Exemple #1
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 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
Exemple #2
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 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
Exemple #3
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 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
Exemple #4
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 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