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