def test_sort_eig_order(self):
"""
Test if eigenvalue sorting has the correct sorting
"""
gyration_tensor = Gyration._gyration_tensor(cluster, None)
eig_val, eig_vec = np.linalg.eig(gyration_tensor)
eig_val, eig_vec = Gyration._sort_eig(eig_val, eig_vec, reverse=False)
assert eig_val[2] >= eig_val[1]
assert eig_val[1] >= eig_val[0]
def test_sort_eig_reverse(self):
"""
Test if eigenvalue sorting has the correct sorting in reverse
"""
gyration_tensor = Gyration._gyration_tensor(cluster, None)
eig_val, eig_vec = np.linalg.eig(gyration_tensor)
eig_val, eig_vec = Gyration._sort_eig(eig_val, eig_vec, reverse=True)
assert eig_val[2] <= eig_val[1]
assert eig_val[1] <= eig_val[0]
def test_sort_eig_transformation(self):
"""
Test if eigenvalue sorting results in the correct sorting via
A*evec_i = eval_i*evec_i
"""
gyration_tensor = Gyration._gyration_tensor(cluster, None)
eig_val, eig_vec = np.linalg.eig(gyration_tensor)
eig_val, eig_vec = Gyration._sort_eig(eig_val, eig_vec, reverse=False)
for i in range(3):
t1 = np.matmul(gyration_tensor, eig_vec[:, i])
t2 = eig_val[i] * eig_vec[:, i]
assert t1 == approx(t2)