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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 math import *
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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, TLegendEntry, 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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#To find whether there are a third layer in directories.
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GetPlotsAfterEachCut = True
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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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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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testFile.cd(key.GetName()+"/"+key2.GetName())
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for key3 in gDirectory.GetListOfKeys():
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if (key3.GetClassName() != "TDirectoryFile"):
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GetPlotsAfterEachCut = False
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if GetPlotsAfterEachCut:
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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[key2.GetName()] = []
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testFile.cd(key.GetName()+"/"+key2.GetName())
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for key3 in gDirectory.GetListOfKeys():
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if (key3.GetClassName() != "TDirectoryFile"):
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continue
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outputFile.cd(key.GetName()+"/"+key2.GetName())
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gDirectory.mkdir(key3.GetName())
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channels[key2.GetName()].append("/"+key3.GetName())
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else:
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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[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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def MakeOneDHist(histogramname,channel,cut):
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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()
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BgTitle = BgMCLegend.AddEntry(0, "Data & Bkgd. MC", "H")
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BgTitle.SetTextAlign(21)
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BgTitle.SetTextFont(62)
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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()
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SignalTitle = SignalMCLegend.AddEntry(0, "Signal MC", "H")
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SignalTitle.SetTextAlign(21)
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SignalTitle.SetTextFont(62)
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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+cut)
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Canvas = TCanvas(histogramName)
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BgMCHistograms = []
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BgMCLegendEntries = []
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SignalMCHistograms = []
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SignalMCLegendEntries = []
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DataHistograms = []
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DataLegendEntries = []
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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" % (condor_dir,sample)
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inputFile = TFile(dataset_file)
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Histogram = inputFile.Get(rootDirectory+"/"+channel+cut+"/"+histogramName).Clone()
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Histogram.SetDirectory(0)
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if arguments.rebinFactor:
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RebinFactor = int(arguments.rebinFactor)
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if 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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legLabel = labels[sample]
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if (arguments.printYields):
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yieldHist = Histogram.Integral()
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legLabel = legLabel + " (%.1f)" % yieldHist
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if( types[sample] == "bgMC"):
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numBgMCSamples += 1
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backgroundIntegral += Histogram.Integral()
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Histogram.SetLineStyle(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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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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BgMCLegendEntries.append(legLabel)
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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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SignalMCLegendEntries.append(legLabel)
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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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dataIntegral += Histogram.Integral()
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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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DataLegendEntries.append(legLabel)
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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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| 267 |
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| 268 |
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| 269 |
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### formatting data histograms and adding to legend
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counter = 0
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for Histogram in DataHistograms:
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BgMCLegend.AddEntry(Histogram,DataLegendEntries[counter],"LEP").SetTextFont (42)
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counter = counter+1
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### creating the histogram to represent the statistical errors on the stack
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if numBgMCSamples is not 0 and not arguments.noStack:
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ErrorHisto = BgMCHistograms[0].Clone("errors")
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| 280 |
ErrorHisto.SetFillStyle(3001)
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| 281 |
ErrorHisto.SetFillColor(13)
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ErrorHisto.SetLineWidth(0)
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BgMCLegend.AddEntry(ErrorHisto,"Stat. Errors","F").SetTextFont (42)
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for Histogram in BgMCHistograms:
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if Histogram is not BgMCHistograms[0]:
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ErrorHisto.Add(Histogram)
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| 288 |
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| 289 |
### formatting bgMC histograms and adding to legend
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counter = numBgMCSamples-1
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for Histogram in reversed(BgMCHistograms):
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if(arguments.noStack):
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BgMCLegend.AddEntry(Histogram,BgMCLegendEntries[counter],"L").SetTextFont (42)
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else:
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| 295 |
BgMCLegend.AddEntry(Histogram,BgMCLegendEntries[counter],"F").SetTextFont (42)
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counter = counter-1
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| 298 |
### formatting signalMC histograms and adding to legend
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counter = 0
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for Histogram in SignalMCHistograms:
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SignalMCLegend.AddEntry(Histogram,SignalMCLegendEntries[counter],"L").SetTextFont (42)
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counter = counter+1
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| 305 |
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| 306 |
### finding the maximum value of anything going on the canvas, and know how to set the y-axis
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| 307 |
finalMax = 0
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| 308 |
if numBgMCSamples is not 0 and not arguments.noStack:
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finalMax = ErrorHisto.GetMaximum() + ErrorHisto.GetBinError(ErrorHisto.GetMaximumBin())
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else:
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| 311 |
for bgMCHist in BgMCHistograms:
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| 312 |
if(bgMCHist.GetMaximum() > finalMax):
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finalMax = bgMCHist.GetMaximum()
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for signalMCHist in SignalMCHistograms:
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| 315 |
if(signalMCHist.GetMaximum() > finalMax):
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finalMax = signalMCHist.GetMaximum()
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for dataHist in DataHistograms:
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| 318 |
if(dataHist.GetMaximum() > finalMax):
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| 319 |
finalMax = dataHist.GetMaximum() + dataHist.GetBinError(dataHist.GetMaximumBin())
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| 320 |
finalMax = 1.1*finalMax
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| 321 |
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| 322 |
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| 323 |
### Drawing histograms to canvas
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| 324 |
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| 325 |
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| 326 |
outputFile.cd(rootDirectory+"/"+channel+cut)
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| 327 |
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| 328 |
makeRatioPlots = arguments.makeRatioPlots
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| 329 |
makeDiffPlots = arguments.makeDiffPlots
|
| 330 |
|
| 331 |
if numBgMCSamples is 0 or numDataSamples is not 1:
|
| 332 |
makeRatioPlots = False
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| 333 |
makeDiffPlots = False
|
| 334 |
if makeRatioPlots or makeDiffPlots:
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| 335 |
Canvas.SetFillStyle(0)
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| 336 |
Canvas.Divide(1,2)
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| 337 |
Canvas.cd(1)
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| 338 |
gPad.SetPad(0.01,0.25,0.99,0.99)
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| 339 |
gPad.SetMargin(0.1,0.05,0.02,0.07)
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| 340 |
gPad.SetFillStyle(0)
|
| 341 |
gPad.Update()
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| 342 |
gPad.Draw()
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| 343 |
Canvas.cd(2)
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| 344 |
gPad.SetPad(0.01,0.01,0.99,0.25)
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| 345 |
#format: gPad.SetMargin(l,r,b,t)
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| 346 |
gPad.SetMargin(0.1,0.05,0.4,0.02)
|
| 347 |
gPad.SetFillStyle(0)
|
| 348 |
gPad.SetGridy(1)
|
| 349 |
gPad.Update()
|
| 350 |
gPad.Draw()
|
| 351 |
|
| 352 |
Canvas.cd(1)
|
| 353 |
|
| 354 |
if numBgMCSamples is not 0:
|
| 355 |
|
| 356 |
if not arguments.noStack:
|
| 357 |
Stack.SetTitle(histoTitle)
|
| 358 |
Stack.Draw("HIST")
|
| 359 |
Stack.GetXaxis().SetTitle(xAxisLabel)
|
| 360 |
Stack.SetMaximum(finalMax)
|
| 361 |
Stack.SetMinimum(0.0001)
|
| 362 |
if makeRatioPlots or makeDiffPlots:
|
| 363 |
Stack.GetHistogram().GetXaxis().SetLabelSize(0)
|
| 364 |
#draw shaded error bands
|
| 365 |
ErrorHisto.Draw("A E2 SAME")
|
| 366 |
|
| 367 |
|
| 368 |
else:
|
| 369 |
BgMCHistograms[0].SetTitle(histoTitle)
|
| 370 |
BgMCHistograms[0].Draw("HIST")
|
| 371 |
BgMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 372 |
BgMCHistograms[0].SetMaximum(finalMax)
|
| 373 |
BgMCHistograms[0].SetMinimum(0.0001)
|
| 374 |
for bgMCHist in BgMCHistograms:
|
| 375 |
bgMCHist.Draw("A HIST SAME")
|
| 376 |
for signalMCHist in SignalMCHistograms:
|
| 377 |
signalMCHist.Draw("A HIST SAME")
|
| 378 |
for dataHist in DataHistograms:
|
| 379 |
dataHist.Draw("A E SAME")
|
| 380 |
|
| 381 |
elif numSignalSamples is not 0:
|
| 382 |
SignalMCHistograms[0].SetTitle(histoTitle)
|
| 383 |
SignalMCHistograms[0].Draw("HIST")
|
| 384 |
SignalMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 385 |
SignalMCHistograms[0].SetMaximum(finalMax)
|
| 386 |
SignalMCHistograms[0].SetMinimum(0.0001)
|
| 387 |
for signalMCHist in SignalMCHistograms:
|
| 388 |
if(signalMCHist is not SignalMCHistograms[0]):
|
| 389 |
signalMCHist.Draw("A HIST SAME")
|
| 390 |
for dataHist in DataHistograms:
|
| 391 |
dataHist.Draw("A E SAME")
|
| 392 |
|
| 393 |
elif(numDataSamples is not 0):
|
| 394 |
DataHistograms[0].SetTitle(histoTitle)
|
| 395 |
DataHistograms[0].Draw("E")
|
| 396 |
DataHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 397 |
DataHistograms[0].SetMaximum(finalMax)
|
| 398 |
DataHistograms[0].SetMinimum(0.0001)
|
| 399 |
for dataHist in DataHistograms:
|
| 400 |
if(dataHist is not DataHistograms[0]):
|
| 401 |
dataHist.Draw("A E SAME")
|
| 402 |
|
| 403 |
|
| 404 |
if(numBgMCSamples is not 0 or numDataSamples is not 0):
|
| 405 |
BgMCLegend.SetX1NDC(0.75)
|
| 406 |
if numBgMCSamples is 0:
|
| 407 |
BgMCLegend.SetY1NDC(0.9-0.05*(1+numDataSamples))
|
| 408 |
else:
|
| 409 |
BgMCLegend.SetY1NDC(0.9-0.05*(2+numBgMCSamples+numDataSamples))
|
| 410 |
BgMCLegend.SetX2NDC(0.9)
|
| 411 |
BgMCLegend.SetY2NDC(0.9)
|
| 412 |
BgMCLegend.Draw()
|
| 413 |
if(numSignalSamples is not 0):
|
| 414 |
SignalMCLegend.SetX1NDC(0.6)
|
| 415 |
SignalMCLegend.SetY1NDC(0.9-0.05*(1+numSignalSamples))
|
| 416 |
SignalMCLegend.SetX2NDC(0.75)
|
| 417 |
SignalMCLegend.SetY2NDC(0.9)
|
| 418 |
SignalMCLegend.Draw()
|
| 419 |
elif numSignalSamples is not 0:
|
| 420 |
SignalMCLegend.SetX1NDC(0.75)
|
| 421 |
SignalMCLegend.SetY1NDC(0.9-0.05*(1+numSignalSamples))
|
| 422 |
SignalMCLegend.SetX2NDC(0.9)
|
| 423 |
SignalMCLegend.SetY2NDC(0.9)
|
| 424 |
SignalMCLegend.Draw()
|
| 425 |
|
| 426 |
if not arguments.normalizeToUnitArea or numDataSamples > 0:
|
| 427 |
LumiLabel.Draw()
|
| 428 |
if arguments.normalizeToData and numBgMCSamples > 0 and numDataSamples > 0:
|
| 429 |
if numSignalSamples is 0:
|
| 430 |
NormLabel = TPaveLabel(0.6,0.85,0.75,0.9,"MC scaled to data","NDC")
|
| 431 |
else:
|
| 432 |
NormLabel = TPaveLabel(0.45,0.85,0.6,0.9,"MC scaled to data","NDC")
|
| 433 |
NormLabel.SetBorderSize(0)
|
| 434 |
NormLabel.SetFillColor(0)
|
| 435 |
NormLabel.SetFillStyle(0)
|
| 436 |
NormLabel.Draw()
|
| 437 |
elif arguments.normalizeToUnitArea:
|
| 438 |
if (numBgMCSamples is not 0 or numDataSamples is not 0) and numSignalSamples is 0:
|
| 439 |
NormLabel = TPaveLabel(0.6,0.85,0.75,0.9,"Scaled to unit area","NDC")
|
| 440 |
else:
|
| 441 |
NormLabel = TPaveLabel(0.45,0.85,0.6,0.9,"Scaled to unit area","NDC")
|
| 442 |
NormLabel.SetBorderSize(0)
|
| 443 |
NormLabel.SetFillColor(0)
|
| 444 |
NormLabel.SetFillStyle(0)
|
| 445 |
NormLabel.Draw()
|
| 446 |
|
| 447 |
|
| 448 |
if makeRatioPlots or makeDiffPlots:
|
| 449 |
Canvas.cd(2)
|
| 450 |
BgSum = Stack.GetStack().Last()
|
| 451 |
Comparison = DataHistograms[0].Clone()
|
| 452 |
Comparison.Add(BgSum,-1)
|
| 453 |
if not makeDiffPlots:
|
| 454 |
Comparison.Divide(BgSum)
|
| 455 |
Comparison.SetTitle("")
|
| 456 |
Comparison.GetXaxis().SetTitle(xAxisLabel)
|
| 457 |
if makeRatioPlots:
|
| 458 |
Comparison.GetYaxis().SetTitle("#frac{Data-MC}{MC}")
|
| 459 |
elif makeDiffPlots:
|
| 460 |
Comparison.GetYaxis().SetTitle("Data-MC")
|
| 461 |
Comparison.GetYaxis().CenterTitle()
|
| 462 |
Comparison.GetYaxis().SetTitleSize(0.1)
|
| 463 |
Comparison.GetYaxis().SetTitleOffset(0.35)
|
| 464 |
Comparison.GetXaxis().SetTitleSize(0.15)
|
| 465 |
Comparison.GetYaxis().SetLabelSize(0.1)
|
| 466 |
Comparison.GetXaxis().SetLabelSize(0.15)
|
| 467 |
if makeRatioPlots:
|
| 468 |
Comparison.GetYaxis().SetRangeUser(-1.15,1.15)
|
| 469 |
elif makeDiffPlots:
|
| 470 |
YMax = Comparison.GetMaximum()
|
| 471 |
YMin = Comparison.GetMinimum()
|
| 472 |
if YMax <= 0 and YMin <= 0:
|
| 473 |
Comparison.GetYaxis().SetRangeUser(-1.2*YMin,0)
|
| 474 |
elif YMax >= 0 and YMin >= 0:
|
| 475 |
Comparison.GetYaxis().SetRangeUser(0,1.2*YMax)
|
| 476 |
else: #axis crosses y=0
|
| 477 |
if abs(YMax) > abs(YMin):
|
| 478 |
Comparison.GetYaxis().SetRangeUser(-1.2*YMax,1.2*YMax)
|
| 479 |
else:
|
| 480 |
Comparison.GetYaxis().SetRangeUser(-1.2*YMin,1.2*YMin)
|
| 481 |
|
| 482 |
Comparison.GetYaxis().SetNdivisions(205)
|
| 483 |
Comparison.Draw()
|
| 484 |
Canvas.Write()
|
| 485 |
|
| 486 |
def MakeTwoDHist(histogramname,channel,cut):
|
| 487 |
numBgMCSamples = 0
|
| 488 |
numDataSamples = 0
|
| 489 |
numSignalSamples = 0
|
| 490 |
|
| 491 |
if(intLumi < 1000.):
|
| 492 |
LumiText = "L_{int} = " + str(intLumi) + " pb^{-1}"
|
| 493 |
else:
|
| 494 |
getcontext().prec = 2
|
| 495 |
LumiInFb = intLumi/1000.
|
| 496 |
LumiText = "L_{int} = " + str(LumiInFb) + " fb^{-1}"
|
| 497 |
|
| 498 |
LumiLabel = TPaveLabel(0.1,0.8,0.34,0.9,LumiText,"NDC")
|
| 499 |
LumiLabel.SetBorderSize(0)
|
| 500 |
LumiLabel.SetFillColor(0)
|
| 501 |
LumiLabel.SetFillStyle(0)
|
| 502 |
|
| 503 |
BgMCLegend = TLegend(0.76,0.65,0.99,0.9)
|
| 504 |
BgMCLegend.AddEntry (0, "Data & Bkgd. MC", "H").SetTextFont (62)
|
| 505 |
BgMCLegend.SetBorderSize(0)
|
| 506 |
BgMCLegend.SetFillColor(0)
|
| 507 |
BgMCLegend.SetFillStyle(0)
|
| 508 |
SignalMCLegend = TLegend(0.76,0.135,0.99,0.377)
|
| 509 |
SignalMCLegend.AddEntry (0, "Signal MC", "H").SetTextFont (62)
|
| 510 |
SignalMCLegend.SetBorderSize(0)
|
| 511 |
SignalMCLegend.SetFillColor(0)
|
| 512 |
SignalMCLegend.SetFillStyle(0)
|
| 513 |
|
| 514 |
outputFile.cd(rootDirectory+"/"+channel+cut)
|
| 515 |
Canvas = TCanvas(histogramName)
|
| 516 |
Canvas.SetRightMargin(0.2413793);
|
| 517 |
BgMCHistograms = []
|
| 518 |
SignalMCHistograms = []
|
| 519 |
DataHistograms = []
|
| 520 |
|
| 521 |
for sample in processed_datasets: # loop over different samples as listed in configurationOptions.py
|
| 522 |
dataset_file = "%s/%s.root" % (condor_dir,sample)
|
| 523 |
inputFile = TFile(dataset_file)
|
| 524 |
Histogram = inputFile.Get(rootDirectory+"/"+channel+"/"+cut+"/"+histogramName).Clone()
|
| 525 |
Histogram.SetDirectory(0)
|
| 526 |
RebinFactor = int(arguments.rebinFactor)
|
| 527 |
if arguments.rebinFactor and Histogram.GetNbinsX() >= RebinFactor*10 and Histogram.GetNbinsY() >= RebinFactor*10:
|
| 528 |
Histogram.Rebin2D(RebinFactor)
|
| 529 |
inputFile.Close()
|
| 530 |
xAxisLabel = Histogram.GetXaxis().GetTitle()
|
| 531 |
yAxisLabel = Histogram.GetYaxis().GetTitle()
|
| 532 |
histoTitle = Histogram.GetTitle()
|
| 533 |
|
| 534 |
if( types[sample] == "bgMC"):
|
| 535 |
|
| 536 |
numBgMCSamples += 1
|
| 537 |
Histogram.SetMarkerColor(colors[sample])
|
| 538 |
Histogram.SetFillColor(colors[sample])
|
| 539 |
BgMCLegend.AddEntry(Histogram,labels[sample],"F").SetTextFont (42)
|
| 540 |
BgMCHistograms.append(Histogram)
|
| 541 |
|
| 542 |
elif( types[sample] == "signalMC"):
|
| 543 |
|
| 544 |
numSignalSamples += 1
|
| 545 |
Histogram.SetMarkerColor(colors[sample])
|
| 546 |
Histogram.SetFillColor(colors[sample])
|
| 547 |
SignalMCLegend.AddEntry(Histogram,labels[sample],"F").SetTextFont (42)
|
| 548 |
SignalMCHistograms.append(Histogram)
|
| 549 |
|
| 550 |
elif( types[sample] == "data"):
|
| 551 |
|
| 552 |
numDataSamples += 1
|
| 553 |
Histogram.SetMarkerColor(colors[sample])
|
| 554 |
Histogram.SetFillColor(colors[sample])
|
| 555 |
BgMCLegend.AddEntry(Histogram,labels[sample],"F").SetTextFont (42)
|
| 556 |
DataHistograms.append(Histogram)
|
| 557 |
|
| 558 |
|
| 559 |
outputFile.cd(rootDirectory+"/"+channel+cut)
|
| 560 |
|
| 561 |
if(numBgMCSamples is not 0):
|
| 562 |
BgMCHistograms[0].SetTitle(histoTitle)
|
| 563 |
BgMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 564 |
BgMCHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 565 |
BgMCHistograms[0].Draw()
|
| 566 |
for signalMCHist in SignalMCHistograms:
|
| 567 |
signalMCHist.Draw("SAME")
|
| 568 |
for dataHist in DataHistograms:
|
| 569 |
dataHist.Draw("SAME")
|
| 570 |
|
| 571 |
elif(numSignalSamples is not 0):
|
| 572 |
SignalMCHistograms[0].SetTitle(histoTitle)
|
| 573 |
SignalMCHistograms[0].Draw()
|
| 574 |
SignalMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 575 |
SignalMCHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 576 |
for signalMCHist in SignalMCHistograms:
|
| 577 |
if(signalMCHist is not SignalMCHistograms[0]):
|
| 578 |
signalMCHist.Draw("SAME")
|
| 579 |
for dataHist in DataHistograms:
|
| 580 |
dataHist.Draw("SAME")
|
| 581 |
|
| 582 |
elif(numDataSamples is not 0):
|
| 583 |
DataHistograms[0].SetTitle(histoTitle)
|
| 584 |
DataHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 585 |
DataHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 586 |
DataHistograms[0].Draw()
|
| 587 |
for dataHist in DataHistograms:
|
| 588 |
if(dataHist is not DataHistograms[0]):
|
| 589 |
dataHist.Draw("SAME")
|
| 590 |
|
| 591 |
|
| 592 |
if(numBgMCSamples is not 0 or numDataSamples is not 0):
|
| 593 |
BgMCLegend.Draw()
|
| 594 |
if(numSignalSamples is not 0):
|
| 595 |
SignalMCLegend.Draw()
|
| 596 |
if not arguments.normalizeToUnitArea or numDataSamples > 0:
|
| 597 |
LumiLabel.Draw()
|
| 598 |
|
| 599 |
Canvas.Write()
|
| 600 |
|
| 601 |
for channel in channels: # loop over final states, which each have their own directory
|
| 602 |
if GetPlotsAfterEachCut:
|
| 603 |
for cut in channels[channel]:
|
| 604 |
testFile.cd(rootDirectory+"/"+channel+cut)
|
| 605 |
|
| 606 |
for key in gDirectory.GetListOfKeys(): # loop over histograms in the current directory
|
| 607 |
histogramName = key.GetName()
|
| 608 |
|
| 609 |
if re.match ('TH1', key.GetClassName()): # plot a 1-D histogram
|
| 610 |
MakeOneDHist(histogramName,channel,cut)
|
| 611 |
if re.match ('TH2', key.GetClassName()) and arguments.draw2DPlots: # plot a 2-D histogram
|
| 612 |
MakeTwoDHist(histogramName,channel,cut)
|
| 613 |
else:
|
| 614 |
testFile.cd(rootDirectory+"/"+channel)
|
| 615 |
|
| 616 |
for key in gDirectory.GetListOfKeys(): # loop over histograms in the current directory
|
| 617 |
histogramName = key.GetName()
|
| 618 |
|
| 619 |
if re.match ('TH1', key.GetClassName()): # plot a 1-D histogram
|
| 620 |
MakeOneDHist(histogramName,channel,"")
|
| 621 |
if re.match ('TH2', key.GetClassName()) and arguments.draw2DPlots: # plot a 2-D histogram
|
| 622 |
MakeTwoDHist(histogramName,channel,"")
|
| 623 |
|
| 624 |
#for dataset in processed_datasets:
|
| 625 |
# dataset_file = "%s/%s.root_tmp" % (condor_dir,dataset)
|
| 626 |
# os.remove(dataset_file)
|
| 627 |
|
| 628 |
outputFile.Close()
|
