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
chi2_results = list(DE.chi2_test(pvalue_threshold=0.05, nbins=-1).values())[0]
print(
f'\n\nchi2: {chi2_results[0]}\nndf: {chi2_results[1]}\np-value of chi2 test: {chi2_results[2]}\n'
)
print(f'fit status: {DE.fit_status}, chi2_test status: {DE.chi2_test_status}')
# Calculate signal significance with toys
# Note that p-value might be zero if bkgr hypo is very unlikely since it might require lots of toys to get to the observed value of test statistic.
c_toy_signif = ROOT.TCanvas()
