def test_ornt_to_affine():
# this orientation indicates that the first output
# axis of the affine is closest to the vector [0,0,-1],
# the last is closest to [1,0,0] and
# that the y coordinate ([0,1,0]) is dropped
ornt = [[2,-1],
[np.nan,np.nan],
[0,1]]
# the reordering/flipping is represented by an affine that
# takes the 3rd output coordinate and maps it to the
# first, takes the 3rd, maps it to first and flips it
A = np.array([[0,0,-1,0],
[1,0,0,0],
[0,0,0,1]])
yield assert_array_equal(A, _ornt_to_affine(ornt))
# a more complicated example. only the 1st, 3rd and 6th
# coordinates appear in the output
ornt = [[3,-1],
[np.nan,np.nan],
[0,1],
[np.nan,np.nan],
[np.nan,np.nan],
[1,-1]]
B = np.array([[0,0,0,-1,0,0,0],
[1,0,0,0,0,0,0],
[0,-1,0,0,0,0,0],
[0,0,0,0,0,0,1]])
yield assert_array_equal(B, _ornt_to_affine(ornt))
def test_drop_coord():
# given a 5x4 affine from slicing an fmri,
# the orientations code should easily reorder and drop the t
# axis
# this affine has output coordinates '-y','z','x' and is at t=16
sliced_fmri_affine = np.array([[0,-1,0,3],
[0,0,2,5],
[3,0,0,4],
[0,0,0,16],
[0,0,0,1]])
ornt = io_orientation(sliced_fmri_affine)
affine_that_drops_t_reorders_and_flips = _ornt_to_affine(ornt)
final_affine = np.dot(affine_that_drops_t_reorders_and_flips,
sliced_fmri_affine)
# the output will be diagonal
# with the 'y' row having been flipped and the 't' row dropped
assert_array_equal(final_affine,
np.array([[3,0,0,4],
[0,1,0,-3],
[0,0,2,5],
[0,0,0,1]]))
