def test_adapt_affine():
# Adapt affine to missing or extra input dimensions
aff_3d = from_matvec(np.arange(9).reshape((3, 3)), [11, 12, 13])
# For 4x4 affine, 3D image, no-op
assert_array_equal(adapt_affine(aff_3d, 3), aff_3d)
# For 4x4 affine, 4D image, add extra identity dimension
assert_array_equal(adapt_affine(aff_3d, 4),
[[ 0, 1, 2, 0, 11],
[ 3, 4, 5, 0, 12],
[ 6, 7, 8, 0, 13],
[ 0, 0, 0, 1, 0],
[ 0, 0, 0, 0, 1]])
# For 5x5 affine, 4D image, identity
aff_4d = from_matvec(np.arange(16).reshape((4, 4)), [11, 12, 13, 14])
assert_array_equal(adapt_affine(aff_4d, 4), aff_4d)
# For 4x4 affine, 2D image, dropped column
assert_array_equal(adapt_affine(aff_3d, 2),
[[ 0, 1, 11],
[ 3, 4, 12],
[ 6, 7, 13],
[ 0, 0, 1]])
# For 4x4 affine, 1D image, 2 dropped columnn
assert_array_equal(adapt_affine(aff_3d, 1),
[[ 0, 11],
[ 3, 12],
[ 6, 13],
[ 0, 1]])
# For 3x3 affine, 2D image, identity
aff_2d = from_matvec(np.arange(4).reshape((2, 2)), [11, 12])
assert_array_equal(adapt_affine(aff_2d, 2), aff_2d)
def test_adapt_affine():
# Adapt affine to missing or extra input dimensions
aff_3d = from_matvec(np.arange(9).reshape((3, 3)), [11, 12, 13])
# For 4x4 affine, 3D image, no-op
assert_array_equal(adapt_affine(aff_3d, 3), aff_3d)
# For 4x4 affine, 4D image, add extra identity dimension
assert_array_equal(adapt_affine(aff_3d, 4),
[[ 0, 1, 2, 0, 11],
[ 3, 4, 5, 0, 12],
[ 6, 7, 8, 0, 13],
[ 0, 0, 0, 1, 0],
[ 0, 0, 0, 0, 1]])
# For 5x5 affine, 4D image, identity
aff_4d = from_matvec(np.arange(16).reshape((4, 4)), [11, 12, 13, 14])
assert_array_equal(adapt_affine(aff_4d, 4), aff_4d)
# For 4x4 affine, 2D image, dropped column
assert_array_equal(adapt_affine(aff_3d, 2),
[[ 0, 1, 11],
[ 3, 4, 12],
[ 6, 7, 13],
[ 0, 0, 1]])
# For 4x4 affine, 1D image, 2 dropped columnn
assert_array_equal(adapt_affine(aff_3d, 1),
[[ 0, 11],
[ 3, 12],
[ 6, 13],
[ 0, 1]])
# For 3x3 affine, 2D image, identity
aff_2d = from_matvec(np.arange(4).reshape((2, 2)), [11, 12])
assert_array_equal(adapt_affine(aff_2d, 2), aff_2d)