def main():
data = make_data('etas-Ds.data')
models = make_models()
prior = make_prior(8)
fitter = CorrFitter(models=make_models())
p0 = None
for N in [1, 2, 3, 4]:
print(30 * '=', 'nterm =', N)
prior = make_prior(N)
fit = fitter.chained_lsqfit(data=data, prior=prior, p0=p0)
p0 = fit.pmean
print_results(fit, prior, data)
if DISPLAYPLOTS:
fitter.display_plots()
# polish fit
print('--- polish fit')
fit = fitter.lsqfit(data=data, prior=fit.p, svdcut=1e-4)
print_results(fit, prior, data)
if DISPLAYPLOTS:
fitter.display_plots()
# fit with structured models
print('--- refit with structured models')
models = [models[:2]] + models[2:]
fitter = CorrFitter(models=make_models())
fit = fitter.chained_lsqfit(data=data, prior=prior, p0=p0)
# polish
print('--- polish fit')
fit = fitter.lsqfit(data=data, prior=fit.p, svdcut=1e-4)
print_results(fit, prior, data)
def doProcess(tmin,tmax,data=data,mdef=mdef):
## -- do a single fit ... set up for parallelizing
for key in data:
mdef[key]['tfit'] = range(tmin,tmax)
models = make_models(data=data,lkey=df.lkey,mdef=mdef)
pdict0 = utf.get_prior_dict(df.define_prior,
df.define_prior['nkey'],df.define_prior['okey'],df.num_nst,df.num_ost)
prior = make_prior(models,prior_dict=pdict0,nst=df.num_nst,ost=df.num_ost)
fitter = CorrFitter(models=models,maxit=df.maxit)
#try: ## -- if catching value error, just exit
## -- p0 = initial values (dictionary)
if df.do_initial:
try:
p0={}
for key in df.define_init:
eokey = utf.get_evenodd(key)
if eokey == 'o':
p0[key] = df.define_init[key][:df.num_ost]
else:
p0[key] = df.define_init[key][:df.num_nst]
fit = fitter.lsqfit(data=data,prior=prior,p0=p0,svdcut=df.svdcut)
except KeyError:
print "Could not use initial point definitions"
fit = fitter.lsqfit(data=data,prior=prior,svdcut=df.svdcut)
else:
fit = fitter.lsqfit(data=data,prior=prior,svdcut=df.svdcut)
## --
print_fit(fit,prior)
print_error_budget(fit)
save_data('fit-timevary/fit_'+str(tmin)+'_'+str(tmax)+'.out',fit,data)
def single_fit(tag_, tstar, CHARGED=False):
w0 = gv.gvar('0.1715(9)')
w0overa = gv.gvar('1.1367(5)')
ZV = gv.gvar('0.9837(20)')
ZVqed = gv.gvar('0.999544(14)') * ZV
hbarc = 0.197326968
a_vcoarse = (w0 / w0overa) / hbarc
if CHARGED == True:
ZV = ZVqed
else:
ZV = ZV
dataf = dsetavg[tag_]
tmin = 2
# replace data after t* with fit
# t* can be changed
fitter = CorrFitter(models=build_models(tmin, 48, tag_))
svdcut = 1e-6
for nexp in [4]:
print('=== nexp =', nexp, ' tmin = ', tmin, ' svdcut = ', svdcut)
fit = fitter.lsqfit(data=dsetavg,
prior=build_prior(nexp),
p0=pfile,
maxit=10000,
svdcut=svdcut) #,fitter='gsl_multifit')
print(fit)
newdata = dataf[:tstar]
#print("Before append/concatenate")
#print(newdata, len(newdata))
fitdata = Corr2(datatag=tag_,
tdata=range(48),
a=('a:rho', 'a:rhoo'),
b=('b:rho', 'b:rhoo'),
dE=('dE:rho', 'dE:rhoo'),
s=(1, -1)).fitfcn(fit.p)
newdata = np.append(np.array(newdata), fitdata[tstar:])
#print(fitdata[tstar:], fitdata[tstar:].size)
#print("After append/concatenate")
#print(newdata, newdata.size)
#sys.exit(0)
# use Z_V from appendix of 1909.00756; divided by u_0 to make the 1-link currents used match
vpol = g2.fourier_vacpol(newdata, Z=ZV, ainv=1 / a_vcoarse, periodic=False)
a_mu = g2.a_mu(vpol, Q=1. / 3.)
print('a_mu:', a_mu, "for", tag_, "t* = ", tstar)
return a_mu
def main():
data, basis = make_data('etab.data')
fitter = CorrFitter(models=make_models(basis))
p0 = None
for N in range(1, 10):
print(30 * '=', 'nterm =', N)
prior = make_prior(N, basis)
fit = fitter.lsqfit(data=data, prior=prior, p0=p0, svdcut=0.0004)
p0 = fit.pmean
print_results(fit, basis, prior, data)
if DISPLAYPLOTS:
fitter.display_plots()
def main():
data = make_data('etas-Ds.data')
models = make_models()
prior = make_prior(8)
fitter = CorrFitter(models=make_models(), ratio=False) # 1
p0 = None
for N in [1, 2]: # 2
print(30 * '=', 'nterm =', N)
prior = make_prior(8) # 3
fit = fitter.lsqfit(data=data, prior=prior, p0=p0, nterm=(N, N)) # 4
p0 = fit.pmean
print_results(fit, prior, data)
if DISPLAYPLOTS:
fitter.display_plots()
test_fit(fitter, 'etas-Ds.data')
def __init__(self, dic, params, childfit, parentfit):
self.name = dic['name']
self.Texts = dic['Text']
self.child_index = dic['child_index']
self.parent_index = dic['parent_index']
self.include2ptdata = dic['include2ptdata']
self.priorfile = dic['priorfile']
self.child = childfit
self.parent = parentfit
self.tmin = childfit.tmin
self.tmax = parentfit.tmin
self.nexp = self.child.nexp
self.dofit = True
if childfit.name == 'pimom0':
self.Von=None
self.Voo=None
else:
self.Von='Von'
self.Voo='Voo'
self.Vnn='Vnn'
self.Vno='Vno'
self.model = self.build_3pt_models(params)
self.fitter = CorrFitter(self.model)
self.prior = self.build_prior(params)
self.prior.update( self.child.prior )
self.prior.update( self.parent.prior )
def __init__(self, dic, params):
#print('Creating 2pt fit')
#print(dic)
self.name = dic['name']
self.tmin = dic['tmin']
self.tmax = dic['tmax']
self.nexp = dic['nexp']
self.noxp = dic['noxp']
self.priorfile = dic['priorfile']
self.p0file = dic['p0file']
self.savep0 = dic['savep0']
self.type = dic['type']
self.dofit = dic['dofit']
name = self.name
if name == 'pimom0':
self.a=(name+':a')
self.dE=(name+':dE')
else:
self.a=(name+':a',name+':ao')
self.dE=(name+':dE',name+':dEo')
# build model
self.model = self.build_model(params)
self.fitter = CorrFitter(self.model)
# build prior
self.prior = self.build_prior(params)
def main(tstr):
dfile = '/home/gray/Desktop/lattice-analysis/data/qed/coarse/rho_coarse_ms.gpl'
madedata = make_data(
dfile, norm=3.) # factor of 3 for colour (missed in extraction)
data = madedata[0]
T = data.size / len(madedata[1]) # extent in time dir
tag01 = madedata[1][0]
tag02 = madedata[1][1]
print("time extent is: ", T)
print(dfile)
suggestedsvdcut = madedata[2]
pfile = None #"vector_fit.p" # last fit
tmin = 2
svdcut = 1e-10 #suggestedsvdcut
fitter = CorrFitter(models=build_models(tag01, tag02, tmin, T))
for nexp in [2, 3, 4, 5]:
fit = fitter.lsqfit(data=data,
prior=build_prior(nexp),
p0=None,
maxit=20000,
svdcut=svdcut,
add_svdnoise=False)
print(fit)
tdata = range(T)
tfit = range(tmin, T + 1 - tmin) # all ts
tp = T
tstar = tstr
tcut = 200
newdata = {}
tags = ['nocharge', 'charge']
newdata[tags[0]] = data[tag01][:tstar + 1]
newdata[tags[1]] = data[tag02][:tstar + 1]
# do replacement of data with fit
fitdatauncharged = Corr2(datatag=tags[0],
tdata=range(T),
a=('a1:vec:s', 'ao:vec:s'),
b=('a1:vec:s', 'ao:vec:s'),
dE=('dE:vec:s', 'dEo:vec:s'),
s=(1., -1.)).fitfcn(fit.p)
for index in range(T):
if index >= tcut:
newdata[tags[0]] = np.append(newdata[tags[0]], 0.)
elif index > tstar:
newdata[tags[0]] = np.append(newdata[tags[0]],
fitdatauncharged[index])
fitdatachargeddown = Corr2(datatag=tags[1],
tdata=range(T),
a=('a1:vec:qed:s', 'ao:vec:qed:s'),
b=('a1:vec:qed:s', 'ao:vec:qed:s'),
dE=('dE:vec:qed:s', 'dEo:vec:qed:s'),
s=(1., -1.)).fitfcn(fit.p)
for index in range(T):
if index >= tcut:
newdata[tags[1]] = np.append(newdata[tags[1]], 0.)
elif index > tstar:
newdata[tags[1]] = np.append(newdata[tags[1]],
fitdatachargeddown[index])
print('THIS IS THE INFORMATION CENTRE')
print(len(newdata[tags[0]]), len(newdata[tags[1]]))
print('tmin, tstar', tmin, tstar)
vpol = g2.fourier_vacpol(newdata[tags[0]],
Z=ZV,
ainv=1 / a,
periodic=False)
unchargedamus = g2.a_mu(vpol, 1 / 3.)
vpol = g2.fourier_vacpol(newdata[tags[1]],
Z=ZVqed,
ainv=1 / a,
periodic=False)
chargedamus = g2.a_mu(vpol, 1 / 3.)
amus_rt = chargedamus / unchargedamus
amu_diff = chargedamus - unchargedamus
amu_diff_rt = amu_diff / unchargedamus
print(
'[s] a_mu[QCD+QED]{0:>20}\n|a_mu[QCD+QED]-a_mu[QCD]{1:>20}\n|a_mu/a_mu {2:>20}'
.format(chargedamus, amu_diff, amu_diff_rt))
init3[key] = utf.truncate_upper_triangle(init3[key],df.num_ost_3pt)
elif eokey == 'no':
init3[key] = np.resize(df.define_init_3pt[key],(df.num_nst_3pt,df.num_ost_3pt))
elif eokey == 'on':
## -- is this correct for symmetric v?
#init3[key] = np.resize(df.define_init_3pt[key],(df.num_ost_3pt,df.num_nst_3pt))
pass
elif eokey == 'n':
init3[key] = df.define_init_3pt[key][:df.num_nst]
elif eokey == 'o':
init3[key] = df.define_init_3pt[key][:df.num_ost]
else:
init3=None
pass
fitter2 = CorrFitter(models=models2,maxit=df.maxit)
fitter3 = CorrFitter(models=models,maxit=df.maxit)
fitterc = CorrFitter(models=[models2,models],maxit=df.maxit)
if df.do_2pt:
print "starting 2pt fit..."
fit2 = fitter2.lsqfit(data=dall,prior=priors2,p0=init2,svdcut=df.svdcut)
## -- fits take a long time, so print prematurely
print_fit(fit2,priors2)
if df.do_3pt:
print "starting 3pt fit..."
fit3 = fitter3.lsqfit(data=dall,prior=priors,p0=init3,svdcut=df.svdcut)
else:
print "Ignoring 3pt fit!"
fit3=None
#fit3 = fitter3.lsqfit(data=dall,prior=priors,svdcut=df.svdcut)
def main(tstr):
dfile = '/home/gray/Desktop/lattice-analysis/data/qed/vcoarse/self_b/rho_vcoarse_7ml.gpl'
madedata = make_data(dfile,norm=3.) # factor of 3 for colour (missed in extraction)
data = madedata[0]
T = data.size / len(madedata[1]) # extent in time dir
tag01 = madedata[1][0]
tag02 = madedata[1][1]
tag03 = madedata[1][2] # =tag01 if no isospin breaking
print("time extent is: ", T)
print(dfile)
suggestedsvdcut = madedata[2]
pfile = None #"vector_fit.p" # last fit
tmin = 2
svdcut = 1e-10 #suggestedsvdcut
fitter = CorrFitter(models=build_models(tag01,tag02,tag03,tmin,T))
for nexp in [2,3,4,5]:
fit = fitter.lsqfit(data=data,prior=build_prior(nexp),p0=None,maxit=20000,svdcut=svdcut,add_svdnoise=False)
print(fit)
tdata = range(T)
tfit = range(tmin,T+1-tmin) # all ts
tp = T
tstar = tstr
tcut = 200
newdata = {}
tags = ['nocharge', 'up_charge', 'down-charge']
newdata[tags[0]] = data[tag01][:tstar+1]
newdata[tags[1]] = data[tag02][:tstar+1]
newdata[tags[2]] = data[tag03][:tstar+1]
# do replacement of data with fit
fitdatauncharged = Corr2(datatag=tags[0],tdata=range(T),a=('a1:vec:u','ao:vec:u'),b=('a1:vec:u','ao:vec:u'),dE=('dE:vec:u','dEo:vec:u'),s=(1.,-1.)).fitfcn(fit.p)
for index in range(T):
if index >= tcut:
newdata[tags[0]] = np.append(newdata[tags[0]],0.)
elif index > tstar:
newdata[tags[0]] = np.append(newdata[tags[0]],fitdatauncharged[index])
fitdatachargedup = Corr2(datatag=tags[1],tdata=range(T),a=('a1:vec:qed:u','ao:vec:qed:u'),b=('a1:vec:qed:u','ao:vec:qed:u'),dE=('dE:vec:qed:u','dEo:vec:qed:u'),s=(1.,-1.)).fitfcn(fit.p)
fitdatachargedown = Corr2(datatag=tags[2],tdata=range(T),a=('a1:vec:qed:d','ao:vec:qed:d'),b=('a1:vec:qed:d','ao:vec:qed:d'),dE=('dE:vec:qed:d','dEo:vec:qed:d'),s=(1.,-1.)).fitfcn(fit.p)
for index in range(T):
if index >= tcut:
newdata[tags[1]] = np.append(newdata[tags[1]],0.)
newdata[tags[2]] = np.append(newdata[tags[2]],0.)
elif index > tstar:
newdata[tags[1]] = np.append(newdata[tags[1]],fitdatachargedup[index])
newdata[tags[2]] = np.append(newdata[tags[2]],fitdatachargedown[index])
print('THIS IS THE INFORMATION CENTRE')
print(len(newdata[tags[0]]), len(newdata[tags[1]]), len(newdata[tags[2]]))
print('tmin, tstar', tmin, tstar)
vpol = g2.fourier_vacpol(newdata[tags[0]], Z=ZV, ainv=1/a, periodic=False)
unchargedamuu = g2.a_mu(vpol,2/3.)
vpol = g2.fourier_vacpol(newdata[tags[1]], Z=ZVqed, ainv=1/a, periodic=False)
chargedamuu = g2.a_mu(vpol,2/3.)
vpol = g2.fourier_vacpol(newdata[tags[0]], Z=ZV, ainv=1/a, periodic=False)
unchargedamud = g2.a_mu(vpol,1/3.)
vpol = g2.fourier_vacpol(newdata[tags[2]], Z=ZVqed, ainv=1/a, periodic=False)
chargedamud = g2.a_mu(vpol,1/3.)
d_rt = chargedamud/unchargedamud
u_rt = chargedamuu/unchargedamuu
amu_qcd = unchargedamud+unchargedamuu
amu_qcdqed = chargedamuu+chargedamud
amu_rt = amu_qcdqed/amu_qcd
amu_diff = (amu_qcdqed-amu_qcd)/amu_qcd
amu_diff2 = amu_qcdqed-amu_qcd
print('[d] a_mu[QCD+QED]||a_mu[QCD+QED]/a_mu[QCD]{0:>20}||{1}'.format(chargedamud,d_rt))
print('[u] a_mu[QCD+QED]||a_mu[QCD+QED]/a_mu[QCD]{0:>20}||{1}'.format(chargedamuu,u_rt))
print('\n[u+d] a_mu[QCD+QED]||a_mu[QCD]||ratio||diff\n{0:20}{1:20}{2:20}{3:20}'.format(amu_qcdqed, amu_qcd, amu_rt, amu_diff))
class Fit2:
def __init__(self, dic, params):
#print('Creating 2pt fit')
#print(dic)
self.name = dic['name']
self.tmin = dic['tmin']
self.tmax = dic['tmax']
self.nexp = dic['nexp']
self.noxp = dic['noxp']
self.priorfile = dic['priorfile']
self.p0file = dic['p0file']
self.savep0 = dic['savep0']
self.type = dic['type']
self.dofit = dic['dofit']
name = self.name
if name == 'pimom0':
self.a=(name+':a')
self.dE=(name+':dE')
else:
self.a=(name+':a',name+':ao')
self.dE=(name+':dE',name+':dEo')
# build model
self.model = self.build_model(params)
self.fitter = CorrFitter(self.model)
# build prior
self.prior = self.build_prior(params)
def build_model(self,params):
model = Corr2( datatag='2pt'+self.name,tp=params.NT,tdata=range(params.NT),
tfit=range(self.tmin,self.tmax+1),
a=self.a,b=self.a,dE=self.dE,s=(1.,-1.) )
return model
def build_prior(self,params):
"""build prior from file"""
prior = BufferDict()
name = self.name
nexp = self.nexp
noxp = self.noxp
prior_file = open(self.priorfile,'r')
pp = yaml.load(prior_file)
prior['log('+name+':dE)'] = [ gvar(0,0) for i in range(nexp) ]
prior['log('+name+':a)'] = [ gvar(0,0) for i in range(nexp) ]
# non-osc. state priors
prior['log('+name+':dE)'][0] = log(gvar(pp['e0'][0],pp['e0'][1]))
prior['log('+name+':a)'][0] = log(gvar(pp['a0'][0],pp['a0'][1]))
for i in range(1,nexp):
prior['log('+name+':dE)'][i] = log(gvar(pp['e1'][0],pp['e1'][1]))
prior['log('+name+':a)'][i] = log(gvar(pp['a1'][0],pp['a1'][1]))
# osc. state priors
if self.name != 'pimom0':
prior['log('+name+':dEo)'] = [ 0 for i in range(noxp) ]
prior['log('+name+':ao)'] = [ 0 for i in range(noxp) ]
prior['log('+name+':dEo)'][0] = log(gvar(pp['o0'][0],pp['o0'][1]))
prior['log('+name+':ao)'][0] = log(gvar(pp['b0'][0],pp['b0'][1]))
for i in range(1,noxp):
prior['log('+name+':dEo)'][i] = log(gvar(pp['o1'][0],pp['o1'][1]))
prior['log('+name+':ao)'][i] = log(gvar(pp['b1'][0],pp['b1'][1]))
#print(prior)
return prior
def print_fit(self,par,outfile):
"""print the energy and amplitude results from a fit"""
fit = self.results
name = self.name
p = fit.p
self.chi2_cal()
self.chi2_aug_part()
self.Q = conf_int(self.chi2/2, par.ncon, self.dof/2)
dE = exp(p['log('+name+':dE)'])
E = [sum(dE[:i+1]) for i in range(self.nexp)]
a = exp(p['log('+name+':a)'])
if self.name != 'pimom0':
dEo = exp(p['log('+name+':dEo)'])
Eo = [sum(dEo[:i+1]) for i in range(self.nexp)]
ao = exp(p['log('+name+':ao)'])
ofile = open(outfile,'a')
if self.dof > 0:
chi2dof = self.chi2/self.dof
else:
chi2dof = 99.9
if chi2dof > 99:
chi2dof = 99.9
form = "{:s} & {:2d} - {:2d} & {:d}+{:d} & {:4.1f} & {:3d} & {:4.2f} "
ena = "& {:<11s} & {:<11s} "
ofile.write( form.format( self.name,self.tmin,self.tmax,self.nexp,self.noxp,chi2dof,self.dof,self.Q ) )
for i in range(self.nexp):
ofile.write( ena.format(E[i].fmt(ndecimal=4),a[i].fmt(ndecimal=4),Eo[i].fmt(ndecimal=4),ao[i].fmt(ndecimal=4)) )
ofile.write(" \\\\ \n")
def print_prior(self,par,outfile):
"""print the priors and result for comparison"""
fit = self.results
p = self.prior
name = self.name
f = fit.p
dE = exp(f['log('+name+':dE)'])
E = [sum(dE[:i+1]) for i in range(self.nexp)]
a = exp(f['log('+name+':a)'])
if self.name != 'pimom0':
dEo = exp(f['log('+name+':dEo)'])
Eo = [sum(dEo[:i+1]) for i in range(self.nexp)]
ao = exp(f['log('+name+':ao)'])
pdE = exp(p['log('+name+':dE)'])
pa = exp(p['log('+name+':a)'])
if self.name != 'pimom0':
pdEo = exp(p['log('+name+':dEo)'])
pao = exp(p['log('+name+':ao)'])
ofile = open(outfile,'a')
ofile.write("{:s} & Prior & Value & & Prior & Value \\\\ # {:d}-{:d} {:d}+{:d} \n".format(
name,self.tmin,self.tmax,self.nexp,self.nexp))
form = "$a_{:d}$ & {:<11s} & {:<11s} & $dE_{:d}$ & {:<11s} & {:<11s} \\\\ \n"
for state in range(self.nexp):
ofile.write( form.format( state,pa[state].fmt(),a[state].fmt(),
state,pdE[state].fmt(),dE[state].fmt() ) )
if self.name != 'pimom0':
form = "$a'_{:d}$ & {:<11s} & {:<11s} & $dE'_{:d}$ & {:<11s} & {:<11s} \\\\ \n"
for state in range(self.nexp):
ofile.write( form.format( state,pao[state].fmt(),ao[state].fmt(),
state,pdEo[state].fmt(),dEo[state].fmt() ) )
def print_model(self,par,outfile):
"""print the fit data and model and the difference"""
fit = self.results
t,g,dg,gth,dgth = self.fitter.collect_fitresults()['2pt'+self.name]
ofile = open(outfile,'a')
ofile.write( " t | {:12s} value | sigma_m sigma_v \n".format('2pt'+self.name) )
for it in range(0,self.tmax-self.tmin):
data = gvar(g[it],dg[it])
model = gvar(gth[it],dgth[it])
diff1 = (data.mean-model.mean)/data.sdev
diff2 = (data.mean-model.mean)/model.sdev
ofile.write( " {:3d} | {:<20s} {:<20s} | {:+5.3f} {:+5.3f} \n".format(
t[it],data.fmt(),model.fmt(),diff1,diff2) )
def chi2_cal(self):
"""calculate chi2 of fit"""
t,g,dg,gth,dgth = self.fitter.collect_fitresults()['2pt'+self.name]
chi2 = 0.
for it in range(0,self.tmax-self.tmin):
chi2 = ( (g[it]-gth[it])/dg )**2
total = sum(chi2)
#print(chi2,total)
parameters = 0
for key in self.prior.keys():
parameters = parameters + len(self.results.p[key])
self.dof = len(g)-parameters
self.chi2 = total
return total,self.dof
def chi2_aug_part(self):
"""calculate the part of the chi2 coming from the priors"""
pth = self.results.p
p = self.prior
aug = 0
for key in p.keys():
for exp in range(self.nexp):
aug = aug + ( (p[key][exp].mean-pth[key][exp].mean)/p[key][exp].sdev )**2
self.aug = aug
return aug
class Fit3:
def __init__(self, dic, params, childfit, parentfit):
self.name = dic['name']
self.Texts = dic['Text']
self.child_index = dic['child_index']
self.parent_index = dic['parent_index']
self.include2ptdata = dic['include2ptdata']
self.priorfile = dic['priorfile']
self.child = childfit
self.parent = parentfit
self.tmin = childfit.tmin
self.tmax = parentfit.tmin
self.nexp = self.child.nexp
self.dofit = True
if childfit.name == 'pimom0':
self.Von=None
self.Voo=None
else:
self.Von='Von'
self.Voo='Voo'
self.Vnn='Vnn'
self.Vno='Vno'
self.model = self.build_3pt_models(params)
self.fitter = CorrFitter(self.model)
self.prior = self.build_prior(params)
self.prior.update( self.child.prior )
self.prior.update( self.parent.prior )
def build_3pt_models(self,params):
models_3pt = []
for T in self.Texts:
models_3pt.append( Corr3( datatag="3pt"+self.name+"T"+str(T),T=T,tdata=range(T+1),
tfit=range(self.tmin,T-self.tmax+1),
a=self.child.a,dEa=self.child.dE,sa=(1.,-1.),
b=self.parent.a,dEb=self.parent.dE,sb=(1.,-1.),
Vnn=self.Vnn,Vno=self.Vno,Von=self.Von,Voo=self.Voo) )
if self.include2ptdata:
models_3pt.append( self.child.build_model(params) )
models_3pt.append( self.parent.build_model(params) )
return models_3pt
def build_prior(self,params):
"""build interaction matrix prior"""
prior = BufferDict()
nexp = self.child.nexp
noxp = self.child.noxp
prior_file = open(self.priorfile,'r')
pp = yaml.load(prior_file)
prior['Vnn'] = [[ gvar(pp['v22'][0],pp['v22'][1]) for i in range(nexp)] for j in range(nexp)]
prior['Vno'] = [[ gvar(pp['v22'][0],pp['v22'][1]) for i in range(nexp)] for j in range(nexp)]
prior['Vnn'][0][0] = gvar(pp['v00'][0],pp['v00'][1])
prior['Vno'][0][0] = gvar(pp['v11'][0],pp['v11'][1])
for i in range(1,nexp):
for j in range(1,nexp):
if i<1 or j<1:
prior['Vnn'][i][j] = gvar(pp['v11'][0],pp['v11'][1])
prior['Vno'][i][j] = gvar(pp['v11'][0],pp['v11'][1])
if self.name != "DPm0":
prior['Voo'] = [[ gvar(pp['v22'][0],pp['v22'][1]) for i in range(nexp)] for j in range(nexp)]
prior['Von'] = [[ gvar(pp['v22'][0],pp['v22'][1]) for i in range(nexp)] for j in range(nexp)]
prior['Voo'][0][0] = gvar(pp['v11'][0],pp['v11'][1])
prior['Von'][0][0] = gvar(pp['v11'][0],pp['v11'][1])
for i in range(1,nexp):
for j in range(1,nexp):
if i<1 or j<1:
prior['Voo'][i][j] = gvar(pp['v11'][0],pp['v11'][1])
prior['Von'][i][j] = gvar(pp['v11'][0],pp['v11'][1])
return prior
def print_fit(self,par,outfile):
"""print the energy and amplitude results from a fit"""
fit = self.results
name = self.name
p = fit.p
self.chi2_cal()
#self.chi2_aug_part()
self.Q = conf_int(self.chi2/2, par.ncon, self.dof/2)
nexp = self.child.nexp
name = self.child.name
CdE = exp(p['log('+name+':dE)'])
CE = [sum(CdE[:i+1]) for i in range(nexp)]
a = exp(p['log('+name+':a)'])
if self.name != 'pimom0':
CdEo = exp(p['log('+name+':dEo)'])
CEo = [sum(CdEo[:i+1]) for i in range(nexp)]
ao = exp(p['log('+name+':ao)'])
name = self.parent.name
PdE = exp(p['log('+name+':dE)'])
PE = [sum(PdE[:i+1]) for i in range(nexp)]
b = exp(p['log('+name+':a)'])
if self.name != 'pimom0':
PdEo = exp(p['log('+name+':dEo)'])
PEo = [sum(PdEo[:i+1]) for i in range(nexp)]
bo = exp(p['log('+name+':ao)'])
# calculating f_0
# DOESN"T WORK FOR D TO K YET!!!!!!!!!
if self.child.name == 'pimom0':
mpi = CE[0]
else:
mpi = gvar(par.pi0,par.pi0_err)
if self.child.name == 'pimom0' or self.child.name == 'pimom2':
m_q = par.m_l
else:
m_q = par.m_s
Epi = CE[0]
mD = PE[0]
v = p['Vnn'][0][0]
self.f_0 = v*sqrt(Epi*mD)*(par.m_c-m_q)/(mD**2-mpi**2)
self.qsq = mpi**2+mD**2-2*mD*Epi
ofile = open(outfile,'a')
if self.dof != 0:
chi2dof = self.chi2/self.dof
else:
chi2dof = 99.9
if chi2dof > 99:
chi2dof = 99.9
pars = "{:s} & {:2d}-{:2d} & {:2d}-{:2d} & {:d}+{:d} & {:4.1f} & {:3d} & {:4.2f} & "
energies = "{:s} & {:<11s} & {:<11s} & "
form = "{:<12s} & {:<12s} \\\\ \n"
ofile.write( pars.format( self.name,self.tmin,self.child.tmax,self.tmax,self.parent.tmax,
nexp,nexp,chi2dof,self.dof,self.Q )
+energies.format( self.child.name,CE[0].fmt(ndecimal=4),a[0].fmt(ndecimal=4) )
+energies.format( self.parent.name,PE[0].fmt(ndecimal=4),b[0].fmt(ndecimal=4) )
+form.format( self.f_0.fmt(ndecimal=4), self.qsq.fmt(ndecimal=4) ) )
def print_model(self,par,outfile):
"""print the fit data and model and the difference"""
fit = self.results
names = ["2pt"+self.child.name,"2pt"+self.parent.name]
[names.append("3pt"+self.name+"T"+str(T)) for T in self.Texts]
print(names)
ofile = open(outfile,'a')
ofile.write("{:5s} models, {:d}+{:d} fit, {:d} to T-{:d} window "+strftime("%H:%M:%S")+"\n".format(self.name,self.nexp,self.nexp,self.child.tmin,self.child.tmax))
for name in names:
t,g,dg,gth,dgth = self.fitter.collect_fitresults()[name]
ofile.write( " t | {:<11s} theory | sigma_data sigma_th \n".format(name) )
for it in range(0,len(t)):
data = gvar(g[it],dg[it])
model = gvar(gth[it],dgth[it])
diff1 = (data.mean-model.mean)/data.sdev
diff2 = (data.mean-model.mean)/model.sdev
ofile.write( " {:3d} | {:<20s} {:<20s} | {:+6.3f} {:+6.3f} \n".format(
t[it],data.fmt(),model.fmt(),diff1,diff2) )
def print_prior(self,par,outfile):
"""print the priors and result for comparison"""
fit = self.results
p = self.prior
name = self.name
f = fit.p
ofile = open(outfile,'a')
ofile.write("{:5s} priors, {:d}+{:d} fit, {:d} to T-{:d} window "+strftime("%H:%M:%S")+"\n".format(name,self.nexp,self.nexp,self.child.tmin,self.child.tmax))
self.print_2pt_prior(par,ofile,self.child.name)
self.print_2pt_prior(par,ofile,self.parent.name)
ofile.write("V \n")
form = "{:<8s} "
self.V_prior_line(ofile,"Vnn prior",form,self.nexp,p['Vnn'])
self.V_prior_line(ofile,"Vnn fittd",form,self.nexp,f['Vnn'])
self.V_prior_line(ofile,"Vno prior",form,self.nexp,p['Vno'])
self.V_prior_line(ofile,"Vno fittd",form,self.nexp,f['Vno'])
if self.child.name != 'pimom0':
self.V_prior_line(ofile,"Von prior",form,self.nexp,p['Von'])
self.V_prior_line(ofile,"Von fittd",form,self.nexp,f['Von'])
self.V_prior_line(ofile,"Voo prior",form,self.nexp,p['Voo'])
self.V_prior_line(ofile,"Voo fittd",form,self.nexp,f['Voo'])
def print_2pt_prior(self,par,ofile,name):
fit = self.results
p = self.prior
f = fit.p
dE = exp(f['log('+name+':dE)'])
E = [sum(dE[:i+1]) for i in range(self.nexp)]
a = exp(f['log('+name+':a)'])
if self.name != 'pimom0':
dEo = exp(f['log('+name+':dEo)'])
Eo = [sum(dEo[:i+1]) for i in range(self.nexp)]
ao = exp(f['log('+name+':ao)'])
pdE = exp(p['log('+name+':dE)'])
pa = exp(p['log('+name+':a)'])
if self.name != 'pimom0':
pdEo = exp(p['log('+name+':dEo)'])
pao = exp(p['log('+name+':ao)'])
ofile.write("{:5s} \n".format(name))
form = "{:<12s} {:<12s} "
self.prior_line(ofile,"odd prior",form,self.nexp,pa,pdE)
self.prior_line(ofile,"odd fittd",form,self.nexp,a,dE)
if self.name != 'pimom0':
self.prior_line(ofile,"evn prior",form,self.nexp,pao,pdEo)
self.prior_line(ofile,"evn fittd",form,self.nexp,ao,dEo)
def prior_line(self,ofile,name,form,nexp,a,E):
ofile.write(name+": ")
ofile.write(form.format(a[0].fmt(),E[0].fmt()))
for state in range(1,self.nexp):
ofile.write("| "+form.format(a[state].fmt(),E[state].fmt()))
ofile.write(" \n")
def V_prior_line(self,ofile,name,form,nexp,V):
ofile.write(name+": ")
ofile.write(form.format(V[0][0].fmt()))
for i in range(1,self.nexp):
for j in range(1,self.nexp):
ofile.write("| "+form.format(V[i][j].fmt()))
ofile.write(" \n")
def chi2_cal(self):
"""calculate chi2 of fit"""
chi2 = 0.
points = 0
for fit in (self.child,self.parent):
t,g,dg,gth,dgth = self.fitter.collect_fitresults()["2pt"+fit.name]
for it in range(0,fit.tmax-fit.tmin):
chi2 = ( (g[it]-gth[it])/dg )**2
total2pt = sum(chi2)
points = points + len(g)
for T in self.Texts:
t,g,dg,gth,dgth = self.fitter.collect_fitresults()["3pt"+self.name+"T"+str(T)]
for it in range(0,T-fit.tmax-fit.tmin):
chi2 = ( (g[it]-gth[it])/dg )**2
total3pt = sum(chi2)
points = points + len(g)
parameters = 0
for key in self.prior.keys():
parameters = parameters + len(self.results.p[key])
self.dof = points-parameters
self.chi2 = total2pt+total3pt
return self.chi2,self.dof
def chi2_aug_part(self):
"""calculate the part of the chi2 coming from the priors"""
####### NOT WORKING ATM #####
pth = self.results.p
p = self.prior
aug = 0
print(pth.values())
qth = np.fromiter(pth.values(),np.float)
q = np.fromiter(p.values(),np.float)
print(qth)
print(q)
for exp in range(self.nexp):
aug = aug + ( (p[key][exp].mean-pth[key][exp].mean)/p[key][exp].sdev )**2
self.aug = aug
return aug
models = make_models(data=data,lkey=df.lkey)
prior = make_prior(models)
## --
## -- DEPRICATED
#if df.do_uncorr:
# ## -- remove the correlations from the data
# dataCorr = data
# datlen = len(gv.evalcov(data)['Gaa','Gaa'])
# fakecov = np.zeros((datlen,datlen))
# for i in range(datlen):
# fakecov[i,i] = np.diag(gv.evalcov(data)['Gaa','Gaa'])[i]
# data['Gaa'] = gv.gvar([data['Gaa'][i].mean for i in range(datlen)],np.array(fakecov))
## --
fitter = CorrFitter(models=models,maxit=df.maxit)
#fit = fitter.chained_lsqfit(data=data, prior=prior)
#fit = fitter.lsqfit(data=data, prior=fit.p)
#fit = fitter.lsqfit(data=data,prior=prior,p0="test.init.out",svdcut=df.svdcut)
if df.do_2pt:
if df.do_initial:
try:
p0={}
for key in df.define_init:
eokey = utf.get_evenodd(key)
if eokey == 'o':
p0[key] = df.define_init[key][:df.num_ost]
else:
p0[key] = df.define_init[key][:df.num_nst]
fit = fitter.lsqfit(data=data,prior=prior,p0=p0,svdcut=df.svdcut)
except KeyError:
## -- temporary fix
if df.do_init3:
#init3={}
#if argsin['override_init']:
init3 = make_adv_init_from_fit_file_3pt(models3,'fit_adv_'+irrepStr+'_3pt',\
fresh_overlap=True,fresh_amplitude=True)
else:
init3=None
pass
## -- temporary
init2=None
#init3=None
#print init3
fitter2 = CorrFitter(models=models2,maxit=df.maxit)
fitter3 = CorrFitter(models=models,maxit=df.maxit)
#print models
#raise ValueError('test')
print
print 'prior: '
for key in sorted(priors):
print key,priors[key]
#print
#print 'init : ',init3
#for key in init3:
# print key,init3[key]
print
if df.do_2pt:
print "starting 2pt fit..."
fit2 = fitter2.lsqfit(data=dall,prior=priorsa2,p0=init2,svdcut=df.svdcut)
def doProcess(nst,ost,n3st=1,o3st=1,data=dall):
## -- do a single fit ... set up for parallelizing
if df.do_2pt and not(df.do_3pt):
pdict3 = utf.get_prior_dict(df.define_prior,
df.define_prior['nkey'],df.define_prior['okey'],nst,ost,do_v_symm=True)
models = make_models(data=dall,lkey=df.lkey)
prior = make_prior(models,prior_dict=pdict3,nst=nst,ost=ost)
fitter = CorrFitter(models=models,maxit=df.maxit)
if df.do_init2:
init={}
if argsin['override_init']:
init = make_init_from_fit_file_3pt(models,'fit_dict')
else:
for key in df.define_init:
## -- does this interfere with underscore tags?
eokey = utf.get_evenodd(key)
if eokey == 'n':
init[key] = df.define_init[key][:nst]
elif eokey == 'o':
init[key] = df.define_init[key][:ost]
else:
init=None
else:
pdict = utf.get_prior_dict(df.define_prior,
df.define_prior['nkey'],df.define_prior['okey'],
nst,ost,do_v_symm=True)
pdict3 = utf.get_prior_dict(df.define_prior_3pt,
df.define_prior_3pt['nkey'],df.define_prior_3pt['okey'],
nst,ost,df.define_prior_3pt['vkey'],nst,ost,do_v_symm=True)
for key in pdict:
pdict3[key] = pdict[key]
models2 = make_models(data=dall,lkey=df.lkey,use_advanced=True)
#models3 = make_models_3pt(data=dall,lkey=df.lkey3)
models3 = make_models_advanced(data=dall,lkey=df.lkey3)
models = list()
for model in models2:
models.append(model)
for model in models3:
models.append(model)
#prior2 = make_prior(models2,prior_dict=pdict,nst=nst,ost=ost)
#prior3 = make_prior_3pt(models3,prior_dict=pdict3,nst=nst,ost=ost,n3st=nst,o3st=ost)
#priors = gv.BufferDict()
#for key in prior2:
# priors[key] = prior2[key]
#for key in prior3:
# priors[key] = prior3[key]
priorsa = truncate_prior_states(df.define_prior_adv,
nst,ost,n3st,o3st)
fitter = CorrFitter(models=models,maxit=df.maxit)
if df.do_init3:
#init3={}
#if argsin['override_init']:
init = make_adv_init_from_fit_file_3pt(models3,'fit_adv_'+irrepStr+'_3pt',\
fresh_overlap=True,fresh_amplitude=True)
else:
init=None
pass
#fit = fitter.lsqfit(data=dall,prior=priors,p0=init,svdcut=df.svdcut)
#print priorsa
#print init
fit = fitter.lsqfit(data=dall,prior=priorsa,p0=init,svdcut=df.svdcut)
## --
print_fit(fit,priorsa,do_v_symm=True)
print_error_budget(fit)
#save_data('fit-stability/fit_'+str(nst)+'_'+str(ost)+'.out',fit,dall)
if df.do_2pt and not(df.do_3pt):
save_init_from_fit(fit,'fit-adv/fit_'+str(nst)+'_'+str(ost)+'.py',do_v_symm=True)
else:
save_init_from_fit(fit,'fit-adv/fit_n'+str(nst)+'_o'+str(ost)\
+'_ng'+str(n3st)+'_og'+str(o3st)+'.py',do_v_symm=True)
def doProcess(nst,ost,data=dall):
## -- do a single fit ... set up for parallelizing
if df.do_2pt and not(df.do_3pt):
pdict3 = utf.get_prior_dict(df.define_prior,
df.define_prior['nkey'],df.define_prior['okey'],nst,ost,do_v_symm=df.do_v_symmetric)
models = make_models(data=dall,lkey=df.lkey)
prior = make_prior(models,prior_dict=pdict3,nst=nst,ost=ost)
fitter = CorrFitter(models=[models],maxit=df.maxit)
if df.do_init2:
init={}
if argsin['override_init']:
init = make_init_from_fit_file_3pt(models,'fit_dict')
else:
for key in df.define_init:
eokey = utf.get_evenodd(key)
if eokey == 'n':
init[key] = df.define_init[key][:nst]
elif eokey == 'o':
init[key] = df.define_init[key][:ost]
else:
init=None
else:
pdict = utf.get_prior_dict(df.define_prior,
df.define_prior['nkey'],df.define_prior['okey'],
nst,ost,do_v_symm=df.do_v_symmetric)
pdict3 = utf.get_prior_dict(df.define_prior_3pt,
df.define_prior_3pt['nkey'],df.define_prior_3pt['okey'],
nst,ost,df.define_prior_3pt['vkey'],nst,ost,do_v_symm=df.do_v_symmetric)
for key in pdict:
pdict3[key] = pdict[key]
models2 = make_models(data=dall,lkey=df.lkey)
models3 = make_models_3pt(data=dall,lkey=df.lkey3)
models = list()
for model in models2:
models.append(model)
for model in models3:
models.append(model)
#print df.lkey+df.lkey3
#for model in models:
# model.all_datatags
#raise ValueError("test")
prior2 = make_prior(models2,prior_dict=pdict,nst=nst,ost=ost)
prior3 = make_prior_3pt(models3,prior_dict=pdict3,nst=nst,ost=ost,n3st=nst,o3st=ost)
priors = gv.BufferDict()
for key in prior2:
priors[key] = prior2[key]
for key in prior3:
priors[key] = prior3[key]
fitter = CorrFitter(models=[models2,models],maxit=df.maxit)
if df.do_init3:
init={}
if argsin['override_init']:
init = make_init_from_fit_file_3pt(models,'fit_dict')
else:
for key in df.define_init_3pt:
eokey = utf.get_evenodd(key)
if eokey == 'nn':
init[key] = np.resize(df.define_init_3pt[key],(nst,nst))
elif eokey == 'no':
init[key] = np.resize(df.define_init_3pt[key],(nst,ost))
elif eokey == 'on':
init[key] = np.resize(df.define_init_3pt[key],(ost,nst))
elif eokey == 'oo':
init[key] = np.resize(df.define_init_3pt[key],(ost,ost))
elif eokey == 'n':
init[key] = df.define_init_3pt[key][:nst]
elif eokey == 'o':
init[key] = df.define_init_3pt[key][:ost]
else:
init=None
fit = fitter.chained_lsqfit(data=dall,prior=priors,p0=init,svdcut=df.svdcut)
## --
print_fit(fit,priors,do_v_symm=df.do_v_symmetric)
print_error_budget(fit)
#save_data('fit-stability/fit_'+str(nst)+'_'+str(ost)+'.out',fit,dall)
if df.do_2pt and not(df.do_3pt):
save_init_from_fit(fit,'fit-stability/fit_'+str(nst)+'_'+str(ost)+'.py',do_v_symm=df.do_v_symmetric)
else:
save_init_from_fit(fit,'fit-stability/fit_n'+str(nst)+'_o'+str(ost)\
+'_ng'+str(nst)+'_og'+str(ost)+'.py',do_v_symm=df.do_v_symmetric)
def main(tstr):
dfile = '/home/gray/Desktop/lattice-analysis/data/qed/vcoarse/comb/comb_rho_vcoarse_physud.gpl'
# tag01 = 'rho_m' + str(mq)
# tag02 = 'rho_m' + str(mq) + '_ucav'
# tag03 = 'rho_m' + str(mq)
# tag04 = 'rho_m' + str(mq) + '_dcav'
madedata = make_data(
dfile, norm=3.) # factor of 3 for colour (missed in extraction)
data = madedata[0]
T = data.size / len(madedata[1]) # extent in time dir
tag01 = madedata[1][0]
tag02 = madedata[1][1]
tag03 = madedata[1][2] # =tag01 if no isospin breaking
tag04 = madedata[1][3]
#sys.exit(0)
#ratiodata = {}
#ratiodata['up'] = data[tag02]/data[tag01]
#ratiodata['down'] = data[tag04]/data[tag03]
suggestedsvdcut = madedata[2]
pfile = "vector_fit.p" # last fit
tmin = 2
svdcut = 1e-10 #suggestedsvdcut
fitter = CorrFitter(
models=build_models(tag01, tag02, tag03, tag04, tmin, T))
for nexp in [2, 3, 4, 5]:
fit = fitter.lsqfit(data=data,
prior=build_prior(nexp),
p0=None,
maxit=20000,
svdcut=svdcut,
add_svdnoise=False)
print(fit)
tdata = range(T)
tfit = range(tmin, T + 1 - tmin) # all ts
tp = T
tstar = tstr
tcut = 200
newdata = {}
tags = ['up', 'up_qed', 'down', 'down_qed']
newdata[tags[0]] = data[tag01][:tstar + 1]
newdata[tags[1]] = data[tag02][:tstar + 1]
newdata[tags[2]] = data[tag03][:tstar + 1]
newdata[tags[3]] = data[tag04][:tstar + 1]
# do replacement of data with fit
fitdataunchargedup = Corr2(datatag=tags[0],
tdata=range(T),
a=('a1:vec:u', 'ao:vec:u'),
b=('a1:vec:u', 'ao:vec:u'),
dE=('dE:vec:u', 'dEo:vec:u'),
s=(1., -1.)).fitfcn(fit.p)
fitdataunchargeddown = Corr2(datatag=tags[2],
tdata=range(T),
a=('a1:vec:d', 'ao:vec:d'),
b=('a1:vec:d', 'ao:vec:d'),
dE=('dE:vec:d', 'dEo:vec:d'),
s=(1., -1.)).fitfcn(fit.p)
for index in range(48):
if index >= tcut:
newdata[tags[0]] = np.append(newdata[tags[0]], 0.)
newdata[tags[2]] = np.append(newdata[tags[2]], 0.)
elif index > tstar:
newdata[tags[0]] = np.append(newdata[tags[0]],
fitdataunchargedup[index])
newdata[tags[2]] = np.append(newdata[tags[2]],
fitdataunchargeddown[index])
fitdatachargedup = Corr2(datatag=tags[1],
tdata=range(T),
a=('a1:vec:qed:u', 'ao:vec:qed:u'),
b=('a1:vec:qed:u', 'ao:vec:qed:u'),
dE=('dE:vec:qed:u', 'dEo:vec:qed:u'),
s=(1., -1.)).fitfcn(fit.p)
fitdatachargedown = Corr2(datatag=tags[3],
tdata=range(T),
a=('a1:vec:qed:d', 'ao:vec:qed:d'),
b=('a1:vec:qed:d', 'ao:vec:qed:d'),
dE=('dE:vec:qed:d', 'dEo:vec:qed:d'),
s=(1., -1.)).fitfcn(fit.p)
for index in range(48):
if index >= tcut:
newdata[tags[1]] = np.append(newdata[tags[1]], 0.)
newdata[tags[3]] = np.append(newdata[tags[3]], 0.)
elif index > tstar:
newdata[tags[1]] = np.append(newdata[tags[1]],
fitdatachargedup[index])
newdata[tags[3]] = np.append(newdata[tags[3]],
fitdatachargedown[index])
w0 = gv.gvar('0.1715(9)')
w0overa = gv.gvar('1.1367(5)')
ZV = gv.gvar('0.9837(20)')
ZVqed = gv.gvar('0.999544(14)') * ZV
hbarc = 0.197326968
a = (w0 / w0overa) / hbarc
print('THIS IS THE INFORMATION CENTRE')
print(len(newdata[tags[0]]), len(newdata[tags[1]]), len(newdata[tags[2]]),
len(newdata[tags[3]]))
print('tmin, tstar', tmin, tstar)
#moments = g2.moments(newdata[tags[0]],Z=ZV,ainv=1/a,periodic=False)
#vpol = g2.vacpol(moments,order=(2,1))
vpol = g2.fourier_vacpol(newdata[tags[0]],
Z=ZV,
ainv=1 / a,
periodic=False)
unchargedamuu = g2.a_mu(vpol, 2 / 3.)
#moments = g2.moments(newdata[tags[1]],Z=ZVqed,ainv=1/a,periodic=False)
#vpol = g2.vacpol(moments,order=(2,1))
vpol = g2.fourier_vacpol(newdata[tags[1]],
Z=ZVqed,
ainv=1 / a,
periodic=False)
chargedamuu = g2.a_mu(vpol, 2 / 3.)
#moments = g2.moments(newdata[tags[2]],Z=ZV,ainv=1/a,periodic=False)
#vpol = g2.vacpol(moments,order=(2,1))
vpol = g2.fourier_vacpol(newdata[tags[2]],
Z=ZV,
ainv=1 / a,
periodic=False)
unchargedamud = g2.a_mu(vpol, 1 / 3.)
#moments = g2.moments(newdata[tags[3]],Z=ZVqed,ainv=1/a,periodic=False)
#vpol = g2.vacpol(moments,order=(2,1))
vpol = g2.fourier_vacpol(newdata[tags[3]],
Z=ZVqed,
ainv=1 / a,
periodic=False)
chargedamud = g2.a_mu(vpol, 1 / 3.)
d_rt = chargedamud / unchargedamud
u_rt = chargedamuu / unchargedamuu
amu_qcd = unchargedamud + unchargedamuu
amu_qcdqed = chargedamuu + chargedamud
amu_rt = amu_qcdqed / amu_qcd
amu_diff = (amu_qcdqed - amu_qcd) / amu_qcd
print('[d] a_mu[QCD+QED]||a_mu[QCD+QED]/a_mu[QCD]{0:>20}||{1}'.format(
chargedamud, d_rt))
print('[u] a_mu[QCD+QED]||a_mu[QCD+QED]/a_mu[QCD]{0:>20}||{1}'.format(
chargedamuu, u_rt))
print(
'\n[u+d] a_mu[QCD+QED]||a_mu[QCD]||ratio||diff\n{0:20}{1:20}{2:20}{3:20}'
.format(amu_qcdqed, amu_qcd, amu_rt, amu_diff))
