def plot_husimi_data(rho, title=""):
"""
"""
# params = {# 'backend': 'ps',
# 'axes.labelsize': 6,
# 'font.size': 6,
# 'xtick.labelsize': 6,
# 'ytick.labelsize': 6,
# 'axes.labelpad': 1,
# 'text.usetex': False,
# 'figure.figsize': fig_size}
# plt.rcParams.update(params)
# rho_tf = dm_to_tf([rho])
# data = batched_expect(ops_batch_husimi, rho_tf)
# x = data.numpy().reshape((len(xvec), len(xvec)))
xvec = np.linspace(-5, 5, 32)
yvec = np.linspace(-5, 5, 32)
x = qfunc(rho, xvec * np.sqrt(2), yvec * np.sqrt(2))
fig, ax = plt.subplots(1, 1, figsize=(fig_width / 3.5, fig_width / 3.5))
im = ax.pcolor(xvec, yvec, x / np.max(x), cmap="hot", vmin=0., vmax=1)
ax.set_aspect("equal")
ax.set_yticks([-5, 0, 5])
ax.set_xlabel(r"Re$(\beta)$", labelpad=0)
ax.set_ylabel(r"Im$(\beta)$", labelpad=-5)
cbar = plt.colorbar(im, fraction=0.0455, ticks=[0, 0.5, 1])
cbar.ax.set_yticklabels(["0", "0.5", "1"])
ax.set_title(title, y=0.93)
cbar.solids.set_edgecolor("face")
return fig, ax
def test_qfunc_dm():
"Husimi Q: Compare with/without precomputation for rand. dm"
N = 20
xvec = np.linspace(-10, 10, 128)
for _ in range(3):
rho = rand_dm(N)
precomp = qfunc_precompute(xvec, xvec, N)
q_default = qfunc(rho, xvec, xvec, precompute=None)
q_false = qfunc(rho, xvec, xvec, precompute=False)
q_true = qfunc(rho, xvec, xvec, precompute=True)
q_precomp = qfunc(rho, xvec, xvec, precompute=precomp)
assert_equal(q_true, q_default) # Default: precompute
assert_equal(q_true,
q_precomp) # Compare precomputing before/during the call
assert_equal(np.sum(np.abs(q_true - q_false)) < 1e-7,
True) # Compare with/without
def update(self, rho):
self.data = qfunc(rho, self.xvecs[0], self.xvecs[1])
def update(self, rho):
self.data = qfunc(rho, self.xvecs[0], self.xvecs[1])