def test_is_valid_cov_thermal(nbar):
""" Test if is_valid_cov for a mixed state"""
hbar = 2
dim = 10
cov = (2 * nbar + 1) * np.identity(dim)
val = is_valid_cov(cov, hbar=hbar)
assert val
def test_is_valid_cov_non_symmetric():
""" Test False if matrix is not symmetric"""
hbar = 2
V = np.zeros([4, 4])
V[0, 1] = 1
val = is_valid_cov(V, hbar=hbar)
assert not val
def test_is_valid_cov_too_certain():
""" Test False if matrix does not satisfy the Heisenberg
uncertainty relation"""
hbar = 2
V = np.random.random([4, 4])
V += V.T
val = is_valid_cov(V, hbar=hbar)
assert not val
def test_hbar(self, hbar, tol):
"""test that the output is a valid covariance matrix, even when not square"""
M = 4
a = np.arange(4 * M**2, dtype=np.float64).reshape((2 * M, 2 * M))
cov = a @ a.T + np.eye(2 * M)
mu = np.arange(2 * M, dtype=np.float64)
T = np.sqrt(0.9) * M**(-0.5) * np.ones((6, M), dtype=np.float64)
_, cov_out = symplectic.passive_transformation(mu, cov, T, hbar=hbar)
assert is_valid_cov(cov_out, hbar=hbar, atol=tol, rtol=0)
def test_valid_cov(self, M, tol):
"""test that the output is a valid covariance matrix, even when not square"""
a = np.arange(4 * M**2, dtype=np.float64).reshape((2 * M, 2 * M))
cov = a @ a.T + np.eye(2 * M)
mu = np.arange(2 * M, dtype=np.float64)
T = np.sqrt(0.9) * M**(-0.5) * np.ones((6, M), dtype=np.float64)
mu_out, cov_out = symplectic.passive_transformation(mu, cov, T)
assert cov_out.shape == (12, 12)
assert len(mu_out) == 12
assert is_valid_cov(cov_out, atol=tol, rtol=0)
def test_is_valid_cov_not_even_dimension():
""" Test False if matrix does not have even dimensions"""
hbar = 2
V = np.zeros([5, 5])
val = is_valid_cov(V, hbar=hbar)
assert not val
def test_is_valid_cov_non_square():
""" Test False if matrix is not square"""
hbar = 2
V = np.ones([3, 4])
val = is_valid_cov(V, hbar=hbar)
assert not val
def test_is_valid_cov():
""" Test if is_valid_cov for a valid covariance matrix """
hbar = 2
val = is_valid_cov(V, hbar=hbar)
assert val