def mres(pion_gv, etas_gv):
prior = dict()
prior['mres'] = gv.gvar(0.0, 1.0)
x = np.arange(len(pion_gv))
c51.scatter_plot(x, pion_gv)
trange = dict()
T = len(pion_gv) - 2
trange['tmin'] = [2, 2]
trange['tmax'] = [T, T]
pionfit = c51.fitscript(trange,
pion_gv,
prior,
c51.mres_fitfcn,
result_flag='off')
print "Pion"
c51.stability_plot(pionfit, 'mres', 'pion')
c51.scatter_plot(x, etas_gv)
etasfit = c51.fitscript(trange,
etas_gv,
prior,
c51.mres_fitfcn,
result_flag='off')
print "Etas"
c51.stability_plot(etasfit, 'mres', 'kaon')
#print kaonfit['post']
return 0
def decay(pion_ss_ps_gv, kaon_ss_ps_gv):
prior = dict()
prior['Z0_s'] = gv.gvar(0.025, 0.01)
prior['Z1_s'] = gv.gvar(0.025, 0.035)
prior['Z2_s'] = gv.gvar(0.025, 0.035)
prior['Z0_p'] = gv.gvar(0.27, 0.15)
prior['Z1_p'] = gv.gvar(0.27, 0.35)
prior['Z2_p'] = gv.gvar(0.27, 0.35)
prior['E0'] = gv.gvar(0.23, 0.2)
prior['E1'] = gv.gvar(0.0, 1.0)
prior['E2'] = gv.gvar(0.0, 1.0)
trange = dict()
trange['tmin'] = [6, 6]
trange['tmax'] = [20, 20]
T = len(pion_ss_ps_gv)
fitfcn = c51.fit_function(T=T, nstates=2)
fit = c51.fitscript(trange,
pion_ss_ps_gv,
prior,
fitfcn.twopt_fitfcn_ss_ps,
sets=2,
result_flag='off')
print "pion"
c51.stability_plot(fit, 'Z0_p')
c51.stability_plot(fit, 'E0')
ml = 0.0158
ms = 0.0902
mres_pi = gv.gvar(0.0009633, 0.0000065)
Z0_p = fit['post'][0]['Z0_p']
E0 = fit['post'][0]['E0']
fpi = Z0_p * np.sqrt(2.) * (2. * ml + 2. * mres_pi) / E0**(3. / 2.)
print 'fpi:', fpi
print "kaon"
prior['Z0_s'] = gv.gvar(0.02, 0.01)
prior['Z1_s'] = gv.gvar(0.02, 0.03)
prior['Z2_s'] = gv.gvar(0.02, 0.03)
prior['Z0_p'] = gv.gvar(0.2, 0.1)
prior['Z1_p'] = gv.gvar(0.2, 0.3)
prior['Z2_p'] = gv.gvar(0.2, 0.3)
prior['E0'] = gv.gvar(0.404, 0.2)
prior['E1'] = gv.gvar(0.0, 1.0)
prior['E2'] = gv.gvar(0.0, 1.0)
fit = c51.fitscript(trange,
kaon_ss_ps_gv,
prior,
fitfcn.twopt_fitfcn_ss_ps,
sets=2,
result_flag='off')
c51.stability_plot(fit, 'Z0_p')
c51.stability_plot(fit, 'E0')
mres_kaon = gv.gvar(0.0006685, 0.0000044)
Z0_p = fit['post'][0]['Z0_p']
E0 = fit['post'][0]['E0']
fk = Z0_p * np.sqrt(2.) * (ml + ms + mres_pi + mres_kaon) / E0**(3. / 2.)
print 'fk:', fk
fkfpi = fk / fpi
print 'fk/fpi:', fkfpi
def mres(pion_gv, etas_gv):
prior = dict()
prior['mres'] = gv.gvar(0.0, 1.0)
x = np.arange(len(pion_gv))
c51.scatter_plot(x,pion_gv)
trange = dict()
T = len(pion_gv)-2
trange['tmin'] = [2, 2]
trange['tmax'] = [T, T]
pionfit = c51.fitscript(trange, pion_gv, prior, c51.mres_fitfcn, result_flag='off')
print "Pion"
c51.stability_plot(pionfit,'mres','pion')
c51.scatter_plot(x,etas_gv)
etasfit = c51.fitscript(trange, etas_gv, prior, c51.mres_fitfcn, result_flag='off')
print "Etas"
c51.stability_plot(etasfit,'mres','kaon')
#print kaonfit['post']
return 0
def decay(pion_ss_ps_gv, kaon_ss_ps_gv):
prior = dict()
prior['Z0_s'] = gv.gvar(0.025, 0.01)
prior['Z1_s'] = gv.gvar(0.025, 0.035)
prior['Z2_s'] = gv.gvar(0.025, 0.035)
prior['Z0_p'] = gv.gvar(0.27, 0.15)
prior['Z1_p'] = gv.gvar(0.27, 0.35)
prior['Z2_p'] = gv.gvar(0.27, 0.35)
prior['E0'] = gv.gvar(0.23, 0.2)
prior['E1'] = gv.gvar(0.0, 1.0)
prior['E2'] = gv.gvar(0.0, 1.0)
trange = dict()
trange['tmin'] = [6,6]
trange['tmax'] = [20,20]
T = len(pion_ss_ps_gv)
fitfcn = c51.fit_function(T=T, nstates=2)
fit = c51.fitscript(trange, pion_ss_ps_gv, prior, fitfcn.twopt_fitfcn_ss_ps, sets=2, result_flag='off')
print "pion"
c51.stability_plot(fit, 'Z0_p')
c51.stability_plot(fit, 'E0')
ml = 0.0158
ms = 0.0902
mres_pi = gv.gvar(0.0009633, 0.0000065)
Z0_p = fit['post'][0]['Z0_p']
E0 = fit['post'][0]['E0']
fpi = Z0_p*np.sqrt(2.)*(2.*ml+2.*mres_pi)/E0**(3./2.)
print 'fpi:', fpi
print "kaon"
prior['Z0_s'] = gv.gvar(0.02, 0.01)
prior['Z1_s'] = gv.gvar(0.02, 0.03)
prior['Z2_s'] = gv.gvar(0.02, 0.03)
prior['Z0_p'] = gv.gvar(0.2, 0.1)
prior['Z1_p'] = gv.gvar(0.2, 0.3)
prior['Z2_p'] = gv.gvar(0.2, 0.3)
prior['E0'] = gv.gvar(0.404, 0.2)
prior['E1'] = gv.gvar(0.0, 1.0)
prior['E2'] = gv.gvar(0.0, 1.0)
fit = c51.fitscript(trange, kaon_ss_ps_gv, prior, fitfcn.twopt_fitfcn_ss_ps, sets=2, result_flag='off')
c51.stability_plot(fit, 'Z0_p')
c51.stability_plot(fit, 'E0')
mres_kaon = gv.gvar(0.0006685, 0.0000044)
Z0_p = fit['post'][0]['Z0_p']
E0 = fit['post'][0]['E0']
fk = Z0_p*np.sqrt(2.)*(ml+ms+mres_pi+mres_kaon)/E0**(3./2.)
print 'fk:', fk
fkfpi = fk/fpi
print 'fk/fpi:', fkfpi
#Plot effective mass
T = len(data) * 0.5
meff = c51.effective_mass(data, 1)
x = np.arange(len(meff))
ylim = c51.find_yrange(meff, 1, 10)
#ylim = [0.47, 0.57]
xr = [1, 15]
c51.scatter_plot(x, meff, 'effective mass', xlim=[xr[0], xr[1]], ylim=ylim)
#ylim = c51.find_yrange(meff, 65, 79)
c51.scatter_plot(x,
meff,
'effective mass ps',
xlim=[T + xr[0], T + xr[1]],
ylim=ylim)
#Fit
inputs = c51.read_yaml('temp.yml')
prior = c51.dict_of_tuple_to_gvar(inputs['prior'])
trange = inputs['trange']
fitfcn = c51.fit_function(T, nstates=2)
fit = c51.fitscript(trange,
data,
prior,
fitfcn.twopt_fitfcn_ss_ps,
sets=2,
result_flag='on')
c51.stability_plot(fit, 'E0')
c51.stability_plot(fit, 'Z0_s')
c51.stability_plot(fit, 'Z0_p')
plt.show()
# bootstrap axial_ls
fk = decay_axial(pr, 'kaon', draws)
# process output and plot distribution
fpi_proc = c51.process_bootstrap(fpi)
fk_proc = c51.process_bootstrap(fk)
fpi_boot0, fpi_bs = fpi_proc()
fk_boot0, fk_bs = fk_proc()
print fpi_boot0
if pr.plot_hist_flag == 'on':
c51.histogram_plot(fpi_bs, 'F0', 'pion F0')
c51.histogram_plot(fpi_bs, 'E0', 'pion E0')
c51.histogram_plot(fk_bs, 'F0', 'kaon F0')
c51.histogram_plot(fk_bs, 'E0', 'kaon F0')
# plot stability for boot0 fits
if pr.plot_stab_flag == 'on':
c51.stability_plot(fpi_boot0, 'F0', 'boot0 fpi')
c51.stability_plot(fpi_boot0, 'E0', 'boot0 m_pi')
c51.stability_plot(fk_boot0, 'F0', 'boot0 fk')
c51.stability_plot(fk_boot0, 'E0', 'boot0 m_k')
# boot0 result
F0_pi = fpi_proc.read_boot0('F0')
E0_pi = fpi_proc.read_boot0('E0')
F0_k = fk_proc.read_boot0('F0')
E0_k = fk_proc.read_boot0('E0')
# calculate decay constant
fpi = -1.0 * F0_pi * np.sqrt(2.0 / E0_pi)
fk = -1.0 * F0_k * np.sqrt(2.0 / E0_k)
table_print = collections.OrderedDict()
table_print['tmin'] = fpi_boot0['tmin']
table_print['tmax'] = fpi_boot0['tmax']
table_print['fk'] = fk
# bootstrap axial_ls
fk = decay_axial(pr, 'kaon', draws)
# process output and plot distribution
fpi_proc = c51.process_bootstrap(fpi)
fk_proc = c51.process_bootstrap(fk)
fpi_boot0, fpi_bs = fpi_proc()
fk_boot0, fk_bs = fk_proc()
print fpi_boot0
if pr.plot_hist_flag == 'on':
c51.histogram_plot(fpi_bs, 'F0', 'pion F0')
c51.histogram_plot(fpi_bs, 'E0', 'pion E0')
c51.histogram_plot(fk_bs, 'F0', 'kaon F0')
c51.histogram_plot(fk_bs, 'E0', 'kaon F0')
# plot stability for boot0 fits
if pr.plot_stab_flag == 'on':
c51.stability_plot(fpi_boot0, 'F0', 'boot0 fpi')
c51.stability_plot(fpi_boot0, 'E0', 'boot0 m_pi')
c51.stability_plot(fk_boot0, 'F0', 'boot0 fk')
c51.stability_plot(fk_boot0, 'E0', 'boot0 m_k')
# boot0 result
F0_pi = fpi_proc.read_boot0('F0')
E0_pi = fpi_proc.read_boot0('E0')
F0_k = fk_proc.read_boot0('F0')
E0_k = fk_proc.read_boot0('E0')
# calculate decay constant
fpi = -1.0*F0_pi*np.sqrt(2.0/E0_pi)
fk = -1.0*F0_k*np.sqrt(2.0/E0_k)
table_print = collections.OrderedDict()
table_print['tmin'] = fpi_boot0['tmin']
table_print['tmax'] = fpi_boot0['tmax']
table_print['fk'] = fk
return np.array([[0, grandfit]])
if __name__ == '__main__':
# read params
pr = c51.process_params()
# read data
corr_avg, T = read_baryon(pr)
# fit
fit = chain_fit(pr, corr_avg, T)
raise SystemExit
# process fit
fit_proc = c51.process_bootstrap(fit)
fit_boot0, fit_bs = fit_proc()
if pr.plot_stab_flag == 'on':
c51.stability_plot(fit_boot0, 'E0', 'proton E0 ')
c51.stability_plot(fit_boot0, 'Z0_p', 'proton Z0_p ')
c51.stability_plot(fit_boot0, 'Z0_s', 'proton Z0_s ')
if pr.print_tbl_flag == 'on':
tbl = c51.tabulate_result(fit_proc, ['Z0_s', 'Z0_p', 'E0'])
print tbl
#c51.heatmap(fit_proc.nstates, fit_proc.tmin, fit_proc.normbayesfactor, [0,1], 'Bayes Factor', 'nstates', 'tmin')
#c51.heatmap(fit_proc.nstates, fit_proc.tmin, fit_proc.chi2dof, [0,3], 'chi2/dof', 'nstates', 'tmin')
# nstate stability
c51.nstate_stability_plot(fit_boot0, 'E0', 'proton E0 ')
c51.nstate_stability_plot(fit_boot0, 'Z0_p', 'proton Z0_p ')
c51.nstate_stability_plot(fit_boot0, 'Z0_s', 'proton Z0_s ')
# model averaging
#bma = c51.bayes_model_avg(fit_proc, ['Z0_p', 'Z0_s', 'E0'])
#print bma
# look at small t values
#datapath = 'prot_w5p6_n94'
filename = 'l2464f211b600m0102m0509m635a_avg.h5'
datapath = 'l2464f211b600m0102m0509m635/wf1p0_m51p2_l58_a51p5_smrw5p0_n75/spectrum/ml0p0126_ms0p0693/pion/corr'
data_ss = c51.read_data(filename, datapath, 0, 0)
data_ps = c51.read_data(filename, datapath, 3, 0)
data = np.concatenate((data_ss, data_ps), axis=1)
data = c51.make_gvars(data)
#data = data_ps
#Plot effective mass
T = len(data)*0.5
meff = c51.effective_mass(data, 1)
x = np.arange(len(meff))
ylim = c51.find_yrange(meff, 1, 10)
#ylim = [0.47, 0.57]
xr = [1,15]
c51.scatter_plot(x, meff, 'effective mass', xlim=[xr[0],xr[1]], ylim=ylim)
#ylim = c51.find_yrange(meff, 65, 79)
c51.scatter_plot(x, meff, 'effective mass ps', xlim=[T+xr[0],T+xr[1]], ylim=ylim)
#Fit
inputs = c51.read_yaml('temp.yml')
prior = c51.dict_of_tuple_to_gvar(inputs['prior'])
trange = inputs['trange']
fitfcn = c51.fit_function(T, nstates=2)
fit = c51.fitscript(trange, data, prior, fitfcn.twopt_fitfcn_ss_ps, sets=2, result_flag='on')
c51.stability_plot(fit, 'E0')
c51.stability_plot(fit, 'Z0_s')
plt.show()
draws = params.bs_draws(draw_n)
# bootstrap mres
mres_pion_fit = mres_bs(params, 'pion', draws)
mres_etas_fit = mres_bs(params, 'etas', draws)
# process bootstrap
mres_pion_proc = c51.process_bootstrap(mres_pion_fit)
mres_etas_proc = c51.process_bootstrap(mres_etas_fit)
if params.print_fit_flag == 'on':
print mres_pion_proc()[0]['rawoutput']
print mres_etas_proc()[0]['rawoutput']
# plot mres stability
if params.plot_stab_flag == 'on':
try:
mres_pion_0, mres_pion_n = mres_pion_proc()
mres_etas_0, mres_etas_n = mres_etas_proc()
c51.stability_plot(mres_pion_0, 'mres', 'pion mres')
c51.stability_plot(mres_etas_0, 'mres', 'etas mres')
except:
print 'error encountered'
#plt.show()
# print results
if params.print_mres_flag == 'on':
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = mres_pion_proc.tmin
tbl_print['tmax'] = mres_pion_proc.tmax
tbl_print['mres_pion_boot0'] = mres_pion_proc.read_boot0('mres')
tbl_print['mres_pion_sdev'] = mres_pion_proc.read_boot0_sdev('mres')
tbl_print['pion_chi2/dof'] = mres_pion_proc.chi2dof
tbl_print['mres_etas_boot0'] = mres_etas_proc.read_boot0('mres')
tbl_print['mres_etas_sdev'] = mres_etas_proc.read_boot0_sdev('mres')
tbl_print['etas_chi2/dof'] = mres_etas_proc.chi2dof
except:
grandfit['nstates'] = n*np.ones(len(fit[k]))
return np.array([[0, grandfit]])
if __name__=='__main__':
# read params
pr = c51.process_params()
# read data
p_avg, T = read_proton(pr)
# fit
fit = fit_proton(pr, p_avg, T)
# process fit
fit_proc = c51.process_bootstrap(fit)
fit_boot0, fit_bs = fit_proc()
if pr.plot_stab_flag == 'on':
c51.stability_plot(fit_boot0, 'E0', 'proton E0 ')
c51.stability_plot(fit_boot0, 'Z0_p', 'proton Z0_p ')
c51.stability_plot(fit_boot0, 'Z0_s', 'proton Z0_s ')
if pr.print_tbl_flag == 'on':
tbl = c51.tabulate_result(fit_proc, ['Z0_s', 'Z0_p', 'E0'])
print tbl
#c51.heatmap(fit_proc.nstates, fit_proc.tmin, fit_proc.normbayesfactor, [0,1], 'Bayes Factor', 'nstates', 'tmin')
#c51.heatmap(fit_proc.nstates, fit_proc.tmin, fit_proc.chi2dof, [0,3], 'chi2/dof', 'nstates', 'tmin')
# nstate stability
c51.nstate_stability_plot(fit_boot0, 'E0', 'proton E0 ')
c51.nstate_stability_plot(fit_boot0, 'Z0_p', 'proton Z0_p ')
c51.nstate_stability_plot(fit_boot0, 'Z0_s', 'proton Z0_s ')
# model averaging
#bma = c51.bayes_model_avg(fit_proc, ['Z0_p', 'Z0_s', 'E0'])
#print bma
# look at small t values
def fittwo_pt(params, twopt_dat, T):
# make data
twopt_gv = c51.make_gvars(twopt_dat)
# priors
priors = params.priors[params.hadron]
priors_gv = c51.dict_of_tuple_to_gvar(priors)
# read trange
trange = params.trange['twopt']
# fit
fitfcn = c51.fit_function(T, nstates=1)
fit = c51.fitscript_v2(trange, T, twopt_gv, priors_gv, fitfcn.twopt_fitfcn_phiqq, params.print_fit_flag)
return np.array([[0, fit]])
if __name__=='__main__':
# read parameters
params = c51.process_params()
# read data
twopt_dat, T = read_data(params)
# fit two point
fit = fittwo_pt(params, twopt_dat, T)
# process fit
fit_proc = c51.process_bootstrap(fit)
fit_boot0, fit_bs = fit_proc()
if params.plot_stab_flag == 'on':
c51.stability_plot(fit_boot0, 'E0', params.hadron+' E0 ')
c51.stability_plot(fit_boot0, 'A0', params.hadron+' A0 ')
if params.print_tbl_flag == 'on':
tbl = c51.tabulate_result(fit_proc, ['A0', 'E0'])
print tbl
plt.show()
fit = c51.fitscript_v2(trange, T, mres_dat_bs, bsp, fitfcn.mres_fitfcn, result_flag='off')
result = [g, fit]
return result
if __name__=='__main__':
# read parameters
params = c51.process_params()
# generate bootstrap list
draw_n = 0
draws = params.bs_draws(draw_n)
# bootstrap mres
mres_pion_fit = mres_bs(params, 'pion', draws)
#mres_etas_fit = mres_bs(params, 'etas', draws, mres_data_flag)
# process bootstrap
mres_pion_proc = c51.process_bootstrap(mres_pion_fit)
#mres_etas_proc = c51.process_bootstrap(mres_etas_fit)
# plot mres stability
if params.plot_stab_flag == 'on':
mres_pion_0, mres_pion_n = mres_pion_proc()
#mres_etas_0, mres_etas_n = mres_etas_proc()
c51.stability_plot(mres_pion_0, 'mres', 'pion mres')
#c51.stability_plot(mres_etas_0, 'mres', 'etas mres')
plt.show()
# print results
if params.print_tbl_flag == 'on':
tblp = c51.tabulate_result(mres_pion_proc, ['mres'])
#tble = c51.tabulate_result(mres_etas_proc, ['mres'])
print params.ens
print tblp
#print tble
draws = params.bs_draws(draw_n)
# bootstrap mres
mres_pion_fit = mres_bs(params, 'pion', draws)
mres_etas_fit = mres_bs(params, 'etas', draws)
# process bootstrap
mres_pion_proc = c51.process_bootstrap(mres_pion_fit)
mres_etas_proc = c51.process_bootstrap(mres_etas_fit)
if params.print_fit_flag == 'on':
print mres_pion_proc()[0]['rawoutput']
print mres_etas_proc()[0]['rawoutput']
# plot mres stability
if params.plot_stab_flag == 'on':
try:
mres_pion_0, mres_pion_n = mres_pion_proc()
mres_etas_0, mres_etas_n = mres_etas_proc()
c51.stability_plot(mres_pion_0, 'mres', 'pion mres')
c51.stability_plot(mres_etas_0, 'mres', 'etas mres')
except:
print 'error encountered'
#plt.show()
# print results
if params.print_mres_flag == 'on':
tbl_print = collections.OrderedDict()
tbl_print['tmin'] = mres_pion_proc.tmin
tbl_print['tmax'] = mres_pion_proc.tmax
tbl_print['mres_pion_boot0'] = mres_pion_proc.read_boot0('mres')
tbl_print['mres_pion_sdev'] = mres_pion_proc.read_boot0_sdev('mres')
tbl_print['pion_chi2/dof'] = mres_pion_proc.chi2dof
tbl_print['mres_etas_boot0'] = mres_etas_proc.read_boot0('mres')
tbl_print['mres_etas_sdev'] = mres_etas_proc.read_boot0_sdev('mres')
tbl_print['etas_chi2/dof'] = mres_etas_proc.chi2dof
result = [g, fit]
return result
if __name__ == '__main__':
# read parameters
params = c51.process_params()
# generate bootstrap list
draw_n = 0
draws = params.bs_draws(draw_n)
# bootstrap mres
mres_pion_fit = mres_bs(params, 'pion', draws)
#mres_etas_fit = mres_bs(params, 'etas', draws, mres_data_flag)
# process bootstrap
mres_pion_proc = c51.process_bootstrap(mres_pion_fit)
#mres_etas_proc = c51.process_bootstrap(mres_etas_fit)
# plot mres stability
if params.plot_stab_flag == 'on':
mres_pion_0, mres_pion_n = mres_pion_proc()
#mres_etas_0, mres_etas_n = mres_etas_proc()
c51.stability_plot(mres_pion_0, 'mres', 'pion mres')
#c51.stability_plot(mres_etas_0, 'mres', 'etas mres')
plt.show()
# print results
if params.print_tbl_flag == 'on':
tblp = c51.tabulate_result(mres_pion_proc, ['mres'])
#tble = c51.tabulate_result(mres_etas_proc, ['mres'])
print params.ens
print tblp
#print tble
