def reorder_voxels_affine(input_ornt, output_ornt, shape, voxel_size):
"""Calculates a linear transformation equivalent to changing voxel order.
Calculates a linear tranformation A such that [a, b, c, 1] = A[x, y, z, 1].
where [x, y, z] is a point in the coordinate system defined by input_ornt
and [a, b, c] is the same point in the coordinate system defined by
output_ornt.
Parameters
----------
input_ornt : array (n, 2)
A description of the orientation of a point in n-space. See
``nibabel.orientation`` or ``dipy.io.bvectxt`` for more information.
output_ornt : array (n, 2)
A description of the orientation of a point in n-space.
shape : tuple of int
Shape of the image in the input orientation.
``map = ornt_mapping(input_ornt, output_ornt)``
voxel_size : int
Voxel size of the image in the input orientation.
Returns
-------
A : array (n+1, n+1)
Affine matrix of the transformation between input_ornt and output_ornt.
See Also
--------
nibabel.orientation
dipy.io.bvectxt.orientation_to_string
dipy.io.bvectxt.orientation_from_string
"""
map = ornt_mapping(input_ornt, output_ornt)
if input_ornt.shape != output_ornt.shape:
raise ValueError("input_ornt and output_ornt must have the same shape")
affine = eye(len(input_ornt)+1)
affine[:3] = affine[map[:, 0]]
corner = asarray(voxel_size) * shape
affine[:3, 3] = (map[:, 1] < 0) * corner[map[:, 0]]
# multiply the rows of affine to get right sign
affine[:3, :3] *= map[:, 1:]
return affine
def reorder_voxels_affine(input_ornt, output_ornt, shape, voxel_size):
"""Calculates a linear transformation equivalent to changing voxel order.
Calculates a linear tranformation A such that [a, b, c, 1] = A[x, y, z, 1].
where [x, y, z] is a point in the coordinate system defined by input_ornt
and [a, b, c] is the same point in the coordinate system defined by
output_ornt.
Parameters
----------
input_ornt : array (n, 2)
A description of the orientation of a point in n-space. See
``nibabel.orientation`` or ``dipy.io.bvectxt`` for more information.
output_ornt : array (n, 2)
A description of the orientation of a point in n-space.
shape : tuple of int
Shape of the image in the input orientation.
``map = ornt_mapping(input_ornt, output_ornt)``
voxel_size : int
Voxel size of the image in the input orientation.
Returns
-------
A : array (n+1, n+1)
Affine matrix of the transformation between input_ornt and output_ornt.
See Also
--------
nibabel.orientation
dipy.io.bvectxt.orientation_to_string
dipy.io.bvectxt.orientation_from_string
"""
map = ornt_mapping(input_ornt, output_ornt)
if input_ornt.shape != output_ornt.shape:
raise ValueError("input_ornt and output_ornt must have the same shape")
affine = eye(len(input_ornt)+1)
affine[:3] = affine[map[:, 0]]
corner = asarray(voxel_size) * shape
affine[:3, 3] = (map[:, 1] < 0) * corner[map[:, 0]]
# multiply the rows of affine to get right sign
affine[:3, :3] *= map[:, 1:]
return affine
