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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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### parse the command-line options
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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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#### 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, THStack, TIter, TKey, TPaveLabel, gPad
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### setting ROOT options so our plots will look awesome and everyone will love us
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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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gStyle.SetCanvasDefH(600)
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gStyle.SetCanvasDefW(600)
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gStyle.SetCanvasDefX(0)
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gStyle.SetCanvasDefY(0)
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gStyle.SetPadTopMargin(0.05)
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gStyle.SetPadBottomMargin(0.13)
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gStyle.SetPadLeftMargin(0.15)
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gStyle.SetPadRightMargin(0.05)
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gStyle.SetTitleColor(1, "XYZ")
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gStyle.SetTitleFont(42, "XYZ")
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gStyle.SetTitleSize(0.05, "XYZ")
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gStyle.SetTitleXOffset(0.95)
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gStyle.SetTitleYOffset(1.25)
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gStyle.SetTextAlign(12)
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gStyle.SetLabelColor(1, "XYZ")
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gStyle.SetLabelFont(42, "XYZ")
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gStyle.SetLabelOffset(0.007, "XYZ")
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gStyle.SetLabelSize(0.05, "XYZ")
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gStyle.SetAxisColor(1, "XYZ")
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gStyle.SetStripDecimals(True)
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gStyle.SetTickLength(0.03, "XYZ")
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gStyle.SetNdivisions(510, "XYZ")
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gStyle.SetPadTickX(1)
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gStyle.SetPadTickY(1)
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gROOT.ForceStyle()
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#set the text for the luminosity label
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if(intLumi < 1000.):
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LumiText = "L_{int} = " + str(intLumi) + " pb^{-1}"
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LumiText = "L_{int} = " + str.format('{0:.1f}', LumiInPb) + " pb^{-1}"
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else:
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LumiInFb = intLumi/1000.
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LumiText = "L_{int} = " + str.format('{0:.1f}', LumiInFb) + " fb^{-1}"
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#bestest place for lumi. label, in top left corner
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topLeft_x_left = 0.1375839
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topLeft_x_right = 0.4580537
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topLeft_y_bottom = 0.8479021
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topLeft_y_top = 0.9475524
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topLeft_y_offset = 0.035
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##########################################################################################################################################
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##########################################################################################################################################
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##########################################################################################################################################
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# some fancy-ass code from Andrzej Zuranski to merge bins in the ratio plot until the error goes below some threshold
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def ratioHistogram( dataHist, mcHist, relErrMax=0.10):
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def groupR(group):
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Data,MC = [float(sum(hist.GetBinContent(i) for i in group)) for hist in [dataHist,mcHist]]
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return (Data-MC)/MC if MC else 0
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def groupErr(group):
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Data,MC = [float(sum(hist.GetBinContent(i) for i in group)) for hist in [dataHist,mcHist]]
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dataErr2,mcErr2 = [sum(hist.GetBinError(i)**2 for i in group) for hist in [dataHist,mcHist]]
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return abs(math.sqrt( (dataErr2+mcErr2)/(Data-MC)**2 + mcErr2/MC**2 ) * (Data-MC)/MC) if Data and MC else 0
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def regroup(groups):
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err,iG = max( (groupErr(g),groups.index(g)) for g in groups )
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if err < relErrMax or len(groups)<3 : return groups
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iH = max( [iG-1,iG+1], key = lambda i: groupErr(groups[i]) if 0<=i<len(groups) else -1 )
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iLo,iHi = sorted([iG,iH])
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return regroup(groups[:iLo] + [groups[iLo]+groups[iHi]] + groups[iHi+1:])
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#don't rebin the histograms of the number of a given object (except for the pileup ones)
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if (dataHist.GetName().find("num") is not -1 and dataHist.GetName().find("Primaryvertexs") is -1) or dataHist.GetName().find("CutFlow") is not -1:
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ratio = dataHist.Clone()
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ratio.Add(mcHist,-1)
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ratio.Divide(mcHist)
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ratio.SetTitle("")
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else:
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groups = regroup( [(i,) for i in range(1,1+dataHist.GetNbinsX())] )
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ratio = TH1F("ratio","",len(groups), array('d', [dataHist.GetBinLowEdge(min(g)) for g in groups ] + [dataHist.GetXaxis().GetBinUpEdge(dataHist.GetNbinsX())]) )
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for i,g in enumerate(groups) :
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ratio.SetBinContent(i+1,groupR(g))
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ratio.SetBinError(i+1,groupErr(g))
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ratio.GetYaxis().SetTitle("#frac{Data-MC}{MC}")
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ratio.SetLineColor(1)
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ratio.SetLineWidth(2)
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return ratio
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##########################################################################################################################################
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##########################################################################################################################################
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##########################################################################################################################################
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def MakeOneDHist(pathToDir,histogramName):
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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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LumiLabel = TPaveLabel(topLeft_x_left,topLeft_y_bottom,topLeft_x_right,topLeft_y_top,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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NormLabel = TPaveLabel()
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NormLabel.SetDrawOption("NDC")
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NormLabel.SetX1NDC(topLeft_x_left)
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NormLabel.SetX2NDC(topLeft_x_right)
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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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NormText = ""
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if arguments.normalizeToUnitArea:
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NormText = "Scaled to unit area"
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elif arguments.normalizeToData:
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NormText = "MC scaled to data"
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NormLabel.SetLabel(NormText)
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BgMCLegend = TLegend()
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BgTitle = BgMCLegend.AddEntry(0, "Data & Bkgd. MC", "H")
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BgTitle.SetTextAlign(22)
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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(22)
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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(pathToDir)
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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(pathToDir+"/"+histogramName).Clone()
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Histogram.SetDirectory(0)
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inputFile.Close()
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if arguments.rebinFactor:
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RebinFactor = int(arguments.rebinFactor)
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#don't rebin histograms which will have less than 5 bins or any gen-matching histograms
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if Histogram.GetNbinsX() >= RebinFactor*5 and Histogram.GetTitle().find("GenMatch") is -1:
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Histogram.Rebin(RebinFactor)
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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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#scaling histograms as per user's specifications
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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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### formatting data histograms and adding to legend
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legendIndex = 0
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for Histogram in DataHistograms:
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BgMCLegend.AddEntry(Histogram,DataLegendEntries[legendIndex],"LEP")
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legendIndex = legendIndex+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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ErrorHisto.SetFillStyle(3001)
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ErrorHisto.SetFillColor(13)
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ErrorHisto.SetLineWidth(0)
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BgMCLegend.AddEntry(ErrorHisto,"Stat. Errors","F")
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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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### formatting bgMC histograms and adding to legend
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legendIndex = 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[legendIndex],"L")
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else:
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BgMCLegend.AddEntry(Histogram,BgMCLegendEntries[legendIndex],"F")
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legendIndex = legendIndex-1
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### formatting signalMC histograms and adding to legend
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legendIndex = 0
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for Histogram in SignalMCHistograms:
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SignalMCLegend.AddEntry(Histogram,SignalMCLegendEntries[legendIndex],"L")
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legendIndex = legendIndex+1
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### finding the maximum value of anything going on the canvas, so we know how to set the y-axis
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finalMax = 0
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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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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() + dataHist.GetBinError(dataHist.GetMaximumBin())
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finalMax = 1.15*finalMax
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### Drawing histograms to canvas
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outputFile.cd(pathToDir)
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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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| 362 |
gPad.SetFillStyle(0)
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| 363 |
gPad.SetGridy(1)
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| 364 |
gPad.Update()
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gPad.Draw()
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Canvas.cd(1)
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| 369 |
if numBgMCSamples is not 0: # the first thing to draw to the canvas is a bgMC sample
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| 371 |
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| 372 |
if not arguments.noStack: # draw unstacked background samples
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Stack.SetTitle(histoTitle)
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Stack.Draw("HIST")
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| 375 |
Stack.GetXaxis().SetTitle(xAxisLabel)
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| 376 |
Stack.SetMaximum(finalMax)
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| 377 |
Stack.SetMinimum(0.0001)
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| 378 |
if makeRatioPlots or makeDiffPlots:
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| 379 |
Stack.GetHistogram().GetXaxis().SetLabelSize(0)
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| 380 |
#draw shaded error bands
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| 381 |
ErrorHisto.Draw("A E2 SAME")
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| 382 |
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| 383 |
else: #draw the stacked backgrounds
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| 384 |
BgMCHistograms[0].SetTitle(histoTitle)
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| 385 |
BgMCHistograms[0].Draw("HIST")
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| 386 |
BgMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
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BgMCHistograms[0].SetMaximum(finalMax)
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| 388 |
BgMCHistograms[0].SetMinimum(0.0001)
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for bgMCHist in BgMCHistograms:
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| 390 |
bgMCHist.Draw("A HIST SAME")
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| 392 |
for signalMCHist in SignalMCHistograms:
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signalMCHist.Draw("A HIST SAME")
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for dataHist in DataHistograms:
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dataHist.Draw("A E SAME")
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| 397 |
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| 398 |
elif numSignalSamples is not 0: # the first thing to draw to the canvas is a signalMC sample
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| 399 |
SignalMCHistograms[0].SetTitle(histoTitle)
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| 400 |
SignalMCHistograms[0].Draw("HIST")
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| 401 |
SignalMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
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| 402 |
SignalMCHistograms[0].SetMaximum(finalMax)
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| 403 |
SignalMCHistograms[0].SetMinimum(0.0001)
|
| 404 |
|
| 405 |
for signalMCHist in SignalMCHistograms:
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| 406 |
if(signalMCHist is not SignalMCHistograms[0]):
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| 407 |
signalMCHist.Draw("A HIST SAME")
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| 408 |
for dataHist in DataHistograms:
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| 409 |
dataHist.Draw("A E SAME")
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| 410 |
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| 411 |
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| 412 |
elif(numDataSamples is not 0): # the first thing to draw to the canvas is a data sample
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| 413 |
DataHistograms[0].SetTitle(histoTitle)
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| 414 |
DataHistograms[0].Draw("E")
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| 415 |
DataHistograms[0].GetXaxis().SetTitle(xAxisLabel)
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| 416 |
DataHistograms[0].SetMaximum(finalMax)
|
| 417 |
DataHistograms[0].SetMinimum(0.0001)
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| 418 |
for dataHist in DataHistograms:
|
| 419 |
if(dataHist is not DataHistograms[0]):
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| 420 |
dataHist.Draw("A E SAME")
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| 421 |
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| 422 |
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| 423 |
|
| 424 |
#legend coordinates, empirically determined :-)
|
| 425 |
x_left = 0.6761745
|
| 426 |
x_right = 0.9328859
|
| 427 |
x_width = x_right - x_left
|
| 428 |
y_max = 0.9335664
|
| 429 |
entry_height = 0.05
|
| 430 |
|
| 431 |
if(numBgMCSamples is not 0 or numDataSamples is not 0): #then draw the data & bgMC legend
|
| 432 |
|
| 433 |
numExtraEntries = 1 # count the legend title
|
| 434 |
BgMCLegend.SetX1NDC(x_left)
|
| 435 |
if numBgMCSamples > 0:
|
| 436 |
numExtraEntries = numExtraEntries + 1 # count the stat. errors entry
|
| 437 |
|
| 438 |
BgMCLegend.SetY1NDC(y_max-entry_height*(numExtraEntries+numBgMCSamples+numDataSamples))
|
| 439 |
BgMCLegend.SetX2NDC(x_right)
|
| 440 |
BgMCLegend.SetY2NDC(y_max)
|
| 441 |
BgMCLegend.Draw()
|
| 442 |
|
| 443 |
if(numSignalSamples is not 0): #then draw the signalMC legend to the left of the other one
|
| 444 |
SignalMCLegend.SetX1NDC(x_left-x_width)
|
| 445 |
SignalMCLegend.SetY1NDC(y_max-entry_height*(1+numSignalSamples)) # add one for the title
|
| 446 |
SignalMCLegend.SetX2NDC(x_left)
|
| 447 |
SignalMCLegend.SetY2NDC(y_max)
|
| 448 |
SignalMCLegend.Draw()
|
| 449 |
|
| 450 |
elif numSignalSamples is not 0: #draw the signalMC legend in the upper right corner
|
| 451 |
SignalMCLegend.SetX1NDC(x_left)
|
| 452 |
SignalMCLegend.SetY1NDC(y_max-entry_height*(1+numSignalSamples)) # add one for the title
|
| 453 |
SignalMCLegend.SetX2NDC(x_right)
|
| 454 |
SignalMCLegend.SetY2NDC(y_max)
|
| 455 |
SignalMCLegend.Draw()
|
| 456 |
|
| 457 |
|
| 458 |
if not arguments.normalizeToUnitArea or numDataSamples > 0: #don't draw the lumi label if there's no data and it's scaled to unit area
|
| 459 |
LumiLabel.Draw()
|
| 460 |
if arguments.normalizeToUnitArea or arguments.normalizeToData:
|
| 461 |
#move the normalization label down before drawing if we drew the lumi. label
|
| 462 |
NormLabel.SetY1NDC(topLeft_y_bottom-topLeft_y_offset)
|
| 463 |
NormLabel.SetY2NDC(topLeft_y_top-topLeft_y_offset)
|
| 464 |
NormLabel.Draw()
|
| 465 |
|
| 466 |
elif arguments.normalizeToUnitArea or arguments.normalizeToData:
|
| 467 |
NormLabel.SetY1NDC(topLeft_y_bottom)
|
| 468 |
NormLabel.SetY2NDC(topLeft_y_top)
|
| 469 |
NormLabel.Draw()
|
| 470 |
|
| 471 |
|
| 472 |
if makeRatioPlots or makeDiffPlots:
|
| 473 |
Canvas.cd(2)
|
| 474 |
BgSum = Stack.GetStack().Last()
|
| 475 |
if makeRatioPlots:
|
| 476 |
Comparison = ratioHistogram(DataHistograms[0],BgSum)
|
| 477 |
elif makeDiffPlots:
|
| 478 |
Comparison = DataHistograms[0].Clone("diff")
|
| 479 |
Comparison.Add(BgSum,-1)
|
| 480 |
Comparison.SetTitle("")
|
| 481 |
Comparison.GetYaxis().SetTitle("Data-MC")
|
| 482 |
Comparison.GetXaxis().SetTitle(xAxisLabel)
|
| 483 |
Comparison.GetYaxis().CenterTitle()
|
| 484 |
Comparison.GetYaxis().SetTitleSize(0.1)
|
| 485 |
Comparison.GetYaxis().SetTitleOffset(0.4)
|
| 486 |
Comparison.GetXaxis().SetTitleSize(0.15)
|
| 487 |
Comparison.GetYaxis().SetLabelSize(0.1)
|
| 488 |
Comparison.GetXaxis().SetLabelSize(0.15)
|
| 489 |
if makeRatioPlots:
|
| 490 |
RatioYRange = 1.15
|
| 491 |
if arguments.ratioYRange:
|
| 492 |
RatioYRange = float(arguments.ratioYRange)
|
| 493 |
Comparison.GetYaxis().SetRangeUser(-1*RatioYRange, RatioYRange)
|
| 494 |
elif makeDiffPlots:
|
| 495 |
YMax = Comparison.GetMaximum()
|
| 496 |
YMin = Comparison.GetMinimum()
|
| 497 |
if YMax <= 0 and YMin <= 0:
|
| 498 |
Comparison.GetYaxis().SetRangeUser(-1.2*YMin,0)
|
| 499 |
elif YMax >= 0 and YMin >= 0:
|
| 500 |
Comparison.GetYaxis().SetRangeUser(0,1.2*YMax)
|
| 501 |
else: #axis crosses y=0
|
| 502 |
if abs(YMax) > abs(YMin):
|
| 503 |
Comparison.GetYaxis().SetRangeUser(-1.2*YMax,1.2*YMax)
|
| 504 |
else:
|
| 505 |
Comparison.GetYaxis().SetRangeUser(-1.2*YMin,1.2*YMin)
|
| 506 |
|
| 507 |
Comparison.GetYaxis().SetNdivisions(205)
|
| 508 |
Comparison.Draw()
|
| 509 |
|
| 510 |
Canvas.Write()
|
| 511 |
if arguments.savePDFs:
|
| 512 |
pathToDirString = pathToDir.replace(' ','_').replace('<','lt').replace('>','gt').replace('(','').replace(')','').replace('=','eq')
|
| 513 |
Canvas.SaveAs(condor_dir+"/stacked_histograms_pdfs/"+pathToDirString+"/"+histogramName+".pdf")
|
| 514 |
|
| 515 |
|
| 516 |
##########################################################################################################################################
|
| 517 |
##########################################################################################################################################
|
| 518 |
##########################################################################################################################################
|
| 519 |
|
| 520 |
def MakeTwoDHist(pathToDir,histogramName):
|
| 521 |
numBgMCSamples = 0
|
| 522 |
numDataSamples = 0
|
| 523 |
numSignalSamples = 0
|
| 524 |
|
| 525 |
LumiLabel = TPaveLabel(0.1,0.8,0.34,0.9,LumiText,"NDC")
|
| 526 |
LumiLabel.SetBorderSize(0)
|
| 527 |
LumiLabel.SetFillColor(0)
|
| 528 |
LumiLabel.SetFillStyle(0)
|
| 529 |
|
| 530 |
BgMCLegend = TLegend(0.76,0.65,0.99,0.9)
|
| 531 |
BgMCLegend.AddEntry (0, "Data & Bkgd. MC", "H").SetTextFont (62)
|
| 532 |
BgMCLegend.SetBorderSize(0)
|
| 533 |
BgMCLegend.SetFillColor(0)
|
| 534 |
BgMCLegend.SetFillStyle(0)
|
| 535 |
SignalMCLegend = TLegend(0.76,0.135,0.99,0.377)
|
| 536 |
SignalMCLegend.AddEntry (0, "Signal MC", "H").SetTextFont (62)
|
| 537 |
SignalMCLegend.SetBorderSize(0)
|
| 538 |
SignalMCLegend.SetFillColor(0)
|
| 539 |
SignalMCLegend.SetFillStyle(0)
|
| 540 |
|
| 541 |
outputFile.cd(pathToDir)
|
| 542 |
Canvas = TCanvas(histogramName)
|
| 543 |
Canvas.SetRightMargin(0.2413793);
|
| 544 |
BgMCHistograms = []
|
| 545 |
SignalMCHistograms = []
|
| 546 |
DataHistograms = []
|
| 547 |
|
| 548 |
for sample in processed_datasets: # loop over different samples as listed in configurationOptions.py
|
| 549 |
dataset_file = "%s/%s.root" % (condor_dir,sample)
|
| 550 |
inputFile = TFile(dataset_file)
|
| 551 |
Histogram = inputFile.Get(pathToDir+"/"+histogramName).Clone()
|
| 552 |
Histogram.SetDirectory(0)
|
| 553 |
RebinFactor = int(arguments.rebinFactor)
|
| 554 |
if arguments.rebinFactor and Histogram.GetNbinsX() >= RebinFactor*10 and Histogram.GetNbinsY() >= RebinFactor*10:
|
| 555 |
Histogram.Rebin2D(RebinFactor)
|
| 556 |
inputFile.Close()
|
| 557 |
xAxisLabel = Histogram.GetXaxis().GetTitle()
|
| 558 |
yAxisLabel = Histogram.GetYaxis().GetTitle()
|
| 559 |
histoTitle = Histogram.GetTitle()
|
| 560 |
|
| 561 |
if( types[sample] == "bgMC"):
|
| 562 |
|
| 563 |
numBgMCSamples += 1
|
| 564 |
Histogram.SetMarkerColor(colors[sample])
|
| 565 |
Histogram.SetFillColor(colors[sample])
|
| 566 |
BgMCLegend.AddEntry(Histogram,labels[sample],"F").SetTextFont (42)
|
| 567 |
BgMCHistograms.append(Histogram)
|
| 568 |
|
| 569 |
elif( types[sample] == "signalMC"):
|
| 570 |
|
| 571 |
numSignalSamples += 1
|
| 572 |
Histogram.SetMarkerColor(colors[sample])
|
| 573 |
Histogram.SetFillColor(colors[sample])
|
| 574 |
SignalMCLegend.AddEntry(Histogram,labels[sample],"F").SetTextFont (42)
|
| 575 |
SignalMCHistograms.append(Histogram)
|
| 576 |
|
| 577 |
elif( types[sample] == "data"):
|
| 578 |
|
| 579 |
numDataSamples += 1
|
| 580 |
Histogram.SetMarkerColor(colors[sample])
|
| 581 |
Histogram.SetFillColor(colors[sample])
|
| 582 |
BgMCLegend.AddEntry(Histogram,labels[sample],"F").SetTextFont (42)
|
| 583 |
DataHistograms.append(Histogram)
|
| 584 |
|
| 585 |
|
| 586 |
outputFile.cd(pathToDir)
|
| 587 |
|
| 588 |
if(numBgMCSamples is not 0):
|
| 589 |
BgMCHistograms[0].SetTitle(histoTitle)
|
| 590 |
BgMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 591 |
BgMCHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 592 |
BgMCHistograms[0].Draw()
|
| 593 |
for signalMCHist in SignalMCHistograms:
|
| 594 |
signalMCHist.Draw("SAME")
|
| 595 |
for dataHist in DataHistograms:
|
| 596 |
dataHist.Draw("SAME")
|
| 597 |
|
| 598 |
elif(numSignalSamples is not 0):
|
| 599 |
SignalMCHistograms[0].SetTitle(histoTitle)
|
| 600 |
SignalMCHistograms[0].Draw()
|
| 601 |
SignalMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 602 |
SignalMCHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 603 |
for signalMCHist in SignalMCHistograms:
|
| 604 |
if(signalMCHist is not SignalMCHistograms[0]):
|
| 605 |
signalMCHist.Draw("SAME")
|
| 606 |
for dataHist in DataHistograms:
|
| 607 |
dataHist.Draw("SAME")
|
| 608 |
|
| 609 |
elif(numDataSamples is not 0):
|
| 610 |
DataHistograms[0].SetTitle(histoTitle)
|
| 611 |
DataHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 612 |
DataHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 613 |
DataHistograms[0].Draw()
|
| 614 |
for dataHist in DataHistograms:
|
| 615 |
if(dataHist is not DataHistograms[0]):
|
| 616 |
dataHist.Draw("SAME")
|
| 617 |
|
| 618 |
|
| 619 |
if(numBgMCSamples is not 0 or numDataSamples is not 0):
|
| 620 |
BgMCLegend.Draw()
|
| 621 |
if(numSignalSamples is not 0):
|
| 622 |
SignalMCLegend.Draw()
|
| 623 |
if not arguments.normalizeToUnitArea or numDataSamples > 0:
|
| 624 |
LumiLabel.Draw()
|
| 625 |
|
| 626 |
Canvas.Write()
|
| 627 |
|
| 628 |
|
| 629 |
|
| 630 |
|
| 631 |
##########################################################################################################################################
|
| 632 |
##########################################################################################################################################
|
| 633 |
##########################################################################################################################################
|
| 634 |
|
| 635 |
processed_datasets = []
|
| 636 |
|
| 637 |
condor_dir = set_condor_output_dir(arguments)
|
| 638 |
|
| 639 |
#### check which input datasets have valid output files
|
| 640 |
for sample in datasets:
|
| 641 |
fileName = condor_dir + "/" + sample + ".root"
|
| 642 |
if not os.path.exists(fileName):
|
| 643 |
continue
|
| 644 |
testFile = TFile(fileName)
|
| 645 |
if testFile.IsZombie() or not testFile.GetNkeys():
|
| 646 |
continue
|
| 647 |
processed_datasets.append(sample)
|
| 648 |
|
| 649 |
if len(processed_datasets) is 0:
|
| 650 |
sys.exit("No datasets have been processed")
|
| 651 |
|
| 652 |
|
| 653 |
#### make output file
|
| 654 |
outputFileName = "stacked_histograms.root"
|
| 655 |
if arguments.outputFileName:
|
| 656 |
outputFileName = arguments.outputFileName
|
| 657 |
|
| 658 |
outputFile = TFile(condor_dir + "/" + outputFileName, "RECREATE")
|
| 659 |
|
| 660 |
|
| 661 |
|
| 662 |
#### use the first input file as a template and make stacked versions of all its histograms
|
| 663 |
inputFile = TFile(condor_dir + "/" + processed_datasets[0] + ".root")
|
| 664 |
inputFile.cd()
|
| 665 |
outputFile.cd()
|
| 666 |
|
| 667 |
if arguments.savePDFs:
|
| 668 |
os.system("rm -r %s/stacked_histograms_pdfs" % (condor_dir))
|
| 669 |
os.system("mkdir %s/stacked_histograms_pdfs" % (condor_dir))
|
| 670 |
|
| 671 |
|
| 672 |
#get root directory in the first layer, generally "OSUAnalysis"
|
| 673 |
for key in inputFile.GetListOfKeys():
|
| 674 |
if (key.GetClassName() != "TDirectoryFile"):
|
| 675 |
continue
|
| 676 |
rootDirectory = key.GetName()
|
| 677 |
outputFile.mkdir(rootDirectory)
|
| 678 |
if arguments.savePDFs:
|
| 679 |
os.system("mkdir %s/stacked_histograms_pdfs/%s" % (condor_dir,rootDirectory.replace(' ','_').replace('<','lt').replace('>','gt').replace('(','').replace(')','').replace('=','eq')))
|
| 680 |
|
| 681 |
#cd to root directory and look for histograms
|
| 682 |
inputFile.cd(rootDirectory)
|
| 683 |
for key2 in gDirectory.GetListOfKeys():
|
| 684 |
|
| 685 |
if re.match ('TH1', key2.GetClassName()): # found a 1-D histogram
|
| 686 |
MakeOneDHist(rootDirectory,key2.GetName())
|
| 687 |
elif re.match ('TH2', key2.GetClassName()) and arguments.draw2DPlots: # found a 2-D histogram
|
| 688 |
MakeTwoDHist(rootDirectory,key2.GetName())
|
| 689 |
|
| 690 |
elif (key2.GetClassName() == "TDirectoryFile"): # found a directory, cd there and look for histograms
|
| 691 |
level2Directory = rootDirectory+"/"+key2.GetName()
|
| 692 |
|
| 693 |
#make a corresponding directory in the output file
|
| 694 |
outputFile.cd(rootDirectory)
|
| 695 |
gDirectory.mkdir(key2.GetName())
|
| 696 |
if arguments.savePDFs:
|
| 697 |
os.system("mkdir %s/stacked_histograms_pdfs/%s" % (condor_dir,level2Directory.replace(' ','_').replace('<','lt').replace('>','gt').replace('(','').replace(')','').replace('=','eq')))
|
| 698 |
|
| 699 |
#####################################################
|
| 700 |
### This layer is typically the "channels" layer ###
|
| 701 |
#####################################################
|
| 702 |
|
| 703 |
inputFile.cd(level2Directory)
|
| 704 |
for key3 in gDirectory.GetListOfKeys():
|
| 705 |
if re.match ('TH1', key3.GetClassName()): # found a 1-D histogram
|
| 706 |
MakeOneDHist(level2Directory,key3.GetName())
|
| 707 |
elif re.match ('TH2', key3.GetClassName()) and arguments.draw2DPlots: # found a 2-D histogram
|
| 708 |
MakeTwoDHist(level2Directory,key3.GetName())
|
| 709 |
|
| 710 |
elif (key3.GetClassName() == "TDirectoryFile"): # found a directory, cd there and look for histograms
|
| 711 |
level3Directory = level2Directory+"/"+key3.GetName()
|
| 712 |
|
| 713 |
#make a corresponding directory in the output file
|
| 714 |
outputFile.cd(level2Directory)
|
| 715 |
gDirectory.mkdir(key3.GetName())
|
| 716 |
if arguments.savePDFs:
|
| 717 |
os.system("mkdir %s/stacked_histograms_pdfs/%s" % (condor_dir,level3Directory.replace(' ','_').replace('<','lt').replace('>','gt').replace('(','').replace(')','').replace('=','eq')))
|
| 718 |
|
| 719 |
#################################################
|
| 720 |
### This layer is typically the "cuts" layer ###
|
| 721 |
#################################################
|
| 722 |
|
| 723 |
inputFile.cd(level3Directory)
|
| 724 |
for key3 in gDirectory.GetListOfKeys():
|
| 725 |
if re.match ('TH1', key3.GetClassName()): # found a 1-D histogram
|
| 726 |
MakeOneDHist(level3Directory,key3.GetName())
|
| 727 |
elif re.match ('TH2', key3.GetClassName()) and arguments.draw2DPlots: # found a 2-D histogram
|
| 728 |
MakeTwoDHist(level3Directory,key3.GetName())
|
| 729 |
|
| 730 |
|
| 731 |
outputFile.Close()
|
