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#!/usr/bin/env python
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import sys
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import os
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import re
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from array import *
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from decimal import *
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from optparse import OptionParser
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from OSUT3Analysis.Configuration.configurationOptions import *
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from OSUT3Analysis.Configuration.processingUtilities import *
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parser = OptionParser()
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parser = set_commandline_arguments(parser)
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(arguments, args) = parser.parse_args()
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if arguments.localConfig:
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sys.path.append(os.getcwd())
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exec("from " + arguments.localConfig.rstrip('.py') + " import *")
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outputFileName = "stacked_histograms.root"
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if arguments.outputFileName:
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outputFileName = arguments.outputFileName
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condor_dir = set_condor_output_dir(arguments)
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#### deal with conflicting arguments
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if arguments.normalizeToData and arguments.normalizeToUnitArea:
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print "Conflicting normalizations requsted, will normalize to unit area"
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arguments.normalizeToData = False
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if arguments.normalizeToData and arguments.noStack:
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print "You have asked to scale non-stacked backgrounds to data. This is a very strange request. Will normalize to unit area instead"
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arguments.normalizeToData = False
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arguments.normalizeToUnitArea = True
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if arguments.makeRatioPlots and arguments.makeDiffPlots:
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print "You have requested both ratio and difference plots. Will make just ratio plots instead"
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arguments.makeRatioPlots = False
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from ROOT import TFile, gROOT, gStyle, gDirectory, TStyle, THStack, TH1F, TCanvas, TString, TLegend, TArrow, THStack, TIter, TKey, TPaveLabel, gPad
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gROOT.SetBatch()
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gStyle.SetOptStat(0)
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gStyle.SetCanvasBorderMode(0)
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gStyle.SetPadBorderMode(0)
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gStyle.SetPadColor(0)
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gStyle.SetCanvasColor(0)
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gStyle.SetTextFont(42)
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gROOT.ForceStyle()
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outputFile = TFile(condor_dir + "/" + outputFileName, "RECREATE")
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channels = []
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processed_datasets = []
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#### check which input datasets have valid output files
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for sample in datasets:
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fileName = condor_dir + "/" + sample + ".root"
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if not os.path.exists(fileName):
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continue
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testFile = TFile(fileName)
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if testFile.IsZombie() or not testFile.GetNkeys():
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continue
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processed_datasets.append(sample)
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if len(processed_datasets) is 0:
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sys.exit("No datasets have been processed")
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#### open first input file and re-make its directory structure in the output file
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testFile = TFile(condor_dir + "/" + processed_datasets[0] + ".root")
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testFile.cd()
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for key in testFile.GetListOfKeys():
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if (key.GetClassName() != "TDirectoryFile"):
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continue
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outputFile.cd()
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outputFile.mkdir(key.GetName())
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rootDirectory = key.GetName()
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testFile.cd(key.GetName())
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for key2 in gDirectory.GetListOfKeys():
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if (key2.GetClassName() != "TDirectoryFile"):
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continue
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outputFile.cd(key.GetName())
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gDirectory.mkdir(key2.GetName())
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channels.append(key2.GetName())
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weight = intLumi / 10000.0
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for dataset in processed_datasets:
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dataset_file = "%s/%s.root" % (condor_dir,dataset)
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fin = TFile (dataset_file)
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flags = fin.Get ("flags")
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noWeights = flags and flags.GetBinContent (1)
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fin.Close ()
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if types[dataset] != "data" and not noWeights:
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os.system("mergeTFileServiceHistograms -i %s -o %s -w %g" % (dataset_file, dataset_file + "_tmp", weight))
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else:
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os.system("mergeTFileServiceHistograms -i %s -o %s -w %g" % (dataset_file, dataset_file + "_tmp", 1.0))
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for channel in channels: # loop over final states, which each have their own directory
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testFile.cd(rootDirectory+"/"+channel)
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for key in gDirectory.GetListOfKeys(): # loop over histograms in the current directory
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histogramName = key.GetName()
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if re.match ('TH1', key.GetClassName()): # plot a 1-D histogram
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numBgMCSamples = 0
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numDataSamples = 0
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numSignalSamples = 0
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Stack = THStack("stack",histogramName)
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if(intLumi < 1000.):
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LumiText = "L_{int} = " + str(intLumi) + " pb^{-1}"
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else:
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getcontext().prec = 2
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LumiInFb = intLumi/1000.
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LumiText = "L_{int} = " + str(LumiInFb) + " fb^{-1}"
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LumiLabel = TPaveLabel(0.1,0.8,0.34,0.9,LumiText,"NDC")
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LumiLabel.SetBorderSize(0)
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LumiLabel.SetFillColor(0)
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LumiLabel.SetFillStyle(0)
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BgMCLegend = TLegend(0.70,0.65,0.94,0.89, "Data & Bkgd. MC")
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BgMCLegend.SetBorderSize(0)
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BgMCLegend.SetFillColor(0)
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BgMCLegend.SetFillStyle(0)
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SignalMCLegend = TLegend(0.45,0.65,0.70,0.89,"Signal MC")
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SignalMCLegend.SetBorderSize(0)
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SignalMCLegend.SetFillColor(0)
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SignalMCLegend.SetFillStyle(0)
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outputFile.cd(rootDirectory+"/"+channel)
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Canvas = TCanvas(histogramName)
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BgMCHistograms = []
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SignalMCHistograms = []
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DataHistograms = []
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backgroundIntegral = 0
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dataIntegral = 0
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scaleFactor = 1
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for sample in processed_datasets: # loop over different samples as listed in configurationOptions.py
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dataset_file = "%s/%s.root_tmp" % (condor_dir,sample)
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inputFile = TFile(dataset_file)
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Histogram = inputFile.Get(rootDirectory+"/"+channel+"/"+histogramName).Clone()
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Histogram.SetDirectory(0)
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RebinFactor = int(arguments.rebinFactor)
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if arguments.rebinFactor and Histogram.GetNbinsX() >= RebinFactor*10:
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Histogram.Rebin(RebinFactor)
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inputFile.Close()
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xAxisLabel = Histogram.GetXaxis().GetTitle()
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histoTitle = Histogram.GetTitle()
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if( types[sample] == "bgMC"):
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numBgMCSamples += 1
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if(arguments.noStack):
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Histogram.SetFillStyle(0)
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Histogram.SetLineColor(colors[sample])
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Histogram.SetLineWidth(2)
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BgMCLegend.AddEntry(Histogram,labels[sample],"L")
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else:
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Histogram.SetFillStyle(1001)
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Histogram.SetFillColor(colors[sample])
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Histogram.SetLineColor(1)
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Histogram.SetLineWidth(1)
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BgMCLegend.AddEntry(Histogram,labels[sample],"F")
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Histogram.SetLineStyle(1)
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backgroundIntegral += Histogram.Integral()
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BgMCHistograms.append(Histogram)
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elif( types[sample] == "signalMC"):
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numSignalSamples += 1
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Histogram.SetFillStyle(0)
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Histogram.SetLineColor(colors[sample])
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Histogram.SetLineStyle(1)
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Histogram.SetLineWidth(2)
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if(arguments.normalizeToUnitArea and Histogram.Integral() > 0):
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Histogram.Scale(1./Histogram.Integral())
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SignalMCLegend.AddEntry(Histogram,labels[sample],"L")
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SignalMCHistograms.append(Histogram)
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elif( types[sample] == "data"):
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numDataSamples += 1
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Histogram.SetFillStyle(0)
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Histogram.SetLineColor(colors[sample])
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Histogram.SetLineStyle(1)
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Histogram.SetLineWidth(2)
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if(arguments.normalizeToUnitArea and Histogram.Integral() > 0):
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Histogram.Scale(1./Histogram.Integral())
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dataIntegral += Histogram.Integral()
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BgMCLegend.AddEntry(Histogram,labels[sample],"LEP")
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DataHistograms.append(Histogram)
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if dataIntegral > 0 and backgroundIntegral > 0:
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scaleFactor = dataIntegral/backgroundIntegral
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for bgMCHist in BgMCHistograms:
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if arguments.normalizeToData:
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bgMCHist.Scale(scaleFactor)
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if arguments.normalizeToUnitArea and not arguments.noStack and backgroundIntegral > 0:
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bgMCHist.Scale(1./backgroundIntegral)
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elif arguments.normalizeToUnitArea and arguments.noStack and bgMCHist.Integral() > 0:
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bgMCHist.Scale(1./bgMCHist.Integral())
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if not arguments.noStack:
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Stack.Add(bgMCHist)
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finalMax = 0
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if not arguments.noStack:
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finalMax = Stack.GetMaximum()
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else:
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for bgMCHist in BgMCHistograms:
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if(bgMCHist.GetMaximum() > finalMax):
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finalMax = bgMCHist.GetMaximum()
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for signalMCHist in SignalMCHistograms:
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if(signalMCHist.GetMaximum() > finalMax):
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finalMax = signalMCHist.GetMaximum()
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for dataHist in DataHistograms:
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if(dataHist.GetMaximum() > finalMax):
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finalMax = dataHist.GetMaximum()
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if len(DataHistograms) is 1:
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dataIntegral += DataHistograms[0].Integral()
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### Drawing histograms to canvas
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outputFile.cd(rootDirectory+"/"+channel)
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makeRatioPlots = arguments.makeRatioPlots
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makeDiffPlots = arguments.makeDiffPlots
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if numBgMCSamples is 0 or numDataSamples is not 1:
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makeRatioPlots = False
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makeDiffPlots = False
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if makeRatioPlots or makeDiffPlots:
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Canvas.SetFillStyle(0)
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Canvas.Divide(1,2)
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Canvas.cd(1)
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gPad.SetPad(0.01,0.25,0.99,0.99)
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gPad.SetMargin(0.1,0.05,0.02,0.07)
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gPad.SetFillStyle(0)
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gPad.Update()
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gPad.Draw()
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Canvas.cd(2)
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gPad.SetPad(0.01,0.01,0.99,0.25)
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#format: gPad.SetMargin(l,r,b,t)
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gPad.SetMargin(0.1,0.05,0.4,0.02)
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gPad.SetFillStyle(0)
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gPad.SetGridy(1)
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gPad.Update()
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gPad.Draw()
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Canvas.cd(1)
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if(numBgMCSamples is not 0):
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if not arguments.noStack:
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Stack.SetTitle(histoTitle)
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Stack.Draw("HIST")
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Stack.GetXaxis().SetTitle(xAxisLabel)
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Stack.SetMaximum(1.1*finalMax)
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Stack.SetMinimum(0.0001)
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if makeRatioPlots or makeDiffPlots:
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Stack.GetHistogram().GetXaxis().SetLabelSize(0)
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else:
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BgMCHistograms[0].SetTitle(histoTitle)
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BgMCHistograms[0].Draw("HIST")
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BgMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
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BgMCHistograms[0].SetMaximum(1.1*finalMax)
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BgMCHistograms[0].SetMinimum(0.0001)
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for bgMCHist in BgMCHistograms:
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bgMCHist.Draw("HIST SAME")
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for signalMCHist in SignalMCHistograms:
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signalMCHist.Draw("HIST SAME")
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for dataHist in DataHistograms:
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dataHist.Draw("E SAME")
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elif(numSignalSamples is not 0):
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SignalMCHistograms[0].SetTitle(histoTitle)
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SignalMCHistograms[0].Draw("HIST")
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SignalMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
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SignalMCHistograms[0].SetMaximum(1.1*finalMax)
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SignalMCHistograms[0].SetMinimum(0.0001)
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for signalMCHist in SignalMCHistograms:
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if(signalMCHist is not SignalMCHistograms[0]):
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signalMCHist.Draw("HIST SAME")
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for dataHist in DataHistograms:
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dataHist.Draw("E SAME")
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elif(numDataSamples is not 0):
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DataHistograms[0].SetTitle(histoTitle)
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DataHistograms[0].Draw("E")
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DataHistograms[0].GetXaxis().SetTitle(xAxisLabel)
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| 310 |
DataHistograms[0].SetMaximum(1.1*finalMax)
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| 311 |
DataHistograms[0].SetMinimum(0.0001)
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for dataHist in DataHistograms:
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if(dataHist is not DataHistograms[0]):
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dataHist.Draw("E SAME")
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| 316 |
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| 317 |
if(numBgMCSamples is not 0 or numDataSamples is not 0):
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BgMCLegend.Draw()
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| 319 |
if(numSignalSamples is not 0):
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| 320 |
SignalMCLegend.Draw()
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| 321 |
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| 322 |
if not arguments.normalizeToUnitArea or numDataSamples > 0:
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| 323 |
LumiLabel.Draw()
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| 324 |
if arguments.normalizeToData and numBgMCSamples > 0 and numDataSamples > 0:
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| 325 |
NormLabel = TPaveLabel(0.1,0.75,0.35,0.85,"MC scaled to data","NDC")
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| 326 |
NormLabel.SetBorderSize(0)
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| 327 |
NormLabel.SetFillColor(0)
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| 328 |
NormLabel.SetFillStyle(0)
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| 329 |
NormLabel.Draw()
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| 330 |
elif arguments.normalizeToUnitArea:
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| 331 |
NormLabel = TPaveLabel(0.1,0.75,0.35,0.85,"Scaled to unit area","NDC")
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| 332 |
NormLabel.SetBorderSize(0)
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| 333 |
NormLabel.SetFillColor(0)
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| 334 |
NormLabel.SetFillStyle(0)
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| 335 |
NormLabel.Draw()
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| 336 |
|
| 337 |
|
| 338 |
if makeRatioPlots or makeDiffPlots:
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| 339 |
Canvas.cd(2)
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| 340 |
BgSum = Stack.GetStack().Last()
|
| 341 |
Comparison = DataHistograms[0].Clone()
|
| 342 |
Comparison.Add(BgSum,-1)
|
| 343 |
if not makeDiffPlots:
|
| 344 |
Comparison.Divide(BgSum)
|
| 345 |
Comparison.SetTitle("")
|
| 346 |
Comparison.GetXaxis().SetTitle(xAxisLabel)
|
| 347 |
if makeRatioPlots:
|
| 348 |
Comparison.GetYaxis().SetTitle("#frac{Data-MC}{MC}")
|
| 349 |
elif makeDiffPlots:
|
| 350 |
Comparison.GetYaxis().SetTitle("Data-MC")
|
| 351 |
Comparison.GetYaxis().CenterTitle()
|
| 352 |
Comparison.GetYaxis().SetTitleSize(0.1)
|
| 353 |
Comparison.GetYaxis().SetTitleOffset(0.35)
|
| 354 |
Comparison.GetXaxis().SetTitleSize(0.15)
|
| 355 |
Comparison.GetYaxis().SetLabelSize(0.1)
|
| 356 |
Comparison.GetXaxis().SetLabelSize(0.15)
|
| 357 |
if makeRatioPlots:
|
| 358 |
Comparison.GetYaxis().SetRangeUser(-1,1)
|
| 359 |
elif makeDiffPlots:
|
| 360 |
YMax = Comparison.GetMaximum()
|
| 361 |
YMin = Comparison.GetMinimum()
|
| 362 |
if YMax <= 0 and YMin <= 0:
|
| 363 |
Comparison.GetYaxis().SetRangeUser(-1.2*YMin,0)
|
| 364 |
elif YMax >= 0 and YMin >= 0:
|
| 365 |
Comparison.GetYaxis().SetRangeUser(0,1.2*YMax)
|
| 366 |
else: #axis crosses y=0
|
| 367 |
if abs(YMax) > abs(YMin):
|
| 368 |
Comparison.GetYaxis().SetRangeUser(-1.2*YMax,1.2*YMax)
|
| 369 |
else:
|
| 370 |
Comparison.GetYaxis().SetRangeUser(-1.2*YMin,1.2*YMin)
|
| 371 |
|
| 372 |
Comparison.GetYaxis().SetNdivisions(205)
|
| 373 |
Comparison.Draw()
|
| 374 |
|
| 375 |
Canvas.Write()
|
| 376 |
|
| 377 |
|
| 378 |
if re.match ('TH2', key.GetClassName()) and arguments.draw2DPlots: # plot a 2-D histogram
|
| 379 |
|
| 380 |
numBgMCSamples = 0
|
| 381 |
numDataSamples = 0
|
| 382 |
numSignalSamples = 0
|
| 383 |
|
| 384 |
if(intLumi < 1000.):
|
| 385 |
LumiText = "L_{int} = " + str(intLumi) + " pb^{-1}"
|
| 386 |
else:
|
| 387 |
getcontext().prec = 2
|
| 388 |
LumiInFb = intLumi/1000.
|
| 389 |
LumiText = "L_{int} = " + str(LumiInFb) + " fb^{-1}"
|
| 390 |
|
| 391 |
LumiLabel = TPaveLabel(0.1,0.8,0.34,0.9,LumiText,"NDC")
|
| 392 |
LumiLabel.SetBorderSize(0)
|
| 393 |
LumiLabel.SetFillColor(0)
|
| 394 |
LumiLabel.SetFillStyle(0)
|
| 395 |
|
| 396 |
BgMCLegend = TLegend(0.76,0.65,0.99,0.9, "Data & Bkgd. MC")
|
| 397 |
BgMCLegend.SetBorderSize(0)
|
| 398 |
BgMCLegend.SetFillColor(0)
|
| 399 |
BgMCLegend.SetFillStyle(0)
|
| 400 |
SignalMCLegend = TLegend(0.76,0.135,0.99,0.377,"Signal MC")
|
| 401 |
SignalMCLegend.SetBorderSize(0)
|
| 402 |
SignalMCLegend.SetFillColor(0)
|
| 403 |
SignalMCLegend.SetFillStyle(0)
|
| 404 |
|
| 405 |
outputFile.cd(rootDirectory+"/"+channel)
|
| 406 |
Canvas = TCanvas(histogramName)
|
| 407 |
Canvas.SetRightMargin(0.2413793);
|
| 408 |
BgMCHistograms = []
|
| 409 |
SignalMCHistograms = []
|
| 410 |
DataHistograms = []
|
| 411 |
|
| 412 |
for sample in processed_datasets: # loop over different samples as listed in configurationOptions.py
|
| 413 |
dataset_file = "%s/%s.root_tmp" % (condor_dir,sample)
|
| 414 |
inputFile = TFile(dataset_file)
|
| 415 |
Histogram = inputFile.Get(rootDirectory+"/"+channel+"/"+histogramName).Clone()
|
| 416 |
Histogram.SetDirectory(0)
|
| 417 |
RebinFactor = int(arguments.rebinFactor)
|
| 418 |
if arguments.rebinFactor and Histogram.GetNbinsX() >= RebinFactor*10 and Histogram.GetNbinsY() >= RebinFactor*10:
|
| 419 |
Histogram.Rebin2D(RebinFactor)
|
| 420 |
inputFile.Close()
|
| 421 |
xAxisLabel = Histogram.GetXaxis().GetTitle()
|
| 422 |
yAxisLabel = Histogram.GetYaxis().GetTitle()
|
| 423 |
histoTitle = Histogram.GetTitle()
|
| 424 |
|
| 425 |
if( types[sample] == "bgMC"):
|
| 426 |
|
| 427 |
numBgMCSamples += 1
|
| 428 |
Histogram.SetMarkerColor(colors[sample])
|
| 429 |
Histogram.SetFillColor(colors[sample])
|
| 430 |
BgMCLegend.AddEntry(Histogram,labels[sample],"F")
|
| 431 |
BgMCHistograms.append(Histogram)
|
| 432 |
|
| 433 |
elif( types[sample] == "signalMC"):
|
| 434 |
|
| 435 |
numSignalSamples += 1
|
| 436 |
Histogram.SetMarkerColor(colors[sample])
|
| 437 |
Histogram.SetFillColor(colors[sample])
|
| 438 |
SignalMCLegend.AddEntry(Histogram,labels[sample],"F")
|
| 439 |
SignalMCHistograms.append(Histogram)
|
| 440 |
|
| 441 |
elif( types[sample] == "data"):
|
| 442 |
|
| 443 |
numDataSamples += 1
|
| 444 |
Histogram.SetMarkerColor(colors[sample])
|
| 445 |
Histogram.SetFillColor(colors[sample])
|
| 446 |
BgMCLegend.AddEntry(Histogram,labels[sample],"F")
|
| 447 |
DataHistograms.append(Histogram)
|
| 448 |
|
| 449 |
|
| 450 |
outputFile.cd(rootDirectory+"/"+channel)
|
| 451 |
|
| 452 |
if(numBgMCSamples is not 0):
|
| 453 |
BgMCHistograms[0].SetTitle(histoTitle)
|
| 454 |
BgMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 455 |
BgMCHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 456 |
BgMCHistograms[0].Draw()
|
| 457 |
for signalMCHist in SignalMCHistograms:
|
| 458 |
signalMCHist.Draw("SAME")
|
| 459 |
for dataHist in DataHistograms:
|
| 460 |
dataHist.Draw("SAME")
|
| 461 |
|
| 462 |
elif(numSignalSamples is not 0):
|
| 463 |
SignalMCHistograms[0].SetTitle(histoTitle)
|
| 464 |
SignalMCHistograms[0].Draw()
|
| 465 |
SignalMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 466 |
SignalMCHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 467 |
for signalMCHist in SignalMCHistograms:
|
| 468 |
if(signalMCHist is not SignalMCHistograms[0]):
|
| 469 |
signalMCHist.Draw("SAME")
|
| 470 |
for dataHist in DataHistograms:
|
| 471 |
dataHist.Draw("SAME")
|
| 472 |
|
| 473 |
elif(numDataSamples is not 0):
|
| 474 |
DataHistograms[0].SetTitle(histoTitle)
|
| 475 |
DataHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 476 |
DataHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 477 |
DataHistograms[0].Draw()
|
| 478 |
for dataHist in DataHistograms:
|
| 479 |
if(dataHist is not DataHistograms[0]):
|
| 480 |
dataHist.Draw("SAME")
|
| 481 |
|
| 482 |
|
| 483 |
if(numBgMCSamples is not 0 or numDataSamples is not 0):
|
| 484 |
BgMCLegend.Draw()
|
| 485 |
if(numSignalSamples is not 0):
|
| 486 |
SignalMCLegend.Draw()
|
| 487 |
if not arguments.normalizeToUnitArea or numDataSamples > 0:
|
| 488 |
LumiLabel.Draw()
|
| 489 |
|
| 490 |
Canvas.Write()
|
| 491 |
|
| 492 |
|
| 493 |
|
| 494 |
for dataset in processed_datasets:
|
| 495 |
dataset_file = "%s/%s.root_tmp" % (condor_dir,dataset)
|
| 496 |
os.remove(dataset_file)
|
| 497 |
|
| 498 |
outputFile.Close()
|
