return fit
if __name__ == '__main__':
# read parameters
pr = c51.process_params()
# bootstrap draws
# if draw number = 0, draws boot0
draw_n = 1000
draws = pr.bs_draws(draw_n)
# bootstrap axial_ll
fpi = decay_axial(pr, 'pion', draws)
# 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')
# grandfit['nstates'] = np.concatenate((grandfit['nstates'], n*np.ones(len(fit[k]))), axis=0)
# 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
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
fit = [g, fit]
return fit
if __name__=='__main__':
# read parameters
pr = c51.process_params()
# bootstrap draws
# if draw number = 0, draws boot0
draw_n = 1000
draws = pr.bs_draws(draw_n)
# bootstrap axial_ll
fpi = decay_axial(pr, 'pion', draws)
# 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')
if mres_etas == 'pion':
constant = Z0_p*np.sqrt(2.)*(2.*ml+2.*mres_pion)/E0**(3./2.)
else:
constant = Z0_p*np.sqrt(2.)*(ml+ms+mres_pion+mres_etas)/E0**(3./2.)
return constant
if __name__=='__main__':
params = c51.process_params()
# generate bootstrap list
draw_n = params.nbs
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
grandfit[k] = fit[k]
try:
grandfit['nstates'] = np.concatenate((grandfit['nstates'], n*np.ones(len(fit[k]))), axis=0)
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
fitfcn = c51.fit_function(T)
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
else:
constant = Z0_p * np.sqrt(2.) * (ml + ms + mres_pion +
mres_etas) / E0**(3. / 2.)
return constant
if __name__ == '__main__':
params = c51.process_params()
# generate bootstrap list
draw_n = params.nbs
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
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
