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 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 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] = 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)
print fmt_reduced_chi2(fit3)
print_fit(fit3,priors,df.do_v_symmetric)
#save_data('./test.fit.out',fit,dall)
print "starting chained fit..."
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:
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)
#bs_avg = make_bootstrap(fitter,dset,df.mdp.n_bs)
print_fit(fit,prior)
print_error_budget(fit)
#save_data(mdp.output_path +'/'+ mdp.fit_fname,fit,data)
save_data('./test.fit.out',fit,data)
save_prior_from_fit(df.define_prior,df.define_model,fit,"test.prior.out",
round_e=2,round_a=1,preserve_e_widths=True,preserve_a_widths=True)
if df.do_plot:
if df.do_default_plot:
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)
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'+irrepStr+'_3pt')
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)
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'],n3st,o3st)
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=n3st,o3st=o3st)
priors = gv.BufferDict()
for key in prior2:
priors[key] = prior2[key]
for key in prior3:
priors[key] = prior3[key]
fitter = CorrFitter(models=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],(n3st,n3st))
elif eokey == 'no':
init[key] = np.resize(df.define_init_3pt[key],(n3st,o3st))
elif eokey == 'on':
init[key] = np.resize(df.define_init_3pt[key],(o3st,n3st))
elif eokey == 'oo':
init[key] = np.resize(df.define_init_3pt[key],(o3st,o3st))
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.lsqfit(data=dall,prior=priors,p0=init,svdcut=df.svdcut)
## --
print_fit(fit,priors)
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')
else:
save_init_from_fit(fit,'fit-stability/fit_n'+str(nst)+'_o'+str(ost)\
+'_ng'+str(n3st)+'_og'+str(o3st)+'.py')
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))
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))
