def plot_from_data(T_data, C_data, fname=None, ax=None, axins=None, Tmax=0.25):
if fname is not None:
base = fname[:-4]
method = os.path.split(fname)[-1].split('+')[0]
if method == 'itwl':
label = r'$1/t$-WL' + r'-$E_{barr}$=0.' + styles.get_barrier(
base)[0]
if method == 'sad':
label = r'SAD' + r'-$E_{barr}$=0.' + styles.get_barrier(base)[0]
if method == 'z':
label = r'ZMC' + r'-$E_{barr}$=0.' + styles.get_barrier(base)[0]
if method == 'tem':
label = r'TEM' + r'-$E_{barr}$=0.' + styles.get_barrier(base)[0]
else:
base = None
method = None
label = 'exact'
_, t_peak, _ = _set_temperatures(ax=ax, axins=axins, Tmax=Tmax)
ax.plot(T_data,
C_data,
label=label,
marker=styles.marker(base),
color=styles.color(base),
linestyle=styles.linestyle(base),
markevery=10)
# inset axes....
#axins = ax.inset_axes( 0.5 * np.array([1, 1, 0.47/0.5, 0.47/0.5]))#[0.005, 0.012, 25, 140])
axins.plot(T_data,
C_data,
label=label,
marker=styles.marker(base),
color=styles.color(base),
linestyle=styles.linestyle(base),
markevery=10)
x1, x2, y1, y2 = 0.002, 0.009, 5, 30
# axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
def plot(S, fname=None, ax=None, axins=None, Tmax=0.25):
timer = Timer(f'heat_capacity.plot {fname}')
if fname is not None:
base = fname[:-8]
method = base[:base.find('-')]
else:
base = None
method = None
t_low, t_peak, t_high = _set_temperatures(ax=ax, axins=axins, Tmax=Tmax)
timer_low_T = Timer('C for low T')
c_low = np.array([C(T, S) for T in t_low])
del timer_low_T
timer_peak = Timer('C for peak T')
c_peak = np.array([C(T, S) for T in t_peak])
del timer_peak
c_high = np.array([C(T, S) for T in t_high])
ax.plot(np.concatenate((t_low, t_peak, t_high)),
np.concatenate((c_low, c_peak, c_high)),
label=styles.pretty_label(base),
color=styles.color(base),
linestyle=styles.linestyle(base),
markevery=10)
# inset axes....
#axins = ax.inset_axes( 0.5 * np.array([1, 1, 0.47/0.5, 0.47/0.5]))#[0.005, 0.012, 25, 140])
axins.plot(np.concatenate((t_low, t_peak, t_high)),
np.concatenate((c_low, c_peak, c_high)),
label=styles.pretty_label(base),
marker=styles.marker(base),
color=styles.color(base),
linestyle=styles.linestyle(base),
markevery=10)
% (seed, ww, ff, N, method)
wildfilename = "data/s%03d/periodic-ww%04.2f-ff%04.2f-N%d-%s-%%s.dat" \
% (seed, ww, ff, N, method)
with open(filename, 'r') as content_file:
content = content_file.read()
init_iters[method].append(int(initialization_iters_regex.findall(content)[0]))
E_data = numpy.loadtxt(wildfilename % 'E', ndmin=2)
Emins[method].append(E_data[:, 0].max())
sample_data = numpy.loadtxt(wildfilename % 'ps', ndmin=2)
samples[method].append(sample_data[len(sample_data[:,1])-1, 1])
plt.figure('iters')
plt.semilogy(all_Ns, init_iters[method], styles.color(method)+'.-',
label=styles.title(method))
plt.figure('emin')
plt.plot(all_Ns, Emins[method],styles.color(method)+'.-', label=styles.title(method))
plt.figure('iters')
plt.xlabel('$N$')
plt.ylabel('Initialization iterations')
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-scaling.pdf" % (ww*100, ff*100))
plt.figure('emin')
plt.xlabel('$N$')
plt.ylabel('Emin')
plt.legend(loc='best').get_frame().set_alpha(0.25)
def plot(S, fname=None, ax=None, axins=None, Tmax=0.25):
timer = Timer(f'heat_capacity.plot {fname}')
if fname is not None:
base = fname[:-8]
method = base[:base.find('-')]
else:
base = None
method = None
# print('peak should be at', T_peak)
T_width = T_peak * 0.7 # this is just a guess
t_low = np.linspace(T_peak / 2, T_peak - T_width, 50)
t_peak = np.linspace(T_peak - T_width, T_peak + T_width, 150)
axins.set_xlim(T_peak - T_width, T_peak + T_width)
t_high = np.linspace(T_peak + T_width, Tmax, 50)
try:
c_low = np.loadtxt(f'{base}-cv_low_saved.txt')
except:
timer_low_T = Timer('C for low T')
c_low = np.array([C(T, S) for T in t_low])
del timer_low_T
np.savetxt(f'{base}-cv_low_saved.txt', c_low)
try:
c_peak = np.loadtxt(f'{base}-cv_peak_saved.txt')
except:
c_peak = np.array([C(T, S) for T in t_peak])
np.savetxt(f'{base}-cv_peak_saved.txt', c_peak)
try:
c_high = np.loadtxt(f'{base}-cv_high_saved.txt')
except:
c_high = np.array([C(T, S) for T in t_high])
np.savetxt(f'{base}-cv_high_saved.txt', c_high)
if method in {'wl', 'itwl', 'sad'}:
ax.plot(np.concatenate((t_low, t_peak, t_high)),
np.concatenate((c_low, c_peak, c_high)),
label=styles.pretty_label(base),
marker=styles.marker(base),
color=styles.color(base),
linestyle=styles.linestyle(base),
markevery=10)
elif method == 'z':
ax.plot(np.concatenate((t_low, t_peak, t_high)),
np.concatenate((c_low, c_peak, c_high)),
label=styles.pretty_label(base),
color=styles.color(base),
linestyle=styles.linestyle(base),
markevery=10)
else:
ax.plot(np.concatenate((t_low, t_peak, t_high)),
np.concatenate((c_low, c_peak, c_high)),
':',
label='exact',
linewidth=2,
color='tab:cyan')
# inset axes....
#axins = ax.inset_axes( 0.5 * np.array([1, 1, 0.47/0.5, 0.47/0.5]))#[0.005, 0.012, 25, 140])
if method in {'wl', 'itwl', 'sad'}:
precision = base[base.rfind('-') + 1:]
axins.plot(t_peak,
c_peak,
label=styles.pretty_label(base),
marker=styles.marker(base),
color=styles.color(base),
linestyle=styles.linestyle(base),
markevery=10)
elif method == 'z':
axins.plot(t_peak,
c_peak,
label=styles.pretty_label(base),
color=styles.color(base),
linestyle=styles.linestyle(base),
markevery=10)
else:
axins.plot(t_peak,
c_peak,
':',
label='exact',
linewidth=2,
color='tab:cyan')
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?
with open(filename, 'r') as content_file:
content = content_file.read()
init_iters[method].append(
int(initialization_iters_regex.findall(content)[0]))
E_data = numpy.loadtxt(wildfilename % 'E', ndmin=2)
Emins[method].append(E_data[:, 0].max())
sample_data = numpy.loadtxt(wildfilename % 'ps', ndmin=2)
samples[method].append(sample_data[len(sample_data[:, 1]) - 1, 1])
plt.figure('iters')
plt.semilogy(all_Ns,
init_iters[method],
styles.color(method) + '.-',
label=styles.title(method))
plt.figure('emin')
plt.plot(all_Ns,
Emins[method],
styles.color(method) + '.-',
label=styles.title(method))
plt.figure('iters')
plt.xlabel('$N$')
plt.ylabel('Initialization iterations')
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-scaling.pdf" %
(ww * 100, ff * 100))
for fname in paths:
print()
start_fname = time.process_time()
base = fname[:-8]
method = base[:base.find('-')]
energy_boundaries, mean_e, my_lnw, my_system, p_exc = compute.read_file(base)
# Create a function for the entropy
l_function, eee, sss = compute.linear_entropy(energy_boundaries, mean_e, my_lnw)
plt.figure('latest-entropy')
#plt.plot(E, normalize_S(l_function(E)), label=base)
if method in {'wl','itwl','sad'}:
plt.plot(E, normalize_S(l_function(E)), label=base, marker = styles.marker(base),
color = styles.color(base), linestyle= styles.linestyle(base), markevery=100)
elif method == 'z':
plt.plot(E, normalize_S(l_function(E)), label=base, color = styles.color(base), linestyle= styles.linestyle(base))
plt.figure('latest heat capacity')
heat_capacity.plot(l_function, fname=fname,ax=ax, axins=axins)
# correct_Cv = [heat_capacity.C(t,l_function) for t in T]
# if method in {'wl','itwl','sad'}:
# plt.plot(T, correct_Cv, label=base, marker = markers[precision], color = colors[method], linestyle= linestyles[method], markevery=5)
# elif method == 'z':
# plt.plot(T, correct_Cv, label=base, color = colors[method], linestyle= linestyles[method])
start_well_histogram = time.process_time()
plt.figure('fraction-well')
mean_which = np.loadtxt(f'{base}-which.dat')
plt.plot(mean_e, mean_which, label=base)
base)[0]
if method == 'tem':
label = r'TEM' + r'-$E_{barr}$=0.' + styles.get_barrier(
base)[0]
fname = tail + 'seed-' + str(seed) + front
data = np.load(fname)
for k in data.keys():
print(k)
E = data['E']
error_dist = data['error_dist_S']
plt.figure('Entropy error distribution')
plt.plot(E,
error_dist,
label=label,
marker=styles.marker(base),
color=styles.color(base),
linestyle=styles.linestyle(base),
markevery=75)
plt.figure('Canonical error incorrect peak')
plt.plot(data['E_dist'],
data['can_error_low_T'],
label=label,
marker=styles.marker(base),
color=styles.color(base),
linestyle=styles.linestyle(base),
markevery=75)
plt.figure('Canonical error phase transition')
plt.plot(data['E_dist'],
data['can_error_high_t'],
label=label,
marker=styles.marker(base),
def _plot_from_data(self, ax, axins, fname, data=None, data_bounds=None, subplot = None, dump_into_thesis = None):
if data is None:
data = self._query_fname(fname)
if data_bounds is not None:
lower_data, upper_data = data_bounds
base = fname[:-4]
method = os.path.split(fname)[-1].split('+')[0]
print(method)
if method == 'itwl':
label = r'$1/t$-WL' + r'-$E_{barr}$=0.'+styles.get_barrier(base)[0]
if method == 'sad':
label = r'SAD' + r'-$E_{barr}$=0.'+styles.get_barrier(base)[0]
if method == 'z':
label = r'ZMC' + r'-$E_{barr}$=0.'+styles.get_barrier(base)[0]
if method == 'tem':
label = r'TEM' + r'-$E_{barr}$=0.'+styles.get_barrier(base)[0]
if method in {'wl','itwl','sad', 'z'}:
plt.figure('fraction-well')
plt.plot(data['mean_e'], data['mean_which'], label=label)
if len(data['hist']) != 0:
plt.figure('histogram')
plt.plot(data['mean_e'], data['hist'], label=label)
plt.figure('latest-entropy')
plt.plot(data['E'][:len(data['S'])], data['S'],
label=label,
marker = styles.marker(base),
color = styles.color(base),
linestyle= styles.linestyle(base),
markevery=25)
plt.figure('convergence')
plt.loglog(data['moves'], data['errors_S'],
label=label,
marker = styles.marker(base),
color = styles.color(base),
linestyle= styles.linestyle(base),
markevery=4)
elif method == 'z':
plt.plot(data['E'], data['S'],
label=label,
color = styles.color(base),
linestyle= styles.linestyle(base))
if data_bounds is not None:
plt.fill_between(lower_data['moves'], lower_data['errors_S'], upper_data['errors_S'],
color = styles.color(base),
linestyle=styles.linestyle(base),
linewidth = 2,
alpha = 0.2)
heat_capacity.plot_from_data(data['T'][:len(data['C'])], data['C'],
fname=fname,
ax=ax,
axins=axins)
plt.figure('convergence-heat-capacity')
plt.loglog(data['moves'], data['errors_C'],
label=label,
marker = styles.marker(base),
color = styles.color(base),
linestyle= styles.linestyle(base),
markevery=4)
if data_bounds is not None:
plt.fill_between(lower_data['moves'], lower_data['errors_C'], upper_data['errors_C'],
color = styles.color(base),
alpha = 0.2)
if False:#subplot is not None:
axs = subplot[0]
axins_subplot = subplot[1]
if method in {'wl','itwl','sad'}:
axs['(c)'].plot(data['E'][:len(data['S'])], data['S'],
label=label,
marker = styles.marker(base),
color = styles.color(base),
linestyle= styles.linestyle(base),
markevery=250)
elif method == 'z':
axs['(c)'].plot(data['E'], data['S'],
label=label,
color = styles.color(base),
linestyle= styles.linestyle(base))
heat_capacity.plot_from_data(data['T'][:len(data['C'])], data['C'],
fname=fname,
ax=axs['(d)'],
axins=axins_subplot)
plt.figure('convergence')
if method in {'wl','itwl','sad'}:
axs['(a)'].loglog(data['moves'], data['errors_S'],
label=label,
marker = styles.marker(base),
color = styles.color(base),
linestyle= styles.linestyle(base),
markevery=2)
elif method == 'z':
axs['(a)'].loglog(data['moves'], data['errors_S'],
label=label,
color = styles.color(base),
linestyle= styles.linestyle(base),
linewidth = 3)
if data_bounds is not None:
axs['(a)'].fill_between(lower_data['moves'], lower_data['errors_S'], upper_data['errors_S'],
color = styles.color(base),
linestyle=styles.linestyle(base),
linewidth = 2,
alpha = 0.2)
plt.figure('convergence-heat-capacity')
if method in {'wl','itwl','sad'}:
axs['(b)'].loglog(data['moves'], data['errors_C'],
label=label,
marker = styles.marker(base),
color = styles.color(base),
linestyle= styles.linestyle(base),
markevery=2)
elif method == 'z':
axs['(b)'].loglog(data['moves'], data['errors_C'],
label=label,
color = styles.color(base),
linestyle= styles.linestyle(base),
linewidth = 3)
if data_bounds is not None:
axs['(b)'].fill_between(lower_data['moves'], lower_data['errors_C'], upper_data['errors_C'],
color = styles.color(base),
alpha = 0.2)
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)
xmin, xmax = plt.xlim()
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)
xmin,xmax = plt.xlim()
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),
markerfacecolor='none', markeredgecolor=styles.color(method),
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],