diff options
Diffstat (limited to 'utils/chi2')
| -rwxr-xr-x | utils/chi2 | 21 |
1 files changed, 13 insertions, 8 deletions
@@ -409,7 +409,7 @@ def do_fit(data,muon,data_mc,weights,atmo_scale_factor,muon_scale_factor,bins,st nlls = [] for universe in range(nuniverses): - data_mc_with_weights = pd.merge(data_mc,weights_dict[universe],how='left',on=['run','evn']) + data_mc_with_weights = pd.merge(data_mc,weights_dict[universe],how='left',on=['run','unique_id']) data_mc_with_weights.weight = data_mc_with_weights.weight.fillna(1.0) nll = make_nll(data,muon,data_mc_with_weights,atmo_scale_factor,muon_scale_factor,bins,reweight=True,print_nll=print_nll) @@ -418,7 +418,7 @@ def do_fit(data,muon,data_mc,weights,atmo_scale_factor,muon_scale_factor,bins,st universe = np.argmin(nlls) if refit: - data_mc_with_weights = pd.merge(data_mc,weights[weights.universe == universe],how='left',on=['run','evn']) + data_mc_with_weights = pd.merge(data_mc,weights[weights.universe == universe],how='left',on=['run','unique_id']) data_mc_with_weights.weight = data_mc_with_weights.weight.fillna(1.0) # Create a new negative log likelihood function with the weighted Monte Carlo. @@ -530,6 +530,11 @@ if __name__ == '__main__': mcpl = load_mcpl_files(args.mcpl) ev_mc = renormalize_data(ev_mc.reset_index(),mcpl) + # Merge weights with MCPL dataframe to get the unique id column in the + # weights dataframe since that is what we use to merge with the Monte + # Carlo. + weights = pd.merge(weights,mcpl[['run','evn','unique_id']],on=['run','evn'],how='left') + # There are a handful of weights which turn out to be slightly negative for # some reason. For example: # @@ -625,8 +630,8 @@ if __name__ == '__main__': xtrue = truncnorm_scaled(PRIORS_LOW,PRIORS_HIGH,PRIORS,PRIOR_UNCERTAINTIES) - data_mc_with_weights = pd.merge(data_mc,weights[weights.universe == 0],how='left',on=['run','evn']) - data_atm_mc_with_weights = pd.merge(data_atm_mc,weights[weights.universe == 0],how='left',on=['run','evn']) + data_mc_with_weights = pd.merge(data_mc,weights[weights.universe == 0],how='left',on=['run','unique_id']) + data_atm_mc_with_weights = pd.merge(data_atm_mc,weights[weights.universe == 0],how='left',on=['run','unique_id']) for i in range(args.coverage): # Calculate expected number of events @@ -660,10 +665,10 @@ if __name__ == '__main__': xopt, universe, samples = do_fit(data,muon,data_mc,weights,atmo_scale_factor,muon_scale_factor,bins,args.steps,args.print_nll,args.walkers,args.thin) - data_mc_with_weights = pd.merge(data_mc,weights[weights.universe == universe],how='left',on=['run','evn']) + data_mc_with_weights = pd.merge(data_mc,weights[weights.universe == universe],how='left',on=['run','unique_id']) data_mc_with_weights.weight = data_mc_with_weights.weight.fillna(1.0) - data_atm_mc_with_weights = pd.merge(data_atm_mc,weights[weights.universe == universe],how='left',on=['run','evn']) + data_atm_mc_with_weights = pd.merge(data_atm_mc,weights[weights.universe == universe],how='left',on=['run','unique_id']) data_atm_mc_with_weights.weight = data_atm_mc_with_weights.weight.fillna(1.0) prob = get_prob(data,muon,data_mc_with_weights,atmo_scale_factor,muon_scale_factor,samples,bins,size=args.multinomial_prob_size) @@ -792,10 +797,10 @@ if __name__ == '__main__': xopt, universe, samples = do_fit(data,muon,data_mc,weights,atmo_scale_factor,muon_scale_factor,bins,args.steps,args.print_nll,args.walkers,args.thin) - data_mc_with_weights = pd.merge(data_mc,weights[weights.universe == universe],how='left',on=['run','evn']) + data_mc_with_weights = pd.merge(data_mc,weights[weights.universe == universe],how='left',on=['run','unique_id']) data_mc_with_weights.weight = data_mc_with_weights.weight.fillna(1.0) - data_atm_mc_with_weights = pd.merge(data_atm_mc,weights[weights.universe == universe],how='left',on=['run','evn']) + data_atm_mc_with_weights = pd.merge(data_atm_mc,weights[weights.universe == universe],how='left',on=['run','unique_id']) data_atm_mc_with_weights.weight = data_atm_mc_with_weights.weight.fillna(1.0) prob = get_prob(data,muon,data_mc_with_weights,atmo_scale_factor,muon_scale_factor,samples,bins,size=args.multinomial_prob_size) |