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]
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 fit_hisq_meson(psql,params,meson, datagv,bootn_fit=False,g=None):
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 'fitting two point', mq1, mq2
tag = params['hisq_fit']['ens']['tag']
stream = params['hisq_fit']['ens']['stream']
nstates = params['hisq_fit']['nstates']
mesp = params[tag][meson]['%s_%s' %(str(mq1),str(mq2))]
T = len(datagv)
# plot effective mass / scaled correlator
if params['flags']['plot_data']:
# unfolded correlator data
c51.scatter_plot(np.arange(len(datagv)), datagv, '%s_%s ss' %(str(mq1),str(mq2)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff = eff.effective_mass(datagv, 1, 'cosh')
xlim = [3, len(meff)/2-2]
ylim = c51.find_yrange(meff, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(meff)), meff, '%s_%s effective mass' %(str(mq1),str(mq2)), xlim = xlim, ylim = ylim)
plt.show()
# scaled correlator
E0 = mesp['priors'][1]['E0'][0]
scaled = eff.scaled_correlator_v2(datagv, E0, phase=1.0)
ylim = c51.find_yrange(scaled, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(scaled)), scaled, '%s_%s scaled correlator (A0)' %(str(mq1),str(mq2)), xlim = xlim, ylim = ylim)
plt.show()
# read priors
if bootn_fit:
if g == 0:
priorn = mesp['priors']
else:
priorn = dict()
for n in mesp['priors'].keys():
priorn[n] = {}
for k in mesp['priors'][n].keys():
priorn[n][k] = [np.random.normal(mesp['priors'][n][k][0],mesp['priors'][n][k][1]), mesp['priors'][n][k][1]]
else:
priorn = mesp['priors']
prior = c51.meson_priors(priorn,nstates)
# read init
try:
tstring = '%s_%s' %(mesp['trange']['tmin'][0], mesp['trange']['tmax'][0])
f_init = open('./hisq_posterior/hisq_%s_%s.yml' %(tag,tstring), 'r')
init = yaml.load(f_init)
f_init.close()
except:
init = None
# read trange
trange = mesp['trange']
# fit boot0
boot0gv = datagv
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T,params['hisq_fit']['nstates'])
boot0fit = c51.fitscript_v2(trange,T,boot0gv,boot0p,fitfcn.mixed_twopt_osc,init=init,bayes=params['flags']['bayes'])
if params['flags']['boot0_update']:
print boot0fit['rawoutput'][0]
post = boot0fit['rawoutput'][0].pmean
mes_dump = dict()
tstring = "%s_%s" %(trange['tmin'][0],trange['tmax'][0])
for k in post.keys():
mes_dump[k] = float(post[k])
f_dump = open('./hisq_posterior/hisq_%s_%s.yml' %(tag,tstring), 'w+')
yaml.dump(mes_dump, f_dump)
f_dump.flush()
f_dump.close()
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['A0'] = [boot0fit['pmean'][t]['A0'] for t in range(len(boot0fit['pmean']))]
tbl_print['dA0'] = [boot0fit['psdev'][t]['A0'] for t in range(len(boot0fit['pmean']))]
tbl_print['E1'] = [boot0fit['pmean'][t]['E1'] for t in range(len(boot0fit['pmean']))]
tbl_print['dE1'] = [boot0fit['psdev'][t]['E1'] for t in range(len(boot0fit['pmean']))]
tbl_print['A1'] = [boot0fit['pmean'][t]['A1'] for t in range(len(boot0fit['pmean']))]
tbl_print['dA1'] = [boot0fit['psdev'][t]['A1'] for t in range(len(boot0fit['pmean']))]
tbl_print['chi2/dof'] = [boot0fit['chi2'][t]/boot0fit['dof'][t] for t in range(len(boot0fit['pmean']))]
tbl_print['logGBF'] = boot0fit['logGBF']
print tabulate(tbl_print, headers='keys')
if params['flags']['fitline_plot']:
p = boot0fit['rawoutput'][0].p
t = np.linspace(0, 30, 1000)
# output fit curve
fitline = fitfcn.mixed_twopt_osc(t,p)
meff = np.arccosh( (np.roll(fitline,-1)+np.roll(fitline,1)) / (2*fitline) )
f_plot = open('./hisq_fitline/%s_mixed_twopt.csv' %(tag), 'w+')
string = 't, y, +-\n'
for i in range(len(t)):
string += '%s, %s, %s\n' %(t[i], meff[i].mean, meff[i].sdev)
f_plot.write(string)
f_plot.flush()
f_plot.close()
return {'hisq_meson_fit': boot0fit['rawoutput'][0]}
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
def fit_mres_bs(psql, params, mq, gv_mp, gv_pp, bootn_fit=False, g=None):
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
if bootn_fit:
if g == 0:
prior = mresp['priors']
else:
prior = dict()
for k in mresp['priors'].keys():
prior[k] = [
np.random.normal(mresp['priors'][k][0],
mresp['priors'][k][1]),
mresp['priors'][k][1]
]
else:
prior = mresp['priors']
# read init
try:
tstring = '%s_%s' % (mresp['trange']['tmin'][0],
mresp['trange']['tmax'][0])
f_init = open('./jmu_posterior/jmu_%s_%s.yml' % (tag, tstring), 'r')
init = yaml.load(f_init)
f_init.close()
except:
init = None
# 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,
init=init,
bayes=params['flags']['bayes'])
if params['flags']['boot0_update']:
post = boot0fit['rawoutput'][0].pmean
res_dump = dict()
tstring = "%s_%s" % (trange['tmin'][0], trange['tmax'][0])
for k in post.keys():
res_dump[k] = float(post[k])
f_dump = open('./jmu_posterior/jmu_%s_%s.yml' % (tag, tstring), 'w+')
yaml.dump(res_dump, f_dump)
f_dump.flush()
f_dump.close()
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')
return {'mres_fit': boot0fit['rawoutput'][0]}
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]}
# bin
boot0 = gv.dataset.bin_data(boot0, binsize=1)
print np.shape(boot0)
gvboot0 = c51.make_gvars(boot0)
fit_proton(psql, params, gvboot0)
res = fit_gA(psql, params, gvboot0)
# bootstrap gA
if params['flags']['bootstrap']:
# read data
b = params['gA_fit']['basak'][0]
nstates = params['gA_fit']['nstates']
fhstates = params['gA_fit']['fhstates']
tau = params['gA_fit']['tau']
fh = gvboot0[len(gvboot0) / 2:]
T = len(fh) / 2
fitfcn = c51.fit_function(T, nstates, fhstates, tau)
x = np.linspace(0, T / 2, 1000)
fit2ptss = []
fit2ptps = []
fit3ptss = []
fit3ptps = []
p = res['gA_fit'].pmean
b02ptss = fitfcn.dwhisq_twopt(x, p, b, 's', 's')
b02ptps = fitfcn.dwhisq_twopt(x, p, b, 'p', 's')
b0mss = np.log(b02ptss / np.roll(b02ptss, -1)) / x[1]
b0mps = np.log(b02ptps / np.roll(b02ptps, -1)) / x[1]
b03ptss = fitfcn.dwhisq_dm(x, p, b, 's', 's')
b03ptps = fitfcn.dwhisq_dm(x, p, b, 'p', 's')
for sfit in tqdm.tqdm(res['gA_fit'].bootstrapped_fit_iter(n=1000)):
p = sfit.pmean
ss2pt = fitfcn.dwhisq_twopt(x, p, b, 's', 's')
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 fit_proton(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 data
if params['flags']['plot_data']:
SSl = spec[:len(spec)/2].reshape((len(basak),T))
PSl = spec[len(spec)/2:].reshape((len(basak),T))
for b in range(len(basak)):
SS = SSl[b]
PS = PSl[b]
# raw correlator
c51.scatter_plot(np.arange(len(SS)), SS, '%s %s ss' %(basak[b],str(mq)))
c51.scatter_plot(np.arange(len(PS)), PS, '%s %s ps' %(basak[b],str(mq)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff_ss = eff.effective_mass(SS, 1, 'log')
meff_ps = eff.effective_mass(PS, 1, 'log')
xlim = [2, len(meff_ss)/3]
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)
#print stuff
#print 'meff_ss'
#for t in range(len(meff_ss)):
# print t, meff_ss[t].mean, meff_ss[t].sdev
#print 'meff_ps'
#for t in range(len(meff_ps)):
# print t, meff_ps[t].mean, meff_ps[t].sdev
plt.show()
# scaled correlator
E0 = barp['priors'][1]['E0'][0]
scaled_ss = eff.scaled_correlator(SS, E0, phase=1.0)
scaled_ps = eff.scaled_correlator(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)
#print stuff
#print 'scaled_ss'
#for t in range(len(scaled_ss)):
# print t, scaled_ss[t].mean, scaled_ss[t].sdev
#print 'scaled_ps'
#for t in range(len(meff_ps)):
# print t, scaled_ps[t].mean, scaled_ps[t].sdev
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'])
print boot0fit['rawoutput'][0]
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'])
return {'nucleon_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]
])
# 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]
def fit_proton(params,gvboot0):
# read data
ens = params['LHCP_params']['ens']
nstates = params['LHCP_params']['nstates']
barp = params[ens]['proton']
basak = ['G1G1', 'G1G2', 'G1G3', 'G2G1', 'G2G2', 'G2G3', 'G3G1', 'G3G2', 'G3G3'] # src snk
mq = 'unitary'
# 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 data
if params['flags']['plot_data']:
SSl = spec[:len(spec)/2].reshape((len(basak),T))
PSl = spec[len(spec)/2:].reshape((len(basak),T))
for b in range(len(basak)):
SS = SSl[b]
PS = PSl[b]
# raw correlator
c51.scatter_plot(np.arange(len(SS)), SS, '%s %s ss' %(basak[b],str(mq)))
c51.scatter_plot(np.arange(len(PS)), PS, '%s %s ps' %(basak[b],str(mq)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff_ss = eff.effective_mass(SS, 1, 'log')
meff_ps = eff.effective_mass(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(SS, E0, phase=1.0)
scaled_ps = eff.scaled_correlator(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.asqtad_twopt_baryon_ss_ps,basak=basak)
print boot0fit['rawoutput'][0]
if params['flags']['stability_plot']:
c51.stability_plot(boot0fit,'E0','%s' %str(mq))
c51.stability_plot(boot0fit,'E1','%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']))]
tbl_print['E1'] = [boot0fit['pmean'][t]['E1'] for t in range(len(boot0fit['pmean']))]
tbl_print['dE1'] = [boot0fit['psdev'][t]['E1'] for t in range(len(boot0fit['pmean']))]
blist = []
for b in 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')
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 = (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_proton(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]
# make gvars
spec = gvboot0[:len(gvboot0) / 2]
T = len(spec) / (2 * len(basak))
# plot data
if params['flags']['plot_data']:
SSl = spec[:len(spec) / 2].reshape((len(basak), T))
PSl = spec[len(spec) / 2:].reshape((len(basak), T))
for b in range(len(basak)):
SS = SSl[b]
PS = PSl[b]
# raw correlator
c51.scatter_plot(np.arange(len(SS)), SS,
'%s %s ss' % (basak[b], str(mq)))
c51.scatter_plot(np.arange(len(PS)), PS,
'%s %s ps' % (basak[b], str(mq)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff_ss = eff.effective_mass(SS, 1, 'log')
meff_ps = eff.effective_mass(PS, 1, 'log')
xlim = [2, len(meff_ss) / 3]
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)
#print stuff
#print 'meff_ss'
#f_meff = open('/Users/cchang5/Documents/Papers/FH/c51_p1/paper/figures/meff_ss.csv', 'w+')
#string = 't_meff, meff, +-\n'
#for t in range(len(meff_ss)):
# string += '%s, %s, %s\n' %(str(t), str(meff_ss[t].mean), str(meff_ss[t].sdev))
#f_meff.write(string)
#f_meff.flush()
#f_meff.close()
#print 'meff_ps'
#for t in range(len(meff_ps)):
# print t, meff_ps[t].mean, meff_ps[t].sdev
plt.show()
# scaled correlator
E0 = barp['priors'][1]['E0'][0]
scaled_ss = np.sqrt(eff.scaled_correlator_v2(SS, E0, phase=1.0))
scaled_ps = eff.scaled_correlator_v2(PS, E0, phase=1.0) / scaled_ss
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 (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 (Z0_p)' %
(basak[b], str(mq)),
xlim=xlim,
ylim=ylim)
#print stuff
#f_scaled = open('/Users/cchang5/Documents/Papers/FH/c51_p1/paper/figures/scaled_corr.csv', 'w+')
#string = 't_scaled, scaled_ss, +-, scaled_ps, +-\n'
#for t in range(len(scaled_ss)):
# string += '%s, %s, %s, %s, %s\n' %(t, scaled_ss[t].mean, scaled_ss[t].sdev, scaled_ps[t].mean, scaled_ps[t].sdev)
#f_scaled.write(string)
#f_scaled.flush()
#f_scaled.close()
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.dwhisq_twopt_ss_ps,
basak=params['gA_fit']['basak'])
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')
scaled_ss = np.sqrt(ss * np.exp(p['E0'] * t))
scaled_ps = ps * np.exp(p['E0'] * t) / scaled_ss
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()
f_plot = open('./fh_fitline/%s_scaled2pt.csv' % (tag), 'w+')
# data scaled two point: propagate E0 error
SSl = spec[:len(spec) / 2].reshape((len(basak), T))[0]
PSl = spec[len(spec) / 2:].reshape((len(basak), T))[0]
t = np.linspace(0, T - 1, T)
scaled_ss = np.sqrt(SSl * np.exp(p['E0'] * t))
scaled_ps = PSl * np.exp(p['E0'] * t) / scaled_ss
string = 't, zss, +-, zps, +-\n'
for i in range(len(t)):
string += '%s, %s, %s, %s, %s\n' % (
t[i], scaled_ss[i].mean, scaled_ss[i].sdev,
scaled_ps[i].mean, scaled_ps[i].sdev)
f_plot = open(
'/Users/cchang5/Documents/Papers/FH/c51_p1/paper/figures/scaled_corr.csv',
'w+')
f_plot.write(string)
f_plot.flush()
f_plot.close()
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']
tbl_print['logGBF'] = boot0fit['logGBF']
tbl_print['Q'] = boot0fit['Q']
print tabulate(tbl_print, headers='keys')
print "tmin, E0%s, +-, chi2dof%s, Q%s, logGBF%s" % (
nstates, nstates, nstates, nstates)
for i in range(len(boot0fit['tmin'])):
print '%s, %s, %s, %s, %s, %s' % (
str(boot0fit['tmin'][i]), str(boot0fit['pmean'][i]['E0']),
str(boot0fit['psdev'][i]['E0']),
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(
[[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'])
return {'nucleon_fit': boot0fit['rawoutput'][0]}
return 0
def fit_decay_bs(psql,
params,
meson,
gv_SS,
gv_PS,
bootn_fit=False,
g=None,
flow=False):
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
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' % (str(mq1), str(mq2)), '$t$', 'C(t)_{SS}')
c51.scatter_plot(np.arange(len(gv_PS)), gv_PS,
'%s %s' % (str(mq1), str(mq2)), '$t$', 'C(t)_{PS}')
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' % (str(mq1), str(mq2)),
'$t$',
'$M^{eff}_{SS}$',
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' % (str(mq1), str(mq2)),
'$t$',
'$M^{eff}_{PS}$',
xlim=xlim,
ylim=ylim)
plt.show()
# scaled correlator
E0 = mesp['priors'][1]['E0'][0]
print E0
scaled_ss = np.sqrt(eff.scaled_correlator_v2(gv_SS, E0, phase=1.0))
scaled_ps = eff.scaled_correlator_v2(gv_PS, E0, phase=1.0) / scaled_ss
ylim = c51.find_yrange(scaled_ss, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(scaled_ss)),
scaled_ss,
'%s %s' % (str(mq1), str(mq2)),
'$t$',
'$Z0_s$',
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' % (str(mq1), str(mq2)),
'$t$',
'$Z0_p$',
xlim=xlim,
ylim=ylim)
plt.show()
# concatenate data
boot0gv = np.concatenate((gv_SS, gv_PS))
# read priors
if bootn_fit:
if g == 0:
priorn = mesp['priors']
else:
priorn = dict()
for n in mesp['priors'].keys():
priorn[n] = {}
for k in mesp['priors'][n].keys():
priorn[n][k] = [
np.random.normal(mesp['priors'][n][k][0],
mesp['priors'][n][k][1]),
mesp['priors'][n][k][1]
]
else:
priorn = mesp['priors']
prior = c51.meson_priors(priorn, nstates)
# read init
try:
tstring = '%s_%s' % (mesp['trange']['tmin'][0],
mesp['trange']['tmax'][0])
f_init = open('./meson_posterior/meson_%s_%s.yml' % (tag, tstring),
'r')
init = yaml.load(f_init)
f_init.close()
except:
init = None
# 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'])
if flow:
boot0fit = c51.fitscript_v2(trange,
T,
boot0gv,
boot0p,
fitfcn.dwhisq_twopt_osc_ss_ps,
bayes=params['flags']['bayes'])
else:
boot0fit = c51.fitscript_v2(trange,
T,
boot0gv,
boot0p,
fitfcn.twopt_fitfcn_ss_ps,
init=init,
bayes=params['flags']['bayes'])
#print boot0fit['rawoutput'][0]
if params['flags']['boot0_update']:
post = boot0fit['rawoutput'][0].pmean
mes_dump = dict()
tstring = "%s_%s" % (trange['tmin'][0], trange['tmax'][0])
for k in post.keys():
mes_dump[k] = float(post[k])
f_dump = open('./meson_posterior/meson_%s_%s.yml' % (tag, tstring),
'w+')
yaml.dump(mes_dump, f_dump)
f_dump.flush()
f_dump.close()
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')
return {'meson_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_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]}
def fit_proton(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]
# make gvars
spec = gvboot0[:len(gvboot0)/2]
T = len(spec)/(2*len(basak))
# plot data
if params['flags']['plot_data']:
SSl = spec[:len(spec)/2].reshape((len(basak),T))
PSl = spec[len(spec)/2:].reshape((len(basak),T))
for b in range(len(basak)):
SS = SSl[b]
PS = PSl[b]
# raw correlator
c51.scatter_plot(np.arange(len(SS)), SS, '%s %s ss' %(basak[b],str(mq)))
c51.scatter_plot(np.arange(len(PS)), PS, '%s %s ps' %(basak[b],str(mq)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff_ss = eff.effective_mass(SS, 1, 'log')
meff_ps = eff.effective_mass(PS, 1, 'log')
xlim = [2, len(meff_ss)/3]
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)
#print stuff
#print 'meff_ss'
#f_meff = open('/Users/cchang5/Documents/Papers/FH/c51_p1/paper/figures/meff_ss.csv', 'w+')
#string = 't_meff, meff, +-\n'
#for t in range(len(meff_ss)):
# string += '%s, %s, %s\n' %(str(t), str(meff_ss[t].mean), str(meff_ss[t].sdev))
#f_meff.write(string)
#f_meff.flush()
#f_meff.close()
#print 'meff_ps'
#for t in range(len(meff_ps)):
# print t, meff_ps[t].mean, meff_ps[t].sdev
plt.show()
# scaled correlator
E0 = barp['priors'][1]['E0'][0]
scaled_ss = np.sqrt(eff.scaled_correlator_v2(SS, E0, phase=1.0))
scaled_ps = eff.scaled_correlator_v2(PS, E0, phase=1.0)/scaled_ss
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 (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 (Z0_p)' %(basak[b],str(mq)), xlim = xlim, ylim = ylim)
#print stuff
#print 'scaled_ss'
#f_scaled = open('/Users/cchang5/Documents/Papers/FH/c51_p1/paper/figures/scaled_ss.csv', 'w+')
#string = 't_scaled, scaled_corr, +-\n'
#for t in range(len(scaled_ss)):
# string += '%s, %s, %s\n' %(str(t), str(scaled_ss[t].mean), str(scaled_ss[t].sdev))
#f_scaled.write(string)
#f_scaled.flush()
#f_scaled.close()
#print 'scaled_ps'
#for t in range(len(meff_ps)):
# print t, scaled_ps[t].mean, scaled_ps[t].sdev
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.dwhisq_twopt_ss_ps,basak=params['gA_fit']['basak'])
print boot0fit['rawoutput'][0]
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']
tbl_print['logGBF'] = boot0fit['logGBF']
tbl_print['Q'] = boot0fit['Q']
print tabulate(tbl_print, headers='keys')
print "tmax, E0, +-, chi2dof, Q, logGBF"
for i in range(len(boot0fit['tmin'])):
print '%s, %s, %s, %s, %s, %s' %(str(boot0fit['tmax'][i]), str(boot0fit['pmean'][i]['E0']), str(boot0fit['psdev'][i]['E0']), boot0fit['chi2'][i]/boot0fit['dof'][i], boot0fit['Q'][i], boot0fit['logGBF'][i])
# 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'])
return {'nucleon_fit': boot0fit['rawoutput'][0]}
return 0
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 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 fit_mixed_meson(psql,params,meson, datagv,bootn_fit=False,g=None):
tag = params['mixed_fit']['ens']['tag']
stream = params['mixed_fit']['ens']['stream']
nstates = params['mixed_fit']['nstates']
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 'fitting two point', mq1, mq2
mesp = params[tag][meson]['%s_%s' %(str(mq1),str(mq2))]
T = len(datagv)
# plot effective mass / scaled correlator
if params['flags']['plot_data']:
# unfolded correlator data
c51.scatter_plot(np.arange(len(datagv)), datagv, '%s_%s ps' %(str(mq1),str(mq2)))
plt.show()
# effective mass
eff = c51.effective_plots(T)
meff = eff.effective_mass(datagv, 1, 'cosh')
xlim = [3, len(meff)/2-2]
ylim = c51.find_yrange(meff, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(meff)), meff, '%s_%s effective mass' %(str(mq1),str(mq2)), xlim = xlim, ylim = ylim)
plt.show()
# scaled correlator
E0 = mesp['priors'][1]['E0'][0]
scaled = eff.scaled_correlator(datagv, E0, phase=1.0)
ylim = c51.find_yrange(scaled, xlim[0], xlim[1])
c51.scatter_plot(np.arange(len(scaled)), scaled, '%s_%s scaled correlator (A0)' %(str(mq1),str(mq2)), xlim = xlim, ylim = ylim)
plt.show()
# read priors
if bootn_fit:
if g == 0:
priorn = mesp['priors']
else:
priorn = dict()
for n in mesp['priors'].keys():
priorn[n] = {}
for k in mesp['priors'][n].keys():
priorn[n][k] = [np.random.normal(mesp['priors'][n][k][0],mesp['priors'][n][k][1]), mesp['priors'][n][k][1]]
else:
priorn = mesp['priors']
prior = c51.meson_priors(priorn,nstates)
# read init
try:
tstring = '%s_%s' %(mesp['trange']['tmin'][0], mesp['trange']['tmax'][0])
f_init = open('./mixed_posterior/mixed_%s_%s.yml' %(tag,tstring), 'r')
init = yaml.load(f_init)
f_init.close()
except:
init = None
# read trange
trange = mesp['trange']
# fit boot0
boot0gv = datagv
boot0p = c51.dict_of_tuple_to_gvar(prior)
#print "T:", T
fitfcn = c51.fit_function(T,params['mixed_fit']['nstates'])
boot0fit = c51.fitscript_v2(trange,T,boot0gv,boot0p,fitfcn.mixed_twopt_osc,init=init,bayes=params['flags']['bayes'])
#print boot0fit['rawoutput'][0]
if params['flags']['boot0_update']:
post = boot0fit['rawoutput'][0].pmean
mes_dump = dict()
tstring = "%s_%s" %(trange['tmin'][0],trange['tmax'][0])
for k in post.keys():
mes_dump[k] = float(post[k])
f_dump = open('./mixed_posterior/mixed_%s_%s.yml' %(tag,tstring), 'w+')
yaml.dump(mes_dump, f_dump)
f_dump.flush()
f_dump.close()
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['A0'] = [boot0fit['pmean'][t]['A0'] for t in range(len(boot0fit['pmean']))]
tbl_print['dA0'] = [boot0fit['psdev'][t]['A0'] for t in range(len(boot0fit['pmean']))]
tbl_print['E1'] = [boot0fit['pmean'][t]['E1'] for t in range(len(boot0fit['pmean']))]
tbl_print['dE1'] = [boot0fit['psdev'][t]['E1'] for t in range(len(boot0fit['pmean']))]
tbl_print['A1'] = [boot0fit['pmean'][t]['A1'] for t in range(len(boot0fit['pmean']))]
tbl_print['dA1'] = [boot0fit['psdev'][t]['A1'] 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')
return {'mixed_meson_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']
gVstates = params['gA_fit']['gVstates']
tau = params['gA_fit']['tau']
barp = params[tag]['proton'][mq]
fhbp = params[tag]['gA'][mq]
gVbp = params[tag]['gV'][mq]
#print "nstates: %s" %nstates
#print "fhstates: %s" %fhstates
# make gvars
spec = gvboot0[:len(gvboot0) / 3]
fh = gvboot0[len(gvboot0) / 3:len(gvboot0) * 2 / 3]
gV = gvboot0[len(gvboot0) * 2 / 3:]
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.
gVRl = gV / spec
gVdM = (np.roll(gVRl, -tau) - gVRl) / float(tau)
# 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))
dMSSl = dM[:len(dM) / 2].reshape((len(basak), T))
dMPSl = dM[len(dM) / 2:].reshape((len(basak), T))
gVSSl = gV[:len(gV) / 2].reshape((len(basak), T))
gVPSl = gV[len(gV) / 2:].reshape((len(basak), T))
gVRSSl = gVRl[:len(gVRl) / 2].reshape((len(basak), T))
gVRPSl = gVRl[len(gVRl) / 2:].reshape((len(basak), T))
gVdMSSl = gVdM[:len(gVdM) / 2].reshape((len(basak), T))
gVdMPSl = gVdM[len(gVdM) / 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]
gVSS = gVSSl[b]
gVPS = gVPSl[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]
print "%s gVSS[1]*exp(E0):" % basak[b], gVSS[1] * np.exp(
E0) #, "fhSS[1]:", fhSS[1]
print "%s gVPS[1]*exp(E0):" % basak[b], gVPS[1] * np.exp(
E0) #, "fhPS[1]:", fhPS[1]
# dmeff R(t+tau) - R(t)
dMSS = dMSSl[b]
dMPS = dMPSl[b]
gVdMSS = gVdMSSl[b]
gVdMPS = gVdMPSl[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)
c51.scatter_plot(np.arange(len(gVdMSS)),
gVdMSS,
'%s %s [R(t+%s)-R(t)]/%s gVss' %
(basak[b], str(mq), str(tau), str(tau)),
xlim=xlim,
ylim=ylim)
c51.scatter_plot(np.arange(len(gVdMPS)),
gVdMPS,
'%s %s [R(t+%s)-R(t)]/%s gVps' %
(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_v2(barp['priors'], fhbp['priors'],
gVbp['priors'], basak, nstates,
fhstates, gVstates)
# read init
try:
#print "found init file"
tstring = '%s_%s_%s_%s_%s_%s' % (
barp['trange']['tmin'][0], barp['trange']['tmax'][0],
fhbp['trange']['tmin'][0], fhbp['trange']['tmax'][0],
gVbp['trange']['tmin'][0], gVbp['trange']['tmax'][0])
f_init = open('./fh_posterior/gAgV_%s_%s.yml' % (tag, tstring),
'r')
init = yaml.load(f_init)
f_init.close()
except:
init = None
#print prior
# read trange
trange = barp['trange']
fhtrange = fhbp['trange']
gVtrange = gVbp['trange']
# fit boot0
boot0gv = np.concatenate((spec, dM, gVdM)) # dM
#boot0gv = gvboot0 # fh
boot0p = c51.dict_of_tuple_to_gvar(prior)
fitfcn = c51.fit_function(T, nstates, fhstates, gVstates, tau)
boot0fit = c51.fitscript_v4(trange,
fhtrange,
gVtrange,
T,
boot0gv,
boot0p,
fitfcn.dwhisq_dm_gVdm_ss_ps,
basak=params['gA_fit']['basak'],
init=init,
bayes=params['flags']['bayes']) # dM
#boot0fit = c51.fitscript_v4(trange,fhtrange,gVtrange,T,boot0gv,boot0p,fitfcn.dwhisq_fh_gVfh_ss_ps,basak=params['gA_fit']['basak'],init=init,bayes=params['flags']['bayes'])
if params['flags']['rawoutput']:
print boot0fit['rawoutput'][0]
if params['flags']['fitline_plot']:
p = boot0fit['rawoutput'][0].p
t = np.linspace(0, 30, 1000)
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')
yss = fitfcn.dwhisq_dm(t, p, b, 's', 's')
yps = fitfcn.dwhisq_dm(t, p, b, 'p', 's')
vss = fitfcn.dwhisq_dm(t, p, b, 's', 's', True)
vps = fitfcn.dwhisq_dm(t, p, b, 'p', 's', True)
f_plot = open('./fh_fitline/%s.csv' % (tag), 'w+')
string = 't, yss, +-, yps, +-, vss, +-, vps, +-\n'
for i in range(len(t)):
string += '%s, %s, %s, %s, %s, %s, %s, %s, %s\n' % (
t[i], yss[i].mean, yss[i].sdev, yps[i].mean, yps[i].sdev,
vss[i].mean, vss[i].sdev, vps[i].mean, vps[i].sdev)
f_plot.write(string)
f_plot.flush()
f_plot.close()
meffss = np.log(ss / np.roll(ss, -1)) / t[1]
meffps = np.log(ps / np.roll(ps, -1)) / t[1]
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()
scaled_ss = np.sqrt(ss * np.exp(p['E0'] * t))
scaled_ps = ps * np.exp(p['E0'] * t) / scaled_ss
#scaled_ss = np.sqrt(ss*np.exp(meffss))
#scaled_ps = ps*np.exp(meffps)/scaled_ss
f_plot = open('./fh_fitline/%s_zeff.csv' % (tag), 'w+')
string = 't, zss, +-, zps, +-\n'
for i in range(len(t)):
string += '%s, %s, %s, %s, %s\n' % (
t[i], scaled_ss[i].mean, scaled_ss[i].sdev,
scaled_ps[i].mean, scaled_ps[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]
gVdMSSl = gVdM[:len(gVdM) / 2].reshape((len(basak), T))[0]
gVdMPSl = gVdM[len(gVdM) / 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+')
#f_data = open('../plots/gA/autocorrelation/%s_bin%s.csv' %(tag,params['gA_fit']['bin']), 'w+')
string = 't_dat, yss_dat, +-, yps_dat, +-, vss_dat, +-, vps_dat, +-\n'
for i in range(len(t)):
string += '%s, %s, %s, %s, %s, %s, %s, %s, %s\n' % (
t[i], dMSSl[i].mean, dMSSl[i].sdev, dMPSl[i].mean,
dMPSl[i].sdev, gVdMSSl[i].mean, gVdMSSl[i].sdev,
gVdMPSl[i].mean, gVdMPSl[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])
tstring = '%s_%s_%s_%s_%s_%s' % (
barp['trange']['tmin'][0], barp['trange']['tmax'][0],
fhbp['trange']['tmin'][0], fhbp['trange']['tmax'][0],
gVbp['trange']['tmin'][0], gVbp['trange']['tmax'][0])
f_dump = open('./fh_posterior/gAgV_%s_%s.yml' % (tag, tstring),
'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))
c51.stability_plot(boot0fit, 'gV00', '%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['gVtmin'] = boot0fit['gVtmin']
tbl_print['gVtmax'] = boot0fit['gVtmax']
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']))
]
tbl_print['gV00'] = [
boot0fit['pmean'][t]['gV00']
for t in range(len(boot0fit['pmean']))
]
tbl_print['dgV00'] = [
boot0fit['psdev'][t]['gV00']
for t in range(len(boot0fit['pmean']))
]
tbl_print['gA/gV'] = [(boot0fit['rawoutput'][t].p['gA00'] /
boot0fit['rawoutput'][t].p['gV00']).mean
for t in range(len(boot0fit['rawoutput']))]
tbl_print['dgA/gV'] = [(boot0fit['rawoutput'][t].p['gA00'] /
boot0fit['rawoutput'][t].p['gV00']).sdev
for t in range(len(boot0fit['rawoutput']))]
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, %s%sE0%s, +-, %s%sgA%s, +-, %s%sgV%s, +-, %s%sgAgV%s, +-, %s%schi2dof%s, %s%sQ%s, %s%slogGBF%s" % (
gVstates, fhstates, nstates, gVstates, fhstates, nstates,
gVstates, fhstates, nstates, gVstates, fhstates, nstates,
gVstates, fhstates, nstates, gVstates, fhstates, nstates,
gVstates, fhstates, nstates)
for i in range(len(boot0fit['fhtmin'])):
gAgV = boot0fit['rawoutput'][i].p['gA00'] / boot0fit[
'rawoutput'][i].p['gV00']
print '%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s' % (
str(boot0fit['gVtmin'][i]), str(
boot0fit['pmean'][i]['E0']),
str(boot0fit['psdev'][i]['E0']),
str(boot0fit['pmean'][i]['gA00']),
str(boot0fit['psdev'][i]['gA00']),
str(boot0fit['pmean'][i]['gV00']),
str(boot0fit['psdev'][i]['gV00']), str(gAgV.mean),
str(gAgV.sdev), boot0fit['chi2'][i] / boot0fit['dof'][i],
boot0fit['Q'][i], boot0fit['logGBF'][i])
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