diff options
| -rwxr-xr-x | utils/chi2 | 11 |
1 files changed, 7 insertions, 4 deletions
@@ -611,13 +611,16 @@ if __name__ == '__main__': p_values = {id: [] for id in (20,22,2020,2022,2222)} p_values_atm = {id: [] for id in (20,22,2020,2022,2222)} - ENERGY_RESOLUTION_UNCERTAINTY = {'e':100.0, 'u': 100.0, 'eu': 100.0} + ENERGY_SCALE_MEAN = {'e': 0.0, 'u': 0.0} + ENERGY_SCALE_UNCERTAINTY = {'e':1.0, 'u': 1.0} + ENERGY_RESOLUTION_MEAN = {'e': 0.0, 'u': 0.0, 'eu': 0.0} + ENERGY_RESOLUTION_UNCERTAINTY = {'e':1.0, 'u': 1.0, 'eu': 1.0} scale = 0.01 muon_scale = 0.01 energy_resolution = 0.1 - true_values = [scale,1.0,energy_resolution,1.0,energy_resolution,1.0,energy_resolution,muon_scale] + true_values = [scale,0.0,energy_resolution,0.0,energy_resolution,energy_resolution,muon_scale] assert(len(true_values) == len(FIT_PARS)) @@ -640,8 +643,8 @@ if __name__ == '__main__': n_muon_atm = np.random.poisson(N_muon_atm) # Sample data from Monte Carlo - data = pd.concat((data_mc.sample(n=n,replace=True,weights='weights'), muon.sample(n=n_muon,replace=True))) - data_atm = pd.concat((data_atm_mc.sample(n=n_atm,replace=True,weights='weights'), muon_atm.sample(n=n_muon_atm,replace=True))) + data = pd.concat((data_mc.sample(n=n,replace=True,weights='weight'), muon.sample(n=n_muon,replace=True))) + data_atm = pd.concat((data_atm_mc.sample(n=n_atm,replace=True,weights='weight'), muon_atm.sample(n=n_muon_atm,replace=True))) # Smear the energies by the additional energy resolution data.ke += np.random.randn(len(data.ke))*data.ke*energy_resolution |