Replace draw_clusters.py

benchmarks/ecal/options/barrel.py

@@ -52,6 +52,7 @@ from Configurables import Jug__Reco__CalorimeterIslandCluster as IslandCluster
 from Configurables import Jug__Reco__ImagingPixelReco as ImCalPixelReco
 from Configurables import Jug__Reco__ImagingTopoCluster as ImagingCluster
 from Configurables import Jug__Reco__ImagingClusterReco as ImagingClusterReco
+from Configurables import Jug__Reco__ClusterRecoCoG as RecoCoG
 
 # branches needed from simulation root file
 sim_coll = [
@@ -108,12 +109,10 @@ if has_ecal_barrel_scfi:
     )  # different sector
     algorithms.append(cb_ecal_cl)
 
-    cb_ecal_clreco = ImagingClusterReco(
+    cb_ecal_clreco = RecoCoG(
         "cb_ecal_clreco",
-        inputProtoClusters=cb_ecal_cl.outputProtoClusterCollection,
-        outputClusters="EcalBarrelImagingClusters",
-        outputLayers="EcalBarrelImagingLayers",
-        mcHits="EcalBarrelHits",
+        inputProtoClusterCollection=cb_ecal_cl.outputProtoClusterCollection,
+        outputClusterCollection="EcalBarrelImagingClusters",
     )
     algorithms.append(cb_ecal_clreco)
 
@@ -155,15 +154,24 @@ else:
     )
     algorithms.append(sciglass_ecal_cl)
 
-    sciglass_ecal_clreco = ImagingClusterReco(
+    sciglass_ecal_clreco = RecoCoG(
         "sciglass_ecal_clreco",
-        inputProtoClusters=sciglass_ecal_cl.outputProtoClusterCollection,
-        mcHits="EcalBarrelHits",
-        outputClusters="EcalBarrelClusters",
-        outputLayers="EcalBarrelLayers",
+        inputProtoClusterCollection=sciglass_ecal_cl.outputProtoClusterCollection,
+        # mcHits="EcalBarrelHits",
+        outputClusterCollection="EcalBarrelClusters",
+        #OutputLevel=DEBUG
     )
     algorithms.append(sciglass_ecal_clreco)
 
+    # sciglass_barrel_clreco = RecoCoG(
+    #    "sciglass_barrel_clreco",
+    #    inputProtoClusterCollection=sciglass_ecal_cl.outputProtoClusterCollection,
+    #    outputClusterCollection="EcalBarrelScFiClusters",
+    #    logWeightBase=6.2,
+    # )
+    # algorithms.append(sciglass_barrel_clreco)
+
+
 podout.outputCommands = [
     "drop *",
     "keep MCParticles",

benchmarks/ecal/run_emcal_benchmarks.py

@@ -25,6 +25,11 @@ default_type = {
         ['barrel.py'],
         ['draw_clusters.py'],
         ['EcalBarrelImagingClusters']],
+    'barrel_sciglass': [
+        ['barrel.py'],
+        ['draw_clusters.py'],
+        ['EcalBarrelClusters']],
+    
 #     'all': [
 #         ['all_ecal.py'],
 #         ['draw_clusters.py'],

benchmarks/ecal/scripts/draw_clusters.py

@@ -58,7 +58,9 @@ if __name__ == '__main__':
     parser.add_argument('--collections', dest='coll', default='',
             help='Collection names for clusters, separated by \",\"')
     args = parser.parse_args()
-
+    
+    #rec_file='home/vaibhavi/eic/benchmarks/reconstruction_benchmarks/rec_barrel_electron_5.0_5.0.root'
+    
     # multi-threading for RDataFrame
     nthreads = os.cpu_count()//2
     ROOT.ROOT.EnableImplicitMT(nthreads)
@@ -69,6 +71,7 @@ if __name__ == '__main__':
     load_root_macros(args.macros)
 
     rdf_rec = ROOT.RDataFrame('events', args.rec_file)
+    #rdf_rec = ROOT.RDataFrame('events', rec_file)
 
     if not args.coll:
         print('No collection names provided, no plots generated.')
@@ -88,14 +91,17 @@ if __name__ == '__main__':
     plots = [
         ('nhits', 'Number of Hits', 50),
         ('energy', 'Energy (GeV)', 50),
-        ('eta', '$\eta$', 50),
+        #('eta', '$\eta$', 50),
     ]
     for coll in [c.strip() for c in args.coll.split(',')]:
         df = flatten_collection(rdf_rec, coll)
         fig, axs = plt.subplots(2, 2, figsize=(12, 8), dpi=160)
-        ncl = df.groupby('event')['{}.ID.value'.format(coll)].nunique().values
-        axs[0][0].hist(ncl, weights=np.repeat(1./float(ncl.shape[0]), ncl.shape[0]),
-                bins=np.arange(0, 10), align='mid', ec='k')
+        #ncl = df.groupby('event')['{}.ID.value'.format(coll)].nunique().values
+        #axs[0][0].hist(ncl, weights=np.repeat(1./float(ncl.shape[0]), ncl.shape[0]),
+                #bins=np.arange(0, 10), align='mid', ec='k')
+        ncl = df.groupby('event')['event'].count()
+        axs[0][0].hist(ncl, weights=np.repeat(1./float(ncl.count()), ncl.count()),
+                bins=np.arange(0, 10), align='mid', ec='k')      
         axs[0][0].set_xlabel('Number of Clusters', fontsize=16)
 
         for ax, (branch, xlabel, bins) in zip(axs.flat[1:], plots):