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)
