updates for 7 GeV pions, use larger cone for matching

hgcalHistHelpers.py

@@ -92,8 +92,8 @@ def histsPrintSaveSameCanvas(histsAndProps, outDir, tag="hists1D_", latexComment
         if (type(hist) == ROOT.TH1F) or (type(hist) == ROOT.TH2F) or (type(hist) == ROOT.TH3F):
             if hist.GetEntries() == 0:
                 continue
-            if (type(hist) == ROOT.TH1F):
-                hist.Rebin()
+            # if (type(hist) == ROOT.TH1F):
+            #     hist.Rebin()
         x_maxs.append(hist.GetBinCenter(hist.FindLastBinAbove(1)))
         hist.Scale(1./hist.Integral())
         hist.GetYaxis().SetTitle("a.u.")
@@ -298,14 +298,14 @@ def fitResolution(graph, fitLineColor = ROOT.kBlue, fitLineStyle = 1, rangeLimit
     stochasticTermLimitDn = 0
     stochasticTermLimitUp = 300
     constantTermLimitDn = 0
-    constantTermLimitUp = 10
+    constantTermLimitUp = 100
     noiseTermLimitDn = 0
-    noiseTermLimitUp = 30
+    noiseTermLimitUp = 500
     # define the fitting gausian and range of its parameters
     fResolution = ROOT.TF1("f", "sqrt([1]*[1] + [0]*[0]/x + [2]*[2]/(x*x))", rangeLimitDn, rangeLimitUp)
     fResolution.SetParLimits(0, stochasticTermLimitDn, stochasticTermLimitUp)  # stochastic term
     fResolution.SetParLimits(1, constantTermLimitDn, constantTermLimitUp)  # constant term
-    # fResolution.SetParLimits(2, noiseTermLimitDn, noiseTermLimitUp) # noise term
+    fResolution.SetParLimits(2, noiseTermLimitDn, noiseTermLimitUp) # noise term
     fResolution.SetLineColor(fitLineColor)
     fResolution.SetLineStyle(fitLineStyle)
     # perform fit and extract params

megaClustering.py

@@ -74,8 +74,12 @@ def getMegaClusters(genParticles, multiClusters, layerClusters, recHits, gun_typ
 
     if multiClusters.shape[0] == 0:
         return pd.DataFrame(columns=['pt', 'eta', 'phi', 'energy'])
-    # for the mega cluster axis, take highest energy multicluster within dR = 0.1
-    bestMultiClusterIndices = hgcalHelpers.getHighestEnergyObjectIndex(selectedGen[['eta', 'phi']], multiClusters[['eta', 'phi']], multiClusters['energy'], 0.1)
+    # for the mega cluster axis, take highest energy multicluster within dR = 0.1, 0.2 for 7 GeV guns
+    bestMultiClusterIndices = None
+    if GEN_engpt <= 7.5:
+        bestMultiClusterIndices = hgcalHelpers.getHighestEnergyObjectIndex(selectedGen[['eta', 'phi']], multiClusters[['eta', 'phi']], multiClusters['energy'], 0.2)
+    else:
+        bestMultiClusterIndices = hgcalHelpers.getHighestEnergyObjectIndex(selectedGen[['eta', 'phi']], multiClusters[['eta', 'phi']], multiClusters['energy'], 0.1)
     # print bestMultiClusterIndices
 
     megaClusters = []

resolutionScalePlotter.py

@@ -105,8 +105,8 @@ def plotComparisons(histsFilesAndInfoMap, resScale_values, pidSelected, GEN_engp
             # prepare basic info and plot these histograms on top of each other
             gMeanCalibEnergy = (resScale_values[etaBinName][phiBinName]['mean'] / 100. + 1.) * GEN_engpt
             gMeanCalibEnergyError = (resScale_values[etaBinName][phiBinName]['meanError'] / 100.) * GEN_engpt
-            # plotComments = ["p_{T,fit} = " + "{0:.2f}".format(gMeanCalibEnergy) + " #pm " + "{0:.2f}".format(gMeanCalibEnergyError) + " GeV",
-            plotComments = []
+            plotComments = ["p_{T,fit} = " + "{0:.2f}".format(gMeanCalibEnergy) + " #pm " + "{0:.2f}".format(gMeanCalibEnergyError) + " GeV"]
+            # plotComments = []
             plotFileTag = "obj_histsOverlayed_" + etaBinName + "_" + phiBinName + "_pid" + str(pidSelected) + "_engpt" + str(int(GEN_engpt))
             # plot and save comparison hists
             hgcalHistHelpers.histsPrintSaveSameCanvas(histsAndProps, outDir, tag=plotFileTag, latexComment=plotComments)
@@ -173,6 +173,18 @@ def setupResScaleScenario(inputdir, gun_type, pidSelected, GEN_engpt, refName, s
                                 "cmp_Pt": {'file': filePFuncalib, 'hist_prefix': "obj_Pt", 'leg': "PF (non-calibrated) cluster", 'color': ROOT.kGreen - 6}}
         resolutionFileAndInfoMap = {'file': filePF, 'hist_prefix': "obj_dEoverE", 'leg': "PF (calibrated) cluster", 'color': ROOT.kBlue,
                                     'renormNoPU': {'file': fileRenormNoPU,  'hist_prefix': "obj_Pt"}}
+    elif (scenario == "PC_noPU"):  # scenario: noPU, resolutoin from PC
+        # list of files and corresponding info
+        filePF = ROOT.TFile.Open(inputdir + "/{}_{}_{}GeV_{}_{}_{}.root".format(gun_type, pidSelected, int(GEN_engpt), refName, "pfcandidate", "noPU"), "read")  # info based on PF energy
+        # filePFuncalib = ROOT.TFile.Open(inputdir + "/{}_{}_{}GeV_{}_{}_{}.root".format(gun_type, pidSelected, int(GEN_engpt), refName, "pfcluster_uncalib", "PU200"), "read")  # info based on PF energy
+        fileRenormNoPU = ROOT.TFile.Open(inputdir + "/{}_{}_{}GeV_{}_{}_{}.root".format(gun_type, pidSelected, int(GEN_engpt), refName, "pfcandidate", "noPU"), "read")  # info based on megacluster energy, noPU
+        # map of histograms and files
+        histsFilesAndInfoMap = {"obj_Pt": {'file': filePF, 'hist_prefix': "obj_Pt", 'leg': "PF candidate", 'color': ROOT.kBlue},
+                                # "ref_Pt": {'file': filePF, 'hist_prefix': "ref_Pt", 'leg': "GEN particle (" + gun_type + "={0:.1f} GeV".format(GEN_engpt) + ")", 'color': ROOT.kRed},
+                                # "cmp_Pt": {'file': filePFuncalib, 'hist_prefix': "obj_Pt", 'leg': "PF (non-calibrated) cluster", 'color': ROOT.kGreen - 6}
+                                }
+        resolutionFileAndInfoMap = {'file': filePF, 'hist_prefix': "obj_dEoverE", 'leg': "PF candidate", 'color': ROOT.kBlue,
+                                    'renormNoPU': {'file': fileRenormNoPU,  'hist_prefix': "obj_Pt"}}
     elif (scenario == "Mega_PU200"):  # scenario: PU, resolutoin from Mega cluster, comparison "Mega vs. PF corrected.
         # list of files and corresponding info
         filePF = ROOT.TFile.Open(inputdir + "/{}_{}_{}GeV_{}_{}_{}.root".format(gun_type, pidSelected, int(GEN_engpt), refName, "pfcluster", "PU200"), "read")  # info based on PF energy