def getMainHistogram(filename, column):
f = ROOT.TFile(
filename.replace("datacard_", "histograms_").replace(".txt", ".root"),
"r")
if f == None:
sys.exit()
h = f.Get(column).Clone()
h.SetDirectory(None)
f.Close()
if h == None:
raise Exception("Error: cannot find histogram '%s' in file %s!" %
(column, filename))
hunc = dataset.RootHistoWithUncertainties(h)
hunc.makeFlowBinsVisible()
return hunc
def doPlot(self, opts, myAllShapeNuisances, f, mass, luminosity, signalTable, rebinList=None):
def rebin(h, rebinList):
if rebinList == None or h == None:
return h
myArray = array.array("d",rebinList)
hnew = h.Rebin(len(rebinList)-1,h.GetTitle(),myArray)
h.Delete()
return hnew
print "Doing plots for:",self._name
hNominal = f.Get(self._cardReader.getHistoNameForColumn(self._name))
hNominal = rebin(hNominal, rebinList)
hNominal.SetFillStyle(0)
hNominalFine = f.Get(self._cardReader.getHistoNameForColumn(self._name)+"_fineBinning")
hNominalFine = rebin(hNominalFine, rebinList)
hNominalHisto = histograms.Histo(hNominal, self._name, drawStyle="HIST")
# Determine label
mySignalLabels = ["HH","HW","Hp"]
mySignalStatus = False
for l in mySignalLabels:
if l in self._name:
mySignalStatus = True
myMCLabels = ["HH","HW","Hp","MC"]
myMCStatus = False
for mclab in myMCLabels:
if mclab in self._name:
myMCStatus = True
histograms.cmsTextMode = histograms.CMSMode.PRELIMINARY
if myMCStatus:
histograms.cmsTextMode = histograms.CMSMode.SIMULATION_PRELIMINARY
x = 0.6
size = 20
myRatioContainer = RatioPlotContainer(self._label)
for uncName in self._uncertaintyShapes:
myShortName = uncName
print "... uncertainty:",myShortName
myLongName = self._cardReader.getHistoNameForNuisance(self._name, uncName)
hup = f.Get("%sUp"%(myLongName))
hup = rebin(hup, rebinList)
hup.SetFillStyle(0)
up = dataset.RootHistoWithUncertainties(hup)
upFine = f.Get("%sUp_fineBinning"%(myLongName))
upFine = rebin(upFine, rebinList)
nom = dataset.RootHistoWithUncertainties(hNominal.Clone())
nom.makeFlowBinsVisible()
hdown = f.Get("%sDown"%(myLongName))
hdown = rebin(hdown, rebinList)
hdown.SetFillStyle(0)
down = dataset.RootHistoWithUncertainties(hdown)
downFine = f.Get("%sDown_fineBinning"%(myLongName))
downFine = rebin(downFine, rebinList)
up.getRootHisto().SetLineColor(ROOT.kRed)
nom.getRootHisto().SetLineColor(ROOT.kBlack)
down.getRootHisto().SetLineColor(ROOT.kBlue)
upHisto = histograms.Histo(up, "Up %.1f"%_integral(up.getRootHisto()), drawStyle="HIST", legendStyle="l")
downHisto = histograms.Histo(down, "Down %.1f"%_integral(down.getRootHisto()), drawStyle="HIST", legendStyle="l")
nomHisto = histograms.Histo(nom, "Nominal %.1f"%_integral(nom.getRootHisto()), drawStyle="HIST", legendStyle="l")
# Add fit uncert. as stat uncert.
for i in range(0,9):
tailFitUp = f.Get("%s_TailFit_par%dUp"%(myLongName,i))
tailFitDown = f.Get("%s_TailFit_par%dDown"%(myLongName,i))
if tailFitUp != None and tailFitDown != None:
nom.addShapeUncertaintyFromVariation("%s_TailFit_par%d"%(self._name,i),tailFitUp,tailFitDown)
tailFitUp.Delete()
tailFitDown.Delete()
tailfitNames = filter(lambda n: "_TailFit_" in n, nom.getShapeUncertaintyNames())
nom.setShapeUncertaintiesAsStatistical(tailfitNames)
# Do plot
plot = plots.ComparisonManyPlot(nomHisto, [upHisto, downHisto])
plot.setLuminosity(luminosity)
plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineWidth(3))
myPlotName = "%s/shapeSyst_%s_syst%s" % (_dirname, self._label, uncName.replace("Up",""))
myParams = {}
myParams["ylabel"] = "Events"
myParams["log"] = False
#myParams["opts2"] = {"ymin": 0.0, "ymax":2.0}
myParams["opts2"] = {"ymin": 0.7, "ymax":1.3}
myParams["opts"] = {"ymin": 0.0}
myParams["ratio"] = True
myParams["ratioType"] = "errorScale"
myParams["ratioYlabel"] = "Var./Nom."
myParams["addLuminosityText"] = True
myParams["customizeBeforeDraw"] = customizeMarker
#myParams["ratioErrorOptions"] = {"numeratorStatSyst": False}
myParams["addMCUncertainty"] = True
plots.drawPlot(plot, myPlotName, **myParams)
myRatioContainer.addRatioPlot(plot, myShortName)
# Analyse up and down variation
if mySignalStatus and upFine != None and downFine != None:
a = abs(upFine.Integral()/hNominalFine.Integral() - 1.0)
b = abs(1.0 - downFine.Integral()/hNominalFine.Integral())
r = a
if b > a:
r = b
if uncName in signalTable.keys():
if r < signalTable[uncName]["min"]:
signalTable[uncName]["min"] = r
if r > signalTable[uncName]["max"]:
signalTable[uncName]["max"] = r
else:
signalTable[uncName] = {}
signalTable[uncName]["min"] = r
signalTable[uncName]["max"] = r
# Create plots with only the ratio plot
if opts.individual:
myRatioContainer.drawIndividually()
else:
myRatioContainer.drawAllInOne(myAllShapeNuisances, luminosity)
def main():
if len(sys.argv) < 2 and not os.path.isdir(sys.argv[1]):
usage()
multicrabdir = sys.argv[1]
signalAnalysisDir = ""
QCDAnalysisDir = os.path.join(multicrabdir,"pseudoMulticrab_QCDMeasurement")
candDirs = execute("ls %s"%multicrabdir)
for d in candDirs:
if "SignalAnalysis_" in d:
signalAnalysisDir = os.path.join(multicrabdir,d)
print "Using",signalAnalysisDir
print " ",QCDAnalysisDir
style = tdrstyle.TDRStyle()
ROOT.gROOT.SetBatch(True)
datasets = dataset.getDatasetsFromMulticrabDirs([signalAnalysisDir], analysisName=analysis)
datasetsQCD = dataset.getDatasetsFromMulticrabDirs([QCDAnalysisDir], analysisName="signalAnalysis")
datasets.extend(datasetsQCD)
plots.mergeRenameReorderForDataMC(datasets)
luminosity = datasets.getDataset("Data").getLuminosity()
# Semi-ugly hack for approval homework, remember improve for the next round
totalErrorFile = ROOT.TFile("outputLight.root")
#totalErrorFile = ROOT.TFile("outputHeavy.root")
## Remove superfluous shape variation uncertainties
totalErrorHistoUp = totalErrorFile.Get("total_background")
rebin = [0,20,40,60,80,100,120,140,160,180,200,220,240,260,280,300,320,340,360,380,400,420,440,460,480,500,600,700,800]
import array
#totalErrorHistoUp.Sumw2()
totalErrorHistoUp = totalErrorHistoUp.Rebin(len(rebin)-1,"foo",array.array("d",rebin))
totalErrorHistoDown = totalErrorHistoUp.Clone("foo2")
for i in xrange(1, totalErrorHistoUp.GetNbinsX()+1):
#print i, totalErrorHistoUp.GetBinContent(i), totalErrorHistoUp.GetBinError(i)
totalErrorHistoUp.SetBinContent(i, totalErrorHistoUp.GetBinError(i))
totalErrorHistoDown.SetBinContent(i, -totalErrorHistoDown.GetBinError(i))
#print i, totalErrorHistoUp.GetBinContent(i), totalErrorHistoDown.GetBinContent(i)
# semi-ugly hack continues below in QCD
myStackList = []
h_data = datasets.getDataset("Data").getDatasetRootHisto("ForDataDrivenCtrlPlots/shapeTransverseMass_POSTFIT").getHistogram()
myRHWU = dataset.RootHistoWithUncertainties(h_data)
myRHWU.makeFlowBinsVisible()
myHisto = histograms.Histo(myRHWU, "Data")
myHisto.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, myHisto)
h_FakeTau = datasets.getDataset("QCDMeasurementMT").getDatasetRootHisto("ForDataDrivenCtrlPlots/shapeTransverseMass_POSTFIT").getHistogram()
myRHWU = dataset.RootHistoWithUncertainties(h_FakeTau)
# semi-ugly hack continues
htemp=myRHWU.getRootHisto()
myRHWU.addAbsoluteShapeUncertainty("toterr", totalErrorHistoUp, totalErrorHistoDown)
#myRHWU.addShapeUncertaintyFromVariation("toterr", totalErrorHistoUp, totalErrorHistoDown)
# semi-ugly hack ends
myRHWU.makeFlowBinsVisible()
myHisto = histograms.Histo(myRHWU, "QCDdata")
myHisto.setIsDataMC(isData=False, isMC=True)
myStackList.insert(1, myHisto)
expectedList = []
expectedList.append("TT")
expectedList.append("WJets")
expectedList.append("SingleTop")
expectedList.append("DYJetsToLL")
expectedList.append("Diboson")
for i in range(0,len(expectedList)):
drh = datasets.getDataset(expectedList[i]).getDatasetRootHisto("ForDataDrivenCtrlPlots/shapeTransverseMass_POSTFIT")
h_bgr = drh.getHistogram()
myRHWU = dataset.RootHistoWithUncertainties(h_bgr)
# myRHWU.addShapeUncertaintyRelative("syst", th1Plus=self._expectedListSystUp[i], th1Minus=self._expectedListSystDown[i])
myRHWU.makeFlowBinsVisible()
myHisto = histograms.Histo(myRHWU, expectedList[i])
myHisto.setIsDataMC(isData=False, isMC=True)
myStackList.append(myHisto)
#myStackList = divideByBinWidth(myStackList)
# no, ugly hack continues here
histograms.uncertaintyMode.set(histograms.Uncertainty.SystOnly)
plots._legendLabels["MCSystError"] = "Bkg. stat.#oplus syst. unc."
plots._legendLabels["BackgroundSystError"] = "Bkg. stat.#oplus syst. unc."
# and stops again here
myStackPlot = plots.DataMCPlot2(myStackList)
myStackPlot.setLuminosity(luminosity)
myStackPlot.setDefaultStyles()
myParams = {}
myParams["ylabel"] = "Events / bin"
myParams["ratioYlabel"] = "Data/Bkg."
myParams["xlabel"] = "m_{T} (GeV)"
myParams["log"] = True
myParams["ratio"] = True
myParams["cmsTextPosition"] = "outframe"
myParams["opts"] = {"ymin": 0.0001, "ymax": 3000.0}
myParams["opts2"] = {"ymin": 0., "ymax":1.99}
myParams["moveLegend"] = {"dx": -0.15, "dy": 0., "dh":0.05} # for data-driven
myParams["ratioMoveLegend"] = {"dx": -0.51, "dy": 0.03}
myParams["stackMCHistograms"] = True
myParams["divideByBinWidth"] = True
myParams["addMCUncertainty"] = True
myParams["ratioType"] = "errorScale"
myParams["ratioCreateLegend"] = True
myParams["ratioMoveLegend"] = dict(dy=-0.45, dh=-0.1, dx=-0.5)
plots.drawPlot(myStackPlot, "TransVerseMassPOSTFIT", **myParams)
def validateRootHistoWithUncertainties(codeValidator):
def createHisto(name, data, dataerr):
h = ROOT.TH1F(name,name,len(data)-2,0,len(data)-2)
for i in range(0,len(data)):
h.SetBinContent(i, data[i])
for i in range(0,len(dataerr)):
h.SetBinError(i, dataerr[i])
return h
def printHistoContents(h):
for i in range(0,h.GetNbinsX()+2):
print i,h.GetBinContent(i),h.GetBinError(i)
def testHistoContents(name,h,data,dataerr):
for i in range(0,len(data)):
codeValidator.test("histoContents(%s) content in bin %d"%(name,i), h.GetBinContent(i), data[i])
for i in range(0,len(dataerr)):
codeValidator.test("histoContents(%s) error in bin %d"%(name,i), h.GetBinError(i), dataerr[i])
def testGraphContents(name,h,data,dataerrup,dataerrdown):
for i in range(0,len(data)):
codeValidator.test("graphContents(%s) content in bin %d"%(name,i), g.GetY()[i], data[i])
for i in range(0,len(dataerrup)):
codeValidator.test("graphContents(%s) error+ in bin %d"%(name,i), h.GetErrorYhigh(i), dataerrup[i])
codeValidator.test("graphContents(%s) error- in bin %d"%(name,i), h.GetErrorYlow(i), dataerrdown[i])
codeValidator.setPackage("tools.dataset.RootHistoWithUncertainties")
# histogram
h = createHisto("h",[3.2,6.4,7.6,10.4,5.3],[1.2,2.1,2.3,1.6,2.0])
rhwu = dataset.RootHistoWithUncertainties(h)
rhwu.makeFlowBinsVisible()
testHistoContents("afterFlowBins", rhwu.getRootHisto(), [0.0,9.6,7.6,15.7,0.0], [0.0,2.4186773245,2.3,2.561249695,0.0])
codeValidator.test("getRate()", rhwu.getRate(), 32.9)
codeValidator.test("getRateStatUncertainty()", rhwu.getRateStatUncertainty(), 4.2071367936)
(systUp,systDown) = rhwu.getRateSystUncertainty()
codeValidator.test("getRateSystUncertainty() up", systUp, 0.0)
codeValidator.test("getRateSystUncertainty() down", systDown, 0.0)
# Add a norm. uncertainty
rhwu.addNormalizationUncertaintyRelative("normA", uncertaintyPlus=0.1, uncertaintyMinus=-0.2)
testHistoContents("after normA", rhwu.getRootHisto(), [0.0,9.6,7.6,15.7,0.0], [0.0,2.4186773245,2.3,2.561249695,0.0])
codeValidator.test("getRate()", rhwu.getRate(), 32.9)
codeValidator.test("getRateStatUncertainty()", rhwu.getRateStatUncertainty(), 4.2071367936)
(systUp,systDown) = rhwu.getRateSystUncertainty() # this tests also Merge
codeValidator.test("getRateSystUncertainty() up", systUp, 3.29)
codeValidator.test("getRateSystUncertainty() down", systDown, 6.58)
g = rhwu.getSystematicUncertaintyGraph(addStatistical=False)
testGraphContents("syst.graph,addStat=False", g, [9.6,7.6,15.7], [0.96,0.76,1.57], [1.92,1.52,3.14])
g = rhwu.getSystematicUncertaintyGraph(addStatistical=True)
testGraphContents("syst.graph,addStat=True", g, [9.6,7.6,15.7], [2.6022298131,2.4223129443,3.0041471335], [3.0881062158,2.7568822971,4.0521105612])
# Add shape uncertainty
htestUp = createHisto("shapeUp", [0,10.5,12.1,13.3,0],[0,0,0,0,0])
htestDown = createHisto("shapeDown", [0,8.8,8.1,15.5,0],[0,0,0,0,0])
rhwu.addShapeUncertaintyFromVariation("shape",htestUp,htestDown)
testHistoContents("after shape", rhwu.getRootHisto(), [0.0,9.6,7.6,15.7,0.0], [0.0,2.4186773245,2.3,2.561249695,0.0])
codeValidator.test("getRate()", rhwu.getRate(), 32.9)
codeValidator.test("getRateStatUncertainty()", rhwu.getRateStatUncertainty(), 4.2071367936)
g = rhwu.getSystematicUncertaintyGraph(addStatistical=False)
testGraphContents("syst.graph,addStat=False", g, [9.6,7.6,15.7], [1.315902732,4.5637265475,1.57], [2.08,1.52,3.9521639642])
(systUp,systDown) = rhwu.getRateSystUncertainty() # this tests also Merge
codeValidator.test("getRateSystUncertainty() up", systUp, 4.4524263048)
codeValidator.test("getRateSystUncertainty() down", systDown, 6.5989696165)
# Clone
rhwuCloned = rhwu.Clone()
testHistoContents("after Clone", rhwuCloned.getRootHisto(), [0.0,9.6,7.6,15.7,0.0], [0.0,2.4186773245,2.3,2.561249695,0.0])
g = rhwuCloned.getSystematicUncertaintyGraph(addStatistical=False)
testGraphContents("syst.graph,addStat=False", g, [9.6,7.6,15.7], [1.315902732,4.5637265475,1.57], [2.08,1.52,3.9521639642])
# Scale
rhwuCloned.Scale(2.0)
testHistoContents("after Scale", rhwuCloned.getRootHisto(), [0.0,9.6*2,7.6*2,15.7*2,0.0], [0.0,2.4186773245*2,2.3*2,2.561249695*2,0.0])
g = rhwuCloned.getSystematicUncertaintyGraph(addStatistical=False)
testGraphContents("syst.graph,addStat=False", g, [9.6*2,7.6*2,15.7*2], [1.315902732*2,4.5637265475*2,1.57*2], [2.08*2,1.52*2,3.9521639642*2])
# ScaleVariationUncertainty
rhwuCloned.ScaleVariationUncertainty("shape",2.0)
testHistoContents("after ScaleVariationUncertainty", rhwuCloned.getRootHisto(), [0.0,9.6*2,7.6*2,15.7*2,0.0], [0.0,2.4186773245*2,2.3*2,2.561249695*2,0.0])
g = rhwuCloned.getSystematicUncertaintyGraph(addStatistical=False)
testGraphContents("syst.graph,addStat=False", g, [9.6*2,7.6*2,15.7*2], [4.08,18.064063773,1.57*2], [4.9985597926,1.52*2,11.4716345827])
# Add second shape uncertainty
htest2Up = createHisto("shape2Up", [0,11.2,13.5,16.3,0],[0,0,0,0,0])
htest2Down = createHisto("shape2Down", [0,8.2,6.1,13.5,0],[0,0,0,0,0])
rhwu.addShapeUncertaintyFromVariation("shape2",htest2Up,htest2Down)
testHistoContents("after shape2", rhwu.getRootHisto(), [0.0,9.6,7.6,15.7,0.0], [0.0,2.4186773245,2.3,2.561249695,0.0])
g = rhwu.getSystematicUncertaintyGraph(addStatistical=False)
testGraphContents("syst.graph,addStat=False", g, [9.6,7.6,15.7], [2.0716177254,7.4590616032,1.6807438829], [2.5072694311,2.1355093069,4.5232289352])
# Add the two rhwu's
rhwuSum = rhwu.Clone()
rhwuSum.Add(rhwuCloned)
testHistoContents("rate after Add", rhwuSum.getRootHisto(), [0,28.8,22.8,47.1,0], [0,5.4083269132,5.1429563482,5.7271284253,0])
#testHistoContents("shape+ after Add", rhwuSum._shapeUncertaintyAbsolutePlus, [0,2.88,2.28,4.71,0], [])
#testHistoContents("shape- after Add", rhwuSum._shapeUncertaintyAbsoluteMinus, [0,5.76,4.56,9.42,0], [])
g = rhwuSum.getSystematicUncertaintyGraph(addStatistical=False)
testGraphContents("syst.graph,addStat=False", g, [28.8,22.8,47.1], [5.5771318794,23.3721714866,4.7480627629], [7.1510558661,4.8003749854,15.4135135514])
(systUp,systDown) = rhwuSum.getRateSystUncertainty() # this tests also Merge
codeValidator.test("getRateSystUncertainty() up", systUp, 19.6983984121)
codeValidator.test("getRateSystUncertainty() down", systDown, 20.5408763202)
# Test add in the other direction
rhwuSum2 = rhwuCloned.Clone()
rhwuSum2.Add(rhwu)
testHistoContents("rate after Add2", rhwuSum2.getRootHisto(), [0,28.8,22.8,47.1,0], [0,5.4083269132,5.1429563482,5.7271284253,0])
#testHistoContents("shape+ after Add2", rhwuSum2._shapeUncertaintyAbsolutePlus, [0,2.88,2.28,4.71,0], [])
#testHistoContents("shape- after Add2", rhwuSum2._shapeUncertaintyAbsoluteMinus, [0,5.76,4.56,9.42,0], [])
g = rhwuSum2.getSystematicUncertaintyGraph(addStatistical=False)
testGraphContents("syst.graph,addStat=False", g, [28.8,22.8,47.1], [5.5771318794,23.3721714866,4.7480627629], [7.1510558661,4.8003749854,15.4135135514])
(systUp,systDown) = rhwuSum2.getRateSystUncertainty() # this tests also Merge
codeValidator.test("getRateSystUncertainty() up", systUp, 19.6983984121)
codeValidator.test("getRateSystUncertainty() down", systDown, 20.5408763202)
#rhwuSum.Debug()
# Bugs found
# rate stat uncert.: last bin was ignored
# rate syst uncert.: normalization uncertainties in bin content, i.e. linear sum used when combining bins (was squared sum)
# syst. uncert: combining of norm