def nll(data, norm):
return atfl.unbinned_nll(model(data), atfl.integral(model(norm)))
def _nll(pars):
return atfl.unbinned_nll(data_model(**pars),
atfl.integral(norm_model(**pars)))
fit_sample = tft.run_toymc(fit_model,
observables_phase_space,
1000000,
1e-20,
chunk=1000000)
tfr.write_tuple("eff_fit_result.root", fit_sample, observables_data)
norm_sample_2 = sqdlz_phsp.rectangular_grid_sample((50, 50)).numpy()
norm_sample_2 = np.concatenate(
[norm_sample_2, np.array(2500 * [md])[:, np.newaxis]], axis=1)
fit = fit_model(norm_sample_2).numpy().reshape((50, 50))
toy_sig = tfr.read_tuple_filtered("test_tuple.root",
branches=["mprime", "thetaprime"],
sel_branches=["md"],
selection="abs(md-1.97)<0.05")
hist = np.histogram2d(toy_sig[:, 0],
toy_sig[:, 1],
bins=(50, 50),
range=observables_bounds[:2])
fit = fit / atfl.integral(fit) * atfl.integral(hist[0])
chi2 = np.sum((fit - hist[0])**2 / fit)
print(fit)
print(hist)
print(norm_sample_2)
print(f"Chi2={chi2}")
def nll(pars):
parslist = [pars[i[0]] for i in parameters_list]
return atfl.unbinned_nll(model(data_sample, parslist),
atfl.integral(model(norm_sample, parslist)))
def _nll(pars):
return atfl.unbinned_nll(
fit_model(data, pars), atfl.integral(fit_model(norm, pars))
)