# input: ["data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-%s.dat" % (seed, ww, ff, N, method, data) for method in methods for data in ["E","lnw"]]
minlog = 0
for method in methods:
e_hist = numpy.loadtxt("data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-E.dat"
% (seed, ww, ff, N, method))
lnw = numpy.loadtxt("data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-lnw.dat"
% (seed, ww, ff, N, method))
energy = -e_hist[:,0]/N
log10w = lnw[e_hist[:,0].astype(int),1]*numpy.log10(numpy.exp(1))
log10_dos = numpy.log10(e_hist[:,1]) - log10w
log10_dos -= log10_dos.max()
if log10_dos.min() < minlog:
minlog = log10_dos.min()
plt.figure('dos')
plt.plot(energy, log10_dos, styles.dots(method),label=styles.title(method))
log10w += log10w.min()
if log10w.min() < minlog:
minlog = log10w.min()
plt.figure('w')
plt.plot(energy, -log10w, styles.dots(method),label=styles.title(method))
def tentothe(n):
if n == 0:
return '1'
if n == 10:
return '10'
if int(n) == n:
return r'$10^{%d}$' % n
golden_u = t_u_cv_s[1]
reference_error = mean_u_err(ww, ff, N, reference, golden_u)
if reference_error == None:
continue
print ' ', reference, reference_error
for method in methods:
plt.figure(2)
method_err = mean_u_err(ww, ff, N, method, golden_u)
if method_err != None:
print ' ', method, method_err
plt.figure(1)
if method not in labels_added:
plt.loglog(reference_error,
method_err,
styles.dots(method),
markerfacecolor='none',
markeredgecolor=styles.color(method),
label=styles.title(method))
labels_added |= set([method])
else:
plt.loglog(reference_error,
method_err,
styles.dots(method),
markerfacecolor='none',
markeredgecolor=styles.color(method))
plt.figure(2)
plt.figure(2)
plt.legend(loc='best')
plt.savefig('figs/energy-for-debugging-ww%04.2f-ff%04.2f-N%i.pdf' %
(ww, ff, N))
ff = float(sys.argv[2])
#arg ff = [0.1, 0.2, 0.3, 0.4]
N = int(sys.argv[3])
#arg N = range(5,21)+[100, 200, 1000]
methods = eval(sys.argv[4])
#arg methods = [["nw","kT0.4","kT0.5","wang_landau","simple_flat","tmmc","oetmmc"]]
seed = int(sys.argv[5])
#arg seed = [0]
# input: ["data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-E.dat" % (seed, ww, ff, N, version) for version in methods]
plt.title('Energy histogram for $\lambda=%g$, $\eta=%g$, and $N=%i$' %
(ww, ff, N))
for version in methods:
data = numpy.loadtxt("data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-E.dat" %
(seed, ww, ff, N, version))
energy = -data[:, 0] / N
DS = data[:, 1]
plt.semilogy(energy, DS, styles.dots(version), label=styles.title(version))
plt.xlabel('$U/N\epsilon$')
plt.ylabel('$H$')
plt.legend(loc='best').get_frame().set_alpha(0.25)
plt.tight_layout(pad=0.2)
plt.savefig("figs/periodic-ww%02.0f-ff%02.0f-N%i-E.pdf" %
(ww * 100, ff * 100, N))
plt.xlabel(r'$T$')
plt.ylabel(r'$\Delta U$')
golden_u = t_u_cv_s[1]
reference_error = mean_u_err(ww, ff, N, reference, golden_u)
if reference_error == None:
continue
print ' ', reference, reference_error
for method in methods:
plt.figure(2)
method_err = mean_u_err(ww, ff, N, method, golden_u)
if method_err != None:
print ' ', method, method_err
plt.figure(1)
if method not in labels_added:
plt.loglog(reference_error, method_err, styles.dots(method),
markerfacecolor='none', markeredgecolor=styles.color(method),
label=styles.title(method))
labels_added |= set([method])
else:
plt.loglog(reference_error, method_err, styles.dots(method),
markerfacecolor='none', markeredgecolor=styles.color(method))
plt.figure(2)
plt.figure(2)
plt.legend(loc='best')
plt.savefig(
'figs/energy-for-debugging-ww%04.2f-ff%04.2f-N%i.pdf' % (ww, ff, N))
min_T = 0.2 # FIXME maybe shouldn't hardcode this?
plt.figure(1)
plt.xlabel(r'$T$')
plt.ylabel(r'$\Delta U$')
golden_u = t_u_cv_s[1]
reference_error = mean_u_err(ww, ff, N, reference, golden_u)
if reference_error == None:
continue
print(' ', reference, reference_error)
for method in methods:
plt.figure(2)
method_err = mean_u_err(ww, ff, N, method, golden_u)
if method_err != None:
print(' ', method, method_err)
plt.figure(1)
if method not in labels_added:
plt.loglog(reference_error, method_err, styles.dots(method),
markerfacecolor='none', markeredgecolor=styles.color(method),
label=styles.title(method))
labels_added |= {method}
else:
plt.loglog(reference_error, method_err, styles.dots(method),
markerfacecolor='none', markeredgecolor=styles.color(method))
plt.figure(2)
plt.figure(2)
plt.legend(loc='best')
plt.savefig(
'figs/energy-for-debugging-ww%04.2f-ff%04.2f-N%i.pdf' % (ww, ff, N))
min_T = 0.2 # FIXME maybe shouldn't hardcode this?
plt.figure(1)
log10_dos = numpy.log10(e_hist[:,1]) - log10w
log10_dos -= log10_dos.max()
return energy, e_hist, log10w, log10_dos
for method in methods:
wildfilename = "../data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-%%s.dat" \
% (seed, ww, ff, N, method)
energy, e_hist, log10w, log10_dos = get_arrays(wildfilename)
if method != 'wang_landau':
plt.figure('dos-poster')
plt.plot(energy, log10_dos, styles.dots(method),
markerfacecolor='none', markeredgecolor=styles.color(method),
label=styles.title(method))
for ax in [ ax_dos, ax_dos_all ]:
ax.plot(energy[log10_dos > -cap], log10_dos[log10_dos > -cap],
styles.dots(method), markerfacecolor='none',
markeredgecolor=styles.color(method), label=styles.title(method))
for ax in [ ax_hist, ax_hist_all ]:
ax.semilogy(energy, e_hist[:,1], styles.dots(method),
markerfacecolor='none', markeredgecolor=styles.color(method),
label=styles.title(method))
for ax in [ ax_lnw, ax_lnw_all ]:
ax.plot(energy[log10w < cap], log10w[log10w < cap], styles.dots(method),
plt.legend(loc='best')
plt.tight_layout(pad=0.2)
plt.savefig("figs/s-scaling.pdf")
### error vs error figures
methods.remove(reference)
plt.figure('u',figsize=fig_size)
plt.figure('cv',figsize=fig_size)
plt.figure('s',figsize=fig_size)
for n in range(len(Ns)):
for method in methods:
plt.figure('u')
plt.loglog(u_errors[reference][n], u_errors[method][n], styles.dots(method),
markerfacecolor='none', markeredgecolor=styles.color(method),
label=styles.title(method))
plt.figure('cv')
plt.loglog(cv_errors[reference][n], cv_errors[method][n], styles.dots(method),
markerfacecolor='none', markeredgecolor=styles.color(method),
label=styles.title(method))
plt.figure('s')
plt.loglog(s_errors[reference][n], s_errors[method][n], styles.dots(method),
markerfacecolor='none', markeredgecolor=styles.color(method),
label=styles.title(method))
for fig in ['u','cv','s']:
plt.figure(fig)
xmin,xmax = plt.xlim()
ymin,ymax = plt.ylim()
ww = float(sys.argv[1])
# arg ww = [1.3, 1.5, 2.0, 3.0]
ff = float(sys.argv[2])
# arg ff = [0.1, 0.2, 0.3, 0.4]
N = int(sys.argv[3])
# arg N = range(5,21)+[100, 200, 1000]
methods = eval(sys.argv[4])
# arg methods = [["nw","kT0.4","kT0.5","wang_landau","simple_flat","tmmc","oetmmc"]]
seed = int(sys.argv[5])
# arg seed = [0]
# input: ["data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-E.dat" % (seed, ww, ff, N, version) for version in methods]
plt.title("Energy histogram for $\lambda=%g$, $\eta=%g$, and $N=%i$" % (ww, ff, N))
for version in methods:
data = numpy.loadtxt("data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-E.dat" % (seed, ww, ff, N, version))
energy = -data[:, 0] / N
DS = data[:, 1]
plt.semilogy(energy, DS, styles.dots(version), label=styles.title(version))
plt.xlabel("$U/N\epsilon$")
plt.ylabel("$H$")
plt.legend(loc="best").get_frame().set_alpha(0.25)
plt.tight_layout(pad=0.2)
plt.savefig("figs/periodic-ww%02.0f-ff%02.0f-N%i-E.pdf" % (ww * 100, ff * 100, N))
# input: ["data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-%s.dat" % (seed, ww, ff, N, method, data) for method in methods for data in ["E","lnw"]]
minlog = 0
for method in methods:
e_hist = numpy.loadtxt("data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-E.dat"
% (seed, ww, ff, N, method))
lnw = numpy.loadtxt("data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-lnw.dat"
% (seed, ww, ff, N, method))
energy = -e_hist[:, 0]/N
log10w = lnw[e_hist[:, 0].astype(int), 1]*numpy.log10(numpy.exp(1))
log10_dos = numpy.log10(e_hist[:, 1]) - log10w
log10_dos -= log10_dos.max()
if log10_dos.min() < minlog:
minlog = log10_dos.min()
plt.figure('dos')
plt.plot(energy, log10_dos, styles.dots(method), label=styles.title(method))
log10w += log10w.min()
if log10w.min() < minlog:
minlog = log10w.min()
plt.figure('w')
plt.plot(energy, -log10w, styles.dots(method), label=styles.title(method))
def tentothe(n):
if n == 0:
return '1'
if n == 10:
return '10'
if int(n) == n:
return r'$10^{%d}$' % n
seed = int(sys.argv[5])
#arg seed = [0]
# input: ["data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-ps.dat" % (seed, ww, ff, N, method) for method in methods]
plt.title('Iterations per sample for $\lambda=%g$, $\eta=%g$, and $N=%i$' % (ww, ff, N))
for method in methods:
data_file = "data/s%03d/periodic-ww%04.2f-ff%04.2f-N%i-%s-ps.dat" \
% (seed, ww, ff, N, method)
with open(data_file,'r') as file_handle:
for line in file_handle:
entries = line.split()
if 'iterations:' in entries:
iterations = int(entries[entries.index('iterations:')+1].replace(',',''))
continue
data = numpy.loadtxt(data_file)
energy = -data[:,0]/N
round_trips = data[:,1]
energy = energy[round_trips != 0]
round_trips = round_trips[round_trips != 0]
if sum(round_trips) > 0:
plt.semilogy(energy, iterations/round_trips, styles.dots(method),
label=styles.title(method))
plt.xlabel('$U/N\epsilon$')
plt.ylabel('Iterations per sample')
plt.legend(loc='best').get_frame().set_alpha(0.25)
plt.tight_layout(pad=0.2)
plt.savefig("figs/periodic-ww%02.0f-ff%02.0f-N%i-sample-rate.pdf" % (ww*100, ff*100, N))
### error vs error figures
methods.remove(reference)
plt.figure("u", figsize=fig_size)
plt.figure("cv", figsize=fig_size)
plt.figure("s", figsize=fig_size)
for n in range(len(Ns)):
for method in methods:
plt.figure("u")
plt.loglog(
u_errors[reference][n],
u_errors[method][n],
styles.dots(method),
markerfacecolor="none",
markeredgecolor=styles.color(method),
label=styles.title(method),
)
plt.figure("cv")
plt.loglog(
cv_errors[reference][n],
cv_errors[method][n],
styles.dots(method),
markerfacecolor="none",
markeredgecolor=styles.color(method),
label=styles.title(method),
)
plt.figure("s")
plt.loglog(
min_energy = 0
for i in range(len(energies)):
min_e = min(energies[i])
if min_e < min_energy:
min_energy = int(min_e)
energy_num = -min_energy + 1
energy[method] = -numpy.array(range(energy_num))
net_rate[method] = numpy.zeros(energy_num)
for i in range(len(seeds)):
for j in range(len(rates[i])):
net_rate[method][-energies[i][j]] += rates[i][j]
net_rate[method] /= len(seeds)
energy[method] = energy[method][net_rate[method] != 0]
net_rate[method] = net_rate[method][net_rate[method] != 0]
for method in methods:
plt.semilogy(energy[method], net_rate[method], styles.dots(method),
markerfacecolor='none', markeredgecolor=styles.color(method),
label=styles.title(method))
plt.axvline(min_e,linewidth=1,color='k',linestyle=':')
plt.xlabel('$E/\epsilon$')
plt.ylabel('$r_p(E)$')
ax.legend(loc='upper right',bbox_to_anchor=(1.65,0.95))
plt.savefig("figs/sample-rates.pdf")