def test_disp_torontonian_single_mode(scale):
"""Calculates the probability of clicking for a single mode state"""
cv = random_covariance(1)
mu = scale * (2 * np.random.rand(2) - 1)
prob_click = threshold_detection_prob(mu, cv, np.array([1]))
expected = 1 - density_matrix_element(mu, cv, [0], [0])
assert np.allclose(prob_click, expected)
def test_numba_tor(N):
"""Tests numba implementations of the torontonian against the default implementation"""
cov = random_covariance(N)
O = Xmat(N) @ Amat(cov)
t1 = tor(O)
t2 = numba_tor(O)
t3 = rec_torontonian(O)
assert np.isclose(t1, t2)
assert np.isclose(t1, t3)
def test_probs_sum_to_1(n, scale):
"""test that threshold probabilities sum to 1"""
cov = random_covariance(n)
mu = scale * (2 * np.random.rand(2 * n) - 1)
p_total = 0
for det_pattern in product([0, 1], repeat=n):
p = threshold_detection_prob(mu, cov, det_pattern)
p_total += p
assert np.isclose(p_total, 1)
def test_numba_ltor(N):
"""Tests numba implementations of the loop torontonian against the default
implementation"""
alpha = np.random.random(N) + np.random.random(N) * 1j
alpha = np.concatenate((alpha, alpha.conj()))
cov = random_covariance(N)
O = Xmat(N) @ Amat(cov)
mu = O @ alpha
t1 = ltor(O, mu)
t2 = numba_ltor(O, mu)
t3 = rec_ltorontonian(O, mu)
assert np.isclose(t1, t2)
assert np.isclose(t1, t3)
def test_disp_torontonian_two_mode(scale):
"""Calculates the probability of clicking for a two mode state"""
cv = random_covariance(2)
mu = scale * (2 * np.random.rand(4) - 1)
prob_click = threshold_detection_prob(mu, cv, [1, 1])
mu0, cv0 = reduced_gaussian(mu, cv, [0])
mu1, cv1 = reduced_gaussian(mu, cv, [1])
expected = (
1
- density_matrix_element(mu0, cv0, [0], [0])
- density_matrix_element(mu1, cv1, [0], [0])
+ density_matrix_element(mu, cv, [0, 0], [0, 0])
)
assert np.allclose(expected, prob_click)
def test_tor_and_threshold_displacement_prob_agree(n_modes):
"""Tests that threshold_detection_prob, ltor and the usual tor expression all agree
when displacements are zero"""
cv = random_covariance(n_modes)
mu = np.zeros([2 * n_modes])
Q = Qmat(cv)
O = Xmat(n_modes) @ Amat(cv)
expected = tor(O) / np.sqrt(np.linalg.det(Q))
prob = threshold_detection_prob(mu, cv, np.array([1] * n_modes))
prob2 = numba_ltor(O, mu) / np.sqrt(np.linalg.det(Q))
prob3 = rec_ltorontonian(O, mu) / np.sqrt(np.linalg.det(Q))
prob4 = numba_vac_prob(mu, Q) * ltor(O, mu)
assert np.isclose(expected, prob)
assert np.isclose(expected, prob2)
assert np.isclose(expected, prob3)
assert np.isclose(expected, prob4)