diff options
| -rwxr-xr-x | utils/dm-search | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/utils/dm-search b/utils/dm-search index 48164f9..c48d970 100755 --- a/utils/dm-search +++ b/utils/dm-search @@ -346,7 +346,7 @@ def do_fit(dm_particle_id,dm_mass,dm_energy,data,muon,data_mc,weights,atmo_scale data_mc_with_weights = pd.merge(data_mc,weights[weights.universe == universe],how='left',on=['run','evn']) 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) + nll = make_nll(dm_particle_id,dm_mass,dm_energy,data,muon,data_mc_with_weights,atmo_scale_factor,muon_scale_factor,bins,reweight=True,print_nll=print_nll) nlls.append(nll(xopt)) universe = np.argmin(nlls) @@ -356,7 +356,7 @@ def do_fit(dm_particle_id,dm_mass,dm_energy,data,muon,data_mc,weights,atmo_scale 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. - nll = make_nll(data,muon,data_mc_with_weights,atmo_scale_factor,muon_scale_factor,bins,reweight=True,print_nll=print_nll) + nll = make_nll(dm_particle_id,dm_mass,dm_energy,data,muon,data_mc_with_weights,atmo_scale_factor,muon_scale_factor,bins,reweight=True,print_nll=print_nll) # Now, we refit with the Monte Carlo weighted by the most likely GENIE # systematics. |