def test_iterate_and_precompute_are_equivalent(self, size, n_xs, n_ys, g):
xs = np.linspace(-1, 1, n_xs)
ys = np.linspace(0, 2, n_ys)
state = qutip.rand_dm(size)
iterate = qutip.qfunc(state, xs, ys, g, precompute_memory=None)
precompute = qutip.qfunc(state, xs, ys, g, precompute_memory=np.inf)
np.testing.assert_allclose(iterate, precompute)
def test_qfunc_warns_if_insufficient_memory(self):
xs = np.linspace(-1, 1, 11)
state = qutip.rand_dm(4)
with pytest.warns(UserWarning) as e:
qutip.qfunc(state, xs, xs, precompute_memory=0)
assert (e[0].message.args[0].startswith(
"Falling back to iterative algorithm"))
def test_failure_if_not_a_Qobj(self, obj):
xs = np.linspace(-1, 1, 11)
with pytest.raises(TypeError) as e:
qutip.qfunc(obj, xs, xs)
assert e.value.args[0].startswith("state must be Qobj")
qfunc = qutip.QFunc(xs, xs)
with pytest.raises(TypeError) as e:
qfunc(obj)
assert e.value.args[0].startswith("state must be Qobj")
def test_failure_if_tensor_hilbert_space(self, dm):
if dm:
state = qutip.rand_dm(4, dims=[[2, 2], [2, 2]])
else:
state = qutip.rand_ket(4, dims=[[2, 2], [1, 1]])
xs = np.linspace(-1, 1, 5)
with pytest.raises(ValueError) as e:
qutip.qfunc(state, xs, xs)
assert "must not have tensor structure" in e.value.args[0]
with pytest.raises(ValueError) as e:
qutip.QFunc(xs, xs)(state)
assert "must not have tensor structure" in e.value.args[0]
def test_failure_if_not_a_state(self, state):
xs = np.linspace(-1, 1, 11)
state = state()
with pytest.raises(ValueError) as e:
qutip.qfunc(state, xs, xs)
assert (e.value.args[0].startswith(
"state must be a ket or density matrix"))
qfunc = qutip.QFunc(xs, xs)
with pytest.raises(ValueError) as e:
qfunc(state)
assert (e.value.args[0].startswith(
"state must be a ket or density matrix"))
def test_failure_if_non_arraylike_coordinates(self, xs):
state = qutip.rand_ket(4)
valid = np.linspace(-1, 1, 5)
with pytest.raises(TypeError) as e:
qutip.qfunc(state, xs, valid)
assert "must be array-like" in e.value.args[0]
with pytest.raises(TypeError) as e:
qutip.qfunc(state, valid, xs)
assert "must be array-like" in e.value.args[0]
with pytest.raises(TypeError) as e:
qutip.QFunc(xs, valid)
assert "must be array-like" in e.value.args[0]
with pytest.raises(TypeError) as e:
qutip.QFunc(valid, xs)
assert "must be array-like" in e.value.args[0]
def test_failure_if_coordinates_not_1d(self, ndim):
state = qutip.rand_ket(4)
valid = np.linspace(-1, 1, 5)
bad = valid.reshape((-1, ) + (1, ) * (ndim - 1))
with pytest.raises(ValueError) as e:
qutip.qfunc(state, bad, valid)
assert "must be 1D" in e.value.args[0]
with pytest.raises(ValueError) as e:
qutip.qfunc(state, valid, bad)
assert "must be 1D" in e.value.args[0]
with pytest.raises(ValueError) as e:
qutip.QFunc(bad, valid)
assert "must be 1D" in e.value.args[0]
with pytest.raises(ValueError) as e:
qutip.QFunc(valid, bad)
assert "must be 1D" in e.value.args[0]
def test_function_and_class_are_equivalent(self, size, dm, n_xs, n_ys, g):
xs = np.linspace(-1, 1, n_xs)
ys = np.linspace(0, 2, n_ys)
state = qutip.rand_dm(size) if dm else qutip.rand_ket(size)
function = qutip.qfunc(state, xs, ys, g)
class_ = qutip.QFunc(xs, ys, g)(state)
np.testing.assert_allclose(function, class_)
def qps(self):
self.qps = []
for rho in self.density_matrix_list:
if self.functype != 'Q':
self.qps.append(qt.wigner(rho, self.xvec, self.yvec))
else:
self.qps.append(qt.qfunc(rho, self.xvec, self.yvec))
return self.qps
def qps(self):
self.qps = []
for rho in self.density_matrix_list:
if self.functype != "Q":
self.qps.append(qt.wigner(rho, self.xvec, self.yvec))
else:
self.qps.append(qt.qfunc(rho, self.xvec, self.yvec))
return self.qps
def test_against_naive_implementation(self, xs, ys, g, size):
state = qutip.rand_dm(size)
state_np = state.full()
x, y = np.meshgrid(xs, ys)
alphas = 0.5 * g * (x + 1j * y)
naive = np.empty(alphas.shape, dtype=np.float64)
for i, alpha in enumerate(alphas.flat):
coh = qutip.coherent(size, alpha, method='analytic').full()
naive.flat[i] = (coh.conj().T @ state_np @ coh).real
naive *= (0.5 * g)**2 / np.pi
np.testing.assert_allclose(naive, qutip.qfunc(state, xs, ys, g))
np.testing.assert_allclose(naive, qutip.QFunc(xs, ys, g)(state))
def f(state, xvec, yvec):
return qt.qfunc(state, xvec, yvec)
ax[1].set_xlabel('$\omega_c-\omega_d$')
ax[1].set_ylabel('$|\langle a \\rangle | ^2 $')
print('saving data ... ', end='', flush=True)
np.save('resonant.npy', data)
print('done')
print('starting qps ', end='', flush=True)
### Q function plots along right hand side
xvec = np.linspace(-8, 8, 100)
yvec = np.linspace(-8, 8, 100)
for sys in enumerate(carmichael_systems[::2][0:4]):
'''for every other system, truncated to four.
get colors for qp backgrounds from lines'''
qp = qt.qfunc(sys[1].rhos_ss[width//2].ptrace(0), xvec, yvec)
print('.', end='', flush=True)
ax[sys[0]+2].contour(xvec, yvec, qp, 40,
linewidths=0.8,
cmap='inferno')
# Set plot parameters
ax[sys[0]+2].set_xlabel('Im(Q)')
ax[sys[0]+2].set_ylabel('Re(Q)')
ax[sys[0]+2].set_title('Q',
loc='right',
fontdict={'fontsize':12, 'verticalalignment':'bottom', 'color': res_childs[0].get_colors()[::2][0:4][sys[0]],
'weight':'bold'})
print('done')
