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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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(options, args) = parser.parse_args()
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if options.localConfig:
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sys.path.append(os.getcwd())
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exec("from " + options.localConfig.rstrip('.py') + " import *")
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condor_dir = set_condor_output_dir(options)
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from ROOT import TFile, gROOT, gStyle, gDirectory, TStyle, THStack, TH1F, TCanvas, TString, TLegend, TArrow, THStack, TIter, TKey, TPaveLabel
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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 + "/stacked_histograms.root", "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 not (testFile.IsZombie()):
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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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os.system("mergeTFileServiceHistograms -i %s -o %s -w %g" % (dataset_file, dataset_file + "_tmp", weight))
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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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histograms = []
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for key in gDirectory.GetListOfKeys():
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if re.match ('TH1', key.GetClassName()):
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histograms.append(key.GetName())
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for histogramName in histograms: # loop over histograms in the current directory
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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(0.70,0.65,0.99,0.89, "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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if(inputFile.IsZombie()):
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continue
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Histogram = inputFile.Get(rootDirectory+"/"+channel+"/"+histogramName).Clone()
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Histogram.SetDirectory(0)
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inputFile.Close()
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xAxisLabel = Histogram.GetXaxis().GetTitle()
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if( types[sample] == "bgMC"):
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numBgMCSamples += 1
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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.SetLineStyle(1)
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Histogram.SetLineWidth(1)
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backgroundIntegral += Histogram.Integral()
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BgMCLegend.AddEntry(Histogram,labels[sample],"F")
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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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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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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 options.normalize:
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bgMCHist.Scale(scaleFactor)
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Stack.Add(bgMCHist)
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stackMax = Stack.GetMaximum()
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finalMax = stackMax
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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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outputFile.cd(rootDirectory+"/"+channel)
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if(numBgMCSamples is not 0):
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Stack.Draw("HIST")
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Stack.SetMaximum(1.1*finalMax)
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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].Draw("HIST")
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SignalMCHistograms[0].SetMaximum(1.1*finalMax)
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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].Draw("E")
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DataHistograms[0].SetMaximum(1.1*finalMax)
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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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if(numBgMCSamples is not 0):
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Stack.GetXaxis().SetTitle(xAxisLabel)
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elif(numSignalSamples is not 0):
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SignalMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
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elif(numDataSamples is not 0):
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DataHistograms[0].GetXaxis().SetTitle(xAxisLabel)
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if(numBgMCSamples is not 0 or numDataSamples is not 0):
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BgMCLegend.Draw()
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if(numSignalSamples is not 0):
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SignalMCLegend.Draw()
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LumiLabel.Draw()
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if options.normalize:
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NormLabel = TPaveLabel(0.1,0.75,0.35,0.85,"MC scaled to data","NDC")
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NormLabel.SetBorderSize(0)
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NormLabel.SetFillColor(0)
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NormLabel.SetFillStyle(0)
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NormLabel.Draw()
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Canvas.Write()
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for dataset in processed_datasets:
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dataset_file = "%s/%s.root_tmp" % (condor_dir,dataset)
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os.remove(dataset_file)
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outputFile.Close()
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