def save_tractogram(fname,
streamlines,
affine,
vox_size=None,
shape=None,
header=None,
reduce_memory_usage=False,
tractogram_file=None):
""" Saves tractogram files (*.trk or *.tck or *.dpy)
Parameters
----------
fname : str
output trk filename
streamlines : list of 2D arrays, generator or ArraySequence
Each 2D array represents a sequence of 3D points (points, 3).
affine : array_like (4, 4)
The mapping from voxel coordinates to streamline points.
vox_size : array_like (3,), optional
The sizes of the voxels in the reference image (default: None)
shape : array, shape (dim,), optional
The shape of the reference image (default: None)
header : dict, optional
Metadata associated to the tractogram file(*.trk). (default: None)
reduce_memory_usage : {False, True}, optional
If True, save streamlines in a lazy manner i.e. they will not be kept
in memory. Otherwise, keep all streamlines in memory until saving.
tractogram_file : class TractogramFile, optional
Define tractogram class type (TrkFile vs TckFile)
Default is None which means auto detect format
"""
if 'dpy' in os.path.splitext(fname)[1].lower():
dpw = Dpy(fname, 'w')
dpw.write_tracks(Streamlines(streamlines))
dpw.close()
return
tractogram_file = tractogram_file or detect_format(fname)
if tractogram_file is None:
raise ValueError("Unknown format for 'fname': {}".format(fname))
if vox_size is not None and shape is not None:
if not isinstance(header, dict):
header = {}
header[Field.VOXEL_TO_RASMM] = affine.copy()
header[Field.VOXEL_SIZES] = vox_size
header[Field.DIMENSIONS] = shape
header[Field.VOXEL_ORDER] = "".join(aff2axcodes(affine))
if reduce_memory_usage and not callable(streamlines):
sg = lambda: (s for s in streamlines)
else:
sg = streamlines
tractogram_loader = LazyTractogram if reduce_memory_usage else Tractogram
tractogram = tractogram_loader(sg)
tractogram.affine_to_rasmm = affine
track_file = tractogram_file(tractogram, header=header)
nib.streamlines.save(track_file, fname)
def streamlines(self, streamlines):
""" Modify streamlines. Creating a new object would be less risky.
Parameters
----------
streamlines : list or ArraySequence (list and deepcopy recommanded)
Streamlines of the tractogram
"""
self._tractogram._streamlines = Streamlines(streamlines)
self.data_per_point = self.data_per_point
self.data_per_streamline = self.data_per_streamline
logging.warning('Streamlines has been modified')
def test_dpy():
fname = 'test.bin'
with InTemporaryDirectory():
dpw = Dpy(fname, 'w')
A = np.ones((5, 3))
B = 2 * A.copy()
C = 3 * A.copy()
dpw.write_track(A)
dpw.write_track(B)
dpw.write_track(C)
dpw.write_tracks(Streamlines([C, B, A]))
all_tracks = np.ascontiguousarray(np.vstack([A, B, C, C, B, A]))
npt.assert_array_equal(all_tracks, dpw.tracks[:])
dpw.close()
dpr = Dpy(fname, 'r')
npt.assert_equal(dpr.version() == u'0.0.1', True)
T = dpr.read_tracksi([0, 1, 2, 0, 0, 2])
T2 = dpr.read_tracks()
npt.assert_equal(len(T2), 6)
dpr.close()
npt.assert_array_equal(A, T[0])
npt.assert_array_equal(C, T[5])