diff options
Diffstat (limited to 'utils/dm-search')
| -rwxr-xr-x | utils/dm-search | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/utils/dm-search b/utils/dm-search index cd91eab..9e1a136 100755 --- a/utils/dm-search +++ b/utils/dm-search @@ -347,13 +347,11 @@ def do_fit(dm_particle_id,dm_mass,dm_energy,data,muon,data_mc,weights,atmo_scale xopt = opt.optimize(x0) # Get the total number of "universes" simulated in the GENIE reweight tool - nuniverses = weights['universe'].max()+1 - - weights_dict = dict(tuple(weights.groupby('universe'))) + nuniverses = max(weights.keys())+1 nlls = [] for universe in range(nuniverses): - data_mc_with_weights = pd.merge(data_mc,weights_dict[universe],how='left',on=['run','unique_id']) + data_mc_with_weights = pd.merge(data_mc,weights[universe],how='left',on=['run','unique_id']) data_mc_with_weights.weight = data_mc_with_weights.weight.fillna(1.0) 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,dm_sample=dm_sample) @@ -362,7 +360,7 @@ def do_fit(dm_particle_id,dm_mass,dm_energy,data,muon,data_mc,weights,atmo_scale universe = np.argmin(nlls) if refit: - data_mc_with_weights = pd.merge(data_mc,weights[weights.universe == universe],how='left',on=['run','unique_id']) + data_mc_with_weights = pd.merge(data_mc,weights[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. @@ -470,7 +468,7 @@ def get_limits(dm_masses,data,muon,data_mc,atmo_scale_factor,muon_scale_factor,b dm_energy = dm_mass xopt, universe, samples = do_fit(dm_particle_id,dm_mass,dm_energy,data,muon,data_mc,weights,atmo_scale_factor,muon_scale_factor,bins,steps,print_nll,walkers,thin) - data_mc_with_weights = pd.merge(data_mc,weights[weights.universe == universe],how='left',on=['run','unique_id']) + data_mc_with_weights = pd.merge(data_mc,weights[universe],how='left',on=['run','unique_id']) data_mc_with_weights.weight = data_mc_with_weights.weight.fillna(1.0) limit = np.percentile(samples[:,6],90) @@ -649,6 +647,8 @@ if __name__ == '__main__': # 15752 154 957 -0.006827 weights = weights[weights.weight > 0] + weights = dict(tuple(weights.groupby('universe'))) + ev_mc = correct_energy_bias(ev_mc) muon_mc = correct_energy_bias(muon_mc) @@ -785,8 +785,8 @@ if __name__ == '__main__': # Set the random seed so we get reproducible results here np.random.seed(0) - 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']) + data_mc_with_weights = pd.merge(data_mc,weights[0],how='left',on=['run','unique_id']) + data_atm_mc_with_weights = pd.merge(data_atm_mc,weights[0],how='left',on=['run','unique_id']) discoveries = 0 |