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: