new branch where the skimmer is modified to include weight informatio…

…n to study strange TT background in vbf search

src/HHbbVV/postprocessing/CreateDatacard.py

@@ -1167,7 +1167,7 @@ def createDatacardABCD(args, templates_dict, templates_summed, shape_vars):
         if args.model_name is not None
         else args.cards_dir
     )
-
+    print(out_dir)
     with open(f"{out_dir}/datacard.txt", "w") as f:
         f.write(helpers.abcd_datacard_template.substitute(datacard_dict))
 

src/HHbbVV/postprocessing/postprocessing.py

@@ -419,9 +419,9 @@ def main(args):
                     )
 
             _add_vbf_columns(df,bb_mask,ptlabel="",mlabel="")
-            
+
             if key == 'Data':
-                break
+                continue
 
             for var in pt_labels:
                 for direction in ["down", "up"]:

src/HHbbVV/processors/bbVVSkimmer.py

@@ -387,6 +387,9 @@ def process(self, events: ak.Array):
                         )
 
         skimmed_events["nGoodVBFJets"] = np.array(ak.sum(vbf_jet_mask, axis=1))
+        # Temp
+        skimmed_events["gen_weights"] = gen_weights
+
 
         # VBF ak4 Jet vars (pt, eta, phi, M, nGoodJets)
         # if self._vbf_search:
@@ -452,7 +455,7 @@ def process(self, events: ak.Array):
             msds = jmsr_shifted_vars["msoftdrop"][shift]
             pnetms = jmsr_shifted_vars["particleNet_mass"][shift]
 
-            if self._save_all:
+            if self._save_all & False:
                 cut = (
                     (pnetms[~bb_mask] >= self.preselection["VVparticleNet_mass"][0])
                     + (msds[~bb_mask] >= self.preselection["VVparticleNet_mass"][0])
@@ -630,7 +633,15 @@ def process(self, events: ak.Array):
                 systematics += list(weights.variations)
 
             # TODO: need to be careful about the sum of gen weights used for the LHE/QCDScale uncertainties
-            logger.debug("weights ", weights._weights.keys())
+            logger.debug("weights ", weights._weights.keys())    
+
+            # TEMP: save each individual weight
+            skimmed_events["L1EcalPrefiring"] = ak.to_numpy(events.L1PreFiringWeight.Nom)
+
+            for key in weights._weights.keys():
+                print(f"single_weight_{key}")
+                skimmed_events[f"single_weight_{key}"] = weights.partial_weight([key])
+
             for systematic in systematics:
                 if systematic in weights.variations:
                     weight = weights.weight(modifier=systematic)
@@ -879,6 +890,7 @@ def getVBFVars(
         # Apply base masks, sort, and calculate vbf dijet (jj) cuts
         vbfJets_mask = ak4_jet_mask  # & electron_muon_overlap_mask & fatjet_overlap_mask
         vbfJets = jets[vbfJets_mask]
+        print()
 
         vbfJets_sorted_pt = vbfJets[ak.argsort(vbfJets.pt, ascending=False)]
         # this is the only which does not guarantee two guys. in the other sorts, the entries are specifically None.