def test_read_write_trk():
sl = [
np.array([[0, 0, 0], [0, 0, 0.5], [0, 0, 1], [0, 0, 1.5]]),
np.array([[0, 0, 0], [0, 0.5, 0.5], [0, 1, 1]])
]
with nbtmp.InTemporaryDirectory() as tmpdir:
fname = op.join(tmpdir, 'sl.trk')
aus.write_trk(fname, sl)
new_sl = aus.read_trk(fname)
npt.assert_equal(list(new_sl), sl)
# What happens if this set of streamlines has some funky affine
# associated with it?
aff = np.eye(4) * np.random.rand()
aff[:3, 3] = np.array([1, 2, 3])
aff[3, 3] = 1
# We move the streamlines, and report the inverse of the affine:
aus.write_trk(fname,
move_streamlines(sl, aff),
affine=np.linalg.inv(aff))
# When we read this, we get back what we put in:
new_sl = aus.read_trk(fname)
# Compare each streamline:
for new, old in zip(new_sl, sl):
npt.assert_almost_equal(new, old, decimal=5)
def streamline_registration(moving,
static,
n_points=100,
native_resampled=False):
"""
Register two collections of streamlines ('bundles') to each other
Parameters
----------
moving, static : lists of 3 by n, or str
The two bundles to be registered. Given either as lists of arrays with
3D coordinates, or strings containing full paths to these files.
n_points : int, optional
How many points to resample to. Default: 100.
native_resampled : bool, optional
Whether to return the moving bundle in the original space, but
resampled in the static space to n_points.
Returns
-------
aligned : list
Streamlines from the moving group, moved to be closely matched to
the static group.
matrix : array (4, 4)
The affine transformation that takes us from 'moving' to 'static'
"""
# Load the streamlines, if you were given a file-name
if isinstance(moving, str):
moving = sut.read_trk(moving)
if isinstance(static, str):
static = sut.read_trk(static)
srr = StreamlineLinearRegistration()
srm = srr.optimize(static=set_number_of_points(static, n_points),
moving=set_number_of_points(moving, n_points))
aligned = srm.transform(moving)
if native_resampled:
aligned = set_number_of_points(aligned, n_points)
aligned = move_streamlines(aligned, np.linalg.inv(srm.matrix))
return aligned, srm.matrix
def streamline_registration(moving, static, n_points=100,
native_resampled=False):
"""
Register two collections of streamlines ('bundles') to each other
Parameters
----------
moving, static : lists of 3 by n, or str
The two bundles to be registered. Given either as lists of arrays with
3D coordinates, or strings containing full paths to these files.
n_points : int, optional
How many points to resample to. Default: 100.
native_resampled : bool, optional
Whether to return the moving bundle in the original space, but
resampled in the static space to n_points.
Returns
-------
aligned : list
Streamlines from the moving group, moved to be closely matched to
the static group.
matrix : array (4, 4)
The affine transformation that takes us from 'moving' to 'static'
"""
# Load the streamlines, if you were given a file-name
if isinstance(moving, str):
moving = sut.read_trk(moving)
if isinstance(static, str):
static = sut.read_trk(static)
srr = StreamlineLinearRegistration()
srm = srr.optimize(static=set_number_of_points(static, n_points),
moving=set_number_of_points(moving, n_points))
aligned = srm.transform(moving)
if native_resampled:
aligned = set_number_of_points(aligned, n_points)
aligned = move_streamlines(aligned, np.linalg.inv(srm.matrix))
return aligned, srm.matrix
def test_read_write_trk():
sl = [np.array([[0, 0, 0], [0, 0, 0.5], [0, 0, 1], [0, 0, 1.5]]),
np.array([[0, 0, 0], [0, 0.5, 0.5], [0, 1, 1]])]
with nbtmp.InTemporaryDirectory() as tmpdir:
fname = op.join(tmpdir, 'sl.trk')
aus.write_trk(fname, sl)
new_sl = aus.read_trk(fname)
npt.assert_equal(list(new_sl), sl)
# What happens if this set of streamlines has some funky affine
# associated with it?
aff = np.eye(4) * np.random.rand()
aff[:3, 3] = np.array([1, 2, 3])
aff[3, 3] = 1
# We move the streamlines, and report the inverse of the affine:
aus.write_trk(fname, move_streamlines(sl, aff),
affine=np.linalg.inv(aff))
# When we read this, we get back what we put in:
new_sl = aus.read_trk(fname)
# Compare each streamline:
for new, old in zip(new_sl, sl):
npt.assert_almost_equal(new, old, decimal=4)
def read_stanford_hardi_tractography():
"""
"""
files, folder = fetch_stanford_hardi_tractography()
files_dict = {}
files_dict['mapping.nii.gz'] = nib.load(
op.join(afq_home,
'stanford_hardi_tractography',
'mapping.nii.gz'))
files_dict['tractography_subsampled.trk'] = read_trk(
op.join(afq_home,
'stanford_hardi_tractography',
'tractography_subsampled.trk'))
return files_dict
def read_stanford_hardi_tractography():
"""
"""
files, folder = fetch_stanford_hardi_tractography()
files_dict = {}
files_dict['mapping.nii.gz'] = nib.load(
op.join(afq_home,
'stanford_hardi_tractography',
'mapping.nii.gz'))
files_dict['tractography_subsampled.trk'] = read_trk(
op.join(afq_home,
'stanford_hardi_tractography',
'tractography_subsampled.trk'))
return files_dict
print("Calculating DTI...")
if not op.exists('./dti_FA.nii.gz'):
dti_params = dti.fit_dti(hardi_fdata,
hardi_fbval,
hardi_fbvec,
out_dir='.')
else:
dti_params = {'FA': './dti_FA.nii.gz', 'params': './dti_params.nii.gz'}
print("Tracking...")
if not op.exists('dti_streamlines.trk'):
streamlines = list(aft.track(dti_params['params']))
aus.write_trk('./dti_streamlines.trk', streamlines, affine=img.affine)
else:
streamlines = aus.read_trk('./dti_streamlines.trk')
# Use only a small portion of the streamlines, for expedience:
streamlines = streamlines[::100]
templates = afd.read_templates()
bundle_names = ["CST", "ILF"]
bundles = {}
for name in bundle_names:
for hemi in ['_R', '_L']:
bundles[name + hemi] = {
'ROIs': [
templates[name + '_roi1' + hemi],
templates[name + '_roi1' + hemi]
],
print("Calculating DTI...")
if not op.exists('./dti_FA.nii.gz'):
dti_params = dti.fit_dti(hardi_fdata, hardi_fbval, hardi_fbvec,
out_dir='.')
else:
dti_params = {'FA': './dti_FA.nii.gz',
'params': './dti_params.nii.gz'}
print("Tracking...")
if not op.exists('dti_streamlines.trk'):
streamlines = list(aft.track(dti_params['params']))
aus.write_trk('./dti_streamlines.trk', streamlines, affine=img.affine)
else:
streamlines = aus.read_trk('./dti_streamlines.trk')
# Use only a small portion of the streamlines, for expedience:
streamlines = streamlines[::100]
templates = afd.read_templates()
bundle_names = ["CST", "ILF"]
bundles = {}
for name in bundle_names:
for hemi in ['_R', '_L']:
bundles[name + hemi] = {'ROIs': [templates[name + '_roi1' + hemi],
templates[name + '_roi1' + hemi]],
'rules': [True, True]}
