def read_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 'readon meson two point', mq1, mq2
tag = params['decay_ward_fit']['ens']['tag']
stream = params['decay_ward_fit']['ens']['stream']
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']))
return SS, PS
def read_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 'reading meson two point', mq1, mq2
tag = params['decay_ward_fit']['ens']['tag']
stream = params['decay_ward_fit']['ens']['stream']
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']))
return SS, PS
def read_hisq_meson(psql,params,meson):
if meson in ['phi_jj_5', 'phi_jj_I']:
mq1 = params['hisq_fit']['ml']
mq2 = mq1
elif meson in ['phi_jr_5', 'phi_jr_I']:
mq1 = params['hisq_fit']['ml']
mq2 = params['hisq_fit']['ms']
elif meson in ['phi_rr_5', 'phi_rr_I']:
mq1 = params['hisq_fit']['ms']
mq2 = mq1
print 'reading meson two point', mq1, mq2
tag = params['hisq_fit']['ens']['tag']
stream = params['hisq_fit']['ens']['stream']
mesp = params[tag][meson]['%s_%s' %(str(mq1),str(mq2))]
# read data
data = c51.fold(psql.data('dwhisq_corr_meson',mesp['meta_id']))
return data
def read_axial(psql, params, meson):
ml = params['axial_fit']['ml']
ms = params['axial_fit']['ms']
print 'reading %s' % (meson)
tag = params['axial_fit']['ens']['tag']
stream = params['axial_fit']['ens']['stream']
Nbs = params['axial_fit']['nbs']
Mbs = params['axial_fit']['mbs']
if meson == 'axial_ll':
axialp = params[tag][meson]['%s_%s' % (ml, ml)]
elif meson == 'axial_ls':
axialp = params[tag][meson]['%s_%s' % (ml, ms)]
# read data
data = c51.fold(psql.data('dwhisq_corr_meson', axialp['meta_id']['PS']),
-1)
#data= psql.data('dwhisq_corr_meson',axialp['meta_id']['PS'])
return data
def read_mixed_meson(psql,params,meson):
tag = params['mixed_fit']['ens']['tag']
stream = params['mixed_fit']['ens']['stream']
ml = "0.%s" %tag.split('m')[1]
ms = "0.%s" %tag.split('m')[2]
print ml, ms
if meson in ['phi_ju']:
mq1 = ml
mq2 = params['mixed_fit']['ml']
elif meson in ['phi_js']:
mq1 = ml
mq2 = params['mixed_fit']['ms']
elif meson in ['phi_ru']:
mq1 = ms
mq2 = params['mixed_fit']['ml']
elif meson in ['phi_rs']:
mq1 = ms
mq2 = params['mixed_fit']['ms']
print 'reading meson two point', mq1, mq2
mesp = params[tag][meson]['%s_%s' %(str(mq1),str(mq2))]
# read data
data = c51.fold(psql.data('dwhisq_corr_meson',mesp['meta_id']))
return data
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 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