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(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)
## -- 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..."
fitc = fitterc.chained_lsqfit(data=dall,prior=priors,p0=init3,svdcut=df.svdcut)
print fmt_reduced_chi2(fitc)
print_fit(fitc,priors,df.do_v_symmetric)
## -- print
#if df.do_2pt:
# print_fit(fit2,priors2)
#print_fit(fitc,priors,df.do_v_symmetric)
#print "starting full fit..."
#fitc2 = fitter3.lsqfit(data=dall,prior=fitc.p,svdcut=df.svdcut)
#print fmt_reduced_chi2(fitc2)
#print_fit(fitc2,priors,df.do_v_symmetric)
## -- save fit as an initial value dictionary
if df.do_irrep == "16":
