def test_thermal_dm(dimensions, n_excitations, nbar_type):
# Ensure that the average number of excitations over all the states is
# much less than the total number of allowed excitations.
if nbar_type == "scalar":
nbars = 0.1 * n_excitations / len(dimensions)
else:
nbars = np.random.rand(len(dimensions))
nbars = (0.1 * n_excitations) / np.sum(nbars)
test_dm = qutip.enr_thermal_dm(dimensions, n_excitations, nbars)
expect_dm = _reference_dm(dimensions, n_excitations, nbars)
np.testing.assert_allclose(test_dm.full(), expect_dm.full(), atol=1e-12)
def test_enr_thermal_dm2():
"Excitation-number-restricted state space: thermal density operator (II)"
dims, excitations = [3, 4, 5], 2
n_vec = 0.1
rho = enr_thermal_dm(dims, excitations, n_vec)
rho_ref = tensor([thermal_dm(d, n_vec) for idx, d in enumerate(dims)])
gonners = [idx for idx, state in enumerate(state_number_enumerate(dims))
if sum(state) > excitations]
rho_ref = rho_ref.eliminate_states(gonners)
rho_ref = rho_ref / rho_ref.tr()
assert_(abs((rho.data - rho_ref.data).data).max() < 1e-12)
def test_enr_thermal_dm2():
"Excitation-number-restricted state space: thermal density operator (II)"
dims, excitations = [3, 4, 5], 2
n_vec = 0.1
rho = enr_thermal_dm(dims, excitations, n_vec)
rho_ref = tensor([thermal_dm(d, n_vec) for idx, d in enumerate(dims)])
gonners = [idx for idx, state in enumerate(state_number_enumerate(dims))
if sum(state) > excitations]
rho_ref = rho_ref.eliminate_states(gonners)
rho_ref = rho_ref / rho_ref.tr()
assert_(abs((rho.data - rho_ref.data).data).max() < 1e-12)