def read_tracksi(self, indices):
""" read tracks with specific indices
"""
tracks = Streamlines()
for i in indices:
off0, off1 = self.offsets[i:i + 2]
tracks.append(self.tracks[off0:off1])
return tracks
def read_tracksi(self, indices):
""" read tracks with specific indices
"""
tracks = Streamlines()
for i in indices:
off0, off1 = self.offsets[i:i + 2]
tracks.append(self.tracks[off0:off1])
return tracks
def read_tracks(self):
""" read the entire tractography
"""
I = self.offsets[:]
TR = self.tracks[:]
tracks = Streamlines()
for i in range(len(I) - 1):
off0, off1 = I[i:i + 2]
tracks.append(TR[off0:off1])
return tracks
def read_tracks(self):
""" read the entire tractography
"""
I = self.offsets[:]
TR = self.tracks[:]
tracks = Streamlines()
for i in range(len(I) - 1):
off0, off1 = I[i:i + 2]
tracks.append(TR[off0:off1])
return tracks
def get_array_sequence(self, item=None):
if item is None:
streamlines = _load_streamlines_from_hdf(self.hdf_group)
else:
streamlines = ArraySequence()
if isinstance(item, int):
streamline = self._get_one_streamline(item)
streamlines.append(streamline)
elif isinstance(item, list) or isinstance(item, np.ndarray):
for i in item:
streamline = self._get_one_streamline(i)
streamlines.append(streamline, cache_build=True)
streamlines.finalize_append()
elif isinstance(item, slice):
offsets = self.hdf_group['offsets'][item]
lengths = self.hdf_group['lengths'][item]
for offset, length in zip(offsets, lengths):
streamline = self.hdf_group['data'][offset:offset + length]
streamlines.append(streamline, cache_build=True)
streamlines.finalize_append()
else:
raise ValueError(
'Item should be either a int, list, '
'np.ndarray or slice but we received {}'.format(
type(item)))
return streamlines