print('maxlndos', maxlndos)
print('numframes', numframes)
for frame in range(numframes):
if frame % 10 == 0:
print('working on frame %d/%d' % (frame, numframes))
plt.cla()
basename = dataformat % frame
e, lndos = readandcompute.e_lndos(basename)
ax.plot(e, lndos, 'k-')
datname = basename + '-lndos.dat'
min_T = readandcompute.minT(datname)
try:
ax.axvline(-readandcompute.max_entropy_state(basename),
color='r',
linestyle=':')
min_important_energy = readandcompute.min_important_energy(basename)
ax.axvline(-min_important_energy, color='b', linestyle=':')
ax.plot(e, (e + min_important_energy) / min_T +
lndos[min_important_energy], 'g--')
ax.axvline(-readandcompute.converged_state(datname),
color='c',
linestyle=':')
except:
pass
e, lnw = readandcompute.e_lnw(basename)
ax.plot(e, -lnw, 'r:')
ax.set_xlabel(r'$E$')
for frame in xrange(numframes):
if frame % 10 == 0:
print 'working on frame %d/%d' % (frame, numframes)
plt.cla()
for suffix_index in range(len(suffixes)):
suffix = suffixes[suffix_index]
basename = dataformat % (suffix, frame*skipby)
try:
e, hist = readandcompute.e_and_total_init_histogram(basename)
ax.plot(e, hist, colors[suffix_index]+'-', label=suffix)
datname = basename+'-transitions.dat'
min_T = readandcompute.minT(datname)
ax.axvline(-readandcompute.max_entropy_state(basename), color='r', linestyle=':')
min_important_energy = readandcompute.min_important_energy(basename)
ax.axvline(-min_important_energy, color='b', linestyle=':')
ax.axvline(-readandcompute.converged_state(datname), color=colors[suffix_index], linestyle=':')
except (KeyboardInterrupt, SystemExit):
raise
except:
pass
ax.set_xlabel(r'$E$')
ax.set_ylim(0, maxhist)
# ax.set_xlim(-5, -0.3)
ax.set_xlim(mine, maxe)
ax.set_ylabel(r'histogram')
# ax.legend(loc='best').get_frame().set_alpha(0.25)
plt.title(r'lv movie from %s ($T_{min} = %g$)' % (filename, min_T))
ffs = eval(sys.argv[2])
#arg ffs = [[0.1,0.2,0.3]]
lenx = float(sys.argv[3])
#arg lenx = [50,100]
lenyz = float(sys.argv[4])
#arg lenyz = [10]
fig, axD = plt.subplots()
axT = plt.twinx()
for ff in ffs:
basename = 'data/lv/ww%.2f-ff%.2f-%gx%g' % (ww, ff, lenx, lenyz)
e, diff = readandcompute.e_diffusion_estimate(basename)
N = readandcompute.read_N(basename)
axD.plot(e, diff, label=r'$\eta = %g$' % ff)
axD.axvline(-readandcompute.max_entropy_state(basename) / N, linestyle=':')
axD.axvline(-readandcompute.min_important_energy(basename) / N,
linestyle='--')
T, u, cv, s, minT = readandcompute.T_u_cv_s_minT(basename)
axT.plot(u / N, T, 'r-')
axT.set_ylim(0, 3)
axT.axhline(minT, color='r', linestyle=':')
e, hist = readandcompute.e_hist(basename)
axT.plot(e / N, 2.5 * hist / hist.max(), 'k:')
e, init_hist = readandcompute.e_and_total_init_histogram(basename)
for i in range(len(e)):
print(e[i], (2.5 * init_hist / init_hist.max())[i])
axT.plot(e, 2.5 * init_hist / init_hist.max(), 'c--')
all_colors = ['b', 'r', 'g', 'k']
colors = {}
for i in xrange(len(ffs)):
colors[ffs[i]] = all_colors[i]
sleeptime = 30*60 # 30 minutes
for frame in xrange(100000):
plt.cla()
for ff in ffs:
basename = 'data/lv/ww%.2f-ff%.2f-%gx%g' % (ww,ff,lenx,lenyz)
e, diff = readandcompute.e_diffusion_estimate(basename)
N = readandcompute.read_N(basename);
try:
ax.axvline(-readandcompute.max_entropy_state(basename)/N, color=colors[ff], linestyle=':')
ax.axvline(-readandcompute.min_important_energy(basename)/N, color=colors[ff], linestyle=':')
T, u, cv, s, minT = readandcompute.T_u_cv_s_minT(basename)
ax.plot(u/N, T, 'k-')
ax.set_ylim(0, 3)
ax.axhline(minT, color='r', linestyle=':')
e, hist = readandcompute.e_hist(basename)
iterations = readandcompute.iterations(basename)
ax.plot(e/N, 2.5*hist/hist.max(), colors[ff]+'-', label=r'$\eta = %g$, %e iterations' % (ff, iterations))
except:
pass
e, init_hist = readandcompute.e_and_total_init_histogram(basename)
ax.plot(e, 2.5*init_hist/init_hist.max(), colors[ff]+'--',
ffs = eval(sys.argv[2])
#arg ffs = [[0.1,0.2,0.3]]
lenx = float(sys.argv[3])
#arg lenx = [50,100]
lenyz = float(sys.argv[4])
#arg lenyz = [10]
fig, axD = plt.subplots()
axT = plt.twinx()
for ff in ffs:
basename = 'data/lv/ww%.2f-ff%.2f-%gx%g' % (ww,ff,lenx,lenyz)
e, diff = readandcompute.e_diffusion_estimate(basename)
N = readandcompute.read_N(basename);
axD.plot(e, diff, label=r'$\eta = %g$' % ff)
axD.axvline(-readandcompute.max_entropy_state(basename)/N, linestyle=':')
axD.axvline(-readandcompute.min_important_energy(basename)/N, linestyle='--')
T, u, cv, s, minT = readandcompute.T_u_cv_s_minT(basename)
axT.plot(u/N, T, 'r-')
axT.set_ylim(0, 3)
axT.axhline(minT, color='r', linestyle=':')
e, hist = readandcompute.e_hist(basename)
axT.plot(e/N, 2.5*hist/hist.max(), 'k:')
e, init_hist = readandcompute.e_and_total_init_histogram(basename)
for i in xrange(len(e)):
print e[i], (2.5*init_hist/init_hist.max())[i]
axT.plot(e, 2.5*init_hist/init_hist.max(), 'c--')