c = ROOT.TCanvas() frame = DE.plot_on_frame() frame.Draw() # Make pull distribution for the fit c_pull = ROOT.TCanvas() frame_pull = DE.plot_pull() frame_pull.Draw() # Plot likelihood profiles c_ll = ROOT.TCanvas() frame_ll = DE.plot_ll(poi=N_sig) frame_ll.Draw() # Check whether there is bias in the fit frame_var, frame_err, frame_pull = DE.check_fit_bias(param_to_study=N_sig, N_toys=100) c_var = ROOT.TCanvas() frame_var.Draw() c_error = ROOT.TCanvas() frame_err.Draw() c_pull = ROOT.TCanvas() frame_pull.Draw() # Export data and model to workspace w = DE.write_to_workspace(poi=N_sig, nuisances=[exp_par, mean, sigma, N_bkgr]) # Calculate statistical significance of signal observation with asymptotic approximation asympt_rrr = DE.asympt_signif(w=w) # DE.asympt_signif_ll(w=w) # another method # Do chi^2 goodness-of-fit test and print fit and test status