xs, ys = [], []
for N in Nlst:
subpath = path + "N_%d" % N
t_MMST, p2_MMST = get_popu_data("%s/traj_MultiEh.txt" % subpath, N)
xs.append(t_MMST)
ys.append(p2_MMST)
return xs, ys
Nlst = [1, 7, 35]
labels = ["N = %d" % N for N in Nlst]
axes = clp.initialize(col=2,
row=1,
width=4.3,
height=4.3 * 0.618 * 2,
fontsize=12,
LaTeX=True,
labelthem=True,
labelthemPosition=[0.04, 0.96])
xs, ys = gather_data(Nlst)
clp.plotone([x / kFGR for x in xs],
ys,
axes[0],
labels=labels,
colors=["r--", "c", "b"],
xlabel="time ($t$)",
ylabel="Avg. excited state popu. $\\bar{\\rho}_{ee}(t)$",
lw=2,
alphaspacing=0.02)
t, p1, p2 = data[:-1,0], data[:-1, 1], data[:-1, 2]
return t, p1, p2
t_QM, p1_QM, p2_QM = get_popu_data(path+"traj_QM.txt")
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))
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, t_QM / np.pi]
y1s = [p1_p, p1_sed, p1_MMST, p1_QM]
y2s = [p2_p, p2_sed, p2_MMST, p2_QM]
labels = [
"Sampling electronic ZPE (self-interaction)",
"Sampling photonic ZPE (vacuum fluctuations)", "Sampling both (MMST)", "QM"
]
colors1 = ["b--", "g--", "r--", 'k--']
colors2 = ["b--", "g-.", "r", 'k--']
ax = clp.initialize(col=1,
row=1,
width=4.6,
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])
return xnew, ynew
def calc_analytical(xs, ys, Nlst):
xnew, ynew = xs, []
for i in range(len(xs)):
x, y = xs[i], ys[i]
N = Nlst[i]
td = x[np.argmax(y)]
y_analytical = kFGR * N / 4.0 * np.cosh(kFGR*N / 2.0 * (x - td))**(-2)
ynew.append(y_analytical)
return xnew, ynew
Nlst = [7, 35]
labels = ["MMST N = %d" %N for N in Nlst]
ax = clp.initialize(col=1, row=1, width=4.3, height=4.3*0.618, fontsize=12, LaTeX=True)
xs, ys = gather_data(Nlst)
xnew, dydt = calculate_derivative(xs,ys)
clp.plotone(xnew, dydt, ax, labels=labels, colors=["c", "b"], xlabel="time ($t$)", ylabel="$d\\bar{\\rho}_{ee}/dt$", lw=2, xlim=[-0.005, 0.2])
xnew, dydt = calc_analytical(xnew,dydt, Nlst)
labels = ["mean-field N = %d" %N for N in Nlst]
clp.plotone(xnew, dydt, ax, labels=labels, colors=["k-.", "k--"], xlabel="time ($t$)", lw=2, xlim=[-0.005, 0.2], ylim=[-2, 30])
# output figure
clp.adjust(tight_layout=True, savefile="SE_Dicke_delay_time.pdf")
return np.convolve(x, np.ones((N, )) / N, mode='valid')
data_QM = get_data(path + "Ez2_QM.txt")
data_EhR = get_data(path + "Ez2_MultiEh.txt")
n = 11
Lcavity = 2.0 * np.pi
spacing_y = np.max(np.max(data_QM[1:-1, :])) * 1.15
fig, ax = clp.initialize(col=1,
row=1,
width=4.3,
height=6.8,
sharex=True,
return_fig_args=True,
sharey=True,
fontsize=12,
LaTeX=True)
N = 50 #average over 50 points
for i in range(n):
X = [data_EhR[:, 0], data_QM[:, 0]]
X = [x / np.pi for x in X]
Y = [data_EhR[:, i + 1], data_QM[:, i + 1]]
Y = [(y + spacing_y * i) / spacing_y for y in Y]
X_avg = [X[0][int(N / 2) - 1:-int(N / 2)]]
Y_avg = [running_mean(Y[0], N)]
X = X + X_avg
Y = Y + Y_avg
t_QM, p1_QM, p2_QM = get_popu_data("%s/traj_QM.txt" % path)
t_MMST, p1_MMST, p2_MMST = get_popu_data("%s/traj_MultiEh.txt" % path)
xs = [t_MMST / kFGR, t_QM / kFGR, t_QM / kFGR]
#y1s = [p1_MMST, p1_QM]
y2s = [p2_MMST, p2_QM, p2_QM[0] * np.exp(-kFGR * t_QM)]
return xs, y2s
labels = ["MMST", "QM", "free space decay"]
colors2 = ["r", "k", "0.7"]
axes = clp.initialize(col=1,
row=3,
width=4.3 * 2,
height=4.3 * 0.618,
fontsize=12,
LaTeX=True,
sharey=True,
labelthem=True,
labelthemPosition=[0.13, 0.95])
xs, y2s = gather_data("SE_101TLS_FDTD_dx_25")
clp.plotone(xs,
y2s,
axes[0],
labels=labels,
colors=colors2,
xlabel="time ($t$)",
ylabel="Excited state population",
alphaspacing=0.1,
lw=2)
def format_func_small(value, tick_number):
# find number of multiples of pi/2
N = value
if N == 0:
return "0"
else:
return r"${0}\pi$".format(int(1000 * N) / 1000.0)
fig, axes = clp.initialize(col=2,
row=4,
width=4.3 * 3,
height=6.3 * 2,
return_fig_args=True,
sharey=True,
fontsize=12,
LaTeX=True,
labelthem=True,
labelthemPosition=[0.08, 0.98])
N = 50 #average over 50 points
paths = [
"EM_FDTD_Full/", "EM_FDTD_Truncated/", "EM_MODE_Full/",
"EM_MODE_Truncated/"
]
methods = ["FDTD 400 modes", "FDTD 100 modes", "XP 400 modes", "XP 100 modes"]
for j in range(4):
data_QM = get_data(paths[j] + "Ez2_QM.txt")
values = np.vstack([m1, m2])
kernel = stats.gaussian_kde(values)
Z = np.reshape(kernel(positions).T, X.shape)
return X, Y, Z
n = 3
NSTART = 0
fig, axes = clp.initialize(col=3,
row=n,
width=6,
height=6,
sharex=True,
sharey=True,
commonX=[0.5, 0.00, "ground state popu."],
commonY=[0.01, 0.5, "excited state popu."],
return_fig_args=True,
LaTeX=True,
labelthem=True,
labelthemPosition=[0.95, 0.95])
Zgood = 0
for k in range(3):
if k == 0:
data_tot = np.loadtxt(path + "ElectronicDist_MultiEh.txt")
elif k == 1:
data_tot = np.loadtxt(path + "ElectronicDist_PreBinSQC.txt")
else:
data_tot = np.loadtxt(path + "ElectronicDist_StochasticED.txt")
for i in range(n):
def gather_data(path):
t_QM, p1_QM, p2_QM = get_popu_data("%s/traj_QM.txt" % path)
t_MMST, p1_MMST, p2_MMST = get_popu_data("%s/traj_MultiEh.txt" % path)
xs = [t_MMST / kFGR, t_QM / kFGR, t_QM / kFGR]
#y1s = [p1_MMST, p1_QM]
y2s = [p2_MMST, p2_QM, p2_QM[0] * np.exp(-kFGR * t_QM)]
return xs, y2s
labels = ["MMST", "QM", "free space decay"]
colors2 = ["r", "k--", "0.7"]
axes = clp.initialize(col=1,
row=2,
width=4.3 * 1.5,
height=4.3 * 0.618,
fontsize=12,
LaTeX=True,
sharey=True)
xs, y2s = gather_data("SE_1TLS_FDTD_near_mirror/dx_25")
clp.plotone(xs,
y2s,
axes[0],
labels=labels,
colors=colors2,
xlabel="time ($t$)",
ylabel="Excited state population",
alphaspacing=0.1,
lw=2,
xlim=[0, 1.0])