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--')