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(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
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(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
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(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)
reference = sys.argv[7]
#arg reference = ['cfw']
seeds = eval(sys.argv[8])
#arg seeds = [range(30)]
u_errors = {}
cv_errors = {}
s_errors = {}
for method in methods:
u_errors[method] = numpy.zeros(len(Ns))
cv_errors[method] = numpy.zeros(len(Ns))
s_errors[method] = numpy.zeros(len(Ns))
for n in range(len(Ns)):
golden_data = readandcompute.t_u_cv_s(ww, ff, Ns[n], golden)
for method in methods:
for seed in seeds:
t_u_cv_s_method = readandcompute.t_u_cv_s(ww, ff, Ns[n], method, seed)
du = abs((t_u_cv_s_method[1]-golden_data[1])[t_u_cv_s_method[0] > min_T]).max()
dcv = abs((t_u_cv_s_method[2]-golden_data[2])[t_u_cv_s_method[0] > min_T]).max()
ds = abs((t_u_cv_s_method[3]-golden_data[3])[t_u_cv_s_method[0] > min_T]).max()
u_errors[method][n] += du
cv_errors[method][n] += dcv
s_errors[method][n] += ds
u_errors[method][n] /= len(seeds)
cv_errors[method][n] /= len(seeds)
s_errors[method][n] /= len(seeds)
reference = sys.argv[7]
# arg reference = ['cfw']
seeds = eval(sys.argv[8])
# arg seeds = [range(30)]
u_errors = {}
cv_errors = {}
s_errors = {}
for method in methods:
u_errors[method] = numpy.zeros(len(Ns))
cv_errors[method] = numpy.zeros(len(Ns))
s_errors[method] = numpy.zeros(len(Ns))
for n in range(len(Ns)):
golden_data = readandcompute.t_u_cv_s(ww, ff, Ns[n], golden)
for method in methods:
for seed in seeds:
t_u_cv_s_method = readandcompute.t_u_cv_s(ww, ff, Ns[n], method, seed)
du = abs((t_u_cv_s_method[1] - golden_data[1])[t_u_cv_s_method[0] > min_T]).max()
dcv = abs((t_u_cv_s_method[2] - golden_data[2])[t_u_cv_s_method[0] > min_T]).max()
ds = abs((t_u_cv_s_method[3] - golden_data[3])[t_u_cv_s_method[0] > min_T]).max()
u_errors[method][n] += du
cv_errors[method][n] += dcv
s_errors[method][n] += ds
u_errors[method][n] /= len(seeds)
cv_errors[method][n] /= len(seeds)
s_errors[method][n] /= len(seeds)
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(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)
