def is_voxel_order_valid(voxel_order):
return is_reference_info_valid(np.eye(4), [1, 1, 1], [1.0, 1.0, 1.0],
voxel_order)
def is_voxel_sizes_valid(voxel_sizes):
return is_reference_info_valid(np.eye(4), [1, 1, 1], voxel_sizes,
'RAS')
def is_affine_valid(affine):
return is_reference_info_valid(affine, [1, 1, 1], [1.0, 1.0, 1.0],
'RAS')
def is_dimensions_valid(dimensions):
return is_reference_info_valid(np.eye(4), dimensions, [1.0, 1.0, 1.0],
'RAS')
def __init__(self, streamlines, reference, space,
origin=Origin.NIFTI,
data_per_point=None, data_per_streamline=None):
""" Create a strict, state-aware, robust tractogram
Parameters
----------
streamlines : list or ArraySequence
Streamlines of the tractogram
reference : Nifti or Trk filename, Nifti1Image or TrkFile,
Nifti1Header, trk.header (dict) or another Stateful Tractogram
Reference that provides the spatial attributes.
Typically a nifti-related object from the native diffusion used for
streamlines generation
space : Enum (dipy.io.stateful_tractogram.Space)
Current space in which the streamlines are (vox, voxmm or rasmm)
After tracking the space is VOX, after loading with nibabel
the space is RASMM
origin : Enum (dipy.io.stateful_tractogram.Origin), optional
Current origin in which the streamlines are (center or corner)
After loading with nibabel the origin is CENTER
data_per_point : dict, optional
Dictionary in which each key has X items, each items has Y_i items
X being the number of streamlines
Y_i being the number of points on streamlines #i
data_per_streamline : dict, optional
Dictionary in which each key has X items
X being the number of streamlines
Notes
-----
Very important to respect the convention, verify that streamlines
match the reference and are effectively in the right space.
Any change to the number of streamlines, data_per_point or
data_per_streamline requires particular verification.
In a case of manipulation not allowed by this object, use Nibabel
directly and be careful.
"""
if data_per_point is None:
data_per_point = {}
if data_per_streamline is None:
data_per_streamline = {}
if isinstance(streamlines, Streamlines):
streamlines = streamlines.copy()
self._tractogram = Tractogram(streamlines,
data_per_point=data_per_point,
data_per_streamline=data_per_streamline)
if isinstance(reference, type(self)):
logger.warning('Using a StatefulTractogram as reference, this '
'will copy only the space_attributes, not '
'the state. The variables space and origin '
'must be specified separately.')
logger.warning('To copy the state from another StatefulTractogram '
'you may want to use the function from_sft '
'(static function of the StatefulTractogram).')
if isinstance(reference, tuple) and len(reference) == 4:
if is_reference_info_valid(*reference):
space_attributes = reference
else:
raise TypeError('The provided space attributes are not '
'considered valid, please correct before '
'using them with StatefulTractogram.')
else:
space_attributes = get_reference_info(reference)
if space_attributes is None:
raise TypeError('Reference MUST be one of the following:\n'
'Nifti or Trk filename, Nifti1Image or '
'TrkFile, Nifti1Header or trk.header (dict).')
(self._affine, self._dimensions,
self._voxel_sizes, self._voxel_order) = space_attributes
self._inv_affine = np.linalg.inv(self._affine)
if space not in Space:
raise ValueError('Space MUST be from Space enum, e.g Space.VOX.')
self._space = space
if origin not in Origin:
raise ValueError('Origin MUST be from Origin enum, '
'e.g Origin.NIFTI.')
self._origin = origin
logger.debug(self)
