def mean_u_err(ww, ff, N, method, golden_u):
toterr = 0.0
numseeds = 0
for seed in seeds:
t_u_cv_s_method = readandcompute.T_u_cv_s_minT(ww, ff, N, method, seed)
if t_u_cv_s_method != None:
du = abs((t_u_cv_s_method[1] -
golden_u)[t_u_cv_s_method[0] > Tmax]).max()
lw = 0.01
if seed == 0:
plt.plot(t_u_cv_s_method[0],
(t_u_cv_s_method[1] - golden_u) / N,
styles.plot(method),
label=styles.title(method),
linewidth=lw)
else:
plt.plot(t_u_cv_s_method[0],
(t_u_cv_s_method[1] - golden_u) / N,
styles.plot(method),
linewidth=lw)
toterr += du
numseeds += 1
if numseeds > 0:
return toterr / numseeds / N
return None
#arg Ns = [[500,1372,4000]]
# make figure with axes labeled using scientific notation
def sci_fig(handle):
fig = plt.figure(handle)
ax = fig.add_subplot(1, 1, 1)
fmt = matplotlib.ticker.ScalarFormatter(useMathText=True)
fmt.set_powerlimits((-2, 3))
fmt.set_scientific(True)
ax.yaxis.set_major_formatter(fmt)
ax.xaxis.set_major_formatter(fmt)
return fig, ax
fig_u, ax_u = sci_fig('u')
plt.title('Specific internal energy for $\lambda=%g$, $\eta=%g$' % (ww, ff))
for N in Ns:
T, U, CV, S, minT = readandcompute.T_u_cv_s_minT(
"data/mc/ww%.2f-ff%.2f-N%d" % (ww, ff, N))
plt.semilogx(T, U / N, '-', label='$N=%d$' % N)
plt.xlabel('$kT/\epsilon$')
plt.ylabel('$U/N\epsilon$')
plt.legend(loc='best')
plt.tight_layout(pad=0.2)
plt.savefig("figs/energy-ww%02.0f-ff%02.0f.pdf" % (ww * 100, ff * 100))
plt.show()
styles.plot(method), linewidth=lw)
toterr += du
numseeds += 1
if numseeds > 0:
return toterr/numseeds/N
return None
labels_added = set()
for golden_comp in all_goldens:
bits = golden_comp.split('-')
ww = float(bits[1][2:])
ff = float(bits[2][2:])
N = int(bits[3][1:])
print('ww = %g, ff = %g, N = %d' % (ww, ff, N))
t_u_cv_s = readandcompute.T_u_cv_s_minT(ww, ff, N, golden)
if t_u_cv_s != None:
plt.figure(2)
plt.cla()
plt.title(r'error in $U/N$ for $N=%d$ and $\eta=%g$ and $\lambda=%g$' % (N, ff, ww))
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)
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--')
axT.spines['right'].set_color('red')
axT.tick_params(axis='y', colors='red')
axT.yaxis.label.set_color('red')