def test_handles_wrong_shapes_well(self):
"""
The cython wrapper raises an error if the shapes are not compatible.
"""
b1, b2 = get_random_coord_arrays(20)
c1, c2 = get_random_coord_arrays(21)
with self.assertRaises(ValueError):
rmsd_qc(b1, c1)
def test_rmsd_bigger_dataset(self):
"""
For large point-clouds, kabsch and qcprot yield the same RMSD
"""
for i in range(RAND_TEST_ITERATIONS):
b1 , b2 = get_random_coord_arrays()
self.assertAlmostEqual(rmsd_kabsch(b1, b2), rmsd_qc(b1, b2))
def test_rmsd_skipping_entries(self):
"""
If the original input array has no contiguouse memory layout, we still don't crash
"""
for i in range(RAND_TEST_ITERATIONS):
b1 , b2 = get_random_coord_arrays()
self.assertAlmostEqual(rmsd_kabsch(b1[::2], b2[::2]), rmsd_qc(b1[::2], b2[::2]))
def test_qcrmsd_centered(self):
"""
Test the is_centered flag of qcprot
"""
for i in range(RAND_TEST_ITERATIONS):
b1 , b2 = get_random_coord_arrays()
b1 = center_coords(b1)
b2 = center_coords(b2)
self.assertAlmostEqual(rmsd_kabsch(b1, b2), rmsd_qc(b1, b2, True))
def rmsd_qc_wrap(coords1, coords2, is_centered=False):
r = rmsd_qc(coords1, coords2, is_centered)
if np.isnan(r):
return rmsd_kabsch(coords1, coords2, is_centered)
return r
def test_rmsd_qc_2(self):
"""
The qcprot algorithm currently does not handle the case of 0 RMSD properly.
"""
self.assertAlmostEqual(rmsd_qc(self.a1, self.a3), 0) #Only translation
self.assertAlmostEqual(rmsd_qc(self.a1, self.a4), 0) #Only rotation
def test_rmsd_qc(self):
"""
Test the py_qcprot rmsd version on trivial examples.
"""
self.assertAlmostEqual(rmsd_qc(self.a1, self.a1), 0) #Special case, treated seperately in python wrapper
self.assertAlmostEqual(rmsd_qc(self.a1, self.a2), math.sqrt(2))
def rmsd_qc_wrap(coords1, coords2, is_centered=False):
r = rmsd_qc(coords1, coords2, is_centered)
if np.isnan(r):
return rmsd_kabsch(coords1, coords2, is_centered)
return r