def test_prefactor_vacuum():
"""test the correct prefactor of 0.5 is calculated for a vacuum state"""
Q = np.identity(2)
beta = np.zeros([2])
res = prefactor(Means(beta), Covmat(Q))
ex = 1
assert np.allclose(res, ex)
def test_gen_Qmat_from_graph():
""" Test the gen_Qmat_from_graph for the analytically solvable case of a single mode"""
A = np.array([[10.0]])
n_mean = 1.0
cov = Covmat(gen_Qmat_from_graph(A, n_mean))
r = np.arcsinh(np.sqrt(n_mean))
cov_e = np.diag([(np.exp(2 * r)), (np.exp(-2 * r))])
assert np.allclose(cov, cov_e)
def test_density_matrix_element_no_disp(t):
"""Test density matrix elements for a state with no displacement"""
beta = Beta(np.zeros([6]))
Q = Qmat(V)
el = t[0]
ex = t[1]
res = density_matrix_element(Means(beta), Covmat(Q), el[0], el[1])
assert np.allclose(ex, res)
def test_prefactor_TMS():
"""test the correct prefactor of 0.5 is calculated for a TMS state"""
q = np.fliplr(np.diag([2.0] * 4))
np.fill_diagonal(q, np.sqrt(2))
Q = np.fliplr(q)
beta = np.zeros([4])
res = prefactor(Means(beta), Covmat(Q))
ex = 0.5
assert np.allclose(res, ex)
def test_density_matrix_element_vacuum():
"""Test density matrix elements for the vacuum"""
Q = np.identity(2)
beta = np.zeros([2])
el = [[0], [0]]
ex = 1
res = density_matrix_element(Means(beta), Covmat(Q), el[0], el[1])
assert np.allclose(ex, res)
el = [[1], [1]]
# res = density_matrix_element(beta, A, Q, el[0], el[1])
res = density_matrix_element(Means(beta), Covmat(Q), el[0], el[1])
assert np.allclose(0, res)
el = [[1], [0]]
# res = density_matrix_element(beta, A, Q, el[0], el[1])
res = density_matrix_element(Means(beta), Covmat(Q), el[0], el[1])
assert np.allclose(0, res)
def test_prefactor_with_displacement():
"""test the correct prefactor of 0.5 is calculated for a TMS state"""
q = np.fliplr(np.diag([2.0] * 4))
np.fill_diagonal(q, np.sqrt(2))
Q = np.fliplr(q)
Qinv = np.linalg.inv(Q)
vect = 1.2 * np.ones([2]) + 1j * np.ones(2)
beta = np.concatenate([vect, vect.conj()])
res = prefactor(Means(beta), Covmat(Q), hbar=2)
ex = np.exp(-0.5 * beta @ Qinv @ beta.conj()) / np.sqrt(np.linalg.det(Q))
assert np.allclose(res, ex)
def test_Covmat():
""" Test the Covmat function by checking that its inverse function is Qmat """
n = 1
B = np.random.rand(n, n) + 1j * np.random.rand(n, n)
B = B + B.T
sc = find_scaling_adjacency_matrix(B, 1)
idm = np.identity(2 * n)
X = Xmat(n)
Bsc = sc * B
A = np.block([[Bsc, 0 * Bsc], [0 * Bsc, Bsc.conj()]])
Q = np.linalg.inv(idm - X @ A)
cov = Covmat(Q)
Qrec = Qmat(cov)
assert np.allclose(Q, Qrec)