t_MMST, p1_MMST, p2_MMST = get_popu_data(path+"traj_MultiEh.txt")
xs = [t_MMST /np.pi, t_QM / np.pi]
y1s = [p1_MMST, p1_QM]
y2s = [p2_MMST, p2_QM]
labels = ["MMST", "QM"]
colors1 = ["r--", "k--"]
colors2 = ["r", "k"]
ax = clp.initialize(col=1, row=1, width=4.3, fontsize=12, LaTeX=True)
clp.plotone(xs, y1s, ax, colors=colors1, alphaspacing=0.0, lw=2)
clp.plotone(xs, y2s, ax, labels=labels, colors=colors2, xlabel="time ($t$)",
ylabel="Electronic population", alphaspacing=0.0, lw=2)
# Plot x-axis as a function of PI
def format_func(value, tick_number):
# find number of multiples of pi/2
N = value
if N == 0:
return "0"
else:
return r"${0}\pi$".format(N)
ax.xaxis.set_major_formatter(FuncFormatter(format_func))
# output figure
clp.adjust(tight_layout=True, savefile="SE_popu_MMST.pdf")
fontsize=12,
LaTeX=True,
sharex=True)
clp.plotone(xs,
y2s,
ax,
labels=labels,
colors=colors2,
xlabel="time (t)",
ylabel="Excited state population",
alphaspacing=0.0,
lw=2,
ylim=[-0.51, 2.0])
# Plot x-axis as a function of PI
def format_func(value, tick_number):
# find number of multiples of pi/2
N = value
if N == 0:
return "0"
else:
return r"${0}\pi$".format(N)
ax.xaxis.set_major_formatter(FuncFormatter(format_func))
# output figure
clp.adjust(tight_layout=True, savefile="SE_popu_turnoff_samplings.pdf")
ys2 = [np.array(k_lst)]
clp.plotone(xs2,
ys2,
axes[1],
labels=["MMST"],
colors=['r-o'],
xlabel="number of TLSs ($N$)",
ylabel="Fitted decay rate ($k_{fit}$)",
lw=2,
markersize=8)
axes[1].legend(loc="lower right")
print(ys2)
# Plot x-axis as a function of PI
def format_func(value, tick_number):
# find number of multiples of pi/2
N = value
if N == 0:
return "0"
else:
return r"${0}\pi$".format(N)
axes[0].xaxis.set_major_formatter(FuncFormatter(format_func))
# output figure
clp.adjust(tight_layout=True, savefile="SE_Dicke.pdf")
showlegend=False)
axes[1].set_title("101 TLSs, $a = \lambda/4$")
xs, y2s = gather_data("SE_101TLS_FDTD_dx_0")
clp.plotone(xs[::-1],
y2s[::-1],
axes[2],
labels=labels[::-1],
colors=colors2[::-1],
xlabel="time ($t$)",
alphaspacing=0.1,
lw=2,
showlegend=False)
axes[2].set_title("101 TLSs, $a = 0$")
# Plot x-axis as a function of PI
def format_func(value, tick_number):
# find number of multiples of pi/2
N = value
if N == 0:
return "0"
else:
return r"${0}\pi$".format(N)
for i in range(3):
axes[i].xaxis.set_major_formatter(FuncFormatter(format_func))
# output figure
clp.adjust(tight_layout=True, savefile="chain_atoms.pdf")
ax,
colors=['r', "k--", "c-."],
labels=["MMST", "QM", "coarse-grained MMST"] if i == 0 else None,
alphaspacing=0.01,
ylim=[-0.2, 12])
ax.set_xlabel("Cavity position")
ax.set_ylabel("E-field Intensity (arbi. units)")
def format_func(value, tick_number):
# find number of multiples of pi/2
N = value
if N == 0:
return "0"
else:
return r"${0}\pi$".format(N)
ax.xaxis.set_major_formatter(FuncFormatter(format_func))
# Add arrow
ax.text(2.25, 5.0, 'time increment', rotation=90, color='b', size=12)
ax.annotate('',
xy=(1.05, 0.0),
xycoords='axes fraction',
xytext=(1.05, 1.0),
arrowprops=dict(arrowstyle="<-", color='b'))
#ax.grid(True, linestyle='--')
clp.adjust(tight_layout=True, savefile="SE_intensity_MMST.pdf")
for k in range(2):
clp.plotone(X,
Y,
axes[k, j],
colors=['r', "k--", "c-."],
labels=["MMST", "QM", "coarse-grained MMST"] if
(i == 0 and j == 0) else None,
alphaspacing=0.01,
ylim=[-0.2, 12],
showlegend=True if (j == 0 and k == 0) else False,
xlim=None if k == 0 else [0.9622, 1.0378])
axes[0, j].set_title(methods[j])
axes[1, j].set_xlabel("Cavity position")
if j is 0:
for k in range(2):
axes[k, j].set_ylabel("E-field Intensity (arbi. units)")
axes[0, j].xaxis.set_major_formatter(FuncFormatter(format_func))
axes[1, j].xaxis.set_major_formatter(FuncFormatter(format_func))
# Add arrow
axes[0, -1].text(2.25, 5.0, 'time increment', rotation=90, color='b', size=12)
#axes[1, -1].text(2.25, 5.0, 'time increment', rotation=90, color='b', size=12)
for k in range(2):
axes[k, -1].annotate('',
xy=(1.05, 0.0),
xycoords='axes fraction',
xytext=(1.05, 1.0),
arrowprops=dict(arrowstyle="<-", color='b'))
#ax.grid(True, linestyle='--')
clp.adjust(tight_layout=True, savefile="EM_compare_MMST.pdf")
m1_centroid = np.mean(m1)
m2_centroid = np.mean(m2)
print(m1_centroid, m2_centroid)
axes[k, i].axvline(x=1.0, color='0.5', linestyle='--', alpha=0.5)
axes[k, i].axhline(y=0.0, color='0.5', linestyle='--', alpha=0.5)
axes[k, i].plot(m1_centroid,
m2_centroid,
'ro',
markersize=5,
alpha=0.8)
axes[k, i].set_xlim([xmin, xmax])
axes[k, i].set_ylim([ymin, ymax])
if i == 0:
axes[k, i].text(xmin + 0.2, ymax - 0.5, "t = 0")
axes[k, i].text(xmin + 0.2,
ymin + 0.2,
methods[k],
color='b',
fontsize=10)
elif i == 1:
axes[k, i].text(xmin + 0.2, ymax - 0.5, "t = $\pi$/3")
elif i == 2:
axes[k, i].text(xmin + 0.2, ymax - 0.5, "t = $\pi$")
else:
axes[k, i].text(xmin + 0.2, ymax - 0.5, "t = %d$\pi$/3" % i)
cb_ax = fig.add_axes([1.0, 0.1, 0.02, 0.8])
cbar = fig.colorbar(im_good, cax=cb_ax)
clp.adjust(tight_layout=True, savefile="ground_state_phase_space_dist.pdf")
t_p, p1_p, p2_p = get_popu_data(path+"traj_PreBinSQC.txt")
t_sed, p1_sed, p2_sed = get_popu_data(path+"traj_StochasticED.txt")
xs = [t_p /np.pi, t_sed/np.pi, t_MMST / np.pi]
y1s = [p1_p, p1_sed, p1_MMST]
y2s = [p2_p, p2_sed, p2_MMST]
labels = ["Sampling electronic ZPE (self-interaction)", "Sampling photonic ZPE (vacuum fluctuations)", "Sampling both (MMST)"]
colors1 = ["b--", "g-.", "r"]
colors2 = ["b--", "g--", "r--"]
ax = clp.initialize(col=1, row=1, width=4.3, fontsize=10, LaTeX=True)
clp.plotone(xs, y2s, ax, labels=labels, colors=colors1, xlabel="time ($t$)",
ylabel="Excited state population", alphaspacing=0.0, lw=2, xlim=[0, 1.2], ylim=[-0.6, 1.0])
# Plot x-axis as a function of PI
def format_func(value, tick_number):
# find number of multiples of pi/2
N = int(value * 10)
if N == 0:
return "0"
else:
return r"${0}\pi$".format(N/10)
ax.xaxis.set_major_formatter(FuncFormatter(format_func))
# output figure
clp.adjust(tight_layout=True, savefile="ground_state_popu.pdf")
axes[0].set_title("1 TLS near mirror, $r = \lambda/2$")
xs, y2s = gather_data("SE_1TLS_FDTD_near_mirror/dx_13")
clp.plotone(xs,
y2s,
axes[1],
labels=labels,
colors=colors2,
xlabel="time ($t$)",
alphaspacing=0.1,
lw=2,
showlegend=False,
xlim=[0, 1.0])
axes[1].set_title("1 TLS near mirror, $r = \lambda/4$")
# Plot x-axis as a function of PI
def format_func(value, tick_number):
# find number of multiples of pi/2
N = value
if N == 0:
return "0"
else:
return r"${0}\pi$".format(int(100 * N) / 100)
for i in range(2):
axes[i].xaxis.set_major_formatter(FuncFormatter(format_func))
# output figure
clp.adjust(tight_layout=True, savefile="SE_near_mirror.pdf")
ks = min(popt[0:2])
kf = max(popt[0:2])
ks_lst.append(ks)
kf_lst.append(kf)
xs2 = [np.array(Nlst)]
ys2 = [1.0 / np.array(ks_lst)]
clp.plotone(xs2,
ys2,
axes[1],
labels=["MMST"],
colors=['r-o'],
xlabel="number of TLSs ($N$)",
ylabel="Fitted slow decay lifetime ($1/k_{s}$)",
lw=2,
markersize=8)
axes[1].legend(loc="lower right")
axes[0].plot(xs[0] / kFGR,
y_fit[0],
"k-.",
alpha=0.5,
label="biexponential fitting")
axes[0].plot(xs[6] / kFGR, y_fit[6], "k-.", alpha=0.5)
print("kslow = ", ks_lst)
print("kfast = ", kf_lst)
# output figure
clp.adjust(tight_layout=True, savefile="SE_Dicke_dilute_subradiance.pdf")