def voxel2streamline(streamline, affine, unique_idx=None):
"""
Maps voxels to streamlines and streamlines to voxels, for setting up
the LiFE equations matrix
Parameters
----------
streamline : list
A collection of streamlines, each n by 3, with n being the number of
nodes in the fiber.
affine : array_like (4, 4)
The mapping from voxel coordinates to streamline points.
The voxel_to_rasmm matrix, typically from a NIFTI file.
unique_idx : array (optional).
The unique indices in the streamlines
Returns
-------
v2f, v2fn : tuple of dicts
The first dict in the tuple answers the question: Given a voxel (from
the unique indices in this model), which fibers pass through it?
The second answers the question: Given a streamline, for each voxel that
this streamline passes through, which nodes of that streamline are in that
voxel?
"""
transformed_streamline = transform_streamlines(streamline, affine)
if unique_idx is None:
all_coords = np.concatenate(transformed_streamline)
unique_idx = unique_rows(np.round(all_coords))
return _voxel2streamline(transformed_streamline,
unique_idx.astype(np.intp))
def voxel2streamline(streamline,
transformed=False,
affine=None,
unique_idx=None):
"""
Maps voxels to streamlines and streamlines to voxels, for setting up
the LiFE equations matrix
Parameters
----------
streamline : list
A collection of streamlines, each n by 3, with n being the number of
nodes in the fiber.
affine : 4 by 4 array (optional)
Defines the spatial transformation from streamline to data.
Default: np.eye(4)
transformed : bool (optional)
Whether the streamlines have been already transformed (in which case
they don't need to be transformed in here).
unique_idx : array (optional).
The unique indices in the streamlines
Returns
-------
v2f, v2fn : tuple of dicts
The first dict in the tuple answers the question: Given a voxel (from
the unique indices in this model), which fibers pass through it?
The second answers the question: Given a streamline, for each voxel that
this streamline passes through, which nodes of that streamline are in that
voxel?
"""
if transformed:
transformed_streamline = streamline
else:
if affine is None:
affine = np.eye(4)
transformed_streamline = transform_streamlines(streamline, affine)
if unique_idx is None:
all_coords = np.concatenate(transformed_streamline)
unique_idx = unique_rows(all_coords.astype(int))
else:
unique_idx = unique_idx
return _voxel2streamline(transformed_streamline, unique_idx)
def voxel2streamline(streamline, transformed=False, affine=None,
unique_idx=None):
"""
Maps voxels to streamlines and streamlines to voxels, for setting up
the LiFE equations matrix
Parameters
----------
streamline : list
A collection of streamlines, each n by 3, with n being the number of
nodes in the fiber.
affine : 4 by 4 array (optional)
Defines the spatial transformation from streamline to data.
Default: np.eye(4)
transformed : bool (optional)
Whether the streamlines have been already transformed (in which case
they don't need to be transformed in here).
unique_idx : array (optional).
The unique indices in the streamlines
Returns
-------
v2f, v2fn : tuple of arrays
The first array in the tuple answers the question: Given a voxel (from
the unique indices in this model), which fibers pass through it? Shape:
(n_voxels, n_fibers).
The second answers the question: Given a voxel, for each fiber, which
nodes are in that voxel? Shape: (n_voxels, max(n_nodes per fiber)).
"""
if transformed:
transformed_streamline = streamline
else:
if affine is None:
affine = np.eye(4)
transformed_streamline = transform_streamlines(streamline, affine)
if unique_idx is None:
all_coords = np.concatenate(transformed_streamline)
unique_idx = unique_rows(all_coords.astype(int))
else:
unique_idx = unique_idx
return _voxel2streamline(transformed_streamline, unique_idx)