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']
barp = params[tag]['proton'][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])
# 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()
# concatenate data
SS = SSl[0]
PS = PSl[0]
for i in range(len(SSl)-1):
SS = np.concatenate((SS,SSl[i+1]),axis=1)
PS = np.concatenate((PS,PSl[i+1]),axis=1)
boot0 = np.concatenate((SS, PS), axis=1)
# read priors
prior = c51.baryon_priors(barp['priors'],basak,nstates)
## read trange
trange = barp['trange']
## fit boot0
boot0gv = c51.make_gvars(boot0)
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'])
print boot0fit['rawoutput'][0]
x_ps_kaon = len(x_ss_kaon) + len(x_ps_pion) + len(x_ss_pion) + len(
x_pps) + len(x_mps) + len(x_ppl) + len(x_mpl) + np.arange(
len(ps_kaon[0]))
print "len mpl:", np.shape(mpl)
print "len ppl:", np.shape(ppl)
print "len mps:", np.shape(mps)
print "len pps:", np.shape(pps)
print "len ss_pion:", np.shape(ss_pion)
print "len ps_pion:", np.shape(ps_pion)
print "len ss_kaon:", np.shape(ss_kaon)
print "len ps_kaon:", np.shape(ps_kaon)
# concatenate
boot0 = np.concatenate(
(mpl, ppl, mps, pps, ss_pion, ps_pion, ss_kaon, ps_kaon), axis=1)
# correlated covariance
gvboot0 = c51.make_gvars(boot0)
# split for projects
gvmpl = gvboot0[x_mpl]
gvppl = gvboot0[x_ppl]
gvmps = gvboot0[x_mps]
gvpps = gvboot0[x_pps]
gvss_pion = gvboot0[x_ss_pion]
gvps_pion = gvboot0[x_ps_pion]
gvss_kaon = gvboot0[x_ss_kaon]
gvps_kaon = gvboot0[x_ps_kaon]
# fit #
# mres
mresl = decay.fit_mres_bs(psql, decay_params,
decay_params['decay_ward_fit']['ml'], gvmpl,
gvppl)
mress = decay.fit_mres_bs(psql, decay_params,
def fit_mres_bs(psql,params,mq,gv_mp,gv_pp):
print 'fitting mres', mq
tag = params['decay_ward_fit']['ens']['tag']
stream = params['decay_ward_fit']['ens']['stream']
Nbs = params['decay_ward_fit']['nbs']
Mbs = params['decay_ward_fit']['mbs']
mresp = params[tag]['mres'][mq]
T = len(gv_pp)
boot0gv = gv_mp/gv_pp
# plot mres
if params['flags']['plot_data']:
c51.scatter_plot(np.arange(len(boot0gv)), boot0gv, '%s mres' %str(mq))
plt.show()
# read priors
prior = mresp['priors']
# read trange
trange = mresp['trange']
# fit boot0
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T)
boot0fit = c51.fitscript_v2(trange,T,boot0gv,boot0p,fitfcn.mres_fitfcn)
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit,'mres',str(mq))
plt.show()
if params['flags']['tabulate']:
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = boot0fit['tmin']
tbl_print['tmax'] = boot0fit['tmax']
tbl_print['mres'] = [boot0fit['pmean'][t]['mres'] for t in range(len(boot0fit['pmean']))]
tbl_print['sdev'] = [boot0fit['psdev'][t]['mres'] 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 = [mresp['meta_id']['mp'],mresp['meta_id']['pp']]
fit_id = c51.select_fitid('mres',params)
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('jmu',corr_lst,fit_id,tmin,tmax,init_id,prior_id,result,params['flags']['update'])
if len(boot0fit['tmin'])!=1 or len(boot0fit['tmax'])!=1:
print "sweeping over time range"
print "skipping bootstrap"
return 0
else: pass
if False: #params['flags']['write']:
bsresult = []
psql.chkbsprior(tag,stream,Nbs,boot0fit['prior'][0])
for g in tqdm.tqdm(range(Nbs)):
# make bs gvar dataset
n = g+1
bs_id,draw = psql.fetchonebs(nbs=n,Mbs=Mbs,ens=tag,stream=stream)
bootn = boot0[draw]
bootngv = c51.make_gvars(bootn)
# read randomized priors
bsprior_id,bsp = psql.bspriorid(tag,stream,g,boot0fit['prior'][0])
bsp = c51.dict_of_tuple_to_gvar(bsp)
# read initial guess
init = {"mres":boot0fit['pmean'][0]['mres']}
#Fit
bsfit = c51.fitscript_v2(trange,T,bootngv,bsp,fitfcn.mres_fitfcn,init)
tmin = bsfit['tmin'][0]
tmax = bsfit['tmax'][0]
boot0_id = psql.select_boot0('jmu',corr_lst,fit_id,tmin,tmax,init_id,prior_id)
bsinit_id = psql.initid(bsfit['p0'][0])
result = c51.make_result(bsfit,0) #"""{"mres":%s, "chi2":%s, "dof":%s}""" %(bsfit['pmean'][t]['mres'],bsfit['chi2'][t],bsfit['dof'][t])
psql.submit_bs('jmu_bs',boot0_id,bs_id,Mbs,bsinit_id,bsprior_id,result,params['flags']['update'])
return {'mres_fit': boot0fit['rawoutput'][0]}
def concatgv(corr1, corr2):
concat = np.concatenate((corr1,corr2),axis=1)
concat_gv = c51.make_gvars(concat)
corr1 = concat_gv[:len(concat_gv)/2]
corr2 = concat_gv[len(concat_gv)/2:]
return corr1, corr2
def fit_decay_bs(psql,params,meson, gv_SS, gv_PS):
if meson=='pion':
mq1 = params['decay_ward_fit']['ml']
mq2 = mq1
elif meson == 'kaon':
mq1 = params['decay_ward_fit']['ml']
mq2 = params['decay_ward_fit']['ms']
elif meson == 'etas':
mq1 = params['decay_ward_fit']['ms']
mq2 = mq1
print 'fitting two point', mq1, mq2
tag = params['decay_ward_fit']['ens']['tag']
stream = params['decay_ward_fit']['ens']['stream']
nstates = params['decay_ward_fit']['nstates']
mesp = params[tag][meson]['%s_%s' %(str(mq1),str(mq2))]
T = len(gv_SS)
# plot effective mass / scaled correlator
if params['flags']['plot_data']:
# unfolded correlator data
c51.scatter_plot(np.arange(len(gv_SS)), gv_SS, '%s_%s ss folded' %(str(mq1),str(mq2)))
c51.scatter_plot(np.arange(len(gv_PS)), gv_PS, '%s_%s ps folded' %(str(mq1),str(mq2)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff_ss = eff.effective_mass(gv_SS, 1, 'cosh')
meff_ps = eff.effective_mass(gv_PS, 1, 'cosh')
xlim = [3, len(meff_ss)/2-2]
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' %(str(mq1),str(mq2)), 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' %(str(mq1),str(mq2)), xlim = xlim, ylim = ylim)
plt.show()
# scaled correlator
E0 = mesp['priors']['1']['E0'][0]
scaled_ss = eff.scaled_correlator(gv_SS, E0, phase=1.0)
scaled_ps = eff.scaled_correlator(gv_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)' %(str(mq1),str(mq2)), 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)' %(str(mq1),str(mq2)), xlim = xlim, ylim = ylim)
plt.show()
# concatenate data
boot0gv = np.concatenate((gv_SS, gv_PS))
# read priors
prior = c51.meson_priors(mesp['priors'],nstates)
# read trange
trange = mesp['trange']
# fit boot0
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T,params['decay_ward_fit']['nstates'])
boot0fit = c51.fitscript_v2(trange,T,boot0gv,boot0p,fitfcn.twopt_fitfcn_ss_ps)
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit,'E0','%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['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['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 = [mesp['meta_id']['SS'],mesp['meta_id']['PS']]
fit_id = c51.select_fitid('meson',params)
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('meson',corr_lst,fit_id,tmin,tmax,init_id,prior_id,result,params['flags']['update'])
if len(boot0fit['tmin'])!=1 or len(boot0fit['tmax'])!=1:
print "sweeping over time range"
print "skipping bootstrap"
return 0
else: pass
if False: #params['flags']['write']:
bsresult = []
psql.chkbsprior(tag,stream,Nbs,boot0fit['prior'][0])
for g in tqdm.tqdm(range(Nbs)):
# make bs gvar dataset
n = g+1
bs_id,draw = psql.fetchonebs(nbs=n,Mbs=Mbs,ens=tag,stream=stream)
bootn = boot0[draw]
bootngv = c51.make_gvars(bootn)
# read randomized priors
bsprior_id,bsp = psql.bspriorid(tag,stream,g,boot0fit['prior'][0])
bsp = c51.dict_of_tuple_to_gvar(bsp)
# read initial guess
init = c51.read_init(boot0fit,0) #{"mres":boot0fit['pmean'][0]['mres']}
#Fit
bsfit = c51.fitscript_v2(trange,T,bootngv,bsp,fitfcn.twopt_fitfcn_ss_ps,init)
tmin = bsfit['tmin'][0]
tmax = bsfit['tmax'][0]
boot0_id = psql.select_boot0('meson',corr_lst,fit_id,tmin,tmax,init_id,prior_id)
bsinit_id = psql.initid(bsfit['p0'][0])
result = c51.make_result(bsfit,0) #"""{"mres":%s, "chi2":%s, "dof":%s}""" %(bsfit['pmean'][t]['mres'],bsfit['chi2'][t],bsfit['dof'][t])
psql.submit_bs('meson_bs',boot0_id,bs_id,Mbs,bsinit_id,bsprior_id,result,params['flags']['update'])
return {'meson_fit': boot0fit['rawoutput'][0]}
# concatenate and build grand covariance matrix # # index x_mp = np.arange(len(mp[0])) x_pp = np.arange(len(pp[0]))+len(x_mp) x_ss_pion = np.arange(len(ss_pion[0]))+len(x_mp)+len(x_pp) x_ps_pion = np.arange(len(ps_pion[0]))+len(x_mp)+len(x_pp)+len(x_ss_pion) x_gAboot0 = np.arange(len(gAboot0[0]))+len(x_mp)+len(x_pp)+len(x_ss_pion)+len(x_ps_pion) print "len mp:", np.shape(mp) print "len pp:", np.shape(pp) print "len ss_pion:", np.shape(ss_pion) print "len ps_pion:", np.shape(ps_pion) print "len gAboot0:", np.shape(gAboot0) # concatenate boot0 = np.concatenate((mp,pp,ss_pion,ps_pion,gAboot0),axis=1) # correlated covariance gvboot0 = c51.make_gvars(boot0) # split to different project subsets gvmp = gvboot0[x_mp] gvpp = gvboot0[x_pp] gvss_pion = gvboot0[x_ss_pion] gvps_pion = gvboot0[x_ps_pion] gvgAboot0 = gvboot0[x_gAboot0] # fit # # mres mresl = decay.fit_mres_bs(psql,decay_params,decay_params['decay_ward_fit']['ml'],gvmp,gvpp) # meson pifit = decay.fit_decay_bs(psql,decay_params,'pion',gvss_pion,gvps_pion) # mN and gA #gAfit = gA.fit_gA(psql,gA_params,gvgAboot0) gAfit = gA.fit_proton(psql,gA_params,gvgAboot0) # chipt parameters
for m in ml:
decay_params['decay_ward_fit']['ml'] = m
ss, ps = decay.read_decay_bs(psql,decay_params,'pion')
x_list['ss_%s_%s' %(str(m),str(m))] = len(data[0]) + np.arange(len(ss[0]))
data = np.concatenate((data,ss), axis=1)
x_list['ps_%s_%s' %(str(m),str(m))] = len(data[0]) + np.arange(len(ps[0]))
data = np.concatenate((data,ps), axis=1)
for s in ms:
decay_params['decay_ward_fit']['ms'] = s
ss, ps = decay.read_decay_bs(psql,decay_params,'kaon')
x_list['ss_%s_%s' %(str(m),str(s))] = len(data[0]) + np.arange(len(ss[0]))
data = np.concatenate((data,ss), axis=1)
x_list['ps_%s_%s' %(str(m),str(s))] = len(data[0]) + np.arange(len(ps[0]))
data = np.concatenate((data,ps), axis=1)
# make gvars
data_gv = c51.make_gvars(data)
### fit ###
result = dict()
# mres
for m in ml:
gv_mp = data_gv[x_list['mp_%s' %str(m)]]
gv_pp = data_gv[x_list['pp_%s' %str(m)]]
fit = decay.fit_mres_bs(psql,decay_params,m,gv_mp,gv_pp)['mres_fit']
result['mres_%s' %str(m)] = fit
for s in ms:
gv_mp = data_gv[x_list['mp_%s' %str(s)]]
gv_pp = data_gv[x_list['pp_%s' %str(s)]]
fit = decay.fit_mres_bs(psql,decay_params,s,gv_mp,gv_pp)['mres_fit']
result['mres_%s' %str(s)] = fit
# mesons
for m in ml:
if __name__=='__main__':
# read master
f = open('./hisq.yml','r')
params = yaml.load(f)
f.close()
# yaml entires
fitmeta = params['hisq_fit']
# log in sql
psqlpwd = pwd.passwd()
psql = sql.pysql('cchang5','cchang5',psqlpwd)
## bootstrap decay constant
# fit phi_jj_5
datajj = read_hisq_meson(psql,params,'phi_jj_5')
datagv = c51.make_gvars(datajj)
resp = fit_hisq_meson(psql,params,'phi_jj_5', datagv)
# fit phi_jr_5
#data = read_hisq_meson(psql,params,'phi_jr_5')
#datagv = c51.make_gvars(data)
#resp = fit_hisq_meson(psql,params,'phi_jr_5', datagv)
# fit phi_rr_5
datarr = read_hisq_meson(psql,params,'phi_rr_5')
datagv = c51.make_gvars(datarr)
resp = fit_hisq_meson(psql,params,'phi_rr_5', datagv)
#print res['meson_fit']
#buffdict.update(res['meson_fit'].p)
if params['flags']['bootstrap']:
params['flags']['plot_data'] = False
params['flags']['stability_plot'] = False
def decay_bs(psql, params, meson):
if meson == "pion":
mq1 = params["decay_ward_fit"]["ml"]
mq2 = mq1
elif meson == "kaon":
mq1 = params["decay_ward_fit"]["ml"]
mq2 = params["decay_ward_fit"]["ms"]
elif meson == "etas":
mq1 = params["decay_ward_fit"]["ms"]
mq2 = mq1
print "fitting twopoint", mq1, mq2
tag = params["decay_ward_fit"]["ens"]["tag"]
stream = params["decay_ward_fit"]["ens"]["stream"]
Nbs = params["decay_ward_fit"]["nbs"]
Mbs = params["decay_ward_fit"]["mbs"]
mesp = params[tag][meson]["%s_%s" % (str(mq1), str(mq2))]
# read data
SS = c51.fold(psql.data("dwhisq_corr_meson", mesp["meta_id"]["SS"]))
PS = c51.fold(psql.data("dwhisq_corr_meson", mesp["meta_id"]["PS"]))
T = len(SS[0])
# plot effective mass / scaled correlator
if params["flags"]["plot_data"]:
# unfolded correlator data
c51.scatter_plot(np.arange(len(SS[0])), c51.make_gvars(SS), "%s_%s ss folded" % (str(mq1), str(mq2)))
c51.scatter_plot(np.arange(len(PS[0])), c51.make_gvars(PS), "%s_%s ps folded" % (str(mq1), str(mq2)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff_ss = eff.effective_mass(c51.make_gvars(SS), 1, "cosh")
meff_ps = eff.effective_mass(c51.make_gvars(PS), 1, "cosh")
xlim = [3, len(meff_ss) / 2 - 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" % (str(mq1), str(mq2)), 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" % (str(mq1), str(mq2)), xlim=xlim, ylim=ylim
)
plt.show()
# scaled correlator
E0 = mesp["priors"]["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)" % (str(mq1), str(mq2)),
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)" % (str(mq1), str(mq2)),
xlim=xlim,
ylim=ylim,
)
plt.show()
# concatenate data
boot0 = np.concatenate((SS, PS), axis=1)
# read priors
prior = mesp["priors"]
# read trange
trange = mesp["trange"]
# fit boot0
boot0gv = c51.make_gvars(boot0)
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T, params["decay_ward_fit"]["nstates"])
boot0fit = c51.fitscript_v2(trange, T, boot0gv, boot0p, fitfcn.twopt_fitfcn_ss_ps)
if params["flags"]["stability_plot"]:
c51.stability_plot(boot0fit, "E0", "%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["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["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 = [mesp["meta_id"]["SS"], mesp["meta_id"]["PS"]]
fit_id = c51.select_fitid("meson", params)
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(
"meson", corr_lst, fit_id, tmin, tmax, init_id, prior_id, result, params["flags"]["update"]
)
if len(boot0fit["tmin"]) != 1 or len(boot0fit["tmax"]) != 1:
print "sweeping over time range"
print "skipping bootstrap"
return 0
else:
pass
if params["flags"]["write"]:
bsresult = []
psql.chkbsprior(tag, stream, Nbs, boot0fit["prior"][0])
for g in tqdm.tqdm(range(Nbs)):
# make bs gvar dataset
n = g + 1
bs_id, draw = psql.fetchonebs(nbs=n, Mbs=Mbs, ens=tag, stream=stream)
bootn = boot0[draw]
bootngv = c51.make_gvars(bootn)
# read randomized priors
bsprior_id, bsp = psql.bspriorid(tag, stream, g, boot0fit["prior"][0])
bsp = c51.dict_of_tuple_to_gvar(bsp)
# read initial guess
init = c51.read_init(boot0fit, 0) # {"mres":boot0fit['pmean'][0]['mres']}
# Fit
bsfit = c51.fitscript_v2(trange, T, bootngv, bsp, fitfcn.twopt_fitfcn_ss_ps, init)
tmin = bsfit["tmin"][0]
tmax = bsfit["tmax"][0]
boot0_id = psql.select_boot0("meson", corr_lst, fit_id, tmin, tmax, init_id, prior_id)
bsinit_id = psql.initid(bsfit["p0"][0])
result = c51.make_result(
bsfit, 0
) # """{"mres":%s, "chi2":%s, "dof":%s}""" %(bsfit['pmean'][t]['mres'],bsfit['chi2'][t],bsfit['dof'][t])
psql.submit_bs("meson_bs", boot0_id, bs_id, Mbs, bsinit_id, bsprior_id, result, params["flags"]["update"])
return 0
def corrfit(psql, params, meson):
mq = params['a13_fit']['ml']
print 'fitting a13', mq
tag = params['a13_fit']['ens']['tag']
stream = params['a13_fit']['ens']['stream']
Nbs = params['a13_fit']['nbs']
Mbs = params['a13_fit']['mbs']
mesp = params[tag][meson][mq]
# read data
SS = c51.fold(psql.data('dwhisq_corr_meson', mesp['meta_id']['SS']))
PS = c51.fold(psql.data('dwhisq_corr_meson', mesp['meta_id']['PS']))
T = len(SS[0])
# plot effective mass / scaled correlator
if params['flags']['plot_data']:
# unfolded correlator data
c51.scatter_plot(np.arange(len(SS[0])), c51.make_gvars(SS),
'%s ss folded' % (str(mq)))
c51.scatter_plot(np.arange(len(PS[0])), c51.make_gvars(PS),
'%s ps folded' % (str(mq)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff_ss = eff.effective_mass(c51.make_gvars(SS), 1, 'cosh')
meff_ps = eff.effective_mass(c51.make_gvars(PS), 1, 'cosh')
xlim = [2, 12]
ylim = [0, 2] #c51.find_yrange(meff_ss, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(meff_ss)),
meff_ss,
'%s ss effective mass' % (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 ps effective mass' % (str(mq)),
xlim=xlim,
ylim=ylim)
plt.show()
# scaled correlator
E0 = mesp['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 ss scaled correlator (take sqrt to get Z0_s)' %
(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 ps scaled correlator (divide by Z0_s to get Z0_p)' % (str(mq)),
xlim=xlim,
ylim=ylim)
plt.show()
# concatenate data
boot0 = np.concatenate((SS, PS), axis=1)
# read priors
prior = mesp['priors']
# read trange
trange = mesp['trange']
# fit boot0
boot0gv = c51.make_gvars(boot0)
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T, params['decay_ward_fit']['nstates'])
boot0fit = c51.fitscript_v2(trange, T, boot0gv, boot0p,
fitfcn.twopt_fitfcn_ss_ps)
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit, 'E0', '%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['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['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 = [mesp['meta_id']['SS'], mesp['meta_id']['PS']]
fit_id = c51.select_fitid('meson', params)
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('meson', corr_lst, fit_id, tmin, tmax, init_id,
prior_id, result, params['flags']['update'])
if len(boot0fit['tmin']) != 1 or len(boot0fit['tmax']) != 1:
print "sweeping over time range"
print "skipping bootstrap"
return 0
else:
pass
if params['flags']['write']:
bsresult = []
psql.chkbsprior(tag, stream, Nbs, boot0fit['prior'][0])
for g in tqdm.tqdm(range(Nbs)):
# make bs gvar dataset
n = g + 1
bs_id, draw = psql.fetchonebs(nbs=n,
Mbs=Mbs,
ens=tag,
stream=stream)
bootn = boot0[draw]
bootngv = c51.make_gvars(bootn)
# read randomized priors
bsprior_id, bsp = psql.bspriorid(tag, stream, g,
boot0fit['prior'][0])
bsp = c51.dict_of_tuple_to_gvar(bsp)
# read initial guess
init = c51.read_init(boot0fit,
0) #{"mres":boot0fit['pmean'][0]['mres']}
#Fit
bsfit = c51.fitscript_v2(trange, T, bootngv, bsp,
fitfcn.twopt_fitfcn_ss_ps, init)
tmin = bsfit['tmin'][0]
tmax = bsfit['tmax'][0]
boot0_id = psql.select_boot0('meson', corr_lst, fit_id, tmin, tmax,
init_id, prior_id)
bsinit_id = psql.initid(bsfit['p0'][0])
result = c51.make_result(
bsfit, 0
) #"""{"mres":%s, "chi2":%s, "dof":%s}""" %(bsfit['pmean'][t]['mres'],bsfit['chi2'][t],bsfit['dof'][t])
psql.submit_bs('meson_bs', boot0_id, bs_id, Mbs, bsinit_id,
bsprior_id, result, params['flags']['update'])
return 0
def mres_bs(psql, params, mq):
print "fitting mres", mq
tag = params["decay_ward_fit"]["ens"]["tag"]
stream = params["decay_ward_fit"]["ens"]["stream"]
Nbs = params["decay_ward_fit"]["nbs"]
Mbs = params["decay_ward_fit"]["mbs"]
mresp = params[tag]["mres"][mq]
# read data
mp = psql.data("dwhisq_corr_jmu", mresp["meta_id"]["mp"])
pp = psql.data("dwhisq_corr_jmu", mresp["meta_id"]["pp"])
T = len(pp[0])
boot0 = mp / pp
# plot mres
if params["flags"]["plot_data"]:
c51.scatter_plot(np.arange(len(boot0[0])), c51.make_gvars(boot0), "%s mres" % str(mq))
plt.show()
# read priors
prior = mresp["priors"]
# read trange
trange = mresp["trange"]
# fit boot0
boot0gv = c51.make_gvars(boot0)
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T)
boot0fit = c51.fitscript_v2(trange, T, boot0gv, boot0p, fitfcn.mres_fitfcn)
if params["flags"]["stability_plot"]:
c51.stability_plot(boot0fit, "mres", str(mq))
plt.show()
if params["flags"]["tabulate"]:
tbl_print = collections.OrderedDict()
tbl_print["tmin"] = boot0fit["tmin"]
tbl_print["tmax"] = boot0fit["tmax"]
tbl_print["mres"] = [boot0fit["pmean"][t]["mres"] for t in range(len(boot0fit["pmean"]))]
tbl_print["sdev"] = [boot0fit["psdev"][t]["mres"] 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 = [mresp["meta_id"]["mp"], mresp["meta_id"]["pp"]]
fit_id = c51.select_fitid("mres", params)
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("jmu", corr_lst, fit_id, tmin, tmax, init_id, prior_id, result, params["flags"]["update"])
if len(boot0fit["tmin"]) != 1 or len(boot0fit["tmax"]) != 1:
print "sweeping over time range"
print "skipping bootstrap"
return 0
else:
pass
if params["flags"]["write"]:
bsresult = []
psql.chkbsprior(tag, stream, Nbs, boot0fit["prior"][0])
for g in tqdm.tqdm(range(Nbs)):
# make bs gvar dataset
n = g + 1
bs_id, draw = psql.fetchonebs(nbs=n, Mbs=Mbs, ens=tag, stream=stream)
bootn = boot0[draw]
bootngv = c51.make_gvars(bootn)
# read randomized priors
bsprior_id, bsp = psql.bspriorid(tag, stream, g, boot0fit["prior"][0])
bsp = c51.dict_of_tuple_to_gvar(bsp)
# read initial guess
init = {"mres": boot0fit["pmean"][0]["mres"]}
# Fit
bsfit = c51.fitscript_v2(trange, T, bootngv, bsp, fitfcn.mres_fitfcn, init)
tmin = bsfit["tmin"][0]
tmax = bsfit["tmax"][0]
boot0_id = psql.select_boot0("jmu", corr_lst, fit_id, tmin, tmax, init_id, prior_id)
bsinit_id = psql.initid(bsfit["p0"][0])
result = c51.make_result(
bsfit, 0
) # """{"mres":%s, "chi2":%s, "dof":%s}""" %(bsfit['pmean'][t]['mres'],bsfit['chi2'][t],bsfit['dof'][t])
psql.submit_bs("jmu_bs", boot0_id, bs_id, Mbs, bsinit_id, bsprior_id, result, params["flags"]["update"])
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]
])
# check outliers
if False:
# corr [basak, config, time]
temp = [SSl, PSl, fhSSl, fhPSl]
ntemp = ['SSl', 'PSl', 'fhSSl', 'fhPSl']
for j in range(len(temp)):
print ntemp[j]
corr = temp[j]
for i in range(len(corr[0, 0])):
corrt = corr[0, :, i] #00 ss 30 ps
minimum = np.amin(corrt)
argmin = np.argmin(corrt) * 5 + 300
median = np.median(corrt)
maximum = np.amax(corrt)
argmax = np.argmax(corrt) * 5 + 300
print i, median - minimum, maximum - median, argmin, argmax
# 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'])
# fit two point
for b in range(len(basak)):
SSspec = (spec[:len(spec) / 2])[b * T:(b + 1) * T]
PSspec = (spec[len(spec) / 2:])[b * T:(b + 1) * T]
boot0gv = np.concatenate((SSspec, PSspec))
boot0fit = c51.fitscript_v2(trange,
T,
boot0gv,
boot0p,
fitfcn.twopt_baryon_ss_ps,
basak=[basak[b]])
boot0p = boot0fit['rawoutput'][0].p
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(
c51.baryon_initpriors(barp['priors'], basak, nstates))
prior_id = psql.priorid(
c51.dict_of_tuple_to_gvar(
c51.baryon_priors(barp['priors'], basak, nstates)))
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)
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T, nstates, tau)
# read trange
trange = barp['trange']
fhtrange = fhbp['trange']
# fit two point
for b in range(len(basak)):
SSspec = (spec[:len(spec) / 2])[b * T:(b + 1) * T]
PSspec = (spec[len(spec) / 2:])[b * T:(b + 1) * T]
boot0gv = np.concatenate((SSspec, PSspec))
boot0fit = c51.fitscript_v2(trange,
T,
boot0gv,
boot0p,
fitfcn.twopt_baryon_ss_ps,
basak=[basak[b]])
boot0p = boot0fit['rawoutput'][0].p
# fit fhcorr
for b in range(len(basak)):
SSdM = (dM[:len(dM) / 2])[b * T:(b + 1) * T]
PSdM = (dM[len(dM) / 2:])[b * T:(b + 1) * T]
boot0gv = np.concatenate((SSdM, PSdM))
boot0fit = c51.fitscript_v2(fhtrange,
T,
boot0gv,
boot0p,
fitfcn.fhbaryon_ss_ps,
basak=[basak[b]])
boot0p = boot0fit['rawoutput'][0].p
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['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_init_id = psql.initid(
c51.baryon_initpriors(barp['priors'], basak, nstates))
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['tmin'][t]
tmax = boot0fit['tmax'][t]
result = c51.make_result(boot0fit, t)
psql.submit_fhboot0('fhproton', corr_lst, baryon_id, fit_id,
tmin, tmax, init_id, prior_id, result,
params['flags']['update'])
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]
dparams = yaml.load(f)
dparams['decay_ward_fit']['ens'] = params['axial_fit']['ens']
dparams['decay_ward_fit']['ml'] = params['axial_fit']['ml']
dparams['decay_ward_fit']['ms'] = params['axial_fit']['ms']
f.close()
# yaml entires
fitmeta = params['axial_fit']
# log in sql
psqlpwd = pwd.passwd()
psql = sql.pysql('cchang5', 'cchang5', psqlpwd)
# read data
ll = read_axial(psql, params, 'axial_ll')
ls = read_axial(psql, params, 'axial_ls')
# read two-point
pionSS, pionPS = decay.read_decay_bs(psql, dparams, 'pion')
kaonSS, kaonPS = decay.read_decay_bs(psql, dparams, 'kaon')
# axial_ll
print np.shape(pionSS), np.shape(pionPS), np.shape(ll)
if params['flags']['fit_axial_ll']:
ll = np.concatenate((pionSS, pionPS, ll), axis=1)
llgv = c51.make_gvars(ll)
fpi = fit_axial(psql, params, dparams, 'axial_ll', llgv, flow=True)
print fphi(fpi['axial_fit'].p['F0'], fpi['axial_fit'].p['E0'])
# axial_ls
if params['flags']['fit_axial_ls']:
ls = np.concatenate((kaonSS, kaonPS, ls), axis=1)
lsgv = c51.make_gvars(ls)
fka = fit_axial(psql, params, dparams, 'axial_ls', lsgv, flow=True)
print fphi(fka['axial_fit'].p['F0'], fka['axial_fit'].p['E0'])
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]
for m in ml:
decay_params['decay_ward_fit']['ml'] = m
ss, ps = decay.read_decay_bs(psql,decay_params,'pion')
x_list['ss_%s_%s' %(str(m),str(m))] = len(data[0]) + np.arange(len(ss[0]))
data = np.concatenate((data,ss), axis=1)
x_list['ps_%s_%s' %(str(m),str(m))] = len(data[0]) + np.arange(len(ps[0]))
data = np.concatenate((data,ps), axis=1)
for s in ms:
decay_params['decay_ward_fit']['ms'] = s
ss, ps = decay.read_decay_bs(psql,decay_params,'kaon')
x_list['ss_%s_%s' %(str(m),str(s))] = len(data[0]) + np.arange(len(ss[0]))
data = np.concatenate((data,ss), axis=1)
x_list['ps_%s_%s' %(str(m),str(s))] = len(data[0]) + np.arange(len(ps[0]))
data = np.concatenate((data,ps), axis=1)
# make gvars
data_gv = c51.make_gvars(data)
### fit ###
result = dict()
# mres
for m in ml:
gv_mp = data_gv[x_list['mp_%s' %str(m)]]
gv_pp = data_gv[x_list['pp_%s' %str(m)]]
fit = decay.fit_mres_bs(psql,decay_params,m,gv_mp,gv_pp)['mres_fit']
result['mres_%s' %str(m)] = fit
for s in ms:
gv_mp = data_gv[x_list['mp_%s' %str(s)]]
gv_pp = data_gv[x_list['pp_%s' %str(s)]]
fit = decay.fit_mres_bs(psql,decay_params,s,gv_mp,gv_pp)['mres_fit']
result['mres_%s' %str(s)] = fit
# mesons
for m in ml:
def concatgv(corr1, corr2):
concat = np.concatenate((corr1, corr2), axis=1)
concat_gv = c51.make_gvars(concat)
corr1 = concat_gv[:len(concat_gv) / 2]
corr2 = concat_gv[len(concat_gv) / 2:]
return corr1, corr2
if __name__=='__main__':
# read master
f = open('./mixed.yml','r')
params = yaml.load(f)
f.close()
# yaml entires
fitmeta = params['mixed_fit']
# log in sql
psqlpwd = pwd.passwd()
psql = sql.pysql('cchang5','cchang5',psqlpwd)
## bootstrap decay constant
# phi_ju
dataju = read_mixed_meson(psql,params,'phi_ju')
gvdataju = c51.make_gvars(dataju)
resp = fit_mixed_meson(psql,params,'phi_ju', gvdataju)
print resp['mixed_meson_fit']
### phi_js
#data = read_mixed_meson(psql,params,'phi_js')
#data = data[:200]
#datagv = c51.make_gvars(data)
#resp = fit_mixed_meson(psql,params,'phi_js', datagv)
### phi_ru
#data = read_mixed_meson(psql,params,'phi_ru')
#data = data[:200]
#datagv = c51.make_gvars(data)
#resp = fit_mixed_meson(psql,params,'phi_ru', datagv)
## phi_rs
#data = read_mixed_meson(psql,params,'phi_rs')
#data = data[:200]
ps_gAs = np.concatenate((ps_gAs, ps_gAt[i]), axis=1)
# index
# meson
x_ss_pion = np.arange(len(ss_pion[0]))
x_ps_pion = np.arange(len(ps_pion[0])) + len(x_ss_pion)
# nucleon
x_N = np.arange(
len(ss_N[0]) + len(ps_N[0]) + len(ss_Ns[0]) +
len(ps_Ns[0])) + len(x_ss_pion) + len(x_ps_pion)
x_ss_N = np.arange(len(ss_N[0])) + len(x_ss_pion) + len(x_ps_pion)
x_ps_N = np.arange(len(
ps_N[0])) + len(x_ss_pion) + len(x_ps_pion) + len(x_ss_N)
x_ss_Ns = np.arange(len(ss_Ns[0])) + len(x_ss_pion) + len(x_ps_pion) + len(
x_ss_N) + len(x_ps_N)
x_ps_Ns = np.arange(len(ps_Ns[0])) + len(x_ss_pion) + len(x_ps_pion) + len(
x_ss_N) + len(x_ps_N) + len(x_ss_Ns)
# concatenate data
data = c51.make_gvars(
np.concatenate((ss_pion, ps_pion, ss_N, ps_N, ss_Ns, ps_Ns), axis=1))
# separate data
gvss_pion = data[x_ss_pion]
gvps_pion = data[x_ps_pion]
gv_N = data[x_N]
# meson
pifit = decay.fit_decay_bs(psql, decay_params, 'pion', gvss_pion,
gvps_pion)
print pifit['meson_fit']
prior = {'Epi': pifit['meson_fit'].p['E0']}
# crazy proton fit sighz
print prior
