def tractography_to_trackvis_file(filename,
tractography,
affine=None,
image_dimensions=None):
trk_header = trackvis.empty_header()
if affine is not None:
pass
elif hasattr(tractography, 'affine'):
affine = tractography.affine
else:
raise ValueError("Affine transform has to be provided")
trackvis.aff_to_hdr(affine, trk_header, True, True)
trk_header['origin'] = 0.
if image_dimensions is not None:
trk_header['dim'] = image_dimensions
elif hasattr(tractography, 'image_dimensions'):
trk_header['dim'] = tractography.image_dimensions
else:
raise ValueError("Image dimensions needed to save a trackvis file")
orig_data = tractography.tracts_data()
data = {}
for k, v in orig_data.items():
if not isinstance(v[0], numpy.ndarray):
continue
if (v[0].ndim > 1 and any(d > 1 for d in v[0].shape[1:])):
warn("Scalar data %s ignored as trackvis "
"format does not handle multivalued data" % k)
else:
data[k] = v
#data_new = {}
# for k, v in data.iteritems():
# if (v[0].ndim > 1 and v[0].shape[1] > 1):
# for i in xrange(v[0].shape[1]):
# data_new['%s_%02d' % (k, i)] = [
# v_[:, i] for v_ in v
# ]
# else:
# data_new[k] = v
trk_header['n_count'] = len(tractography.tracts())
trk_header['n_properties'] = 0
trk_header['n_scalars'] = len(data)
if len(data) > 10:
raise ValueError('At most 10 scalars permitted per point')
trk_header['scalar_name'][:len(data)] = numpy.array([n[:20] for n in data],
dtype='|S20')
trk_tracts = []
for i, sl in enumerate(tractography.tracts()):
scalars = None
if len(data) > 0:
scalars = numpy.vstack([
data[k.decode('utf8')][i].squeeze()
for k in trk_header['scalar_name'][:len(data)]
]).T
trk_tracts.append((sl, scalars, None))
trackvis.write(filename, trk_tracts, trk_header, points_space='rasmm')
if not(np.allclose(line[0], line[-1])):
# new_line = [line[0]] # default line
new_line = [line[0] + normal * normal_length] # added normal step outside
for index in range(len(line)):
if not(np.allclose(line[index], new_line[-1], atol=epsilon)):
new_line += [line[index]]
new_line = np.array(new_line)
new_lines += [new_line]
"""
new_lines += [np.array(line)]
del lines
hdr = nib.trackvis.empty_header()
trackvis.aff_to_hdr(img_affine, hdr, True, True)
"""
pixels_shape = [img.shape[0], img.shape[1], img.shape[2]]
hdr['voxel_size'] = img_zoom
hdr['voxel_order'] = 'LAS'
hdr['dim'] = pixels_shape
hdr['n_count'] = len(new_lines)
"""
streamlines = []
for fibre in list(new_lines):
streamlines += [(fibre, None, None)]
nib.trackvis.write(trk_filename, streamlines, hdr, points_space='rasmm')
def tractography_to_trackvis_file(filename, tractography, affine=None, image_dimensions=None):
trk_header = trackvis.empty_header()
if affine is not None:
pass
elif hasattr(tractography, 'affine'):
affine = tractography.affine
else:
raise ValueError("Affine transform has to be provided")
trackvis.aff_to_hdr(affine, trk_header, True, True)
trk_header['origin'] = 0.
if image_dimensions is not None:
trk_header['dim'] = image_dimensions
elif hasattr(tractography, 'image_dimensions'):
trk_header['dim'] = tractography.image_dimensions
else:
raise ValueError("Image dimensions needed to save a trackvis file")
orig_data = tractography.tracts_data()
data = {}
for k, v in orig_data.items():
if not isinstance(v[0], numpy.ndarray):
continue
if (v[0].ndim > 1 and any(d > 1 for d in v[0].shape[1:])):
warn(
"Scalar data %s ignored as trackvis "
"format does not handle multivalued data" % k
)
else:
data[k] = v
#data_new = {}
# for k, v in data.iteritems():
# if (v[0].ndim > 1 and v[0].shape[1] > 1):
# for i in xrange(v[0].shape[1]):
# data_new['%s_%02d' % (k, i)] = [
# v_[:, i] for v_ in v
# ]
# else:
# data_new[k] = v
trk_header['n_count'] = len(tractography.tracts())
trk_header['n_properties'] = 0
trk_header['n_scalars'] = len(data)
if len(data) > 10:
raise ValueError('At most 10 scalars permitted per point')
trk_header['scalar_name'][:len(data)] = numpy.array(
[n[:20] for n in data],
dtype='|S20'
)
trk_tracts = []
for i, sl in enumerate(tractography.tracts()):
scalars = None
if len(data) > 0:
scalars = numpy.vstack([
data[k.decode('utf8')][i].squeeze()
for k in trk_header['scalar_name'][:len(data)]
]).T
trk_tracts.append((sl, scalars, None))
trackvis.write(filename, trk_tracts, trk_header, points_space='rasmm')