(seed_mask_img.get_data(), affine, density=5)
stopping_criterion = ThresholdStoppingCriterion(FA, 0.2)
prob_dg = ProbabilisticDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
max_angle=20.,
sphere=sphere)
streamline_generator = LocalTracking(prob_dg,
stopping_criterion,
seeds,
affine,
step_size=.2,
max_cross=1)
streamlines = Streamlines(streamline_generator)
# sft = StatefulTractogram(streamlines, seed_mask_img, Space.RASMM)
# streamlines_file_path = "/root/streamlines.trk"
# save_trk(sft, streamlines_file_path)
###########################################################################
# Compute 3D volumes for the IronTract Challenge. For 'EPFL', we only #
# keep streamlines with length > 1mm. We compute the visitation count #
# image and apply a small gaussian smoothing. The gaussian smoothing #
# is especially usefull to increase voxel coverage of deterministic #
# algorithms. The log of the smoothed visitation count map is then #
# iteratively thresholded producing 200 volumes/operation points. #
# For VUMC, additional streamline filtering is done using anatomical #
# priors (keeping only streamlines that intersect with at least one ROI). #
###########################################################################
if postprocessing in ["EPFL", "ALL"]:
context.set_progress(message='Processing density map (EPFL)')
volume_folder = "/root/vol_epfl"
output_epfl_zip_file_path = "/root/TrackyMcTrackface_EPFL_example.zip"
os.mkdir(volume_folder)
lengths = length(streamlines)
streamlines = streamlines[lengths > 1]
density = utils.density_map(streamlines, affine, seed_mask_img.shape)
density = scipy.ndimage.gaussian_filter(density.astype("float32"), 0.5)
log_density = np.log10(density + 1)
max_density = np.max(log_density)
for i, t in enumerate(np.arange(0, max_density, max_density / 200)):
nbr = str(i)
nbr = nbr.zfill(3)
mask = log_density >= t
vol_filename = os.path.join(
volume_folder, "vol" + nbr + "_t" + str(t) + ".nii.gz")
nib.Nifti1Image(mask.astype("int32"), affine,
seed_mask_img.header).to_filename(vol_filename)
shutil.make_archive(output_epfl_zip_file_path[:-4], 'zip',
volume_folder)
if postprocessing in ["VUMC", "ALL"]:
context.set_progress(message='Processing density map (VUMC)')
ROIs_img = nib.load(VUMC_ROIs_file_path)
volume_folder = "/root/vol_vumc"
output_vumc_zip_file_path = "/root/TrackyMcTrackface_VUMC_example.zip"
os.mkdir(volume_folder)
lengths = length(streamlines)
streamlines = streamlines[lengths > 1]
rois = ROIs_img.get_fdata().astype(int)
_, grouping = utils.connectivity_matrix(streamlines,
affine,
rois,
inclusive=True,
return_mapping=True,
mapping_as_streamlines=False)
streamlines = streamlines[grouping[(0, 1)]]
density = utils.density_map(streamlines, affine, seed_mask_img.shape)
density = scipy.ndimage.gaussian_filter(density.astype("float32"), 0.5)
log_density = np.log10(density + 1)
max_density = np.max(log_density)
for i, t in enumerate(np.arange(0, max_density, max_density / 200)):
nbr = str(i)
nbr = nbr.zfill(3)
mask = log_density >= t
vol_filename = os.path.join(
volume_folder, "vol" + nbr + "_t" + str(t) + ".nii.gz")
nib.Nifti1Image(mask.astype("int32"), affine,
seed_mask_img.header).to_filename(vol_filename)
shutil.make_archive(output_vumc_zip_file_path[:-4], 'zip',
volume_folder)
###################
# Upload the data #
###################
context.set_progress(message='Uploading results...')
# context.upload_file(fa_file_path, 'fa.nii.gz')
# context.upload_file(fod_file_path, 'fod.nii.gz')
# context.upload_file(streamlines_file_path, 'streamlines.trk')
if postprocessing in ["EPFL", "ALL"]:
context.upload_file(output_epfl_zip_file_path,
'TrackyMcTrackface_EPFL_example.zip')
if postprocessing in ["VUMC", "ALL"]:
context.upload_file(output_vumc_zip_file_path,
'TrackyMcTrackface_VUMC_example.zip')
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')
print(s1.shape)
print(s2.shape)
print(s3.shape)
streamlines = Streamlines()
streamlines.append(s1)
streamlines.append(s2)
streamlines.append(s3)
tractograms = [streamlines]
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=False, interactive=False)
#
affine = np.diag([2., 1, 1, 1]).astype('f8')
#
data = 255 * np.random.rand(150, 150, 150)
#
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')
save_nifti(fimg, data, affine)
save_tractogram(ftrk, streamlines, affine)
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)
def track(peaks,
seed_image,
max_nr_fibers=2000,
smooth=None,
compress=0.1,
bundle_mask=None,
start_mask=None,
end_mask=None,
tracking_uncertainties=None,
dilation=0,
next_step_displacement_std=0.15,
nr_cpus=-1,
verbose=True):
"""
Great speedup was archived by:
- only seeding in bundle_mask instead of entire image (seeding took very long)
- calculating fiber length on the fly instead of using extra function which has to iterate over entire fiber a
second time
Args:
peaks:
seed_image:
max_nr_fibers:
peak_threshold:
smooth:
compress:
bundle_mask:
start_mask:
end_mask:
dilation:
nr_cpus:
verbose:
Returns:
"""
import psutil
peaks[:, :, :, 0] *= -1 # how to flip along x axis to work properly
# Add +1 dilation for start and end mask to be more robust
start_mask = binary_dilation(start_mask,
iterations=dilation + 1).astype(np.uint8)
end_mask = binary_dilation(end_mask,
iterations=dilation + 1).astype(np.uint8)
if dilation > 0:
bundle_mask = binary_dilation(bundle_mask,
iterations=dilation).astype(np.uint8)
if tracking_uncertainties is not None:
tracking_uncertainties = img_utils.scale_to_range(
tracking_uncertainties, range=(0, 1))
global _PEAKS
_PEAKS = peaks
global _BUNDLE_MASK
_BUNDLE_MASK = bundle_mask
global _START_MASK
_START_MASK = start_mask
global _END_MASK
_END_MASK = end_mask
global _TRACKING_UNCERTAINTIES
_TRACKING_UNCERTAINTIES = tracking_uncertainties
# Get list of coordinates of each voxel in mask to seed from those
mask_coords = np.array(np.where(bundle_mask == 1)).transpose()
nr_voxels = mask_coords.shape[0]
spacing = seed_image.header.get_zooms()[0]
# max_nr_seeds = 250 * max_nr_fibers
max_nr_seeds = 100 * max_nr_fibers # after how many seeds to abort (to avoid endless runtime)
# How many seeds to process in each pool.map iteration
seeds_per_batch = 5000
if nr_cpus == -1:
nr_processes = psutil.cpu_count()
else:
nr_processes = nr_cpus
streamlines = []
fiber_ctr = 0
seed_ctr = 0
# Processing seeds in batches so we can stop after we reached desired nr of streamlines. Not ideal. Could be
# optimised by more multiprocessing fanciness.
while fiber_ctr < max_nr_fibers:
pool = multiprocessing.Pool(processes=nr_processes)
streamlines_tmp = pool.map(
partial(process_seedpoint,
next_step_displacement_std=next_step_displacement_std,
spacing=spacing),
seed_generator(mask_coords, seeds_per_batch))
# streamlines_tmp = [process_seedpoint(seed, spacing=spacing) for seed in
# seed_generator(mask_coords, seeds_per_batch)] # single threaded for debug
pool.close()
pool.join()
streamlines_tmp = [sl for sl in streamlines_tmp
if len(sl) > 0] # filter empty
streamlines += streamlines_tmp
fiber_ctr = len(streamlines)
if verbose:
print("nr_fibs: {}".format(fiber_ctr))
seed_ctr += seeds_per_batch
if seed_ctr > max_nr_seeds:
if verbose:
print("Early stopping because max nr of seeds reached.")
break
if verbose:
print("final nr streamlines: {}".format(len(streamlines)))
streamlines = streamlines[:
max_nr_fibers] # remove surplus of fibers (comes from multiprocessing)
streamlines = Streamlines(streamlines) # Generate streamlines object
# Move from convention "0mm is in voxel corner" to convention "0mm is in voxel center". Most toolkit use the
# convention "0mm is in voxel center".
streamlines = fiber_utils.add_to_each_streamline(streamlines, -0.5)
# move streamlines to coordinate space
# This is doing: streamlines(coordinate_space) = affine * streamlines(voxel_space)
streamlines = list(transform_streamlines(streamlines, seed_image.affine))
# Smoothing does not change overall results at all because is just little smoothing. Just removes small unevenness.
if smooth:
streamlines = fiber_utils.smooth_streamlines(streamlines,
smoothing_factor=smooth)
if compress:
streamlines = fiber_utils.compress_streamlines(streamlines,
error_threshold=0.1,
nr_cpus=nr_cpus)
return streamlines
def read_bundles_2_subjects(subj_id='subj_1',
metrics=['fa'],
bundles=['af.left', 'cst.right', 'cc_1']):
r"""Read images and streamlines from 2 subjects of the SNAIL dataset.
Parameters
----------
subj_id : string
Either ``subj_1`` or ``subj_2``.
metrics : list
Either ['fa'] or ['t1'] or ['fa', 't1']
bundles : list
E.g., ['af.left', 'cst.right', 'cc_1']. See all the available bundles
in the ``exp_bundles_maps/bundles_2_subjects`` directory of your
``$HOME/.dipy`` folder.
Returns
-------
dix : dict
Dictionary with data of the metrics and the bundles as keys.
Notes
-----
If you are using these datasets please cite the following publications.
References
----------
.. [1] Renauld, E., M. Descoteaux, M. Bernier, E. Garyfallidis,
K. Whittingstall, "Morphology of thalamus, LGN and optic radiation do not
influence EEG alpha waves", Plos One (under submission), 2015.
.. [2] Garyfallidis, E., O. Ocegueda, D. Wassermann,
M. Descoteaux. Robust and efficient linear registration of fascicles in the
space of streamlines , Neuroimage, 117:124-140, 2015.
"""
dname = pjoin(dipy_home, 'exp_bundles_and_maps', 'bundles_2_subjects')
from dipy.io.streamline import load_tractogram
from dipy.tracking.streamline import Streamlines
res = {}
if 't1' in metrics:
data, affine = load_nifti(pjoin(dname, subj_id, 't1_warped.nii.gz'))
res['t1'] = data
if 'fa' in metrics:
fa, affine = load_nifti(pjoin(dname, subj_id, 'fa_1x1x1.nii.gz'))
res['fa'] = fa
res['affine'] = affine
for bun in bundles:
streams = load_tractogram(pjoin(dname, subj_id, 'bundles',
'bundles_' + bun + '.trk'),
'same',
bbox_valid_check=False).streamlines
streamlines = Streamlines(streams)
res[bun] = streamlines
return res
step_size=step_size,
average_voxel_size=voxel_size)
# Particle Filtering Tractography
pft_streamline_generator = ParticleFilteringTracking(dg,
cmc_classifier,
seeds,
affine,
max_cross=1,
step_size=step_size,
maxlen=1000,
pft_back_tracking_dist=2,
pft_front_tracking_dist=1,
particle_count=15,
return_all=False)
streamlines = Streamlines(pft_streamline_generator)
save_trk("tractogram_pft.trk", streamlines, affine, shape)
if has_fury:
r = window.Renderer()
r.add(actor.line(streamlines, colormap.line_colors(streamlines)))
window.record(r, out_path='tractogram_pft.png', size=(800, 800))
if interactive:
window.show(r)
"""
.. figure:: tractogram_pft.png
:align: center
**Corpus Callosum using particle filtering tractography**
"""
data,
default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=white_matter)
classifier = ThresholdStoppingCriterion(csa_peaks.gfa, .25)
seed_mask = labels == 2
seeds = utils.seeds_from_mask(seed_mask, density=[1, 1, 1], affine=affine)
# Initialization of LocalTracking. The computation happens in the next step.
streamlines = LocalTracking(csa_peaks, classifier, seeds, affine, step_size=2)
# Compute streamlines and store as a list.
streamlines = Streamlines(streamlines)
###############################################################################
# We will create a streamline actor from the streamlines.
streamlines_actor = actor.line(streamlines, line_colors(streamlines))
###############################################################################
# Next, we create a surface actor from the corpus callosum seed ROI. We
# provide the ROI data, the affine, the color in [R,G,B], and the opacity as
# a decimal between zero and one. Here, we set the color as blue/green with
# 50% opacity.
surface_opacity = 0.5
surface_color = [0, 1, 1]
def test_gaussian_weights():
# Some bogus x,y,z coordinates
x = np.arange(10).astype(float)
y = np.arange(10).astype(float)
z = np.arange(10).astype(float)
# Create a distribution for which we can predict the weights we would
# expect to get:
bundle = Streamlines([np.array([x, y, z]).T + 1,
np.array([x, y, z]).T - 1])
# In this case, all nodes receives an equal weight of 0.5:
w = gaussian_weights(bundle, n_points=10)
npt.assert_almost_equal(w, np.ones((len(bundle), 10)) * 0.5)
# Test when asked to return Mahalanobis, instead of weights
w = gaussian_weights(bundle, n_points=10, return_mahalnobis=True)
npt.assert_almost_equal(w, np.ones((len(bundle), 10)))
# Here, some nodes are twice as far from the mean as others
bundle = Streamlines([np.array([x, y, z]).T + 2,
np.array([x, y, z]).T + 1,
np.array([x, y, z]).T - 1,
np.array([x, y, z]).T - 2])
w = gaussian_weights(bundle, n_points=10)
# And their weights should be halved:
npt.assert_almost_equal(w[0], w[1] / 2)
npt.assert_almost_equal(w[-1], w[2] / 2)
# Test the situation where all the streamlines have an identical node:
arr1 = np.array([x, y, z]).T + 2
arr2 = np.array([x, y, z]).T + 1
arr3 = np.array([x, y, z]).T - 1
arr4 = np.array([x, y, z]).T - 2
arr1[0] = np.array([1, 1, 1])
arr2[0] = np.array([1, 1, 1])
arr3[0] = np.array([1, 1, 1])
arr4[0] = np.array([1, 1, 1])
bundle_w_id_node = Streamlines([arr1, arr2, arr3, arr4])
w = gaussian_weights(Streamlines(bundle_w_id_node), n_points=10)
# For this case, the result should be a weight of 1/n_streamlines in that
# node for all streamlines:
npt.assert_equal(w[:, 0],
np.ones(len(bundle_w_id_node)) * 1/len(bundle_w_id_node))
# Test the situation where all the streamlines are copies of each other:
bundle_w_copies = Streamlines([bundle[0], bundle[0], bundle[0], bundle[0]])
w = gaussian_weights(bundle_w_copies, n_points=10)
# In this case, the entire array should be equal to 1/n_streamlines:
npt.assert_equal(w,
np.ones(w.shape) * 1/len(bundle_w_id_node))
# Test with bundle of length 1:
bundle_len_1 = Streamlines([bundle[0]])
w = gaussian_weights(bundle_len_1, n_points=10)
npt.assert_equal(w, np.ones(w.shape))
bundle_len_1 = Streamlines([bundle[0]])
w = gaussian_weights(bundle_len_1, n_points=10, return_mahalnobis=True)
npt.assert_equal(w, np.ones(w.shape) * np.nan)
def test_orient_by_rois():
streamlines = Streamlines([
np.array([[0, 0., 0], [1, 0., 0.], [2, 0., 0.]]),
np.array([[2, 0., 0.], [1, 0., 0], [0, 0, 0.]])
])
# Make two ROIs:
mask1_vol = np.zeros((4, 4, 4), dtype=bool)
mask2_vol = np.zeros_like(mask1_vol)
mask1_vol[0, 0, 0] = True
mask2_vol[1, 0, 0] = True
mask1_coords = np.array(np.where(mask1_vol)).T
mask2_coords = np.array(np.where(mask2_vol)).T
# If there is an affine, we'll use it:
affine = np.eye(4)
affine[:, 3] = [-1, 100, -20, 1]
# Transform the streamlines:
x_streamlines = Streamlines([sl + affine[:3, 3] for sl in streamlines])
# After reorientation, this should be the answer:
flipped_sl = Streamlines([streamlines[0], streamlines[1][::-1]])
new_streamlines = orient_by_rois(streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=False)
npt.assert_array_equal(new_streamlines, flipped_sl)
npt.assert_(new_streamlines is not streamlines)
# Test with affine:
x_flipped_sl = Streamlines([s + affine[:3, 3] for s in flipped_sl])
new_streamlines = orient_by_rois(x_streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=affine,
as_generator=False)
npt.assert_array_equal(new_streamlines, x_flipped_sl)
npt.assert_(new_streamlines is not x_streamlines)
# Test providing coord ROIs instead of vol ROIs:
new_streamlines = orient_by_rois(x_streamlines,
mask1_coords,
mask2_coords,
in_place=False,
affine=affine,
as_generator=False)
npt.assert_array_equal(new_streamlines, x_flipped_sl)
# Test with as_generator set to True
new_streamlines = orient_by_rois(streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=True)
npt.assert_(isinstance(new_streamlines, types.GeneratorType))
ll = Streamlines(new_streamlines)
npt.assert_array_equal(ll, flipped_sl)
# Test with as_generator set to True and with the affine
new_streamlines = orient_by_rois(x_streamlines,
mask1_vol,
mask2_vol,
in_place=False,
affine=affine,
as_generator=True)
npt.assert_(isinstance(new_streamlines, types.GeneratorType))
ll = Streamlines(new_streamlines)
npt.assert_array_equal(ll, x_flipped_sl)
# Test with generator input:
new_streamlines = orient_by_rois(generate_sl(streamlines),
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=True)
npt.assert_(isinstance(new_streamlines, types.GeneratorType))
ll = Streamlines(new_streamlines)
npt.assert_array_equal(ll, flipped_sl)
# Generator output cannot take a True `in_place` kwarg:
npt.assert_raises(ValueError, orient_by_rois,
*[generate_sl(streamlines), mask1_vol, mask2_vol],
**dict(in_place=True, affine=None, as_generator=True))
# But you can input a generator and get a non-generator as output:
new_streamlines = orient_by_rois(generate_sl(streamlines),
mask1_vol,
mask2_vol,
in_place=False,
affine=None,
as_generator=False)
npt.assert_(not isinstance(new_streamlines, types.GeneratorType))
npt.assert_array_equal(new_streamlines, flipped_sl)
# Modify in-place:
new_streamlines = orient_by_rois(streamlines,
mask1_vol,
mask2_vol,
in_place=True,
affine=None,
as_generator=False)
npt.assert_array_equal(new_streamlines, flipped_sl)
# The two objects are one and the same:
npt.assert_(new_streamlines is streamlines)
def test_length():
# Test length of only one streamline
length_streamline_cython = length(streamline)
length_streamline_python = length_python(streamline)
assert_almost_equal(length_streamline_cython, length_streamline_python)
length_streamline_cython = length(streamline_64bit)
length_streamline_python = length_python(streamline_64bit)
assert_almost_equal(length_streamline_cython, length_streamline_python)
# Test computing length of multiple streamlines of different nb_points
length_streamlines_cython = length(streamlines)
for i, s in enumerate(streamlines):
length_streamline_python = length_python(s)
assert_array_almost_equal(length_streamlines_cython[i],
length_streamline_python)
length_streamlines_cython = length(streamlines_64bit)
for i, s in enumerate(streamlines_64bit):
length_streamline_python = length_python(s)
assert_array_almost_equal(length_streamlines_cython[i],
length_streamline_python)
# ArraySequence
# Test length of only one streamline
length_streamline_cython = length(streamline_64bit)
length_streamline_arrseq = length(Streamlines([streamline]))
assert_almost_equal(length_streamline_arrseq, length_streamline_cython)
length_streamline_cython = length(streamline_64bit)
length_streamline_arrseq = length(Streamlines([streamline_64bit]))
assert_almost_equal(length_streamline_arrseq, length_streamline_cython)
# Test computing length of multiple streamlines of different nb_points
length_streamlines_cython = length(streamlines)
length_streamlines_arrseq = length(Streamlines(streamlines))
assert_array_almost_equal(length_streamlines_arrseq,
length_streamlines_cython)
length_streamlines_cython = length(streamlines_64bit)
length_streamlines_arrseq = length(Streamlines(streamlines_64bit))
assert_array_almost_equal(length_streamlines_arrseq,
length_streamlines_cython)
# Test on a sliced ArraySequence
length_streamlines_cython = length(streamlines_64bit[::2])
length_streamlines_arrseq = length(Streamlines(streamlines_64bit)[::2])
assert_array_almost_equal(length_streamlines_arrseq,
length_streamlines_cython)
length_streamlines_cython = length(streamlines[::-1])
length_streamlines_arrseq = length(Streamlines(streamlines)[::-1])
assert_array_almost_equal(length_streamlines_arrseq,
length_streamlines_cython)
# Test streamlines having mixed dtype
streamlines_mixed_dtype = [
streamline,
streamline.astype(np.float64),
streamline.astype(np.int32),
streamline.astype(np.int64)
]
lengths_mixed_dtype = [length(s) for s in streamlines_mixed_dtype]
assert_array_equal(length(streamlines_mixed_dtype), lengths_mixed_dtype)
# Test streamlines with different shape
length_streamlines_cython = length(heterogeneous_streamlines)
for i, s in enumerate(heterogeneous_streamlines):
length_streamline_python = length_python(s)
assert_array_almost_equal(length_streamlines_cython[i],
length_streamline_python)
# Test streamline having integer dtype
length_streamline = length(streamline.astype('int'))
assert_equal(length_streamline.dtype, np.float64)
# Test empty list
assert_equal(length([]), 0.0)
# Test streamline having only one point
assert_equal(length(np.array([[1, 2, 3]])), 0.0)
# We do not support list of lists, it should be numpy ndarray.
streamline_unsupported = [[1, 2, 3], [4, 5, 5], [2, 1, 3], [4, 2, 1]]
assert_raises(AttributeError, length, streamline_unsupported)
# Test setting computing length of a numpy with flag WRITABLE=False
streamlines_readonly = []
for s in streamlines:
streamlines_readonly.append(s.copy())
streamlines_readonly[-1].setflags(write=False)
assert_array_almost_equal(length(streamlines_readonly),
[length_python(s) for s in streamlines_readonly])
streamlines_readonly = []
for s in streamlines_64bit:
streamlines_readonly.append(s.copy())
streamlines_readonly[-1].setflags(write=False)
assert_array_almost_equal(length(streamlines_readonly),
[length_python(s) for s in streamlines_readonly])
def test_set_number_of_points():
# Test resampling of only one streamline
nb_points = 12
new_streamline_cython = set_number_of_points(streamline, nb_points)
new_streamline_python = set_number_of_points_python(streamline, nb_points)
assert_equal(len(new_streamline_cython), nb_points)
# Using a 5 digits precision because of streamline is in float32.
assert_array_almost_equal(new_streamline_cython, new_streamline_python, 5)
new_streamline_cython = set_number_of_points(streamline_64bit, nb_points)
new_streamline_python = set_number_of_points_python(
streamline_64bit, nb_points)
assert_equal(len(new_streamline_cython), nb_points)
assert_array_almost_equal(new_streamline_cython, new_streamline_python)
res = []
simple_streamline = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]], 'f4')
for nb_points in range(2, 200):
new_streamline_cython = set_number_of_points(simple_streamline,
nb_points)
res.append(nb_points - len(new_streamline_cython))
assert_equal(np.sum(res), 0)
# Test resampling of multiple streamlines of different nb_points
nb_points = 12
new_streamlines_cython = set_number_of_points(streamlines, nb_points)
for i, s in enumerate(streamlines):
new_streamline_python = set_number_of_points_python(s, nb_points)
# Using a 5 digits precision because of streamline is in float32.
assert_array_almost_equal(new_streamlines_cython[i],
new_streamline_python, 5)
# ArraySequence
arrseq = Streamlines(streamlines)
new_streamlines_as_seq_cython = set_number_of_points(arrseq, nb_points)
assert_array_almost_equal(new_streamlines_as_seq_cython,
new_streamlines_cython)
new_streamlines_cython = set_number_of_points(streamlines_64bit, nb_points)
for i, s in enumerate(streamlines_64bit):
new_streamline_python = set_number_of_points_python(s, nb_points)
assert_array_almost_equal(new_streamlines_cython[i],
new_streamline_python)
# ArraySequence
arrseq = Streamlines(streamlines_64bit)
new_streamlines_as_seq_cython = set_number_of_points(arrseq, nb_points)
assert_array_almost_equal(new_streamlines_as_seq_cython,
new_streamlines_cython)
# Test streamlines with mixed dtype
streamlines_mixed_dtype = [
streamline,
streamline.astype(np.float64),
streamline.astype(np.int32),
streamline.astype(np.int64)
]
nb_points_mixed_dtype = [
len(s)
for s in set_number_of_points(streamlines_mixed_dtype, nb_points)
]
assert_array_equal(nb_points_mixed_dtype,
[nb_points] * len(streamlines_mixed_dtype))
# Test streamlines with different shape
new_streamlines_cython = set_number_of_points(heterogeneous_streamlines,
nb_points)
for i, s in enumerate(heterogeneous_streamlines):
new_streamline_python = set_number_of_points_python(s, nb_points)
assert_array_almost_equal(new_streamlines_cython[i],
new_streamline_python)
# Test streamline with integer dtype
new_streamline = set_number_of_points(streamline.astype(np.int32))
assert_equal(new_streamline.dtype, np.float32)
new_streamline = set_number_of_points(streamline.astype(np.int64))
assert_equal(new_streamline.dtype, np.float64)
# Test empty list
assert_equal(set_number_of_points([]), [])
# Test streamline having only one point
assert_raises(ValueError, set_number_of_points, np.array([[1, 2, 3]]))
# We do not support list of lists, it should be numpy ndarray.
streamline_unsupported = [[1, 2, 3], [4, 5, 5], [2, 1, 3], [4, 2, 1]]
assert_raises(AttributeError, set_number_of_points, streamline_unsupported)
# Test setting number of points of a numpy with flag WRITABLE=False
streamline_readonly = streamline.copy()
streamline_readonly.setflags(write=False)
assert_equal(len(set_number_of_points(streamline_readonly, nb_points=42)),
42)
# Test setting computing length of a numpy with flag WRITABLE=False
streamlines_readonly = []
for s in streamlines:
streamlines_readonly.append(s.copy())
streamlines_readonly[-1].setflags(write=False)
assert_equal(len(set_number_of_points(streamlines_readonly, nb_points=42)),
len(streamlines_readonly))
streamlines_readonly = []
for s in streamlines_64bit:
streamlines_readonly.append(s.copy())
streamlines_readonly[-1].setflags(write=False)
assert_equal(len(set_number_of_points(streamlines_readonly, nb_points=42)),
len(streamlines_readonly))
# Test if nb_points is less than 2
assert_raises(ValueError,
set_number_of_points, [np.ones(
(10, 3)), np.ones((10, 3))],
nb_points=1)
def save_roisubset(streamlines, roislist, roisexcel, labelmask, stringstep, ratios, trkpath, subject, affine, header):
#atlas_legends = BIGGUS_DISKUS + "/atlases/CHASSSYMM3AtlasLegends.xlsx"
df = pd.read_excel(roisexcel, sheet_name='Sheet1')
df['Structure'] = df['Structure'].str.lower()
for rois in roislist:
if len(rois)==1:
roiname = "_" + rois[0] + "_"
elif len(rois)>1:
roiname="_"
for roi in rois:
roiname = roiname + roi[0:4]
roiname = roiname + "_"
labelslist=[]#fimbria
for roi in rois:
rslt_df = df.loc[df['Structure'] == roi.lower()]
if roi.lower() == "wholebrain" or roi.lower() == "brain":
labelslist=None
else:
labelslist=np.concatenate((labelslist,np.array(rslt_df.index2)))
if isempty(labelslist) and roi.lower() != "wholebrain" and roi.lower() != "brain":
txt = "Warning: Unrecognized roi, will take whole brain as ROI. The roi specified was: " + roi
print(txt)
#bvec_orient=[-2,1,3]
if isempty(labelslist):
roimask = np.where(labelmask == 0, False, True)
else:
if labelmask is None:
raise ("Bad label data, could not define ROI for streams")
roimask = np.zeros(np.shape(labelmask),dtype=int)
for label in labelslist:
roimask = roimask + (labelmask == label)
if not isempty(labelslist):
trkroipath = trkpath + '/' + subject + roiname + "_stepsize_" + stringstep + '.trk'
if not os.path.exists(trkroipath):
affinetemp=np.eye(4)
trkstreamlines = target(streamlines, affinetemp, roimask, include=True, strict="longstring")
trkstreamlines = Streamlines(trkstreamlines)
myheader = create_tractogram_header(trkroipath, *header)
roi_sl = lambda: (s for s in trkstreamlines)
save_trk_heavy_duty(trkroipath, streamlines=roi_sl,
affine=affine, header=myheader)
else:
trkdata = load_trk(trkroipath, 'same')
trkdata.to_vox()
if hasattr(trkdata, 'space_attribute'):
header = trkdata.space_attribute
elif hasattr(trkdata, 'space_attributes'):
header = trkdata.space_attributes
trkstreamlines = trkdata.streamlines
for ratio in ratios:
if ratio != 1:
trkroiminipath = trkpath + '/' + subject + '_ratio_' + ratios + roiname + "_stepsize_" + stringstep + '.trk'
if not os.path.exists(trkroiminipath):
ministream = []
for idx, stream in enumerate(trkstreamlines):
if (idx % ratio) == 0:
ministream.append(stream)
trkstreamlines = ministream
myheader = create_tractogram_header(trkminipath, *header)
ratioed_roi_sl_gen = lambda: (s for s in trkstreamlines)
if allsave:
save_trk_heavy_duty(trkroiminipath, streamlines=ratioed_roi_sl_gen,
affine=affine, header=myheader)
else:
trkdata = load_trk(trkminipath, 'same')
trkdata.to_vox()
if hasattr(trkdata, 'space_attribute'):
header = trkdata.space_attribute
elif hasattr(trkdata, 'space_attributes'):
header = trkdata.space_attributes
trkstreamlines = trkdata.streamlines
tensor_model = dti.TensorModel(gtab)
tenfit = tensor_model.fit(data, mask=white_matter)
FA = fractional_anisotropy(tenfit.evals)
classifier = ThresholdTissueClassifier(FA, .2)
from dipy.data import default_sphere
from dipy.direction import DeterministicMaximumDirectionGetter
from dipy.io.streamline import save_trk, load_trk
detmax_dg = DeterministicMaximumDirectionGetter.from_shcoeff(
csd_fit.shm_coeff, max_angle=40., sphere=default_sphere)
from dipy.tracking.streamline import Streamlines
streamlines = Streamlines(
LocalTracking(detmax_dg, classifier, seeds, affine, step_size=.1))
long_streamlines = np.ones((len(streamlines)), bool)
for i in range(0, len(streamlines)):
if streamlines[i].shape[0] < 70:
long_streamlines[i] = False
streamlines = streamlines[long_streamlines]
from dipy.viz import window, actor, colormap as cmap
streamlines_actor = actor.line(streamlines, cmap.line_colors(streamlines))
# weighted streamlines:
from dipy.tracking.streamline import transform_streamlines
''' FA weighting:
fa_name = r'20190211_134016ep2dd155D60MB3APs004a001_FA.nii'
def test_values_from_volume():
decimal = 4
data3d = np.arange(2000).reshape(20, 10, 10)
# Test two cases of 4D data (handled differently)
# One where the last dimension is length 3:
data4d_3vec = np.arange(6000).reshape(20, 10, 10, 3)
# The other where the last dimension is not 3:
data4d_2vec = np.arange(4000).reshape(20, 10, 10, 2)
for dt in [np.float32, np.float64]:
for data in [data3d, data4d_3vec, data4d_2vec]:
sl1 = [
np.array([[1, 0, 0], [1.5, 0, 0], [2, 0, 0], [2.5, 0,
0]]).astype(dt),
np.array([[2, 0, 0], [3.1, 0, 0], [3.9, 0, 0], [4.1, 0,
0]]).astype(dt)
]
ans1 = [[
data[1, 0,
0], data[1, 0, 0] + (data[2, 0, 0] - data[1, 0, 0]) / 2,
data[2, 0,
0], data[2, 0, 0] + (data[3, 0, 0] - data[2, 0, 0]) / 2
],
[
data[2, 0, 0],
data[3, 0, 0] + (data[4, 0, 0] - data[3, 0, 0]) * 0.1,
data[3, 0, 0] + (data[4, 0, 0] - data[3, 0, 0]) * 0.9,
data[4, 0, 0] + (data[5, 0, 0] - data[4, 0, 0]) * 0.1
]]
vv = values_from_volume(data, sl1)
npt.assert_almost_equal(vv, ans1, decimal=decimal)
vv = values_from_volume(data, np.array(sl1))
npt.assert_almost_equal(vv, ans1, decimal=decimal)
vv = values_from_volume(data, Streamlines(sl1))
npt.assert_almost_equal(vv, ans1, decimal=decimal)
affine = np.eye(4)
affine[:, 3] = [-100, 10, 1, 1]
x_sl1 = ut.move_streamlines(sl1, affine)
x_sl2 = ut.move_streamlines(sl1, affine)
vv = values_from_volume(data, x_sl1, affine=affine)
npt.assert_almost_equal(vv, ans1, decimal=decimal)
# The generator has already been consumed so needs to be
# regenerated:
x_sl1 = list(ut.move_streamlines(sl1, affine))
vv = values_from_volume(data, x_sl1, affine=affine)
npt.assert_almost_equal(vv, ans1, decimal=decimal)
# Test that the streamlines haven't mutated:
l_sl2 = list(x_sl2)
npt.assert_equal(x_sl1, l_sl2)
vv = values_from_volume(data, np.array(x_sl1), affine=affine)
npt.assert_almost_equal(vv, ans1, decimal=decimal)
npt.assert_equal(np.array(x_sl1), np.array(l_sl2))
# Test for lists of streamlines with different numbers of nodes:
sl2 = [sl1[0][:-1], sl1[1]]
ans2 = [ans1[0][:-1], ans1[1]]
vv = values_from_volume(data, sl2)
for ii, v in enumerate(vv):
npt.assert_almost_equal(v, ans2[ii], decimal=decimal)
# We raise an error if the streamlines fed don't make sense. In this
# case, a tuple instead of a list, generator or array
nonsense_sl = (np.array([[1, 0, 0], [1.5, 0, 0], [2, 0, 0], [2.5, 0, 0]]),
np.array([[2, 0, 0], [3.1, 0, 0], [3.9, 0, 0], [4.1, 0,
0]]))
npt.assert_raises(RuntimeError, values_from_volume, data, nonsense_sl)
# For some use-cases we might have singleton streamlines (with only one
# node each):
data3D = np.ones((2, 2, 2))
streamlines = np.ones((10, 1, 3))
npt.assert_equal(values_from_volume(data3D, streamlines).shape, (10, 1))
data4D = np.ones((2, 2, 2, 2))
streamlines = np.ones((10, 1, 3))
npt.assert_equal(values_from_volume(data4D, streamlines).shape, (10, 1, 2))
import numpy as np
import nibabel as nib
from numpy.testing import (assert_equal,
assert_almost_equal,
run_module_suite)
from dipy.data import get_fnames
from dipy.segment.bundles import RecoBundles
from dipy.tracking.distances import bundles_distances_mam
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)
def plot_bundles_with_metric(bundle_path,
endings_path,
brain_mask_path,
bundle,
metrics,
output_path,
tracking_format="trk_legacy",
show_color_bar=True):
import seaborn as sns # import in function to avoid error if not installed (this is only needed in this function)
from dipy.viz import actor, window
from tractseg.libs import vtk_utils
def _add_extra_point_to_last_streamline(sl):
# Coloring broken as soon as all streamlines have same number of points -> why???
# Add one number to last streamline to make it have a different number
sl[-1] = np.append(sl[-1], [sl[-1][-1]], axis=0)
return sl
# Settings
NR_SEGMENTS = 100
ANTI_INTERPOL_MULT = 1 # increase number of points to avoid interpolation to blur the colors
algorithm = "distance_map" # equal_dist | distance_map | cutting_plane
# colors = np.array(sns.color_palette("coolwarm", NR_SEGMENTS)) # colormap blue to red (does not fit to colorbar)
colors = np.array(sns.light_palette(
"red", NR_SEGMENTS)) # colormap only red, which fits to color_bar
img_size = (1000, 1000)
# Tractometry skips first and last element. Therefore we only have 98 instead of 100 elements.
# Here we duplicate the first and last element to get back to 100 elements
metrics = list(metrics)
metrics = np.array([metrics[0]] + metrics + [metrics[-1]])
metrics_max = metrics.max()
metrics_min = metrics.min()
if metrics_max == metrics_min:
metrics = np.zeros(len(metrics))
else:
metrics = img_utils.scale_to_range(
metrics,
range=(0, 99)) # range needs to be same as segments in colormap
orientation = dataset_specific_utils.get_optimal_orientation_for_bundle(
bundle)
# Load mask
beginnings_img = nib.load(endings_path)
beginnings = beginnings_img.get_fdata().astype(np.uint8)
for i in range(1):
beginnings = binary_dilation(beginnings)
# Load trackings
if tracking_format == "trk_legacy":
streams, hdr = trackvis.read(bundle_path)
streamlines = [s[0] for s in streams]
else:
sl_file = nib.streamlines.load(bundle_path)
streamlines = sl_file.streamlines
# Reduce streamline count
streamlines = streamlines[::2]
# Reorder to make all streamlines have same start region
streamlines = fiber_utils.add_to_each_streamline(streamlines, 0.5)
streamlines_new = []
for idx, sl in enumerate(streamlines):
startpoint = sl[0]
# Flip streamline if not in right order
if beginnings[int(startpoint[0]),
int(startpoint[1]),
int(startpoint[2])] == 0:
sl = sl[::-1, :]
streamlines_new.append(sl)
streamlines = fiber_utils.add_to_each_streamline(streamlines_new, -0.5)
if algorithm == "distance_map" or algorithm == "equal_dist":
streamlines = fiber_utils.resample_fibers(
streamlines, NR_SEGMENTS * ANTI_INTERPOL_MULT)
elif algorithm == "cutting_plane":
streamlines = fiber_utils.resample_to_same_distance(
streamlines,
max_nr_points=NR_SEGMENTS,
ANTI_INTERPOL_MULT=ANTI_INTERPOL_MULT)
# Cut start and end by percentage
# streamlines = FiberUtils.resample_fibers(streamlines, NR_SEGMENTS * ANTI_INTERPOL_MULT)
# remove = int((NR_SEGMENTS * ANTI_INTERPOL_MULT) * 0.15) # remove X% in beginning and end
# streamlines = np.array(streamlines)[:, remove:-remove, :]
# streamlines = list(streamlines)
if algorithm == "equal_dist":
segment_idxs = []
for i in range(len(streamlines)):
segment_idxs.append(list(range(NR_SEGMENTS * ANTI_INTERPOL_MULT)))
segment_idxs = np.array(segment_idxs)
elif algorithm == "distance_map":
metric = AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=100., metric=metric)
clusters = qb.cluster(streamlines)
centroids = Streamlines(clusters.centroids)
_, segment_idxs = cKDTree(centroids.data, 1,
copy_data=True).query(streamlines, k=1)
elif algorithm == "cutting_plane":
streamlines_resamp = fiber_utils.resample_fibers(
streamlines, NR_SEGMENTS * ANTI_INTERPOL_MULT)
metric = AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=100., metric=metric)
clusters = qb.cluster(streamlines_resamp)
centroid = Streamlines(clusters.centroids)[0]
# index of the middle cluster
middle_idx = int(NR_SEGMENTS / 2) * ANTI_INTERPOL_MULT
middle_point = centroid[middle_idx]
segment_idxs = fiber_utils.get_idxs_of_closest_points(
streamlines, middle_point)
# Align along the middle and assign indices
segment_idxs_eqlen = []
for idx, sl in enumerate(streamlines):
sl_middle_pos = segment_idxs[idx]
before_elems = sl_middle_pos
after_elems = len(sl) - sl_middle_pos
base_idx = 1000 # use higher index to avoid negative numbers for area below middle
r = range((base_idx - before_elems), (base_idx + after_elems))
segment_idxs_eqlen.append(r)
segment_idxs = segment_idxs_eqlen
# Add extra point otherwise coloring BUG
streamlines = _add_extra_point_to_last_streamline(streamlines)
renderer = window.Renderer()
colors_all = [] # final shape will be [nr_streamlines, nr_points, 3]
for jdx, sl in enumerate(streamlines):
colors_sl = []
for idx, p in enumerate(sl):
if idx >= len(segment_idxs[jdx]):
seg_idx = segment_idxs[jdx][idx - 1]
else:
seg_idx = segment_idxs[jdx][idx]
m = metrics[int(seg_idx / ANTI_INTERPOL_MULT)]
color = colors[int(m)]
colors_sl.append(color)
colors_all.append(
colors_sl
) # this can not be converted to numpy array because last element has one more elem
sl_actor = actor.streamtube(streamlines,
colors=colors_all,
linewidth=0.2,
opacity=1)
renderer.add(sl_actor)
# plot brain mask
mask = nib.load(brain_mask_path).get_fdata().astype(np.uint8)
cont_actor = vtk_utils.contour_from_roi_smooth(
mask,
affine=beginnings_img.affine,
color=[.9, .9, .9],
opacity=.2,
smoothing=50)
renderer.add(cont_actor)
if show_color_bar:
lut_cmap = actor.colormap_lookup_table(scale_range=(metrics_min,
metrics_max),
hue_range=(0.0, 0.0),
saturation_range=(0.0, 1.0))
renderer.add(actor.scalar_bar(lut_cmap))
if orientation == "sagittal":
renderer.set_camera(position=(-412.95, -34.38, 80.15),
focal_point=(102.46, -16.96, -11.71),
view_up=(0.1806, 0.0, 0.9835))
elif orientation == "coronal":
renderer.set_camera(position=(-48.63, 360.31, 98.37),
focal_point=(-20.16, 92.89, 36.02),
view_up=(-0.0047, -0.2275, 0.9737))
elif orientation == "axial":
pass
else:
raise ValueError("Invalid orientation provided")
# Use this to interatively get new camera angle
# window.show(renderer, size=img_size, reset_camera=False)
# print(renderer.get_camera())
window.record(renderer, out_path=output_path, size=img_size)
[68.90222168, 93.46326447, 122.01765442],
[68.99872589, 93.30039978, 122.84759521],
[69.04119873, 93.05428314, 123.66156769],
[69.05086517, 92.74394989, 124.45450592],
[69.02742004, 92.40427399, 125.23509979],
[68.95466614, 92.09059143, 126.02339935],
[68.84975433, 91.79674531, 126.81564331],
[68.72673798, 91.53726196, 127.61715698],
[68.60685731, 91.30300141, 128.42681885],
[68.50636292, 91.12481689, 129.25317383],
[68.39311218, 91.01572418, 130.08976746],
[68.25946808, 90.94654083, 130.92756653]],
dtype=np.float32)
streamlines = Streamlines([
streamline[[0, 10]], streamline, streamline[::2], streamline[::3],
streamline[::5], streamline[::6]
])
def io_tractogram(extension):
with InTemporaryDirectory():
fname = 'test.{}'.format(extension)
in_affine = np.eye(4)
in_dimensions = np.array([50, 50, 50])
in_voxel_sizes = np.array([2, 1.5, 1.5])
nii_header = create_nifti_header(in_affine, in_dimensions,
in_voxel_sizes)
sft = StatefulTractogram(streamlines, nii_header, space=Space.RASMM)
save_tractogram(sft, fname, bbox_valid_check=False)
def test_cluster_confidence():
mysl = np.array([np.arange(10)] * 3, 'float').T
# a short streamline (<20 mm) should raise an error unless override=True
test_streamlines = Streamlines()
test_streamlines.append(mysl)
assert_raises(ValueError, cluster_confidence, test_streamlines)
cci = cluster_confidence(test_streamlines, override=True)
# two identical streamlines should raise an error
test_streamlines = Streamlines()
test_streamlines.append(mysl, cache_build=True)
test_streamlines.append(mysl)
test_streamlines.finalize_append()
assert_raises(ValueError, cluster_confidence, test_streamlines)
# 3 offset collinear streamlines
test_streamlines = Streamlines()
test_streamlines.append(mysl, cache_build=True)
test_streamlines.append(mysl+1)
test_streamlines.append(mysl+2)
test_streamlines.finalize_append()
cci = cluster_confidence(test_streamlines, override=True)
assert_equal(cci[0], cci[2])
assert_true(cci[1] > cci[0])
# 3 parallel streamlines
mysl = np.zeros([10, 3])
mysl[:, 0] = np.arange(10)
mysl2 = mysl.copy()
mysl2[:, 1] = 1
mysl3 = mysl.copy()
mysl3[:, 1] = 2
mysl4 = mysl.copy()
mysl4[:, 1] = 4
mysl5 = mysl.copy()
mysl5[:, 1] = 5000
test_streamlines_p1 = Streamlines()
test_streamlines_p1.append(mysl, cache_build=True)
test_streamlines_p1.append(mysl2)
test_streamlines_p1.append(mysl3)
test_streamlines_p1.finalize_append()
test_streamlines_p2 = Streamlines()
test_streamlines_p2.append(mysl, cache_build=True)
test_streamlines_p2.append(mysl3)
test_streamlines_p2.append(mysl4)
test_streamlines_p2.finalize_append()
test_streamlines_p3 = Streamlines()
test_streamlines_p3.append(mysl, cache_build=True)
test_streamlines_p3.append(mysl2)
test_streamlines_p3.append(mysl3)
test_streamlines_p3.append(mysl5)
test_streamlines_p3.finalize_append()
cci_p1 = cluster_confidence(test_streamlines_p1, override=True)
cci_p2 = cluster_confidence(test_streamlines_p2, override=True)
# test relative distance
assert_array_equal(cci_p1, cci_p2*2)
# test simple cci calculation
expected_p1 = np.array([1./1+1./2, 1./1+1./1, 1./1+1./2])
expected_p2 = np.array([1./2+1./4, 1./2+1./2, 1./2+1./4])
assert_array_equal(expected_p1, cci_p1)
assert_array_equal(expected_p2, cci_p2)
# test power variable calculation (dropoff with distance)
cci_p1_pow2 = cluster_confidence(test_streamlines_p1, power=2,
override=True)
expected_p1_pow2 = np.array([np.power(1./1, 2)+np.power(1./2, 2),
np.power(1./1, 2)+np.power(1./1, 2),
np.power(1./1, 2)+np.power(1./2, 2)])
assert_array_equal(cci_p1_pow2, expected_p1_pow2)
# test max distance (ignore distant sls)
cci_dist = cluster_confidence(test_streamlines_p3,
max_mdf=5, override=True)
expected_cci_dist = np.concatenate([cci_p1, np.zeros(1)])
assert_array_equal(cci_dist, expected_cci_dist)
def evaluate_streamline_plausibility(dwi_data,
gtab,
mask_data,
streamlines,
affine=np.eye(4),
sphere='repulsion724'):
"""
Linear Fascicle Evaluation (LiFE) takes any connectome and uses a
forward modelling approach to predict diffusion measurements in the
same brain.
Parameters
----------
dwi_data : array
4D array of dwi data.
gtab : Obj
DiPy object storing diffusion gradient information.
mask_data : array
3D Brain mask.
streamlines : ArraySequence
DiPy list/array-like object of streamline points from tractography.
Returns
-------
streamlines : ArraySequence
DiPy list/array-like object of streamline points from tractography.
References
----------
.. [1] Pestilli, F., Yeatman, J, Rokem, A. Kay, K. and Wandell B.A. (2014).
Validation and statistical inference in living connectomes.
Nature Methods 11: 1058-1063. doi:10.1038/nmeth.3098
"""
import dipy.tracking.life as life
import dipy.core.optimize as opt
from dipy.tracking._utils import _mapping_to_voxel
# from dipy.data import get_sphere
from dipy.tracking import utils
from dipy.tracking.streamline import Streamlines
original_count = len(streamlines)
streamlines_long = nib.streamlines. \
array_sequence.ArraySequence(
[
s
for s in streamlines
if len(s) >= float(10)
]
)
print('Removing streamlines with negative voxel indices...')
# Remove any streamlines with negative voxel indices
lin_T, offset = _mapping_to_voxel(np.eye(4))
streamlines_positive = []
for sl in streamlines_long:
inds = np.dot(sl, lin_T)
inds += offset
if not inds.min().round(decimals=6) < 0:
streamlines_positive.append(sl)
del streamlines_long
# Filter resulting streamlines by those that stay entirely
# inside the ROI of interest
mask_data = np.array(mask_data, dtype=bool, copy=False)
streamlines_in_brain = Streamlines(
utils.target(streamlines_positive, np.eye(4), mask_data, include=True))
streamlines_in_brain = [i for i in streamlines_in_brain]
del streamlines_positive
print('Fitting fiber model...')
# ! Remember this 4d masking function !
data_in_mask = np.nan_to_num(
np.broadcast_to(mask_data[..., None], dwi_data.shape).astype('bool') *
dwi_data)
# ! Remember this 4d masking function !
fiber_model = life.FiberModel(gtab)
fiber_fit = fiber_model.fit(data_in_mask,
streamlines_in_brain,
affine=affine,
sphere=False)
# sphere = get_sphere(sphere)
# fiber_fit = fiber_model.fit(data_in_mask, streamlines_in_brain,
# affine=affine,
# sphere=sphere)
streamlines = list(
np.array(streamlines_in_brain)[np.where(fiber_fit.beta > 0)[0]])
pruned_count = len(streamlines)
if pruned_count == 0:
print(
UserWarning('\nWarning LiFE skipped due to implausible values '
'detected in model betas. This does not '
'necessarily invalidate the '
'tractography. Rather it could indicate that '
'you\'ve sampled too few streamlines, or that the '
'sampling scheme is simply incompatible with the '
'LiFE model. Is your acquisition hemispheric? '
'Also check the gradient table for errors. \n'))
return streamlines_in_brain
else:
del streamlines_in_brain
model_predict = fiber_fit.predict()
model_error = model_predict - fiber_fit.data
model_rmse = np.sqrt(np.mean(model_error[:, 10:]**2, -1))
beta_baseline = np.zeros(fiber_fit.beta.shape[0])
pred_weighted = np.reshape(
opt.spdot(fiber_fit.life_matrix, beta_baseline),
(fiber_fit.vox_coords.shape[0], np.sum(~gtab.b0s_mask)))
mean_pred = np.empty((fiber_fit.vox_coords.shape[0], gtab.bvals.shape[0]))
S0 = fiber_fit.b0_signal
mean_pred[..., gtab.b0s_mask] = S0[:, None]
mean_pred[...,
~gtab.b0s_mask] = (pred_weighted +
fiber_fit.mean_signal[:, None]) * S0[:, None]
mean_error = mean_pred - fiber_fit.data
mean_rmse = np.sqrt(np.mean(mean_error**2, -1))
print(f"Original # Streamlines: {original_count}")
print(f"Final # Streamlines: {pruned_count}")
print(f"Streamlines removed: {pruned_count - original_count}")
print(f"Mean RMSE: {np.mean(mean_rmse)}")
print(f"Mean Model RMSE: {np.mean(model_rmse)}")
print(f"Mean Reduction RMSE: {np.mean(mean_rmse - model_rmse)}")
return streamlines
def localTracking(self):
if self.graddev is None:
#multiply by the jacobian (zero out z-direction)
graddev = np.zeros([3, 3])
graddev[0, 0] = 1
graddev[1, 1] = 1
graddev[2, 2] = 1
new_peak_dirsp = np.einsum('ab,ijkvb->aijkv', graddev,
self.peaks.peak_dirs)
shape = new_peak_dirsp.shape
new_peak_dirsp = new_peak_dirsp.reshape(3, -1)
new_peak_dirs = copy.deepcopy(new_peak_dirsp)
for i in range(0, new_peak_dirs.shape[-1]):
norm = np.linalg.norm(new_peak_dirsp[:, i])
if norm != 0:
new_peak_dirs[:, i] = new_peak_dirsp[:, i] / norm
new_peak_dirs = new_peak_dirs.reshape(shape)
new_peak_dirs = np.moveaxis(new_peak_dirs, 0, -1)
new_peak_dirs = new_peak_dirs.reshape(
[-1, self.peaks.peak_indices.shape[-1], 3])
#update self.peaks.peak_indices
peak_indices = np.zeros(self.peaks.peak_indices.shape)
peak_indices = peak_indices.reshape(
[-1, self.peaks.peak_indices.shape[-1]])
for i in range(0, peak_indices.shape[0]):
for k in range(0, self.peaks.peak_indices.shape[-1]):
peak_indices[i, k] = self.sphere.find_closest(
new_peak_dirs[i, k, :])
self.peaks.peak_indices = peak_indices.reshape(
self.peaks.peak_indices.shape)
streamlines_generator = LocalTracking(self.peaks,
self.stopping_criterion,
self.seeds,
self.affine,
step_size=abs(
self.affine[0, 0] / 6))
self.streamlines = Streamlines(streamlines_generator)
else:
shape = self.graddev.shape
self.graddev = self.graddev.reshape(shape[0:3] + (3, 3), order='F')
#self.graddev[:, :, :, :, 2] = 0
#self.graddev[:, :, :, 2, :] = 0
#self.graddev[:, :, :, 2, 2] = -1
self.graddev = (self.graddev.reshape([-1, 3, 3]) + np.eye(3))
self.graddev = self.graddev.reshape(shape[0:3] + (3, 3))
#multiply by the jacobian
new_peak_dirsp = np.einsum('ijkab,ijkvb->aijkv', self.graddev,
self.peaks.peak_dirs)
shape = new_peak_dirsp.shape
new_peak_dirsp = new_peak_dirsp.reshape(3, -1)
new_peak_dirs = copy.deepcopy(new_peak_dirsp)
for i in range(0, new_peak_dirs.shape[-1]):
norm = np.linalg.norm(new_peak_dirsp[:, i])
if norm != 0:
new_peak_dirs[:, i] = new_peak_dirsp[:, i] / norm
new_peak_dirs = new_peak_dirs.reshape(shape)
new_peak_dirs = np.moveaxis(new_peak_dirs, 0, -1)
new_peak_dirs = new_peak_dirs.reshape(
[-1, self.peaks.peak_indices.shape[-1], 3])
#update self.peaks.peak_indices
peak_indices = np.zeros(self.peaks.peak_indices.shape)
peak_indices = peak_indices.reshape(
[-1, self.peaks.peak_indices.shape[-1]])
for i in range(0, peak_indices.shape[0]):
for k in range(0, self.peaks.peak_indices.shape[-1]):
peak_indices[i, k] = self.sphere.find_closest(
new_peak_dirs[i, k, :])
self.peaks.peak_indices = peak_indices.reshape(
self.peaks.peak_indices.shape)
streamlines_generator = LocalTracking(self.peaks,
self.stopping_criterion,
self.seeds,
self.affine,
step_size=self.affine[0, 0] /
6)
self.streamlines = Streamlines(streamlines_generator)
self.NpointsPerLine = pointsPerLine(self.streamlines)
# Make a corpus callosum seed mask for tracking
seed_mask = labels == 2
seeds = utils.seeds_from_mask(seed_mask, density=[1, 1, 1], affine=affine)
# Make a streamline bundle model of the corpus callosum ROI connectivity
streamlines = LocalTracking(csa_peaks, classifier, seeds, affine,
step_size=2)
streamlines = Streamlines(streamlines)
"""
We do not want our results inflated by short streamlines, so we remove
streamlines shorter than 40mm prior to calculating the CCI.
"""
lengths = list(length(streamlines))
long_streamlines = Streamlines()
for i, sl in enumerate(streamlines):
if lengths[i] > 40:
long_streamlines.append(sl)
"""
Now we calculate the Cluster Confidence Index using the corpus callosum
streamline bundle and visualize them.
"""
cci = cluster_confidence(long_streamlines)
# Visualize the streamlines, colored by cci
ren = window.Renderer()
s_list = []
'''new func:'''
for i in range(id.__len__()): #
for j in range(i + 1): #
edge_s_list = []
# print(i,j)
if (i + 1, j + 1) in streamline_dict and mat_medians[i, j] > 0:
edge_s_list += streamline_dict[(i + 1, j + 1)]
if (j + 1, i + 1) in streamline_dict and mat_medians[i, j] > 0:
edge_s_list += streamline_dict[(j + 1, i + 1)]
edge_vec_vols = [mat_medians[i, j]] * edge_s_list.__len__()
s_list = s_list + edge_s_list
vec_vols = vec_vols + edge_vec_vols
s = Streamlines(s_list)
cci = cluster_confidence(s)
keep_streamlines = Streamlines()
for i, sl in enumerate(s):
if cci[i] >= 1:
keep_streamlines.append(sl)
# Visualize the streamlines we kept
ren = window.Renderer()
keep_streamlines_actor = actor.line(keep_streamlines, linewidth=0.1)
ren.add(keep_streamlines_actor)
