r.par(cex=options.cex) inv_beta = arange(options.inv_beta_min, options.inv_beta_max, 0.01) beta = 1.0/inv_beta lnZ = vectorize(cp.lnZ)(beta) r.plot(inv_beta, lnZ, type='l', xlab=r("expression(beta**-1)"), ylab=r("""expression(paste("ln ", Z(beta)))""")) data.append((cp.number, "lnZ", (inv_beta, lnZ))) betaF = vectorize(cp.betaF)(beta) r.plot(inv_beta, betaF, type='l', xlab=r("expression(beta**-1)"), ylab=r("expression(F(beta) * beta)")) data.append((cp.number, "betaF", (inv_beta, betaF))) S = vectorize(cp.S)(beta) r.plot(inv_beta, S, type='l', xlab=r("expression(beta**-1)"), ylab=r("expression(S(beta) / k[B])")) data.append((cp.number, "S", (inv_beta, S))) E = vectorize(cp.E)(beta) r.plot(inv_beta, E, type='l', xlab=r("expression(beta**-1)"), ylab=r("expression(bar(E)(beta))")) data.append((cp.number, "E", (inv_beta, E))) C = vectorize(cp.C)(beta) r.plot(inv_beta, C, type='l', xlab=r("expression(beta**-1)"), ylab=r("expression(C(beta)/k[B])")) data.append((cp.number, "C", (inv_beta, C))) r.graphics_off() # Dump the output as a pickle if options.pickle!=None: pickle_to_file(data, options.pickle)
if args.N: p = plot_entry_list(pdf, log_dict.get('N',[]), binning, binning_dict, None, None, xlab=args.xlab, ylab=r"$N$", normalize_log_space=False, xmin=xmin, xmax=xmax, main=args.main, bin_numbers=args.bin_numbers, color=args.color) points += p if args.s: p = plot_sum_N(pdf, log_dict.get('N',[]), binning, binning_dict, bin_widths, bin_widths_dict, xlab=args.xlab, ylab=r"$N$", normalize_log_space=False, xmin=xmin, xmax=xmax, main=args.main, bin_numbers=args.bin_numbers, color=args.color) points += p if args.S: p = plot_sum_N(pdf, log_dict.get('N',[]), binning, binning_dict, None, None, xlab=args.xlab, ylab=r"$N$", normalize_log_space=False, xmin=xmin, xmax=xmax, main=args.main, bin_numbers=args.bin_numbers, color=args.color) points += p if args.bins: plot_binning(pdf, binning, main=args.main, color=args.color) plot_bin_widths(pdf, bin_widths, log_space=False, main=args.main, color=args.color) plot_bin_widths(pdf, bin_widths, log_space=True, main=args.main, color=args.color) pdf.close() # Dump the output as a pickle if args.pickle!=None: pickle_to_file(points, args.pickle)
if options.s: p = plot_sum_N(log_dict.get('N',[]), binning, binning_dict, bin_widths, bin_widths_dict, xlab=options.xlab, ylab="N", normalize_log_space=False, xmin=xmin, xmax=xmax, main=options.main, bin_numbers=options.bin_numbers) points += p if options.S: p = plot_sum_N(log_dict.get('N',[]), binning, binning_dict, None, None, xlab=options.xlab, ylab="N", normalize_log_space=False, xmin=xmin, xmax=xmax, main=options.main, bin_numbers=options.bin_numbers) points += p if options.bins: plot_binning(binning, main=options.main) plot_bin_widths(bin_widths, log_space=False, main=options.main) plot_bin_widths(bin_widths, log_space=True, main=options.main) try: r.graphics_off() except: r['graphics.off']() # Dump the output as a pickle if options.pickle!=None: pickle_to_file(points, options.pickle)
ax = fig.add_subplot(111) ax.plot(inv_beta, S, color=args.color) ax.set_xlabel(r"$\beta^{-1}$") ax.set_ylabel(r"$S(\beta) / k_\mathrm{B}$") pdf.savefig() data.append((cp.number, "S", (inv_beta, S))) E = vectorize(cp.E)(beta) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(inv_beta, E, color=args.color) ax.set_xlabel(r"$\beta^{-1}$") ax.set_ylabel(r"$\bar{E}(\beta)$") pdf.savefig() data.append((cp.number, "E", (inv_beta, E))) C = vectorize(cp.C)(beta) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(inv_beta, C, color=args.color) ax.set_xlabel(r"$\beta^{-1}$") ax.set_ylabel(r"$C(\beta) / k_\mathrm{B}$") pdf.savefig() data.append((cp.number, "C", (inv_beta, C))) pdf.close() # Dump the output as a pickle if args.pickle != None: pickle_to_file(data, args.pickle)