CV = {} # heat capacity
S = {} # entropy
# we want to keep our methods distinct from our golden
if golden in methods:
methods.remove(golden)
fig_size = (6,4.5)
plt.figure('u',figsize=fig_size)
plt.figure('hc',figsize=fig_size)
plt.figure('s',figsize=fig_size)
plt.figure('u_err',figsize=fig_size)
plt.figure('hc_err',figsize=fig_size)
plt.figure('s_err',figsize=fig_size)
array_len = len(readandcompute.u_cv_s(ww, ff, N, methods[0], seeds[0])[0])
for method in [golden]+methods:
U[method] = numpy.zeros(array_len)
CV[method] = numpy.zeros(array_len)
S[method] = numpy.zeros(array_len)
for seed in seeds:
u_cv_s = readandcompute.u_cv_s(ww, ff, N, method, seed)
U[method] += u_cv_s[0] # internal energy
CV[method] += u_cv_s[1] # heat capacity
S[method] += u_cv_s[2] # entropy
U[method] /= len(seeds)
CV[method] /= len(seeds)
S[method] /= len(seeds)
tex.write(string.join([" & %d " % samples[m][i] for m in methods]))
tex.write("\\\\\n")
tex.write(r"""\end{tabular}
""")
######################################################################
# error scaling figures
######################################################################
u_errors = {}
cv_errors = {}
s_errors = {}
for N in all_Ns:
u_cv_s = readandcompute.u_cv_s(ww, ff, N, golden)
if u_cv_s != None:
for method in methods:
u_cv_s_method = readandcompute.u_cv_s(ww, ff, N, method, seed)
if u_cv_s_method != None:
du = abs(u_cv_s_method[0]-u_cv_s[0]).max()
if method not in u_errors:
u_errors[method] = numpy.array([[N, du]])
else:
u_errors[method] = numpy.vstack([u_errors[method], [N, du]])
dcv = abs(u_cv_s_method[1]-u_cv_s[1]).max()
if method not in cv_errors:
cv_errors[method] = numpy.array([[N, dcv]])
else:
cv_errors[method] = numpy.vstack([u_errors[method], [N, dcv]])
ds = abs(u_cv_s_method[2]-u_cv_s[2]).max()
tex.write(string.join([" & %d " % samples[m][i] for m in methods]))
tex.write("\\\\\n")
tex.write(r"""\end{tabular}
""")
######################################################################
# error scaling figures
######################################################################
u_errors = {}
cv_errors = {}
s_errors = {}
for N in all_Ns:
u_cv_s = readandcompute.u_cv_s(ww, ff, N, golden)
if u_cv_s != None:
for method in methods:
u_cv_s_method = readandcompute.u_cv_s(ww, ff, N, method, seed)
if u_cv_s_method != None:
du = abs(u_cv_s_method[0] - u_cv_s[0]).max()
if method not in u_errors:
u_errors[method] = numpy.array([[N, du]])
else:
u_errors[method] = numpy.vstack(
[u_errors[method], [N, du]])
dcv = abs(u_cv_s_method[1] - u_cv_s[1]).max()
if method not in cv_errors:
cv_errors[method] = numpy.array([[N, dcv]])
else:
cv_errors[method] = numpy.vstack(
# input: ["data/periodic-ww%04.2f-ff%04.2f-N%i-tmmc-golden-%s.dat" % (ww, ff, N, data) for data in ["E","lnw"]]
max_T = 1.4
T_bins = 1e3
dT = max_T / T_bins
T_range = numpy.arange(dT, max_T, dT)
min_T = 0 # we will adjust this
# make dictionaries which we can index by method name
U = {} # internal energy
CV = {} # heat capacity
S = {} # entropy
for method in set(methods + [golden]):
u_cv_s = readandcompute.u_cv_s(ww, ff, N, method)
if u_cv_s != None:
U[method] = u_cv_s[0] # internal energy
CV[method] = u_cv_s[1] # heat capacity
S[method] = u_cv_s[2] # entropy
plt.figure('u')
plt.plot(T_range,
U[method] / N,
styles.plot(method),
label=styles.title(method))
plt.figure('hc')
plt.plot(T_range,
CV[method] / N,
styles.plot(method),
CV = {} # heat capacity
S = {} # entropy
# we want to keep our methods distinct from our golden
if golden in methods:
methods.remove(golden)
fig_size = (6, 4.5)
plt.figure('u', figsize=fig_size)
plt.figure('hc', figsize=fig_size)
plt.figure('s', figsize=fig_size)
plt.figure('u_err', figsize=fig_size)
plt.figure('hc_err', figsize=fig_size)
plt.figure('s_err', figsize=fig_size)
array_len = len(readandcompute.u_cv_s(ww, ff, N, methods[0], seeds[0])[0])
for method in [golden] + methods:
U[method] = numpy.zeros(array_len)
CV[method] = numpy.zeros(array_len)
S[method] = numpy.zeros(array_len)
for seed in seeds:
u_cv_s = readandcompute.u_cv_s(ww, ff, N, method, seed)
U[method] += u_cv_s[0] # internal energy
CV[method] += u_cv_s[1] # heat capacity
S[method] += u_cv_s[2] # entropy
U[method] /= len(seeds)
CV[method] /= len(seeds)
S[method] /= len(seeds)
# input: ["data/periodic-ww%04.2f-ff%04.2f-N%i-tmmc-golden-%s.dat" % (ww, ff, N, data) for data in ["E","lnw"]]
max_T = 1.4
T_bins = 1e3
dT = max_T/T_bins
T_range = numpy.arange(dT,max_T,dT)
min_T = 0 # we will adjust this
# make dictionaries which we can index by method name
U = {} # internal energy
CV = {} # heat capacity
S = {} # entropy
for method in set(methods+[golden]):
u_cv_s = readandcompute.u_cv_s(ww, ff, N, method)
if u_cv_s != None:
U[method] = u_cv_s[0] # internal energy
CV[method] = u_cv_s[1] # heat capacity
S[method] = u_cv_s[2] # entropy
plt.figure('u')
plt.plot(T_range,U[method]/N,styles.plot(method),label=styles.title(method))
plt.figure('hc')
plt.plot(T_range,CV[method]/N,styles.plot(method),label=styles.title(method))
plt.figure('s')
plt.plot(T_range,S[method]/N,styles.plot(method),label=styles.title(method))
for method in methods:
