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'$E$')
ax.set_ylim(1.1 * minlndos, maxlndos)
# ax.set_xlim(-5, -0.3)
ax.set_xlim(mine, maxe)
ax.set_ylabel(r'$\ln DOS$')
# ax.legend(loc='best').get_frame().set_alpha(0.25)
if 'tmi' in sys.argv:
plt.title(
plt.cla()
basename = dataformat % frame
old_min_T = min_T
min_T = readandcompute.minT_from_transitions(basename)
# e, diff = readandcompute.e_diffusion_estimate(basename)
try:
N = readandcompute.read_N(basename)
ax.axvline(-readandcompute.max_entropy_state(basename),
color='r',
linestyle=':')
ax.axvline(-readandcompute.min_important_energy(basename),
color='b',
linestyle=':')
ax.axvline(-readandcompute.converged_state(basename + '-lndos.dat'),
color='c',
linestyle=':')
except:
pass
e, init_hist = readandcompute.e_and_total_init_histogram(basename)
if min_T != old_min_T:
print('min_T goes from', old_min_T, 'to', min_T)
baseline_init_hist = init_hist
baseline_e = e
ax.plot(e,
init_hist,
'b-',
label=r'%e initialization iterations' %
(sum(init_hist) / float(N)))
for frame in xrange(numframes):
if frame % 10 == 0:
print 'working on frame %d/%d' % (frame, numframes)
plt.cla()
basename = dataformat % frame
old_min_T = min_T
min_T = readandcompute.minT_from_transitions(basename)
# e, diff = readandcompute.e_diffusion_estimate(basename)
try:
N = readandcompute.read_N(basename)
ax.axvline(-readandcompute.max_entropy_state(basename), color='r', linestyle=':')
ax.axvline(-readandcompute.min_important_energy(basename), color='b', linestyle=':')
ax.axvline(-readandcompute.converged_state(basename+'-lndos.dat'), color='c', linestyle=':')
except:
pass
e, init_hist = readandcompute.e_and_total_init_histogram(basename)
if min_T != old_min_T:
print 'min_T goes from', old_min_T, 'to', min_T
baseline_init_hist = init_hist
baseline_e = e
ax.plot(e, init_hist, 'b-',
label=r'%e initialization iterations' % (sum(init_hist)/float(N)))
# newstuff below is init_hist - baseline_init_hist, but takes into
# account the fact that these arrays might not be the same size.
# So we look up the element with the corresponding energy.
if len(e) != len(baseline_e):
newstuff = 1.0*init_hist
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))
plt.legend(loc='best')
fname = '%s/frame%06d.png' % (moviedir, frame)
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'$E$')
ax.set_ylim(1.1*minlndos, maxlndos)
# ax.set_xlim(-5, -0.3)
ax.set_xlim(mine, maxe)
ax.set_ylabel(r'$\ln DOS$')
# ax.legend(loc='best').get_frame().set_alpha(0.25)
if 'tmi' in sys.argv:
plt.title(r'lv movie with $\lambda = %g$, $\eta = %g$, $%g\times %g$ ($T_{min} = %g$) tmi'
% (ww, ff, lenx, lenyz, min_T))
elif 'toe' in sys.argv: