def report(Zs):
Z2s = []
abses = []
phzes = []
reals = []
imags = []
for Z in Zs:
Z2s.append(conj(Z) * Z)
abses.append(abs(Z))
phzes.append(phz(Z))
reals.append(real(Z))
imags.append(imag(Z))
if (print_details):
print "%11.7f %11.7f" % (Z.real, Z.imag)
#print "%11.7f %11.7f" % (abs(Z), phz(Z))
print
print >> sys.stderr, "E[Z] =", stats_m.find_mean(Zs)
print >> sys.stderr, "E[|Z|^2] =", stats_m.find_mean(Z2s)
print >> sys.stderr, "E[abs(Z)] =", stats_m.find_mean(abses)
print >> sys.stderr, "E[phz(Z)] =", stats_m.find_mean(phzes)
print >> sys.stderr, "E[re(Z)] =", stats_m.find_mean(reals)
print >> sys.stderr, "E[im(Z)] =", stats_m.find_mean(imags)
print
def report(Zs): Z2s = [] abses = [] phzes = [] reals = [] imags = [] for Z in Zs: Z2s.append(conj(Z)*Z) abses.append(abs(Z)) phzes.append(phz(Z)) reals.append(real(Z)) imags.append(imag(Z)) if (print_details): print "%11.7f %11.7f" % (Z.real, Z.imag) #print "%11.7f %11.7f" % (abs(Z), phz(Z)) print print >> sys.stderr, "E[Z] =", stats_m.find_mean(Zs) print >> sys.stderr, "E[|Z|^2] =", stats_m.find_mean(Z2s) print >> sys.stderr, "E[abs(Z)] =", stats_m.find_mean(abses) print >> sys.stderr, "E[phz(Z)] =", stats_m.find_mean(phzes) print >> sys.stderr, "E[re(Z)] =", stats_m.find_mean(reals) print >> sys.stderr, "E[im(Z)] =", stats_m.find_mean(imags) print
expected_mean_t = 0.0 expected_var_s = s expected_var_t = t expected_cov_st = min(s, t) elif which_process == 'bbbm' or which_process == 'bbsde': expected_mean_s = X0 + s/T*(XT-X0) expected_mean_t = X0 + t/T*(XT-X0) expected_var_s = s*(T-s)/T expected_var_t = t*(T-t)/T if s < t: expected_cov_st = s*(T-t)/T else: expected_cov_st = t*(T-s)/T mean_s = stats_m.find_mean(slice_s) mean_t = stats_m.find_mean(slice_t) var_s = stats_m.find_sampvaraux(slice_s, mean_s) var_t = stats_m.find_sampvaraux(slice_t, mean_t) cov_st = stats_m.find_sample_covariance(slice_s, slice_t) diff_mean_s = mean_s - expected_mean_s diff_mean_t = mean_t - expected_mean_t diff_var_s = var_s - expected_var_s diff_var_t = var_t - expected_var_t diff_cov_st = cov_st - expected_cov_st if which_process == 'bm': print 'T = %11.7f' % T elif which_process == 'bbbm' or which_process == 'bbsde': print 'X0 = %11.7f' % X0
expected_mean_t = 0.0
expected_var_s = s
expected_var_t = t
expected_cov_st = min(s, t)
elif which_process == 'bbbm' or which_process == 'bbsde':
expected_mean_s = X0 + s / T * (XT - X0)
expected_mean_t = X0 + t / T * (XT - X0)
expected_var_s = s * (T - s) / T
expected_var_t = t * (T - t) / T
if s < t:
expected_cov_st = s * (T - t) / T
else:
expected_cov_st = t * (T - s) / T
mean_s = stats_m.find_mean(slice_s)
mean_t = stats_m.find_mean(slice_t)
var_s = stats_m.find_sampvaraux(slice_s, mean_s)
var_t = stats_m.find_sampvaraux(slice_t, mean_t)
cov_st = stats_m.find_sample_covariance(slice_s, slice_t)
diff_mean_s = mean_s - expected_mean_s
diff_mean_t = mean_t - expected_mean_t
diff_var_s = var_s - expected_var_s
diff_var_t = var_t - expected_var_t
diff_cov_st = cov_st - expected_cov_st
if which_process == 'bm':
print 'T = %11.7f' % T
elif which_process == 'bbbm' or which_process == 'bbsde':
print 'X0 = %11.7f' % X0
k = len(Nis)
# Compute stratum weights
wis = [0.0] * k
for i in range(0, k):
wis[i] = (1.0 * Nis[i]) / N
# Compute sample size n = sum_{i=1}^k w[i]*n[i]
n = 0
for i in range(0, k):
n += wis[i] * nis[i]
n = int(n)
# Compute the simple and stratified means
mis = map(stats_m.find_mean, strata)
m = stats_m.find_mean(population)
# Compute the simple and stratified variances
vis = [0.0] * k
for i in range(0, k):
vis[i] = stats_m.find_popvaraux(strata[i], mis[i])
v = stats_m.find_popvaraux(population, m)
# ----------------------------------------------------------------
if (1):
print "N = %6d k = %6d m = %7.4f v = %7.4f" % (N, k, m, v)
if (N < 20):
print
for i in range(0, k):
print "Stratum", i+1, " = ", strata[i]
