def gA_bs(psql,params):
# read data
mq = params['gA_fit']['ml']
basak = copy.deepcopy(params['gA_fit']['basak'])
tag = params['gA_fit']['ens']['tag']
stream = params['gA_fit']['ens']['stream']
Nbs = params['gA_fit']['nbs']
Mbs = params['gA_fit']['mbs']
nstates = params['gA_fit']['nstates']
tau = params['gA_fit']['tau']
barp = params[tag]['proton'][mq]
fhbp = params[tag]['gA'][mq]
print "fitting for gA mq %s, basak %s, ens %s%s, Nbs %s, Mbs %s" %(str(mq),str(basak),str(tag),str(stream),str(Nbs),str(Mbs))
# read two point
SSl = np.array([psql.data('dwhisq_corr_baryon',idx) for idx in [barp['meta_id']['SS'][i] for i in basak]])
PSl = np.array([psql.data('dwhisq_corr_baryon',idx) for idx in [barp['meta_id']['PS'][i] for i in basak]])
T = len(SSl[0,0])
# read fh correlator
fhSSl = np.array([psql.data('dwhisq_fhcorr_baryon',idx) for idx in [fhbp['meta_id']['SS'][i] for i in basak]])
fhPSl = np.array([psql.data('dwhisq_fhcorr_baryon',idx) for idx in [fhbp['meta_id']['PS'][i] for i in basak]])
# concatenate and make gvars to preserve correlations
SS = SSl[0]
PS = PSl[0]
for i in range(len(SSl)-1): # loop over basak operators
SS = np.concatenate((SS,SSl[i+1]),axis=1)
PS = np.concatenate((PS,PSl[i+1]),axis=1)
fhSS = fhSSl[0]
fhPS = fhPSl[0]
for i in range(len(fhSSl)-1):
fhSS = np.concatenate((fhSS,fhSSl[i+1]),axis=1)
fhPS = np.concatenate((fhPS,fhPSl[i+1]),axis=1)
boot0 = np.concatenate((SS, PS, fhSS, fhPS), axis=1)
# make gvars
gvboot0 = c51.make_gvars(boot0)
spec = gvboot0[:len(gvboot0)/2]
fh = gvboot0[len(gvboot0)/2:]
# R(t)
Rl = fh/spec
# dmeff [R(t+tau) - R(t)] / tau
dM = (np.roll(Rl,-tau)-Rl)/float(tau)
# plot data
if params['flags']['plot_data']:
for b in range(len(basak)):
SS = SSl[b]
PS = PSl[b]
# raw correlator
c51.scatter_plot(np.arange(len(SS[0])), c51.make_gvars(SS), '%s %s ss' %(basak[b],str(mq)))
c51.scatter_plot(np.arange(len(PS[0])), c51.make_gvars(PS), '%s %s ps' %(basak[b],str(mq)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff_ss = eff.effective_mass(c51.make_gvars(SS), 1, 'log')
meff_ps = eff.effective_mass(c51.make_gvars(PS), 1, 'log')
xlim = [2, len(meff_ss)/3-2]
ylim = c51.find_yrange(meff_ss, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(meff_ss)), meff_ss, '%s %s ss effective mass' %(basak[b],str(mq)), xlim = xlim, ylim = ylim)
ylim = c51.find_yrange(meff_ps, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(meff_ps)), meff_ps, '%s %s ps effective mass' %(basak[b],str(mq)), xlim = xlim, ylim = ylim)
plt.show()
# scaled correlator
E0 = barp['priors'][1]['E0'][0]
scaled_ss = eff.scaled_correlator(c51.make_gvars(SS), E0, phase=1.0)
scaled_ps = eff.scaled_correlator(c51.make_gvars(PS), E0, phase=1.0)
ylim = c51.find_yrange(scaled_ss, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(scaled_ss)), scaled_ss, '%s %s ss scaled correlator (take sqrt to get Z0_s)' %(basak[b],str(mq)), xlim = xlim, ylim = ylim)
ylim = c51.find_yrange(scaled_ps, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(scaled_ps)), scaled_ps, '%s %s ps scaled correlator (divide by Z0_s to get Z0_p)' %(basak[b],str(mq)), xlim = xlim, ylim = ylim)
plt.show()
if params['flags']['fit_twopt']:
# data already concatenated previously
# read priors
prior = c51.baryon_priors(barp['priors'],basak,nstates)
## read trange
trange = barp['trange']
## fit boot0
boot0gv = spec
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T,nstates)
boot0fit = c51.fitscript_v2(trange,T,boot0gv,boot0p,fitfcn.twopt_baryon_ss_ps,basak=params['gA_fit']['basak'])
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit,'E0','%s' %str(mq))
plt.show()
if params['flags']['tabulate']:
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = boot0fit['tmin']
tbl_print['tmax'] = boot0fit['tmax']
tbl_print['E0'] = [boot0fit['pmean'][t]['E0'] for t in range(len(boot0fit['pmean']))]
tbl_print['dE0'] = [boot0fit['psdev'][t]['E0'] for t in range(len(boot0fit['pmean']))]
blist = []
for b in params['gA_fit']['basak']:
blist.append(b[2:])
blist.append(b[:2])
blist = np.unique(blist)
for b in blist:
tbl_print['%s_Z0s' %b] = [boot0fit['pmean'][t]['%s_Z0s'%b] for t in range(len(boot0fit['pmean']))]
tbl_print['%s_dZ0s' %b] = [boot0fit['psdev'][t]['%s_Z0s' %b] for t in range(len(boot0fit['pmean']))]
tbl_print['%s_Z0p' %b] = [boot0fit['pmean'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
tbl_print['%s_dZ0p' %b] = [boot0fit['psdev'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
tbl_print['chi2/dof'] = np.array(boot0fit['chi2'])/np.array(boot0fit['dof'])
tbl_print['logGBF'] = boot0fit['logGBF']
print tabulate(tbl_print, headers='keys')
# submit boot0 to db
if params['flags']['write']:
corr_lst = np.array([[barp['meta_id']['SS'][i] for i in params['gA_fit']['basak']],[barp['meta_id']['PS'][i] for i in params['gA_fit']['basak']]]).flatten()
fit_id = c51.select_fitid('baryon',nstates=nstates,basak=params['gA_fit']['basak'])
for t in range(len(boot0fit['tmin'])):
init_id = psql.initid(boot0fit['p0'][t])
prior_id = psql.priorid(boot0fit['prior'][t])
tmin = boot0fit['tmin'][t]
tmax = boot0fit['tmax'][t]
result = c51.make_result(boot0fit,t)
psql.submit_boot0('proton',corr_lst,fit_id,tmin,tmax,init_id,prior_id,result,params['flags']['update'])
# plot fh correlator
if params['flags']['plot_fhdata']:
for b in range(len(basak)):
# raw correlator dC_lambda/dlambda
fhSS = fhSSl[b]
fhPS = fhPSl[b]
c51.scatter_plot(np.arange(len(fhSS[0])), c51.make_gvars(fhSS), '%s %s fh ss' %(basak[b],str(mq)))
c51.scatter_plot(np.arange(len(fhPS[0])), c51.make_gvars(fhPS), '%s %s fh ps' %(basak[b],str(mq)))
plt.show()
# R(t)
RSS = (Rl[:len(Rl)/2])[b*len(Rl)/(2*len(basak)):(b+1)*len(Rl)/(2*len(basak))]
RPS = (Rl[len(Rl)/2:])[b*len(Rl)/(2*len(basak)):(b+1)*len(Rl)/(2*len(basak))]
c51.scatter_plot(np.arange(len(RSS)), RSS, '%s %s R(t) ss' %(basak[b],str(mq)))
c51.scatter_plot(np.arange(len(RPS)), RPS, '%s %s R(t) ps' %(basak[b],str(mq)))
plt.show()
# dmeff R(t+tau) - R(t)
dMSS = (dM[:len(dM)/2])[b*len(dM)/(2*len(basak)):(b+1)*len(dM)/(2*len(basak))]
dMPS = (dM[len(dM)/2:])[b*len(dM)/(2*len(basak)):(b+1)*len(dM)/(2*len(basak))]
c51.scatter_plot(np.arange(len(dMSS)), dMSS, '%s %s [R(t+%s)-R(t)]/%s ss' %(basak[b],str(mq),str(tau),str(tau)))
c51.scatter_plot(np.arange(len(dMPS)), dMPS, '%s %s [R(t+%s)-R(t)]/%s ps' %(basak[b],str(mq),str(tau),str(tau)))
plt.show()
# fit fh correlator
if params['flags']['fit_gA']:
# data concatenated previously
# read priors
prior = c51.fhbaryon_priors(barp['priors'],fhbp['priors'],basak,nstates)
print prior
# read trange
trange = barp['trange']
fhtrange = fhbp['trange']
# fit boot0
boot0gv = np.concatenate((spec, dM))
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T,nstates,tau)
boot0fit = c51.fitscript_v3(trange,fhtrange,T,boot0gv,boot0p,fitfcn.baryon_fhbaryon_ss_ps,basak=params['gA_fit']['basak'])
#print {k: boot0fit['p0'][0][k] for k in [bk for n in range(nstates) for bk in barp['priors'][n+1].keys()]}
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit,'E0','%s' %str(mq))
c51.stability_plot(boot0fit,'gA00','%s' %str(mq))
plt.show()
if params['flags']['tabulate']:
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = boot0fit['tmin']
tbl_print['tmax'] = boot0fit['tmax']
tbl_print['fhtmin'] = boot0fit['fhtmin']
tbl_print['fhtmax'] = boot0fit['fhtmax']
#tbl_print['E0'] = [boot0fit['pmean'][t]['E0'] for t in range(len(boot0fit['pmean']))]
#tbl_print['dE0'] = [boot0fit['psdev'][t]['E0'] for t in range(len(boot0fit['pmean']))]
tbl_print['gA00'] = [boot0fit['pmean'][t]['gA00'] for t in range(len(boot0fit['pmean']))]
tbl_print['dgA00'] = [boot0fit['psdev'][t]['gA00'] for t in range(len(boot0fit['pmean']))]
#blist = []
#for b in params['gA_fit']['basak']:
# blist.append(b[2:])
# blist.append(b[:2])
#blist = np.unique(blist)
#for b in blist:
# tbl_print['%s_Z0s' %b] = [boot0fit['pmean'][t]['%s_Z0s'%b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0s' %b] = [boot0fit['psdev'][t]['%s_Z0s' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_Z0p' %b] = [boot0fit['pmean'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0p' %b] = [boot0fit['psdev'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
tbl_print['chi2/dof'] = np.array(boot0fit['chi2'])/np.array(boot0fit['dof'])
tbl_print['chi2'] = boot0fit['chi2']
tbl_print['chi2f'] = boot0fit['chi2f']
tbl_print['logGBF'] = boot0fit['logGBF']
print tabulate(tbl_print, headers='keys')
# submit boot0 to db
if params['flags']['write']:
corr_lst = np.array([[fhbp['meta_id']['SS'][i] for i in params['gA_fit']['basak']],[fhbp['meta_id']['PS'][i] for i in params['gA_fit']['basak']]]).flatten()
fit_id = c51.select_fitid('fhbaryon',nstates=nstates,tau=params['gA_fit']['tau'])
for t in range(len(boot0fit['tmin'])):
baryon_corr_lst = np.array([[barp['meta_id']['SS'][i] for i in params['gA_fit']['basak']],[barp['meta_id']['PS'][i] for i in params['gA_fit']['basak']]]).flatten()
baryon_fit_id = c51.select_fitid('baryon',nstates=nstates,basak=params['gA_fit']['basak'])
baryon_tmin = trange['tmin'][0]
baryon_tmax = trange['tmax'][0]
baryon_p0 = {k: boot0fit['p0'][0][k] for k in [bk for n in range(nstates) for bk in barp['priors'][n+1].keys()]}
baryon_init_id = psql.initid(baryon_p0)
baryon_prior_id = psql.priorid(c51.dict_of_tuple_to_gvar(c51.baryon_priors(barp['priors'],basak,nstates)))
baryon_id = psql.select_boot0("proton", baryon_corr_lst, baryon_fit_id, baryon_tmin, baryon_tmax, baryon_init_id, baryon_prior_id)
init_id = psql.initid(boot0fit['p0'][t])
prior_id = psql.priorid(boot0fit['prior'][t])
tmin = boot0fit['fhtmin'][t]
tmax = boot0fit['fhtmax'][t]
result = c51.make_result(boot0fit,t)
print tmin, tmax
psql.submit_fhboot0('fhproton',corr_lst,baryon_id,fit_id,tmin,tmax,init_id,prior_id,result,params['flags']['update'])
if params['flags']['csvformat']:
for t in range(len(boot0fit['fhtmin'])):
print nstates,',',boot0fit['tmin'][t],',',boot0fit['tmax'][t],',',boot0fit['fhtmin'][t],',',boot0fit['fhtmax'][t],',',boot0fit['pmean'][t]['gA00'],',',boot0fit['psdev'][t]['gA00'],',',(np.array(boot0fit['chi2'])/np.array(boot0fit['dof']))[t],',',(np.array(boot0fit['chi2'])/np.array(boot0fit['chi2f']))[t],',',boot0fit['logGBF'][t]
def fit_axial(psql,
params,
dparams,
meson,
datagv,
inherit_prior=None,
flow=False):
if meson == 'axial_ll':
mq1 = params['axial_fit']['ml']
mq2 = mq1
dmeson = 'pion'
elif meson == 'axial_ls':
mq1 = params['axial_fit']['ml']
mq2 = params['axial_fit']['ms']
dmeson = 'kaon'
print 'fitting axial two point', mq1, mq2
tag = params['axial_fit']['ens']['tag']
stream = params['axial_fit']['ens']['stream']
nstates = params['axial_fit']['nstates']
axialp = params[tag][meson]['%s_%s' % (str(mq1), str(mq2))]
mesonp = dparams[tag][dmeson]['%s_%s' % (str(mq1), str(mq2))]
T = len(datagv) / 3
SS = datagv[:T]
PS = datagv[T:2 * T]
ax = datagv[2 * T:]
# plot effective mass / scaled correlator
if params['flags']['plot_data']:
# unfolded correlator data
c51.scatter_plot(np.arange(T), ax, '%s_%s ps' % (str(mq1), str(mq2)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff_ps = eff.effective_mass(ax[1:T / 2], 1, 'cosh')
xlim = [2, len(meff_ps) / 2 - 2]
ylim = c51.find_yrange(meff_ps, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(meff_ps)),
meff_ps,
'$%s %s$ ps effective mass' % (str(mq1), str(mq2)),
xlim=xlim,
ylim=ylim)
plt.show()
# scaled correlator
E0 = mesonp['priors'][1]['E0'][0]
print E0
z_s = np.sqrt(eff.scaled_correlator_v2(SS, E0, phase=1.0))
scaled_ax = eff.scaled_correlator_v2(ax, E0, phase=1.0) / z_s
ylim = c51.find_yrange(scaled_ax, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(scaled_ax)),
scaled_ax,
'$%s %s$ f0 scaled axial correlator' %
(str(mq1), str(mq2)),
xlim=xlim,
ylim=ylim)
plt.show()
# read priors
if inherit_prior == None:
prior = c51.meson_priors(mesonp['priors'], nstates)
axp = c51.meson_priors(axialp['priors'], nstates)
for k in axp.keys():
prior[k] = axp[k]
prior = c51.dict_of_tuple_to_gvar(prior)
else:
prior = c51.meson_priors(mesonp['priors'], nstates)
axp = c51.meson_priors(axialp['priors'], nstates)
for k in axp.keys():
prior[k] = axp[k]
prior = c51.dict_of_tuple_to_gvar(prior)
for k in inherit_prior.keys():
prior[k] = inherit_prior[k]
# read trange
trange = axialp['trange']
dtrange = mesonp['trange']
print "PRIOR:", prior
# fit
fitfcn = c51.fit_function(T, params['axial_fit']['nstates'])
if flow:
boot0fit = c51.fitscript_v3(dtrange,
trange,
T,
datagv,
prior,
fitfcn.dwhisq_twopt_osc_axial,
axial=True)
else:
boot0fit = c51.fitscript_v3(dtrange,
trange,
T,
datagv,
prior,
fitfcn.dwhisq_twopt_axial,
axial=True)
#print boot0fit['rawoutput'][0]
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit, 'E0', '%s_%s' % (str(mq1), str(mq2)))
c51.stability_plot(boot0fit, 'F0', '%s_%s' % (str(mq1), str(mq2)))
plt.show()
if params['flags']['tabulate']:
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = boot0fit['tmin']
tbl_print['tmax'] = boot0fit['tmax']
tbl_print['Fphi'] = [
fphi(boot0fit['post'][t]['F0'], boot0fit['post'][t]['E0']).mean
for t in range(len(boot0fit['pmean']))
]
tbl_print['dFphi'] = [
fphi(boot0fit['post'][t]['F0'], boot0fit['post'][t]['E0']).sdev
for t in range(len(boot0fit['pmean']))
]
tbl_print['E0'] = [
boot0fit['pmean'][t]['E0'] for t in range(len(boot0fit['pmean']))
]
tbl_print['dE0'] = [
boot0fit['psdev'][t]['E0'] for t in range(len(boot0fit['pmean']))
]
tbl_print['Z0_s'] = [
boot0fit['pmean'][t]['Z0_s'] for t in range(len(boot0fit['pmean']))
]
tbl_print['dZ0_s'] = [
boot0fit['psdev'][t]['Z0_s'] for t in range(len(boot0fit['pmean']))
]
tbl_print['Z0_p'] = [
boot0fit['pmean'][t]['Z0_p'] for t in range(len(boot0fit['pmean']))
]
tbl_print['dZ0_p'] = [
boot0fit['psdev'][t]['Z0_p'] for t in range(len(boot0fit['pmean']))
]
tbl_print['F0'] = [
boot0fit['pmean'][t]['F0'] for t in range(len(boot0fit['pmean']))
]
tbl_print['dF0'] = [
boot0fit['psdev'][t]['F0'] for t in range(len(boot0fit['pmean']))
]
tbl_print['F0*Z0_s'] = [(boot0fit['rawoutput'][t].p['F0'] *
boot0fit['rawoutput'][t].p['Z0_s']).mean
for t in range(len(boot0fit['rawoutput']))]
tbl_print['dF0*Z0_s'] = [(boot0fit['rawoutput'][t].p['F0'] *
boot0fit['rawoutput'][t].p['Z0_s']).sdev
for t in range(len(boot0fit['rawoutput']))]
tbl_print['chi2/dof'] = np.array(boot0fit['chi2']) / np.array(
boot0fit['dof'])
tbl_print['logGBF'] = boot0fit['logGBF']
print tabulate(tbl_print, headers='keys')
return {'axial_fit': boot0fit['rawoutput'][0]}
def fit_gA(psql,params,gvboot0):
# read data
mq = params['gA_fit']['ml']
basak = copy.deepcopy(params['gA_fit']['basak'])
tag = params['gA_fit']['ens']['tag']
stream = params['gA_fit']['ens']['stream']
Nbs = params['gA_fit']['nbs']
Mbs = params['gA_fit']['mbs']
nstates = params['gA_fit']['nstates']
fhstates = params['gA_fit']['fhstates']
tau = params['gA_fit']['tau']
barp = params[tag]['proton'][mq]
fhbp = params[tag]['gA'][mq]
print "nstates: %s" %nstates
print "fhstates: %s" %fhstates
# make gvars
spec = gvboot0[:len(gvboot0)/2]
fh = gvboot0[len(gvboot0)/2:]
T = len(fh)/(2*len(basak))
# R(t)
Rl = fh/spec
# dmeff [R(t+tau) - R(t)] / tau
#dM = Rl
dM = (np.roll(Rl,-tau)-Rl)/float(tau) #This is needed to plot gA correctly in the effective plots, but will give wrong data to fit with.
# plot fh correlator
if params['flags']['plot_fhdata']:
fhSSl = fh[:len(fh)/2].reshape((len(basak),T))
fhPSl = fh[len(fh)/2:].reshape((len(basak),T))
RSSl = Rl[:len(Rl)/2].reshape((len(basak),T))
RPSl = Rl[len(Rl)/2:].reshape((len(basak),T))
dM_plot = (np.roll(Rl,-tau)-Rl)/float(tau)
dMSSl = dM_plot[:len(dM_plot)/2].reshape((len(basak),T))
dMPSl = dM_plot[len(dM_plot)/2:].reshape((len(basak),T))
for b in range(len(basak)):
# ground state nucleon mass prior central value
E0 = barp['priors'][1]['E0'][0]
# raw correlator dC_lambda/dlambda
fhSS = fhSSl[b]
fhPS = fhPSl[b]
#c51.scatter_plot(np.arange(len(fhSS)), fhSS, '%s %s fh ss' %(basak[b],str(mq)))
#c51.scatter_plot(np.arange(len(fhPS)), fhPS, '%s %s fh ps' %(basak[b],str(mq)))
print "%s fhSS[1]*exp(E0):" %basak[b], fhSS[1]*np.exp(E0) #, "fhSS[1]:", fhSS[1]
print "%s fhPS[1]*exp(E0):" %basak[b], fhPS[1]*np.exp(E0) #, "fhPS[1]:", fhPS[1]
plt.show()
# dmeff R(t+tau) - R(t)
dMSS = dMSSl[b]
dMPS = dMPSl[b]
xlim = [0, 20]
ylim = [0.5, 2.0]
c51.scatter_plot(np.arange(len(dMSS)), dMSS, '%s %s [R(t+%s)-R(t)]/%s ss' %(basak[b],str(mq),str(tau),str(tau)),xlim=xlim,ylim=ylim)
c51.scatter_plot(np.arange(len(dMPS)), dMPS, '%s %s [R(t+%s)-R(t)]/%s ps' %(basak[b],str(mq),str(tau),str(tau)),xlim=xlim,ylim=ylim)
if False:
for i in range(len(dMSS)):
print i,',',dMSS[i].mean,',',dMSS[i].sdev,',',dMPS[i].mean,',',dMPS[i].sdev
plt.show()
# fit fh correlator
if params['flags']['fit_gA']:
# data concatenated previously
# read priors
prior = c51.fhbaryon_priors(barp['priors'],fhbp['priors'],basak,nstates,fhstates)
print prior
# read trange
trange = barp['trange']
fhtrange = fhbp['trange']
# fit boot0
boot0gv = gvboot0 #np.concatenate((spec, fh))
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T,nstates,fhstates,tau)
boot0fit = c51.fitscript_v3(trange,fhtrange,T,boot0gv,boot0p,fitfcn.dwhisq_fh_ss_ps,basak=params['gA_fit']['basak'])
print boot0fit['rawoutput'][0]
#print {k: boot0fit['p0'][0][k] for k in [bk for n in range(nstates) for bk in barp['priors'][n+1].keys()]}
if False: # plot R(t+1) - R(t)
posterior = boot0fit['post'][0]
x = np.arange(20)
ssline, psline = fitfcn.baryon_rt_fitline(x,posterior)
for i in range(len(ssline)):
print x[i],',',ssline[i].mean,',',ssline[i].sdev,',',psline[i].mean,',',psline[i].sdev
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit,'E0','%s' %str(mq))
c51.stability_plot(boot0fit,'gA00','%s' %str(mq))
plt.show()
if params['flags']['tabulate']:
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = boot0fit['tmin']
tbl_print['tmax'] = boot0fit['tmax']
tbl_print['fhtmin'] = boot0fit['fhtmin']
tbl_print['fhtmax'] = boot0fit['fhtmax']
tbl_print['E0'] = [boot0fit['pmean'][t]['E0'] for t in range(len(boot0fit['pmean']))]
tbl_print['dE0'] = [boot0fit['psdev'][t]['E0'] for t in range(len(boot0fit['pmean']))]
tbl_print['gA00'] = [boot0fit['pmean'][t]['gA00'] for t in range(len(boot0fit['pmean']))]
tbl_print['dgA00'] = [boot0fit['psdev'][t]['gA00'] for t in range(len(boot0fit['pmean']))]
#blist = []
#for b in params['gA_fit']['basak']:
# blist.append(b[2:])
# blist.append(b[:2])
#blist = np.unique(blist)
#for b in blist:
# tbl_print['%s_Z0s' %b] = [boot0fit['pmean'][t]['%s_Z0s'%b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0s' %b] = [boot0fit['psdev'][t]['%s_Z0s' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_Z0p' %b] = [boot0fit['pmean'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0p' %b] = [boot0fit['psdev'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
tbl_print['chi2/dof'] = np.array(boot0fit['chi2'])/np.array(boot0fit['dof'])
tbl_print['chi2'] = boot0fit['chi2']
tbl_print['chi2f'] = boot0fit['chi2f']
tbl_print['logGBF'] = boot0fit['logGBF']
tbl_print['Q'] = boot0fit['Q']
print tabulate(tbl_print, headers='keys')
print "tmin, E0, +-, gA, +-, chi2dof, Q, logGBF"
for i in range(len(boot0fit['fhtmin'])):
print '%s, %s, %s, %s, %s, %s, %s, %s' %(str(boot0fit['fhtmin'][i]), str(boot0fit['pmean'][i]['E0']), str(boot0fit['psdev'][i]['E0']), str(boot0fit['pmean'][i]['gA00']), str(boot0fit['psdev'][i]['gA00']), boot0fit['chi2'][i]/boot0fit['dof'][i], boot0fit['Q'][i], boot0fit['logGBF'][i])
# submit boot0 to db
if params['flags']['write']:
corr_lst = np.array([[fhbp['meta_id']['SS'][i] for i in params['gA_fit']['basak']],[fhbp['meta_id']['PS'][i] for i in params['gA_fit']['basak']]]).flatten()
fit_id = c51.select_fitid('fhbaryon',nstates=nstates,tau=params['gA_fit']['tau'])
for t in range(len(boot0fit['tmin'])):
baryon_corr_lst = np.array([[barp['meta_id']['SS'][i] for i in params['gA_fit']['basak']],[barp['meta_id']['PS'][i] for i in params['gA_fit']['basak']]]).flatten()
baryon_fit_id = c51.select_fitid('baryon',nstates=nstates,basak=params['gA_fit']['basak'])
baryon_tmin = trange['tmin'][0]
baryon_tmax = trange['tmax'][0]
baryon_p0 = {k: boot0fit['p0'][0][k] for k in [bk for n in range(nstates) for bk in barp['priors'][n+1].keys()]}
baryon_init_id = psql.initid(baryon_p0)
baryon_prior_id = psql.priorid(c51.dict_of_tuple_to_gvar(c51.baryon_priors(barp['priors'],basak,nstates)))
baryon_id = psql.select_boot0("proton", baryon_corr_lst, baryon_fit_id, baryon_tmin, baryon_tmax, baryon_init_id, baryon_prior_id)
init_id = psql.initid(boot0fit['p0'][t])
prior_id = psql.priorid(boot0fit['prior'][t])
tmin = boot0fit['fhtmin'][t]
tmax = boot0fit['fhtmax'][t]
result = c51.make_result(boot0fit,t)
print tmin, tmax
psql.submit_fhboot0('fhproton',corr_lst,baryon_id,fit_id,tmin,tmax,init_id,prior_id,result,params['flags']['update'])
if params['flags']['csvformat']:
for t in range(len(boot0fit['fhtmin'])):
print nstates,',',boot0fit['tmin'][t],',',boot0fit['tmax'][t],',',boot0fit['fhtmin'][t],',',boot0fit['fhtmax'][t],',',boot0fit['pmean'][t]['gA00'],',',boot0fit['psdev'][t]['gA00'],',',(np.array(boot0fit['chi2'])/np.array(boot0fit['dof']))[t],',',(np.array(boot0fit['chi2'])/np.array(boot0fit['chi2f']))[t],',',boot0fit['logGBF'][t]
return {'gA_fit': boot0fit['rawoutput'][0]}
return 0
def fit_gA(psql, params, gvboot0):
# read data
mq = params['gA_fit']['ml']
basak = copy.deepcopy(params['gA_fit']['basak'])
tag = params['gA_fit']['ens']['tag']
stream = params['gA_fit']['ens']['stream']
Nbs = params['gA_fit']['nbs']
Mbs = params['gA_fit']['mbs']
nstates = params['gA_fit']['nstates']
fhstates = params['gA_fit']['fhstates']
tau = params['gA_fit']['tau']
barp = params[tag]['proton'][mq]
fhbp = params[tag]['gA'][mq]
#print "nstates: %s" %nstates
#print "fhstates: %s" %fhstates
# make gvars
spec = gvboot0[:len(gvboot0) / 2]
fh = gvboot0[len(gvboot0) / 2:]
T = len(fh) / (2 * len(basak))
# R(t)
Rl = fh / spec
dM = (np.roll(Rl, -tau) - Rl) / float(
tau
) #This is needed to plot gA correctly in the effective plots, but will give wrong data to fit with.
# plot fh correlator
if params['flags']['plot_fhdata']:
fhSSl = fh[:len(fh) / 2].reshape((len(basak), T))
fhPSl = fh[len(fh) / 2:].reshape((len(basak), T))
RSSl = Rl[:len(Rl) / 2].reshape((len(basak), T))
RPSl = Rl[len(Rl) / 2:].reshape((len(basak), T))
dM_plot = (np.roll(Rl, -tau) - Rl) / float(tau)
dMSSl = dM_plot[:len(dM_plot) / 2].reshape((len(basak), T))
dMPSl = dM_plot[len(dM_plot) / 2:].reshape((len(basak), T))
for b in range(len(basak)):
# ground state nucleon mass prior central value
E0 = barp['priors'][1]['E0'][0]
# raw correlator dC_lambda/dlambda
fhSS = fhSSl[b]
fhPS = fhPSl[b]
#c51.scatter_plot(np.arange(len(fhSS)), fhSS, '%s %s fh ss' %(basak[b],str(mq)))
#c51.scatter_plot(np.arange(len(fhPS)), fhPS, '%s %s fh ps' %(basak[b],str(mq)))
print "%s fhSS[1]*exp(E0):" % basak[b], fhSS[1] * np.exp(
E0) #, "fhSS[1]:", fhSS[1]
print "%s fhPS[1]*exp(E0):" % basak[b], fhPS[1] * np.exp(
E0) #, "fhPS[1]:", fhPS[1]
plt.show()
# dmeff R(t+tau) - R(t)
dMSS = dMSSl[b]
dMPS = dMPSl[b]
xlim = [0, 20]
ylim = [0.5, 2.0]
c51.scatter_plot(np.arange(len(dMSS)),
dMSS,
'%s %s [R(t+%s)-R(t)]/%s ss' %
(basak[b], str(mq), str(tau), str(tau)),
xlim=xlim,
ylim=ylim)
c51.scatter_plot(np.arange(len(dMPS)),
dMPS,
'%s %s [R(t+%s)-R(t)]/%s ps' %
(basak[b], str(mq), str(tau), str(tau)),
xlim=xlim,
ylim=ylim)
if False:
for i in range(len(dMSS)):
print i, ',', dMSS[i].mean, ',', dMSS[i].sdev, ',', dMPS[
i].mean, ',', dMPS[i].sdev
plt.show()
# fit fh correlator
if params['flags']['fit_gA']:
# data concatenated previously
# read priors
prior = c51.fhbaryon_priors(barp['priors'], fhbp['priors'], basak,
nstates, fhstates)
# read init
try:
#print "found init file"
f_init = open('./fh_posterior/%s.yml' % (tag), 'r')
init = yaml.load(f_init)
f_init.close()
except:
init = None
#print prior
# read trange
trange = barp['trange']
fhtrange = fhbp['trange']
# fit boot0
boot0gv = gvboot0 #np.concatenate((spec, fh))
#boot0gv = np.concatenate((spec,dM))
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T, nstates, fhstates, tau)
boot0fit = c51.fitscript_v3(trange,
fhtrange,
T,
boot0gv,
boot0p,
fitfcn.dwhisq_fh_ss_ps,
basak=params['gA_fit']['basak'],
init=None,
bayes=params['flags']['bayes'])
#boot0fit = c51.fitscript_v3(trange,fhtrange,T,boot0gv,boot0p,fitfcn.dwhisq_dm_ss_ps,basak=params['gA_fit']['basak'],init=None,bayes=params['flags']['bayes'])
print boot0fit['rawoutput'][0]
if params['flags']['fitline_plot']:
p = boot0fit['rawoutput'][0].p
t = np.linspace(0, T / 2, 100 * T / 2)
b = params['gA_fit']['basak'][0]
# output fit curve
ss = fitfcn.dwhisq_twopt(t, p, b, 's', 's')
ps = fitfcn.dwhisq_twopt(t, p, b, 'p', 's')
fhss = fitfcn.dwhisq_fh(t, p, b, 's', 's', False)
fhps = fitfcn.dwhisq_fh(t, p, b, 'p', 's', False)
rss = fhss / ss
rps = fhps / ps
yss = (np.roll(rss, -100) - rss) / t[100]
yps = (np.roll(rps, -100) - rps) / t[100]
#f_plot = open('./fh_fitline/%s.csv' %(tag), 'w+')
f_plot = open('./fh_fitline/%s_fh.csv' % (tag), 'w+')
string = 't, yss, +-, yps, +-\n'
for i in range(len(t)):
#string += '%s, %s, %s, %s, %s\n' %(t[i], yss[i].mean, yss[i].sdev, yps[i].mean, yps[i].sdev)
string += '%s, %s, %s, %s, %s\n' % (t[i], fhss[i].mean,
fhss[i].sdev, fhps[i].mean,
fhps[i].sdev)
f_plot.write(string)
f_plot.flush()
f_plot.close()
meffss = np.log(ss / np.roll(ss, -100)) / t[100]
meffps = np.log(ps / np.roll(ps, -100)) / t[100]
f_plot = open('./fh_fitline/%s_twopt.csv' % (tag), 'w+')
string = 't, css, +-, cps, +-\n'
for i in range(len(t)):
string += '%s, %s, %s, %s, %s\n' % (
t[i], meffss[i].mean, meffss[i].sdev, meffps[i].mean,
meffps[i].sdev)
f_plot.write(string)
f_plot.flush()
f_plot.close()
# output data
dMSSl = fh[:len(fh) / 2].reshape((len(basak), T))[0]
dMPSl = fh[len(fh) / 2:].reshape((len(basak), T))[0]
#dMSSl = dM[:len(dM)/2].reshape((len(basak),T))[0]
#dMPSl = dM[len(dM)/2:].reshape((len(basak),T))[0]
t = np.linspace(0, 30, 31)
f_data = open('./fh_fitline/%s_fh_dat.csv' % (tag), 'w+')
#f_data = open('./fh_fitline/%s_dat.csv' %(tag), 'w+')
string = 't_dat, yss_dat, +-, yps_dat, +-\n'
for i in range(len(t)):
string += '%s, %s, %s, %s, %s\n' % (
t[i], dMSSl[i].mean, dMSSl[i].sdev, dMPSl[i].mean,
dMPSl[i].sdev)
f_data.write(string)
f_data.flush()
f_data.close()
SSl = spec[:len(spec) / 2].reshape((len(basak), T))[0]
PSl = spec[len(spec) / 2:].reshape((len(basak), T))[0]
meffSS = np.log(SSl / np.roll(SSl, -1))
meffPS = np.log(PSl / np.roll(PSl, -1))
f_data = open('./fh_fitline/%s_twopt_dat.csv' % (tag), 'w+')
string = 't_dat, ss_dat, +-, ps_dat, +-\n'
for i in range(len(t)):
string += '%s, %s, %s, %s, %s\n' % (
t[i], meffSS[i].mean, meffSS[i].sdev, meffPS[i].mean,
meffPS[i].sdev)
f_data.write(string)
f_data.flush()
f_data.close()
if params['flags']['boot0_update']:
fh_post = boot0fit['rawoutput'][0].pmean
fh_dump = dict()
for k in fh_post.keys():
fh_dump[k] = float(fh_post[k])
f_dump = open('./fh_posterior/%s.yml' % (tag), 'w+')
yaml.dump(fh_dump, f_dump)
f_dump.flush()
f_dump.close()
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit, 'E0', '%s' % str(mq))
c51.stability_plot(boot0fit, 'gA00', '%s' % str(mq))
plt.show()
if params['flags']['tabulate']:
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = boot0fit['tmin']
tbl_print['tmax'] = boot0fit['tmax']
tbl_print['fhtmin'] = boot0fit['fhtmin']
tbl_print['fhtmax'] = boot0fit['fhtmax']
tbl_print['E0'] = [
boot0fit['pmean'][t]['E0']
for t in range(len(boot0fit['pmean']))
]
tbl_print['dE0'] = [
boot0fit['psdev'][t]['E0']
for t in range(len(boot0fit['pmean']))
]
tbl_print['gA00'] = [
boot0fit['pmean'][t]['gA00']
for t in range(len(boot0fit['pmean']))
]
tbl_print['dgA00'] = [
boot0fit['psdev'][t]['gA00']
for t in range(len(boot0fit['pmean']))
]
#blist = []
#for b in params['gA_fit']['basak']:
# blist.append(b[2:])
# blist.append(b[:2])
#blist = np.unique(blist)
#for b in blist:
# tbl_print['%s_Z0s' %b] = [boot0fit['pmean'][t]['%s_Z0s'%b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0s' %b] = [boot0fit['psdev'][t]['%s_Z0s' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_Z0p' %b] = [boot0fit['pmean'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0p' %b] = [boot0fit['psdev'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
tbl_print['chi2/dof'] = np.array(boot0fit['chi2']) / np.array(
boot0fit['dof'])
tbl_print['chi2'] = boot0fit['chi2']
tbl_print['chi2f'] = boot0fit['chi2f']
tbl_print['logGBF'] = boot0fit['logGBF']
tbl_print['Q'] = boot0fit['Q']
print tabulate(tbl_print, headers='keys')
print "tmin, %sE0%s, +-, %sgA%s, +-, %schi2dof%s, %sQ%s, %slogGBF%s" % (
fhstates, nstates, fhstates, nstates, fhstates, nstates,
fhstates, nstates, fhstates, nstates)
for i in range(len(boot0fit['fhtmin'])):
print '%s, %s, %s, %s, %s, %s, %s, %s' % (
str(boot0fit['fhtmin'][i]), str(
boot0fit['pmean'][i]['E0']),
str(boot0fit['psdev'][i]['E0']),
str(boot0fit['pmean'][i]['gA00']),
str(boot0fit['psdev'][i]['gA00']),
boot0fit['chi2'][i] / boot0fit['dof'][i], boot0fit['Q'][i],
boot0fit['logGBF'][i])
# submit boot0 to db
if False: #params['flags']['write']:
corr_lst = np.array(
[[fhbp['meta_id']['SS'][i] for i in params['gA_fit']['basak']],
[fhbp['meta_id']['PS'][i]
for i in params['gA_fit']['basak']]]).flatten()
fit_id = c51.select_fitid('fhbaryon',
nstates=nstates,
tau=params['gA_fit']['tau'])
for t in range(len(boot0fit['tmin'])):
baryon_corr_lst = np.array([[
barp['meta_id']['SS'][i] for i in params['gA_fit']['basak']
], [
barp['meta_id']['PS'][i] for i in params['gA_fit']['basak']
]]).flatten()
baryon_fit_id = c51.select_fitid(
'baryon', nstates=nstates, basak=params['gA_fit']['basak'])
baryon_tmin = trange['tmin'][0]
baryon_tmax = trange['tmax'][0]
baryon_p0 = {
k: boot0fit['p0'][0][k]
for k in [
bk for n in range(nstates)
for bk in barp['priors'][n + 1].keys()
]
}
baryon_init_id = psql.initid(baryon_p0)
baryon_prior_id = psql.priorid(
c51.dict_of_tuple_to_gvar(
c51.baryon_priors(barp['priors'], basak, nstates)))
baryon_id = psql.select_boot0("proton", baryon_corr_lst,
baryon_fit_id, baryon_tmin,
baryon_tmax, baryon_init_id,
baryon_prior_id)
init_id = psql.initid(boot0fit['p0'][t])
prior_id = psql.priorid(boot0fit['prior'][t])
tmin = boot0fit['fhtmin'][t]
tmax = boot0fit['fhtmax'][t]
result = c51.make_result(boot0fit, t)
print tmin, tmax
psql.submit_fhboot0('fhproton', corr_lst, baryon_id, fit_id,
tmin, tmax, init_id, prior_id, result,
params['flags']['update'])
if params['flags']['csvformat']:
for t in range(len(boot0fit['fhtmin'])):
print nstates, ',', boot0fit['tmin'][t], ',', boot0fit['tmax'][
t], ',', boot0fit['fhtmin'][t], ',', boot0fit['fhtmax'][
t], ',', boot0fit['pmean'][t]['gA00'], ',', boot0fit[
'psdev'][t]['gA00'], ',', (
np.array(boot0fit['chi2']) /
np.array(boot0fit['dof']))[t], ',', (
np.array(boot0fit['chi2']) /
np.array(boot0fit['chi2f'])
)[t], ',', boot0fit['logGBF'][t]
return {'gA_fit': boot0fit['rawoutput'][0]}
return 0
def fit_gA(psql,params,gvboot0):
# read data
mq = params['gA_fit']['ml']
basak = copy.deepcopy(params['gA_fit']['basak'])
tag = params['gA_fit']['ens']['tag']
stream = params['gA_fit']['ens']['stream']
Nbs = params['gA_fit']['nbs']
Mbs = params['gA_fit']['mbs']
nstates = params['gA_fit']['nstates']
tau = params['gA_fit']['tau']
barp = params[tag]['proton'][mq]
fhbp = params[tag]['gA'][mq]
# make gvars
spec = gvboot0[:len(gvboot0)/2]
fh = gvboot0[len(gvboot0)/2:]
T = len(fh)/(2*len(basak))
# R(t)
Rl = fh/spec
# dmeff [R(t+tau) - R(t)] / tau
#dM = Rl
dM = (np.roll(Rl,-tau)-Rl)/float(tau) #This is needed to plot gA correctly in the effective plots, but will give wrong data to fit with.
# plot fh correlator
if params['flags']['plot_fhdata']:
fhSSl = fh[:len(fh)/2].reshape((len(basak),T))
fhPSl = fh[len(fh)/2:].reshape((len(basak),T))
RSSl = Rl[:len(Rl)/2].reshape((len(basak),T))
RPSl = Rl[len(Rl)/2:].reshape((len(basak),T))
dM_plot = (np.roll(Rl,-tau)-Rl)/float(tau)
dMSSl = dM_plot[:len(dM_plot)/2].reshape((len(basak),T))
dMPSl = dM_plot[len(dM_plot)/2:].reshape((len(basak),T))
for b in range(len(basak)):
# raw correlator dC_lambda/dlambda
fhSS = fhSSl[b]
fhPS = fhPSl[b]
c51.scatter_plot(np.arange(len(fhSS)), fhSS, '%s %s fh ss' %(basak[b],str(mq)))
c51.scatter_plot(np.arange(len(fhPS)), fhPS, '%s %s fh ps' %(basak[b],str(mq)))
plt.show()
# R(t)
RSS = RSSl[b]
RPS = RPSl[b]
#c51.scatter_plot(np.arange(len(RSS)), RSS, '%s %s R(t) ss' %(basak[b],str(mq)))
#c51.scatter_plot(np.arange(len(RPS)), RPS, '%s %s R(t) ps' %(basak[b],str(mq)))
plt.show()
# dmeff R(t+tau) - R(t)
dMSS = dMSSl[b]
dMPS = dMPSl[b]
xlim = [0, 20]
ylim = [0.5, 2.0]
#c51.scatter_plot(np.arange(len(dMSS)), dMSS, '%s %s [R(t+%s)-R(t)]/%s ss' %(basak[b],str(mq),str(tau),str(tau)),xlim=xlim,ylim=ylim)
#c51.scatter_plot(np.arange(len(dMPS)), dMPS, '%s %s [R(t+%s)-R(t)]/%s ps' %(basak[b],str(mq),str(tau),str(tau)),xlim=xlim,ylim=ylim)
if False:
for i in range(len(dMSS)):
print i,',',dMSS[i].mean,',',dMSS[i].sdev,',',dMPS[i].mean,',',dMPS[i].sdev
plt.show()
# fit fh correlator
if params['flags']['fit_gA']:
# data concatenated previously
# read priors
prior = c51.fhbaryon_priors(barp['priors'],fhbp['priors'],basak,nstates)
#print prior
# read trange
trange = barp['trange']
fhtrange = fhbp['trange']
# fit boot0
boot0gv = np.concatenate((spec, dM))
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T,nstates,tau)
boot0fit = c51.fitscript_v3(trange,fhtrange,T,boot0gv,boot0p,fitfcn.baryon_dm_ss_ps,basak=params['gA_fit']['basak'])
print boot0fit['rawoutput'][0]
#print {k: boot0fit['p0'][0][k] for k in [bk for n in range(nstates) for bk in barp['priors'][n+1].keys()]}
if False: # plot R(t+1) - R(t)
posterior = boot0fit['post'][0]
x = np.arange(20)
ssline, psline = fitfcn.baryon_rt_fitline(x,posterior)
for i in range(len(ssline)):
print x[i],',',ssline[i].mean,',',ssline[i].sdev,',',psline[i].mean,',',psline[i].sdev
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit,'E0','%s' %str(mq))
c51.stability_plot(boot0fit,'gA00','%s' %str(mq))
plt.show()
if params['flags']['tabulate']:
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = boot0fit['tmin']
tbl_print['tmax'] = boot0fit['tmax']
tbl_print['fhtmin'] = boot0fit['fhtmin']
tbl_print['fhtmax'] = boot0fit['fhtmax']
tbl_print['E0'] = [boot0fit['pmean'][t]['E0'] for t in range(len(boot0fit['pmean']))]
tbl_print['dE0'] = [boot0fit['psdev'][t]['E0'] for t in range(len(boot0fit['pmean']))]
tbl_print['gA00'] = [boot0fit['pmean'][t]['gA00'] for t in range(len(boot0fit['pmean']))]
tbl_print['dgA00'] = [boot0fit['psdev'][t]['gA00'] for t in range(len(boot0fit['pmean']))]
#blist = []
#for b in params['gA_fit']['basak']:
# blist.append(b[2:])
# blist.append(b[:2])
#blist = np.unique(blist)
#for b in blist:
# tbl_print['%s_Z0s' %b] = [boot0fit['pmean'][t]['%s_Z0s'%b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0s' %b] = [boot0fit['psdev'][t]['%s_Z0s' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_Z0p' %b] = [boot0fit['pmean'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0p' %b] = [boot0fit['psdev'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
tbl_print['chi2/dof'] = np.array(boot0fit['chi2'])/np.array(boot0fit['dof'])
tbl_print['chi2'] = boot0fit['chi2']
tbl_print['chi2f'] = boot0fit['chi2f']
tbl_print['logGBF'] = boot0fit['logGBF']
print tabulate(tbl_print, headers='keys')
# submit boot0 to db
if params['flags']['write']:
corr_lst = np.array([[fhbp['meta_id']['SS'][i] for i in params['gA_fit']['basak']],[fhbp['meta_id']['PS'][i] for i in params['gA_fit']['basak']]]).flatten()
fit_id = c51.select_fitid('fhbaryon',nstates=nstates,tau=params['gA_fit']['tau'])
for t in range(len(boot0fit['tmin'])):
baryon_corr_lst = np.array([[barp['meta_id']['SS'][i] for i in params['gA_fit']['basak']],[barp['meta_id']['PS'][i] for i in params['gA_fit']['basak']]]).flatten()
baryon_fit_id = c51.select_fitid('baryon',nstates=nstates,basak=params['gA_fit']['basak'])
baryon_tmin = trange['tmin'][0]
baryon_tmax = trange['tmax'][0]
baryon_p0 = {k: boot0fit['p0'][0][k] for k in [bk for n in range(nstates) for bk in barp['priors'][n+1].keys()]}
baryon_init_id = psql.initid(baryon_p0)
baryon_prior_id = psql.priorid(c51.dict_of_tuple_to_gvar(c51.baryon_priors(barp['priors'],basak,nstates)))
baryon_id = psql.select_boot0("proton", baryon_corr_lst, baryon_fit_id, baryon_tmin, baryon_tmax, baryon_init_id, baryon_prior_id)
init_id = psql.initid(boot0fit['p0'][t])
prior_id = psql.priorid(boot0fit['prior'][t])
tmin = boot0fit['fhtmin'][t]
tmax = boot0fit['fhtmax'][t]
result = c51.make_result(boot0fit,t)
print tmin, tmax
psql.submit_fhboot0('fhproton',corr_lst,baryon_id,fit_id,tmin,tmax,init_id,prior_id,result,params['flags']['update'])
if params['flags']['csvformat']:
for t in range(len(boot0fit['fhtmin'])):
print nstates,',',boot0fit['tmin'][t],',',boot0fit['tmax'][t],',',boot0fit['fhtmin'][t],',',boot0fit['fhtmax'][t],',',boot0fit['pmean'][t]['gA00'],',',boot0fit['psdev'][t]['gA00'],',',(np.array(boot0fit['chi2'])/np.array(boot0fit['dof']))[t],',',(np.array(boot0fit['chi2'])/np.array(boot0fit['chi2f']))[t],',',boot0fit['logGBF'][t]
return {'gA_fit': boot0fit['rawoutput'][0]}
return 0
def gA_bs(psql, params):
# read data
mq = params['gA_fit']['ml']
basak = copy.deepcopy(params['gA_fit']['basak'])
tag = params['gA_fit']['ens']['tag']
stream = params['gA_fit']['ens']['stream']
Nbs = params['gA_fit']['nbs']
Mbs = params['gA_fit']['mbs']
nstates = params['gA_fit']['nstates']
tau = params['gA_fit']['tau']
barp = params[tag]['proton'][mq]
fhbp = params[tag]['gA'][mq]
print "fitting for gA mq %s, basak %s, ens %s%s, Nbs %s, Mbs %s" % (
str(mq), str(basak), str(tag), str(stream), str(Nbs), str(Mbs))
# read two point
SSl = np.array([
psql.data('dwhisq_corr_baryon', idx)
for idx in [barp['meta_id']['SS'][i] for i in basak]
])
PSl = np.array([
psql.data('dwhisq_corr_baryon', idx)
for idx in [barp['meta_id']['PS'][i] for i in basak]
])
T = len(SSl[0, 0])
# read fh correlator
fhSSl = np.array([
psql.data('dwhisq_fhcorr_baryon', idx)
for idx in [fhbp['meta_id']['SS'][i] for i in basak]
])
fhPSl = np.array([
psql.data('dwhisq_fhcorr_baryon', idx)
for idx in [fhbp['meta_id']['PS'][i] for i in basak]
])
# concatenate and make gvars to preserve correlations
SS = SSl[0]
PS = PSl[0]
for i in range(len(SSl) - 1): # loop over basak operators
SS = np.concatenate((SS, SSl[i + 1]), axis=1)
PS = np.concatenate((PS, PSl[i + 1]), axis=1)
fhSS = fhSSl[0]
fhPS = fhPSl[0]
for i in range(len(fhSSl) - 1):
fhSS = np.concatenate((fhSS, fhSSl[i + 1]), axis=1)
fhPS = np.concatenate((fhPS, fhPSl[i + 1]), axis=1)
boot0 = np.concatenate((SS, PS, fhSS, fhPS), axis=1)
# make gvars
gvboot0 = c51.make_gvars(boot0)
spec = gvboot0[:len(gvboot0) / 2]
fh = gvboot0[len(gvboot0) / 2:]
# R(t)
Rl = fh / spec
# dmeff [R(t+tau) - R(t)] / tau
#dM = Rl
dM = (np.roll(Rl, -tau) - Rl) / float(
tau
) #This is needed to plot gA correctly in the effective plots, but will give wrong data to fit with.
# plot data
if params['flags']['plot_data']:
for b in range(len(basak)):
SS = SSl[b]
PS = PSl[b]
# raw correlator
c51.scatter_plot(np.arange(len(SS[0])), c51.make_gvars(SS),
'%s %s ss' % (basak[b], str(mq)))
c51.scatter_plot(np.arange(len(PS[0])), c51.make_gvars(PS),
'%s %s ps' % (basak[b], str(mq)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff_ss = eff.effective_mass(c51.make_gvars(SS), 1, 'log')
meff_ps = eff.effective_mass(c51.make_gvars(PS), 1, 'log')
xlim = [2, len(meff_ss) / 3 - 2]
ylim = c51.find_yrange(meff_ss, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(meff_ss)),
meff_ss,
'%s %s ss effective mass' % (basak[b], str(mq)),
xlim=xlim,
ylim=ylim)
ylim = c51.find_yrange(meff_ps, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(meff_ps)),
meff_ps,
'%s %s ps effective mass' % (basak[b], str(mq)),
xlim=xlim,
ylim=ylim)
plt.show()
# scaled correlator
E0 = barp['priors'][1]['E0'][0]
scaled_ss = eff.scaled_correlator(c51.make_gvars(SS),
E0,
phase=1.0)
scaled_ps = eff.scaled_correlator(c51.make_gvars(PS),
E0,
phase=1.0)
ylim = c51.find_yrange(scaled_ss, xlim[0], xlim[1])
c51.scatter_plot(
np.arange(len(scaled_ss)),
scaled_ss,
'%s %s ss scaled correlator (take sqrt to get Z0_s)' %
(basak[b], str(mq)),
xlim=xlim,
ylim=ylim)
ylim = c51.find_yrange(scaled_ps, xlim[0], xlim[1])
c51.scatter_plot(
np.arange(len(scaled_ps)),
scaled_ps,
'%s %s ps scaled correlator (divide by Z0_s to get Z0_p)' %
(basak[b], str(mq)),
xlim=xlim,
ylim=ylim)
plt.show()
if params['flags']['fit_twopt']:
# data already concatenated previously
# read priors
prior = c51.baryon_priors(barp['priors'], basak, nstates)
## read trange
trange = barp['trange']
## fit boot0
boot0gv = spec
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T, nstates)
boot0fit = c51.fitscript_v2(trange,
T,
boot0gv,
boot0p,
fitfcn.twopt_baryon_ss_ps,
basak=params['gA_fit']['basak'])
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit, 'E0', '%s' % str(mq))
plt.show()
if params['flags']['tabulate']:
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = boot0fit['tmin']
tbl_print['tmax'] = boot0fit['tmax']
tbl_print['E0'] = [
boot0fit['pmean'][t]['E0']
for t in range(len(boot0fit['pmean']))
]
tbl_print['dE0'] = [
boot0fit['psdev'][t]['E0']
for t in range(len(boot0fit['pmean']))
]
blist = []
for b in params['gA_fit']['basak']:
blist.append(b[2:])
blist.append(b[:2])
blist = np.unique(blist)
for b in blist:
tbl_print['%s_Z0s' % b] = [
boot0fit['pmean'][t]['%s_Z0s' % b]
for t in range(len(boot0fit['pmean']))
]
tbl_print['%s_dZ0s' % b] = [
boot0fit['psdev'][t]['%s_Z0s' % b]
for t in range(len(boot0fit['pmean']))
]
tbl_print['%s_Z0p' % b] = [
boot0fit['pmean'][t]['%s_Z0p' % b]
for t in range(len(boot0fit['pmean']))
]
tbl_print['%s_dZ0p' % b] = [
boot0fit['psdev'][t]['%s_Z0p' % b]
for t in range(len(boot0fit['pmean']))
]
tbl_print['chi2/dof'] = np.array(boot0fit['chi2']) / np.array(
boot0fit['dof'])
tbl_print['logGBF'] = boot0fit['logGBF']
print tabulate(tbl_print, headers='keys')
# submit boot0 to db
if params['flags']['write']:
corr_lst = np.array(
[[barp['meta_id']['SS'][i] for i in params['gA_fit']['basak']],
[barp['meta_id']['PS'][i]
for i in params['gA_fit']['basak']]]).flatten()
fit_id = c51.select_fitid('baryon',
nstates=nstates,
basak=params['gA_fit']['basak'])
for t in range(len(boot0fit['tmin'])):
init_id = psql.initid(boot0fit['p0'][t])
prior_id = psql.priorid(boot0fit['prior'][t])
tmin = boot0fit['tmin'][t]
tmax = boot0fit['tmax'][t]
result = c51.make_result(boot0fit, t)
psql.submit_boot0('proton', corr_lst, fit_id, tmin, tmax,
init_id, prior_id, result,
params['flags']['update'])
# plot fh correlator
if params['flags']['plot_fhdata']:
for b in range(len(basak)):
# raw correlator dC_lambda/dlambda
fhSS = fhSSl[b]
fhPS = fhPSl[b]
c51.scatter_plot(np.arange(len(fhSS[0])), c51.make_gvars(fhSS),
'%s %s fh ss' % (basak[b], str(mq)))
c51.scatter_plot(np.arange(len(fhPS[0])), c51.make_gvars(fhPS),
'%s %s fh ps' % (basak[b], str(mq)))
plt.show()
# R(t)
RSS = (Rl[:len(Rl) / 2])[b * len(Rl) / (2 * len(basak)):(b + 1) *
len(Rl) / (2 * len(basak))]
RPS = (Rl[len(Rl) / 2:])[b * len(Rl) / (2 * len(basak)):(b + 1) *
len(Rl) / (2 * len(basak))]
c51.scatter_plot(np.arange(len(RSS)), RSS,
'%s %s R(t) ss' % (basak[b], str(mq)))
c51.scatter_plot(np.arange(len(RPS)), RPS,
'%s %s R(t) ps' % (basak[b], str(mq)))
plt.show()
# dmeff R(t+tau) - R(t)
dMSS = (dM[:len(dM) / 2])[b * len(dM) / (2 * len(basak)):(b + 1) *
len(dM) / (2 * len(basak))]
dMPS = (dM[len(dM) / 2:])[b * len(dM) / (2 * len(basak)):(b + 1) *
len(dM) / (2 * len(basak))]
xlim = [2, 15]
ylim = [0.5, 2] #c51.find_yrange(dMSS, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(dMSS)),
dMSS,
'%s %s [R(t+%s)-R(t)]/%s ss' %
(basak[b], str(mq), str(tau), str(tau)),
xlim=xlim,
ylim=ylim)
ylim = [0.5, 2] #c51.find_yrange(dMPS, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(dMPS)),
dMPS,
'%s %s [R(t+%s)-R(t)]/%s ps' %
(basak[b], str(mq), str(tau), str(tau)),
xlim=xlim,
ylim=ylim)
if True:
for i in range(len(dMSS)):
print i, ',', dMSS[i].mean, ',', dMSS[i].sdev, ',', dMPS[
i].mean, ',', dMPS[i].sdev
plt.show()
# fit fh correlator
if params['flags']['fit_gA']:
# data concatenated previously
# read priors
prior = c51.fhbaryon_priors(barp['priors'], fhbp['priors'], basak,
nstates)
print prior
# read trange
trange = barp['trange']
fhtrange = fhbp['trange']
# fit boot0
boot0gv = np.concatenate((spec, dM))
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T, nstates, tau)
boot0fit = c51.fitscript_v3(trange,
fhtrange,
T,
boot0gv,
boot0p,
fitfcn.baryon_dm_ss_ps,
basak=params['gA_fit']['basak'])
print boot0fit['rawoutput'][0]
#print {k: boot0fit['p0'][0][k] for k in [bk for n in range(nstates) for bk in barp['priors'][n+1].keys()]}
if False: # plot R(t+1) - R(t)
posterior = boot0fit['post'][0]
x = np.arange(20)
ssline, psline = fitfcn.baryon_rt_fitline(x, posterior)
for i in range(len(ssline)):
print x[i], ',', ssline[i].mean, ',', ssline[
i].sdev, ',', psline[i].mean, ',', psline[i].sdev
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit, 'E0', '%s' % str(mq))
c51.stability_plot(boot0fit, 'gA00', '%s' % str(mq))
plt.show()
if params['flags']['tabulate']:
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = boot0fit['tmin']
tbl_print['tmax'] = boot0fit['tmax']
tbl_print['fhtmin'] = boot0fit['fhtmin']
tbl_print['fhtmax'] = boot0fit['fhtmax']
#tbl_print['E0'] = [boot0fit['pmean'][t]['E0'] for t in range(len(boot0fit['pmean']))]
#tbl_print['dE0'] = [boot0fit['psdev'][t]['E0'] for t in range(len(boot0fit['pmean']))]
tbl_print['gA00'] = [
boot0fit['pmean'][t]['gA00']
for t in range(len(boot0fit['pmean']))
]
tbl_print['dgA00'] = [
boot0fit['psdev'][t]['gA00']
for t in range(len(boot0fit['pmean']))
]
#blist = []
#for b in params['gA_fit']['basak']:
# blist.append(b[2:])
# blist.append(b[:2])
#blist = np.unique(blist)
#for b in blist:
# tbl_print['%s_Z0s' %b] = [boot0fit['pmean'][t]['%s_Z0s'%b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0s' %b] = [boot0fit['psdev'][t]['%s_Z0s' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_Z0p' %b] = [boot0fit['pmean'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0p' %b] = [boot0fit['psdev'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
tbl_print['chi2/dof'] = np.array(boot0fit['chi2']) / np.array(
boot0fit['dof'])
tbl_print['chi2'] = boot0fit['chi2']
tbl_print['chi2f'] = boot0fit['chi2f']
tbl_print['logGBF'] = boot0fit['logGBF']
print tabulate(tbl_print, headers='keys')
# submit boot0 to db
if params['flags']['write']:
corr_lst = np.array(
[[fhbp['meta_id']['SS'][i] for i in params['gA_fit']['basak']],
[fhbp['meta_id']['PS'][i]
for i in params['gA_fit']['basak']]]).flatten()
fit_id = c51.select_fitid('fhbaryon',
nstates=nstates,
tau=params['gA_fit']['tau'])
for t in range(len(boot0fit['tmin'])):
baryon_corr_lst = np.array([[
barp['meta_id']['SS'][i] for i in params['gA_fit']['basak']
], [
barp['meta_id']['PS'][i] for i in params['gA_fit']['basak']
]]).flatten()
baryon_fit_id = c51.select_fitid(
'baryon', nstates=nstates, basak=params['gA_fit']['basak'])
baryon_tmin = trange['tmin'][0]
baryon_tmax = trange['tmax'][0]
baryon_p0 = {
k: boot0fit['p0'][0][k]
for k in [
bk for n in range(nstates)
for bk in barp['priors'][n + 1].keys()
]
}
baryon_init_id = psql.initid(baryon_p0)
baryon_prior_id = psql.priorid(
c51.dict_of_tuple_to_gvar(
c51.baryon_priors(barp['priors'], basak, nstates)))
baryon_id = psql.select_boot0("proton", baryon_corr_lst,
baryon_fit_id, baryon_tmin,
baryon_tmax, baryon_init_id,
baryon_prior_id)
init_id = psql.initid(boot0fit['p0'][t])
prior_id = psql.priorid(boot0fit['prior'][t])
tmin = boot0fit['fhtmin'][t]
tmax = boot0fit['fhtmax'][t]
result = c51.make_result(boot0fit, t)
print tmin, tmax
psql.submit_fhboot0('fhproton', corr_lst, baryon_id, fit_id,
tmin, tmax, init_id, prior_id, result,
params['flags']['update'])
if params['flags']['csvformat']:
for t in range(len(boot0fit['fhtmin'])):
print nstates, ',', boot0fit['tmin'][t], ',', boot0fit['tmax'][
t], ',', boot0fit['fhtmin'][t], ',', boot0fit['fhtmax'][
t], ',', boot0fit['pmean'][t]['gA00'], ',', boot0fit[
'psdev'][t]['gA00'], ',', (
np.array(boot0fit['chi2']) /
np.array(boot0fit['dof']))[t], ',', (
np.array(boot0fit['chi2']) /
np.array(boot0fit['chi2f'])
)[t], ',', boot0fit['logGBF'][t]
def fit_gA(params,gvboot0):
# read data
ens = params['LHCP_params']['ens']
nstates = params['LHCP_params']['nstates']
barp = params[ens]['proton']
fhbp = params[ens]['gA']
basak = ['G1G1', 'G1G2', 'G1G3', 'G2G1', 'G2G2', 'G2G3', 'G3G1', 'G3G2', 'G3G3'] # src snk
mq = 'unitary'
tau = 1
# make gvars
spec = gvboot0[:len(gvboot0)/2]
fh = gvboot0[len(gvboot0)/2:]
T = len(fh)/(2*len(basak))
# R(t)
Rl = fh/spec
# dmeff [R(t+tau) - R(t)] / tau
dM = Rl
#dM = (np.roll(Rl,-tau)-Rl)/float(tau) #This is needed to plot gA correctly in the effective plots, but will give wrong data to fit with.
# plot fh correlator
if params['flags']['plot_fhdata']:
fhSSl = fh[:len(fh)/2].reshape((len(basak),T))
fhPSl = fh[len(fh)/2:].reshape((len(basak),T))
RSSl = Rl[:len(Rl)/2].reshape((len(basak),T))
RPSl = Rl[len(Rl)/2:].reshape((len(basak),T))
dM_plot = (np.roll(Rl,-tau)-Rl)/float(tau)
dMSSl = dM_plot[:len(dM_plot)/2].reshape((len(basak),T))
dMPSl = dM_plot[len(dM_plot)/2:].reshape((len(basak),T))
for b in range(len(basak)):
# raw correlator dC_lambda/dlambda
fhSS = fhSSl[b]
fhPS = fhPSl[b]
c51.scatter_plot(np.arange(len(fhSS)), fhSS, '%s %s fh ss' %(basak[b],str(mq)))
c51.scatter_plot(np.arange(len(fhPS)), fhPS, '%s %s fh ps' %(basak[b],str(mq)))
plt.show()
# R(t)
RSS = RSSl[b]
RPS = RPSl[b]
c51.scatter_plot(np.arange(len(RSS)), RSS, '%s %s R(t) ss' %(basak[b],str(mq)))
c51.scatter_plot(np.arange(len(RPS)), RPS, '%s %s R(t) ps' %(basak[b],str(mq)))
plt.show()
# dmeff R(t+tau) - R(t)
dMSS = dMSSl[b]
dMPS = dMPSl[b]
xlim = [0, 20]
ylim = [0.5, 2.0]
c51.scatter_plot(np.arange(len(dMSS)), dMSS, '%s %s [R(t+%s)-R(t)]/%s ss' %(basak[b],str(mq),str(tau),str(tau)),xlim=xlim,ylim=ylim)
c51.scatter_plot(np.arange(len(dMPS)), dMPS, '%s %s [R(t+%s)-R(t)]/%s ps' %(basak[b],str(mq),str(tau),str(tau)),xlim=xlim,ylim=ylim)
if False:
for i in range(len(dMSS)):
print i,',',dMSS[i].mean,',',dMSS[i].sdev,',',dMPS[i].mean,',',dMPS[i].sdev
plt.show()
# fit fh correlator
if params['flags']['fit_gA']:
# data concatenated previously
# read priors
prior = c51.fhbaryon_priors(barp['priors'],fhbp['priors'],basak,nstates)
#print prior
# read trange
trange = barp['trange']
fhtrange = fhbp['trange']
# fit boot0
boot0gv = np.concatenate((spec, dM))
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T,nstates,tau)
boot0fit = c51.fitscript_v3(trange,fhtrange,T,boot0gv,boot0p,fitfcn.asqtad_baryon_rt_ss_ps,basak=basak)
#print boot0fit['rawoutput'][0]
#print {k: boot0fit['p0'][0][k] for k in [bk for n in range(nstates) for bk in barp['priors'][n+1].keys()]}
if False: # plot R(t+1) - R(t)
posterior = boot0fit['post'][0]
x = np.arange(20)
ssline, psline = fitfcn.baryon_rt_fitline(x,posterior)
for i in range(len(ssline)):
print x[i],',',ssline[i].mean,',',ssline[i].sdev,',',psline[i].mean,',',psline[i].sdev
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit,'E0','%s' %str(mq))
c51.stability_plot(boot0fit,'gA00','%s' %str(mq))
plt.show()
if params['flags']['tabulate']:
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = boot0fit['tmin']
tbl_print['tmax'] = boot0fit['tmax']
tbl_print['fhtmin'] = boot0fit['fhtmin']
tbl_print['fhtmax'] = boot0fit['fhtmax']
#tbl_print['E0'] = [boot0fit['pmean'][t]['E0'] for t in range(len(boot0fit['pmean']))]
#tbl_print['dE0'] = [boot0fit['psdev'][t]['E0'] for t in range(len(boot0fit['pmean']))]
tbl_print['gA00'] = [boot0fit['pmean'][t]['gA00'] for t in range(len(boot0fit['pmean']))]
tbl_print['dgA00'] = [boot0fit['psdev'][t]['gA00'] for t in range(len(boot0fit['pmean']))]
#blist = []
#for b in params['gA_fit']['basak']:
# blist.append(b[2:])
# blist.append(b[:2])
#blist = np.unique(blist)
#for b in blist:
# tbl_print['%s_Z0s' %b] = [boot0fit['pmean'][t]['%s_Z0s'%b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0s' %b] = [boot0fit['psdev'][t]['%s_Z0s' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_Z0p' %b] = [boot0fit['pmean'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
# tbl_print['%s_dZ0p' %b] = [boot0fit['psdev'][t]['%s_Z0p' %b] for t in range(len(boot0fit['pmean']))]
tbl_print['chi2/dof'] = np.array(boot0fit['chi2'])/np.array(boot0fit['dof'])
tbl_print['chi2'] = boot0fit['chi2']
tbl_print['chi2f'] = boot0fit['chi2f']
tbl_print['logGBF'] = boot0fit['logGBF']
print tabulate(tbl_print, headers='keys')
if params['flags']['csvformat']:
for t in range(len(boot0fit['fhtmin'])):
print nstates,',',boot0fit['tmin'][t],',',boot0fit['tmax'][t],',',boot0fit['fhtmin'][t],',',boot0fit['fhtmax'][t],',',boot0fit['pmean'][t]['gA00'],',',boot0fit['psdev'][t]['gA00'],',',(np.array(boot0fit['chi2'])/np.array(boot0fit['dof']))[t],',',(np.array(boot0fit['chi2'])/np.array(boot0fit['chi2f']))[t],',',boot0fit['logGBF'][t]
return {'gA_fit': boot0fit['rawoutput'][0]}
return 0
