def __radd__(self, gg):
gg = deepcopy(gg)
if self.name:
gg.ylab = self.name.title()
if self.labels and self.labels in LABEL_FORMATS:
format_func = LABEL_FORMATS[self.labels]
gg.ytick_formatter = FuncFormatter(format_func)
if self.limits:
gg.ylimits = self.limits
if self.breaks:
gg.ybreaks = self.breaks
return gg
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")
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")
def plot_tau_preview(self, on_id="on", off_id="off", pyrlevel=2):
"""Plot a preview of current tau_on, tau_off and AA images.
AA is plotted twice in 1st row of subplots in 2 diffent value ranges.
:param str on_id: string ID of onband filter ("on")
:param str off_id: string ID of offband filter ("off")
:param pyrlevel: provide any integer here to reduce the image
sizes using a gaussian pyramide approach (2)
"""
lists = {}
tm = {on_id: 1, off_id: 1}
def fmt(num):
return '{:.1e}'.format(num)
for list_id in [on_id, off_id]:
try:
l = self.get_list(list_id)
lists[list_id] = l
if not l.bg_model.ready_2_go():
print("Tau preview could not be plotted, bg model is not "
" ready for filter: %s" % list_id)
return 0
if not l.tau_mode:
tm[list_id] = 0
l.activate_tau_mode()
except BaseException:
print(format_exc())
return 0
fig, axes = subplots(2, 2, figsize=(16, 10))
tau_on = lists[on_id].current_img().pyr_down(pyrlevel)
t_on_str = lists[on_id].current_time_str()
tau_off = lists[off_id].current_img().pyr_down(pyrlevel)
t_off_str = lists[off_id].current_time_str()
aa = tau_on - tau_off # AA image object
tau_max = max([tau_on.img.max(), tau_off.img.max(), aa.img.max()])
tau_min = max([tau_on.img.min(), tau_off.img.min(), aa.img.min()])
# make a color map for the index range
cmap = shifted_color_map(tau_min, tau_max)
im = axes[0, 0].imshow(tau_on.img,
cmap=cmap,
vmin=tau_min,
vmax=tau_max)
fig.colorbar(im, ax=axes[0, 0], format=FuncFormatter(fmt))
axes[0, 0].set_title("tau on: %s" % t_on_str)
im = axes[0, 1].imshow(tau_off.img,
cmap=cmap,
vmin=tau_min,
vmax=tau_max)
fig.colorbar(im, ax=axes[0, 1], format=FuncFormatter(fmt))
axes[0, 1].set_title("tau off: %s" % t_off_str)
im = axes[1, 0].imshow(aa.img, cmap=cmap, vmin=tau_min, vmax=tau_max)
fig.colorbar(im, ax=axes[1, 0], format=FuncFormatter(fmt))
axes[1, 0].set_title("AA (vals scaled)")
cmap = shifted_color_map(aa.img.min(), aa.img.max())
im = axes[1, 1].imshow(aa.img, cmap=cmap)
fig.colorbar(im, ax=axes[1, 1], format=FuncFormatter(fmt))
axes[1, 1].set_title("AA")
tight_layout()
for k, v in six.iteritems(tm):
lists[k].activate_tau_mode(v)
return axes