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| author | tlatorre <tlatorre@uchicago.edu> | 2020-12-08 12:45:39 -0600 |
|---|---|---|
| committer | tlatorre <tlatorre@uchicago.edu> | 2020-12-08 12:45:39 -0600 |
| commit | ff4904530a099a2adb50830f53d82fb9bd220542 (patch) | |
| tree | 58a2b566dc00861e2415384dbef1d42477228e85 | |
| parent | 9eabe86a5a1b2d4613aafdb6bdc763764c2a930a (diff) | |
| download | sddm-ff4904530a099a2adb50830f53d82fb9bd220542.tar.gz sddm-ff4904530a099a2adb50830f53d82fb9bd220542.tar.bz2 sddm-ff4904530a099a2adb50830f53d82fb9bd220542.zip | |
fix more typos
| -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. |