print ''
print '24^3 lattice'
for scheme in 'g', 'q':
print 'scheme', scheme
cfac = C_S(alpha_s2, scheme)*ZAc
Z = data_0c[0].Z_S[scheme]
sig = data_0c[0].Z_S_sigmaJK[scheme]
print ' 2 GeV: {0:.4f} +/- {1:.4f}'.format(cfac*Z, cfac*sig)
cfac = C_S(alpha_s3, scheme)*ZAc
# Small interpolation in mu required.
d1 = data_0c[-2]
d2 = data_0c[-1]
#print d1.mu, d1.Z_S[scheme]
#print d2.mu, d2.Z_S[scheme]
fit = fits.line_fit2([(d1.mu, d1.Z_S[scheme], d1.Z_S_sigmaJK[scheme]),
(d2.mu, d2.Z_S[scheme], d2.Z_S_sigmaJK[scheme])])
Z = fit.b*3 + fit.a
#print Z
sig = max(d1.Z_S_sigmaJK[scheme], d2.Z_S_sigmaJK[scheme]) # Fudge.
print ' 3 GeV: {0:.4f} +/- {1:.4f}'.format(cfac*Z, cfac*sig)
print '\n\n Z_P'
print '32^3 lattice'
for scheme in 'g', 'q':
print 'scheme', scheme
cfac = C_S(alpha_s2, scheme)*ZAf
Z = data_0f[0].Z_P[scheme]
sig = data_0f[0].Z_P_sigmaJK[scheme]
print ' 2 GeV: {0:.4f} +/- {1:.4f}'.format(cfac*Z, cfac*sig)
cfac = C_S(alpha_s3, scheme)*ZAf
def pole_subtract_Data2(*Dat):
'''Remove 1/m dependence in data pts by fitting m*Zinv to a straight line.'''
for d in Dat:
assert isinstance(d, Data)
Dat = list(Dat)
# Multiply by am and find intercept.
mres = Dat[0].mres
points = [(mres + d.m, (mres + d.m)*d.Zinv, (mres + d.m)*d.Zinv_sigmaJK)
for d in Dat]
fit = line_fit2(points)
for d in Dat:
d.polefit_params = fit # Save these parameters.
d.Zinv_sub = d.Zinv - fit.a/(d.m + mres)
d.Zinv_sub_sigma = sqrt(d.Zinv_sigmaJK**2)# + (fit.sig_a/(d.m+mres))**2)
# Jackknife values.
# This proceeds in a few steps. First we calculate the fit parameter 'a'
# many times by removing individual configs. Then we enlarge the
# "jackknife space" of each data point to include the effect on the
# subtracted value coming from the other mass points.
aJK = []
for dummy in range(len(Dat)):
d, ds = Dat[0], Dat[1:] # Split off first element.
d.Zinv_subJK = []
for jk in d.Zinv_JK:
# We do not recompute d.Zinv_sigmaJK which would require some
# insane double jackknife.
points = [(mres + x.m, (mres + x.m)*x.Zinv,
(mres + x.m)*x.Zinv_sigmaJK) for x in ds] +\
[(mres + d.m, (mres + d.m)*jk, (mres + d.m)*d.Zinv_sigmaJK)]
fit = line_fit2(points)
aJK += [fit.a]
ds.append(d) # Return element to end.
Dat = ds
# Initialize Zinv_subJK. The central value d.Zinv is used when considering
# the effect from other mass points. In the following loop we correct
# the values in the same mass point.
ct = 0
Zinv_subJK = []
for d in Dat:
d.Zinv_JKexpand = [d.Zinv]*len(aJK) # Init. expanded 'regular' JK.
for x in range(len(d.Zinv_JK)):
Zinv_subJK += [d.Zinv - aJK[ct]/(mres+d.m)]
d.Zinv_JKexpand[ct] = d.Zinv_JK[x] # Fill JK values in correct spots.
ct +=1
assert ct == len(aJK)
ct = 0
for dummy in range(len(Dat)):
d, ds = Dat[0], Dat[1:]
d.Zinv_subJK = Zinv_subJK # Initialize.
for jk in d.Zinv_JK:
d.Zinv_subJK[ct] = jk - aJK[ct]/(mres+d.m) # Corrects wrong values.
ct += 1
d.Zinv_sub_sigma2 = JKsigma(d.Zinv_subJK, d.Zinv_sub)
ds.append(d)
Dat = ds
assert ct == len(aJK)
