print('maxe', maxe)
print('minlndos', minlndos)
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:')
]
first_method = True
the_first_method = ''
first_temperature = [True, True, True]
methods = [
'-tmi',
'-toe',
'-tmmc',
]
for i in range(len(methods)):
method = methods[i]
fbase = 'data/lv/ww%.2f-ff%.2f-%gx%g%s' % (ww, ff, lenx, lenyz, method)
fname = fbase + '-density.dat'
try:
minT = readandcompute.minT(fname)
convergedT = readandcompute.convergedT(fname)
for T in Ts:
if T >= minT and T >= convergedT * 0.8:
density, x = readandcompute.density_x(fbase, T)
plt.plot(x / 2, density, color(T) + lines[i])
if first_method or method == the_first_method:
if first_temperature[i]:
plt.plot(x / 2,
density,
color(T) + lines[i],
label='T=%g %s (converged to %.2g)' %
(T, method[1:], convergedT))
first_temperature[i] = False
else:
print 'numframes', numframes
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)
try:
return colors[T]
except:
return ''
lines = ['-', '--', ':',]
first_method = True
the_first_method = ''
first_temperature = [True, True, True]
methods = ['-tmi', '-toe', '-tmmc', ]
for i in range(len(methods)):
method = methods[i]
fbase = 'data/lv/ww%.2f-ff%.2f-%gx%g%s' % (ww,ff,lenx,lenyz,method)
fname = fbase + '-density.dat'
try:
minT = readandcompute.minT(fname)
convergedT = readandcompute.convergedT(fname)
for T in Ts:
if T >= minT and T >= convergedT*0.8:
density, x = readandcompute.density_x(fbase, T)
plt.plot(x/2, density, color(T)+lines[i])
if first_method or method == the_first_method:
if first_temperature[i]:
plt.plot(x/2, density, color(T)+lines[i],
label='T=%g %s (converged to %.2g)' % (T, method[1:], convergedT))
first_temperature[i] = False
else:
plt.plot(x/2, density, color(T)+lines[i], label='T=%g' % T)
the_first_method = method
first_method = False
