def zGammaCut(plot="photons[0].pt", merge=False):
cut = "[email protected]() && [email protected]()"
nunuTree = readTree("slimZGammaNuNu_V03.19b_tree.root", "photonTree")
llTree = readTree("slimZGammaLL_V02.19b_tree.root", "photonTree")
zIncTree = readTree("slimZGamma_V03.18_tree.root", "photonTree")
nBins = range(60, 500, 10) if plot == "photons[0].pt" or plot == "genPhotons[0].pt" else readAxisConf(plot)[2]
h_nunu = getHisto(nunuTree, plot, cut + "&&genPhotons[0].pt>130", nBins=nBins, color=2)
h_ll = getHisto(llTree, plot, cut, nBins=nBins, color=5)
h_zInc = getHisto(zIncTree, plot, cut, nBins=nBins)
llTree.AddFriend("photonTreeAddVariables", llTree.GetFile().GetName())
llPlot = "metLL" if plot == "met" else plot
zMod = getHisto(llTree, llPlot, "genPhotons[0].pt<130", nBins=nBins, color=4)
zMod.Scale(20.0 / (2.0 * 3.363))
mh = Multihisto()
mh.orderByIntegral = False
mh.setMinimum(0.01)
mh.addHisto(h_nunu, "#gammaZ#rightarrow#gamma#nu#nu(p_{T#gamma}^{gen}#geq130GeV)", draw="e", toStack=True)
mh.addHisto(zMod, "ll to #slash{E}_{T}", toStack=True)
mh.addHisto(h_ll, "#gammaZ#rightarrow#gammall", draw="e", toStack=True)
mh.addHisto(h_zInc, "#gammaZ", draw="e")
if merge:
h_nunuPart = getHisto(nunuTree, plot, cut + "&&genPhotons[0].pt>=130", nBins=nBins, color=6)
h_zIncPart = getHisto(zIncTree, plot, cut + "&&genPhotons[0].pt<130", nBins=nBins, color=6)
merged = addHistos([h_nunuPart, h_zIncPart])
mh.addHisto(merged, "merged", draw="e")
mh.Draw()
total = mh.stack.GetStack().Last().Clone(randomName())
total.SetFillStyle(3002)
total.SetFillColor(1)
total.Draw("same e2")
if plot == "photons[0].pt":
l = ROOT.TLine(130, 0, 130, 1)
l.Draw()
if merge:
plot += "_merged"
SavePad("zGammaCut_%s" % plot)
def drawMCStack( treeName="photonTree" ): mh = Multihisto() mh.orderByIntegral = False datasetsToStack = [gz,tt,wjets,qcd,gjets] mh = fillMh( mh, datasetsToStack, treeName ) can = ROOT.TCanvas() can.cd() mh.Draw() info = PlotCaption(treeName=treeName) info.Draw() allDatasetAbbr = ''.join([x.datasetAbbr for x in datasetsToStack ]) plot = "met" SaveAs( can, "stackedHisto_%s_%s_%s"%(treeName, plot,allDatasetAbbr) )
def drawMCStack(treeName="photonTree"):
mh = Multihisto()
mh.orderByIntegral = False
datasetsToStack = [gz, tt, wjets, qcd, gjets]
mh = fillMh(mh, datasetsToStack, treeName)
can = ROOT.TCanvas()
can.cd()
mh.Draw()
info = PlotCaption(treeName=treeName)
info.Draw()
allDatasetAbbr = ''.join([x.datasetAbbr for x in datasetsToStack])
plot = "met"
SaveAs(can, "stackedHisto_%s_%s_%s" % (treeName, plot, allDatasetAbbr))
def finalDistributionMC( plot ): # Sample names treeVersion = 12 g1 = "slimGJets_200_400_V03.%s_tree.root"%treeVersion g2 = "slimGJets_400_inf_V03.%s_tree.root"%treeVersion q1 = "slimQCD_250_500_V03.%s_tree.root"%treeVersion q2 = "slimQCD_500_1000_V03.%s_tree.root"%treeVersion q3 = "slimQCD_1000_inf_V03.%s_tree.root"%treeVersion tt = "slimTTJets_V03.%s_tree.root"%treeVersion w1 = "slimWJets_250_300_V03.%s_tree.root"%treeVersion w2 = "slimWJets_300_400_V03.%s_tree.root"%treeVersion w3 = "slimWJets_400_inf_V03.%s_tree.root"%treeVersion wg1 = "slimWGamma_50_130_V03.%s_tree.root"%treeVersion wg2 = "slimWGamma_130_inf_V03.%s_tree.root"%treeVersion tg = "slimTTGamma_V03.%s_tree.root"%treeVersion zgn = "slimZGammaNuNu_V03.%s_tree.root"%treeVersion data = [ g1, g2, q1, q2, q3, tt, w1, w2, w3, zgn, wg1,wg2,tg ] # additional ISR uncertainty ewkUncertainty = 0.11 isrUncertaintyZ = 0.5 isrUncertaintyW = 0.5 isrUncertaintyT = 0.5 isrUncertainty = 0.5 leptonPtCut = 25 # only larger than 15 make sense here, since this is the reprocessing cut commonCut = "([email protected]() || Max$(electrons.pt)<{0}) && ([email protected]() || Max$(muons.pt)<{0})".format(leptonPtCut) #commonCut = "[email protected]() && [email protected]()" # Compute the weights: #weight2D = getMixedWeigthHisto( data, data, commonCut ) #attachWeightsToFiles( data, weight2D, "foWeights" ) #from qcdClosure import drawWeightHisto #drawWeightHisto( weight2D, "MC" ) # Get Histograms dataHist = getHists( data, plot, commonCut ) fgammaHist, fgammaWeightError = predictionHistos( data, plot, commonCut, modifyEmptyBins=False ) egammaHist = multiDimFakeRate( data, plot, commonCut, isData=False ) egammaHistsys = setRelativeUncertainty( egammaHist.Clone(randomName()), ewkUncertainty ) fsrZ = getHists( [zgn], plot, commonCut ) fsrZsys = setRelativeUncertainty( fsrZ, isrUncertaintyZ ) fsrW = getHists( [wg1,wg2], plot, commonCut ) fsrWsys = setRelativeUncertainty( fsrW, isrUncertaintyW ) fsrT = getHists( [tg], plot, commonCut ) fsrTsys = setRelativeUncertainty( fsrT, isrUncertaintyT ) fsr = addHistos( [fsrT, fsrW, fsrZ ] ) fsrSys = setRelativeUncertainty( fsr, isrUncertainty ) #signal1 = getMetHisto( "W", 900, 1720 ) #signal2 = getMetHisto( "B", 1700, 1120 ) signal1 = getHists( ["slimW_1700_720_375_V03.06_tree.root"], plot, commonCut ) signal2 = getHists( ["slimW_900_1720_375_V03.06_tree.root"], plot, commonCut ) signal3 = getHists( ["slimB_1300_1720_375_V03.06_tree.root"], plot, commonCut ) signal4 = getHists( ["slimB_1700_1120_375_V03.06_tree.root"], plot, commonCut ) for i, signal in enumerate([signal1, signal2, signal3, signal4]): signal.SetLineColor( ROOT.kGreen + i ) signal.SetLineColor( ROOT.kBlue + i ) #signal.SetLineStyle(5+i) # prettify histograms fgammaHist.SetLineColor(7) egammaHist.SetLineColor( 3 ) fsrZ.SetLineColor( ROOT.kRed-7 ) fsrW.SetLineColor( ROOT.kRed-9 ) fsrT.SetLineColor( ROOT.kRed ) mh = Multihisto() mh.orderByIntegral = False mh.addHisto( fsrT, "#gamma t#bar{t}", True ) mh.addHisto( fsrW, "#gamma W", True ) mh.addHisto( fsrZ, "#gamma Z", True ) mh.addHisto( egammaHist, "e#rightarrow#gamma", True ) mh.addHisto( fgammaHist, "Multijet", True ) dataLegName = "Data" mh.addHisto( dataHist, dataLegName, draw="pe" ) mh.addHisto( signal2, "Bino-like #chi_{1}^{0}", draw="hist" ) mh.addHisto( signal1, "Wino-like #chi_{1}^{0}", draw="hist" ) # get all SYSTEMATICAL uncertainties: systematicUncertHistStack = ROOT.THStack() systematicUncertHistStack.Add( fgammaWeightError ) systematicUncertHistStack.Add( egammaHistsys ) #systematicUncertHistStack.Add( fsrTsys ) #systematicUncertHistStack.Add( fsrWsys ) #systematicUncertHistStack.Add( fsrZsys ) systematicUncertHistStack.Add( fsrSys ) #if plot == "met": # writeDataCard( treeVersion, dataHist, fgammaHist, fgammaWeightError, # egammaHist, egammaHistsys, # fsr, fsrSys ) # draw stuff luminosity = 19.7 infoText = ROOT.TLatex(0,.96, "CMS Private Work - %sfb^{-1} #sqrt{s}=8TeV #geq1#gamma_{tight},#geq2jets"%luminosity ) infoText.SetNDC() infoText.SetTextSize(.04) can = ROOT.TCanvas() mh.Draw() errorBand = mh.stack.GetStack().Last().Clone( randomName() ) sysUncert = systematicUncertHistStack.GetStack().Last() for bin in range(errorBand.GetNbinsX()+2): errorBand.SetBinError( bin, errorBand.GetBinError(bin) |qPlus| sysUncert.GetBinError(bin) ) errorBand.SetFillStyle(3002) errorBand.SetMarkerSize(0) errorBand.SetFillColor(1) errorBand.Draw("e2 same") from myRatio import Ratio r = Ratio( "Data / Bkg", dataHist, mh.stack.GetStack().Last(), systematicUncertHistStack.GetStack().Last() ) r.draw(0.5,1.5) infoText.Draw() SaveAs( can, "finalDistributionData_%s"%plot )
def drawStackedBackground( plot, treeName, listOfFiles, sumBinned, order=False ): cut = "[email protected]() && [email protected]()" # if a data histogram is present, the mc integral will scaled to data mcHists = [] dataHists = [] for fileName in listOfFiles: datasetAbbr = getDatasetAbbr( fileName ) try: color = colors[datasetAbbr] except: color = 1 tree = readTree( fileName, treeName ) histo = getHisto( tree, plot, color=color, cut=cut ) if "PhotonHad" in fileName: dataHists.append( (datasetAbbr, histo ) ) else: mcHists.append( ( datasetAbbr, histo ) ) # merge datahists if there dataHist = addHistos( [histo for datasetAbbr, histo in dataHists] ) if dataHists else None scale = 1.*dataHist.Integral()/addHistos( [histo for datasetAbbr, histo in mcHists] ).Integral() if dataHists else 1 scale = 1. # combine MC datasets if sumBinned: for combiAbbr, abbrList in combinedDatasets.iteritems(): if set(abbrList).issubset( set( [ a for a, h in mcHists ] ) ): #print "a valid combination was found" histosToAdd = [ h for a,h in mcHists if a in abbrList ] thisSum = addHistos( histosToAdd ) mcHists = [ (a,h) for a,h in mcHists if a not in abbrList]+[(combiAbbr,thisSum)] mh = Multihisto() mh.setMinimum(0.01) mh.leg.SetX1NDC(0.5) mh.leg.SetY1NDC(0.5) mh.orderByIntegral = order if dataHist: mh.addHisto( dataHist, "Data", draw="e0" ) for abbr, hist in mcHists: hist.Scale( scale ) mh.addHisto( hist, datasetToLatex(abbr), toStack=True, draw="hist" ) # add signal histos #signalFiles = ["slimW_1000_1020_375_V02.44_tree.root", "slimW_1200_1120_375_V02.44_tree.root"] signalFiles = ["slimW_1200_1120_375_V02.44_tree.root"] signalFiles = [] for iColor, sf in enumerate(signalFiles): tree = readTree( sf, treeName ) histo = getHisto( tree, plot, color=ROOT.kMagenta+iColor, cut=cut ) mh.addHisto( histo, datasetToLatex(getDatasetAbbr(sf)), draw="hist" ) can = ROOT.TCanvas() can.cd() mh.Draw() info = PlotCaption(treeName=treeName) info.Draw() if dataHist: from myRatio import Ratio den = mh.stack.GetStack().Last().Clone( randomName() ) den.SetLineColor(2) r = Ratio( "Data/Sim", dataHist, den ) r.draw(0,2) allDatasetAbbr = getSaveNameFromDatasets( listOfFiles ) SaveAs( can, "stackedHisto_%s_%s_%s"%(treeName, plot,allDatasetAbbr) )
def drawStackedBackground(plot, treeName, listOfFiles, sumBinned, order=False):
tightCut = " photons[0].sigmaIetaIeta<0.011 && photons[0].chargedIso<0.7 && photons[0].neutralIso < 0.4+0.04*photons[0].pt && photons[0].photonIso < 0.5+0.005*photons[0].pt && photons[0].r9<0.9"
cut = "[email protected]() && [email protected]()"
#cut = cut +"&&" + tightCut
cut = "std::abs(photons[0].eta)>1.4442"
# if a data histogram is present, the mc integral will scaled to data
mcHists = []
dataHists = []
for fileName in listOfFiles:
datasetAbbr = getDatasetAbbr(fileName)
try:
color = colors[datasetAbbr]
except:
color = 1
tree = readTree(fileName, treeName)
histo = getHisto(tree, plot, color=color, cut=cut)
if "PhotonHad" in fileName:
dataHists.append((datasetAbbr, histo))
else:
mcHists.append((datasetAbbr, histo))
# merge datahists if there
dataHist = addHistos([histo for datasetAbbr, histo in dataHists
]) if dataHists else None
scale = 1. * dataHist.Integral() / addHistos(
[histo
for datasetAbbr, histo in mcHists]).Integral() if dataHists else 1
scale = 1.
# combine MC datasets
if sumBinned:
for combiAbbr, abbrList in combinedDatasets.iteritems():
if set(abbrList).issubset(set([a for a, h in mcHists])):
indices = []
for a, b in mcHists:
if a in abbrList:
indices.append(mcHists.index((a, b)))
#print "a valid combination was found"
histosToAdd = [h for a, h in mcHists if a in abbrList]
thisSum = addHistos(histosToAdd)
mcHists = [(a, h) for a, h in mcHists if a not in abbrList]
mcHists.insert(min(indices), (combiAbbr, thisSum))
mh = Multihisto()
mh.setMinimum(0.01)
mh.leg.SetFillStyle(0)
mh.leg.SetNColumns(2)
mh.orderByIntegral = order
if dataHist:
mh.addHisto(dataHist, "Data", draw="ep")
for abbr, hist in mcHists:
hist.Scale(scale)
mh.addHisto(hist, datasetToLatex(abbr), toStack=True, draw="hist")
egammaHist = True
if egammaHist:
egammaHist = multiDimFakeRate(
[filename for filename in listOfFiles if "PhotonHad" in filename],
plot, cut)
egammaHist.SetLineColor(ROOT.kGreen)
mh.addHisto(egammaHist, "e#rightarrow#gamma", toStack=True)
#totalBG = addHistos( [ h for abbr, h in mcHists ] )
#totalBG.SetLineColor(2)
#mh.addHisto( totalBG, "SM Simulation", toStack=False, draw="hist" )
# add signal histos
#signalFiles = ["slimW_1000_1020_375_V02.44_tree.root", "slimW_1200_1120_375_V02.44_tree.root"]
signalFiles = ["slimW_1200_1120_375_V02.44_tree.root"]
signalFiles = ["slimW_1700_720_375_V03.22_tree.root"]
for iColor, sf in enumerate(signalFiles):
tree = readTree(sf, treeName)
histo = getHisto(tree, plot, color=ROOT.kBlue + iColor, cut=cut)
#mh.addHisto( histo, datasetToLatex(getDatasetAbbr(sf)), draw="hist" )
mh.addHisto(histo, "Signal", draw="hist")
infoText = ROOT.TLatex(
0, .97,
"#text{CMS Private Work }#geq1#gamma_{#text{pixel}},#geq2#text{jets}")
infoText.SetNDC()
infoText.SetTextSize(0.05)
can = ROOT.TCanvas()
can.cd()
mh.Draw()
infoText.Draw()
#if dataHist:
# from myRatio import Ratio
# den = totalBG
#den = mh.stack.GetStack().Last().Clone( randomName() )
# den.SetLineColor(2)
# r = Ratio( "Data/Sim.", dataHist, den )
# r.draw(0,2)
allDatasetAbbr = getSaveNameFromDatasets(listOfFiles)
SaveAs(can, "stackedHisto_%s_%s_%s" % (treeName, plot, allDatasetAbbr))
def finalDistributionData(plot):
# Sample names
treeVersion = "31"
wg = [ "slimWGamma_50_130_V03.%s_tree.root"%treeVersion, \
"slimWGamma_130_inf_V03.%s_tree.root"%treeVersion ]
tg = ["slimTTGamma_V03.%s_tree.root" % treeVersion]
zgn = ["slimZGammaNuNu_V03.%s_tree.root" % treeVersion]
zgll = ["slimZGammaLL_V02.19b_tree.root"]
data = [
"PhotonHad%s_V03.%s_tree.root" % (x, treeVersion)
for x in ["A", "B", "C", "D"]
]
# additional ISR uncertainty
ewkUncertainty = 0.11
isrUncertaintyZ = 0.5
isrUncertaintyW = 0.5
isrUncertaintyT = 0.5
isrUncertainty = 0.5
leptonPtCut = 25 # only larger than 15 make sense here, since this is the reprocessing cut
#commonCut = "([email protected]() || Max$(electrons.pt)<{0}) && ([email protected]() || Max$(muons.pt)<{0})".format(leptonPtCut)
commonCut = "[email protected]() && [email protected]() && thisPt>0 && recoilChr > 0"
# Compute the weights:
weight2D = getMixedWeigthHisto(data, data, commonCut)
attachWeightsToFiles(data, weight2D, "foWeights")
drawWeightHisto(weight2D, "Data", writeWeightFile=True)
# Get Histograms
dataHist = getHists(data, plot, commonCut)
fgammaHist, fgammaWeightError = predictionHistos(data,
plot,
commonCut,
modifyEmptyBins=False)
egammaHist = multiDimFakeRate(data, plot, commonCut)
egammaHistsys = setRelativeUncertainty(egammaHist.Clone(randomName()),
ewkUncertainty)
fsrZ = getHists(zgn, plot, commonCut + "&&genPhotons[0].pt>130")
fsrZll = getHists(zgll, plot, commonCut)
if plot == "met":
fsrZ2 = getHists(zgll, plot + "LL",
commonCut + "&&genPhotons[0].pt<130")
else:
fsrZ2 = getHists(zgll, plot, commonCut + "&&0")
fsrZ2.Scale(20. / (2. * 3.363))
fsrW = getHists(wg, plot, commonCut)
fsrT = getHists(tg, plot, commonCut)
# apply common scale factor
for h in fsrW, fsrT, fsrZ, fsrZ2, fsrZll:
h.Scale(1.5)
fsr = addHistos([fsrT, fsrW, fsrZ, fsrZ2, fsrZll])
fsr.SetLineColor(ROOT.kRed)
fsrSys = setRelativeUncertainty(fsr, isrUncertainty)
#signal1 = getMetHisto( "W", 900, 1720 )
#signal2 = getMetHisto( "B", 1700, 1120 )
signal1 = getHists(["slimW_1700_720_375_V03.06_tree.root"], plot,
commonCut)
signal2 = getHists(["slimW_900_1720_375_V03.06_tree.root"], plot,
commonCut)
signal3 = getHists(["slimB_1300_1720_375_V03.06_tree.root"], plot,
commonCut)
signal4 = getHists(["slimB_1700_1120_375_V03.06_tree.root"], plot,
commonCut)
for i, signal in enumerate([signal1, signal2, signal3, signal4]):
signal.SetLineColor(ROOT.kGreen + i)
signal.SetLineColor(ROOT.kBlue + i)
#signal.SetLineStyle(5+i)
# prettify histograms
fgammaHist.SetLineColor(7)
egammaHist.SetLineColor(3)
fsrZ.SetLineColor(ROOT.kRed - 7)
fsrW.SetLineColor(ROOT.kRed - 9)
fsrT.SetLineColor(ROOT.kRed)
mh = Multihisto()
mh.setMinimum(0.2)
mh.orderByIntegral = False
mh.addHisto(fsr, "ISR", True)
mh.addHisto(egammaHist, "e#rightarrow#gamma", True)
mh.addHisto(fgammaHist, "Multijet", True)
dataLegName = "Data"
mh.addHisto(dataHist, dataLegName, draw="pe x0")
mh.addHisto(signal2, "Bino-like #chi_{1}^{0}", draw="hist")
mh.addHisto(signal1, "Wino-like #chi_{1}^{0}", draw="hist")
# get all SYSTEMATICAL uncertainties:
systematicUncertHistStack = ROOT.THStack()
systematicUncertHistStack.Add(fgammaWeightError)
systematicUncertHistStack.Add(egammaHistsys)
systematicUncertHistStack.Add(fsrSys)
if plot == "met":
writeDataCard(treeVersion, dataHist, fgammaHist, fgammaWeightError,
egammaHist, egammaHistsys, fsr, fsrSys)
# draw stuff
luminosity = 19.7
infoText = ROOT.TLatex(
0, .96,
"CMS Private Work - %sfb^{-1} #sqrt{s}=8TeV #geq1#gamma_{tight},#geq2jets"
% luminosity)
infoText.SetNDC()
infoText.SetTextSize(.04)
can = ROOT.TCanvas()
mh.Draw()
statUncert = mh.stack.GetStack().Last().Clone(randomName())
systUncert = systematicUncertHistStack.GetStack().Last().Clone(
randomName())
totalUncert = statUncert.Clone(randomName())
for bin in range(totalUncert.GetNbinsX() + 2):
totalUncert.SetBinError(
bin,
statUncert.GetBinError(bin) | qPlus | systUncert.GetBinError(bin))
for h in statUncert, systUncert, totalUncert:
h.SetMarkerSize(0)
totalUncert.SetFillStyle(3002)
totalUncert.SetFillColor(1)
totalUncert.Draw("same e2")
systUncert.SetFillStyle(3254)
systUncert.SetFillColor(2)
systUncert.Draw("same e2")
statUncert.SetLineWidth(3)
statUncert.SetLineColor(2)
statUncert.Draw("same e x0")
dataHist.Draw("same pe x0")
from myRatio import Ratio
r = Ratio("Data / Bkg", dataHist,
mh.stack.GetStack().Last(),
systematicUncertHistStack.GetStack().Last())
r.draw(.5, 1.5)
infoText.Draw()
SaveAs(can, "finalDistributionData_%s" % plot)
def zGammaCut(plot="photons[0].pt", merge=False):
cut = "[email protected]() && [email protected]()"
nunuTree = readTree("slimZGammaNuNu_V03.19b_tree.root", "photonTree")
llTree = readTree("slimZGammaLL_V02.19b_tree.root", "photonTree")
zIncTree = readTree("slimZGamma_V03.18_tree.root", "photonTree")
nBins = range(
60, 500, 10
) if plot == "photons[0].pt" or plot == "genPhotons[0].pt" else readAxisConf(
plot)[2]
h_nunu = getHisto(nunuTree,
plot,
cut + "&&genPhotons[0].pt>130",
nBins=nBins,
color=2)
h_ll = getHisto(llTree, plot, cut, nBins=nBins, color=5)
h_zInc = getHisto(zIncTree, plot, cut, nBins=nBins)
llTree.AddFriend("photonTreeAddVariables", llTree.GetFile().GetName())
llPlot = "metLL" if plot == "met" else plot
zMod = getHisto(llTree,
llPlot,
"genPhotons[0].pt<130",
nBins=nBins,
color=4)
zMod.Scale(20. / (2. * 3.363))
mh = Multihisto()
mh.orderByIntegral = False
mh.setMinimum(0.01)
mh.addHisto(h_nunu,
"#gammaZ#rightarrow#gamma#nu#nu(p_{T#gamma}^{gen}#geq130GeV)",
draw="e",
toStack=True)
mh.addHisto(zMod, "ll to #slash{E}_{T}", toStack=True)
mh.addHisto(h_ll, "#gammaZ#rightarrow#gammall", draw="e", toStack=True)
mh.addHisto(h_zInc, "#gammaZ", draw="e")
if merge:
h_nunuPart = getHisto(nunuTree,
plot,
cut + "&&genPhotons[0].pt>=130",
nBins=nBins,
color=6)
h_zIncPart = getHisto(zIncTree,
plot,
cut + "&&genPhotons[0].pt<130",
nBins=nBins,
color=6)
merged = addHistos([h_nunuPart, h_zIncPart])
mh.addHisto(merged, "merged", draw="e")
mh.Draw()
total = mh.stack.GetStack().Last().Clone(randomName())
total.SetFillStyle(3002)
total.SetFillColor(1)
total.Draw("same e2")
if plot == "photons[0].pt":
l = ROOT.TLine(130, 0, 130, 1)
l.Draw()
if merge:
plot += "_merged"
SavePad("zGammaCut_%s" % plot)
def finalDistributionMC(plot):
# Sample names
treeVersion = 12
g1 = "slimGJets_200_400_V03.%s_tree.root" % treeVersion
g2 = "slimGJets_400_inf_V03.%s_tree.root" % treeVersion
q1 = "slimQCD_250_500_V03.%s_tree.root" % treeVersion
q2 = "slimQCD_500_1000_V03.%s_tree.root" % treeVersion
q3 = "slimQCD_1000_inf_V03.%s_tree.root" % treeVersion
tt = "slimTTJets_V03.%s_tree.root" % treeVersion
w1 = "slimWJets_250_300_V03.%s_tree.root" % treeVersion
w2 = "slimWJets_300_400_V03.%s_tree.root" % treeVersion
w3 = "slimWJets_400_inf_V03.%s_tree.root" % treeVersion
wg1 = "slimWGamma_50_130_V03.%s_tree.root" % treeVersion
wg2 = "slimWGamma_130_inf_V03.%s_tree.root" % treeVersion
tg = "slimTTGamma_V03.%s_tree.root" % treeVersion
zgn = "slimZGammaNuNu_V03.%s_tree.root" % treeVersion
data = [g1, g2, q1, q2, q3, tt, w1, w2, w3, zgn, wg1, wg2, tg]
# additional ISR uncertainty
ewkUncertainty = 0.11
isrUncertaintyZ = 0.5
isrUncertaintyW = 0.5
isrUncertaintyT = 0.5
isrUncertainty = 0.5
leptonPtCut = 25 # only larger than 15 make sense here, since this is the reprocessing cut
commonCut = "([email protected]() || Max$(electrons.pt)<{0}) && ([email protected]() || Max$(muons.pt)<{0})".format(
leptonPtCut)
#commonCut = "[email protected]() && [email protected]()"
# Compute the weights:
#weight2D = getMixedWeigthHisto( data, data, commonCut )
#attachWeightsToFiles( data, weight2D, "foWeights" )
#from qcdClosure import drawWeightHisto
#drawWeightHisto( weight2D, "MC" )
# Get Histograms
dataHist = getHists(data, plot, commonCut)
fgammaHist, fgammaWeightError = predictionHistos(data,
plot,
commonCut,
modifyEmptyBins=False)
egammaHist = multiDimFakeRate(data, plot, commonCut, isData=False)
egammaHistsys = setRelativeUncertainty(egammaHist.Clone(randomName()),
ewkUncertainty)
fsrZ = getHists([zgn], plot, commonCut)
fsrZsys = setRelativeUncertainty(fsrZ, isrUncertaintyZ)
fsrW = getHists([wg1, wg2], plot, commonCut)
fsrWsys = setRelativeUncertainty(fsrW, isrUncertaintyW)
fsrT = getHists([tg], plot, commonCut)
fsrTsys = setRelativeUncertainty(fsrT, isrUncertaintyT)
fsr = addHistos([fsrT, fsrW, fsrZ])
fsrSys = setRelativeUncertainty(fsr, isrUncertainty)
#signal1 = getMetHisto( "W", 900, 1720 )
#signal2 = getMetHisto( "B", 1700, 1120 )
signal1 = getHists(["slimW_1700_720_375_V03.06_tree.root"], plot,
commonCut)
signal2 = getHists(["slimW_900_1720_375_V03.06_tree.root"], plot,
commonCut)
signal3 = getHists(["slimB_1300_1720_375_V03.06_tree.root"], plot,
commonCut)
signal4 = getHists(["slimB_1700_1120_375_V03.06_tree.root"], plot,
commonCut)
for i, signal in enumerate([signal1, signal2, signal3, signal4]):
signal.SetLineColor(ROOT.kGreen + i)
signal.SetLineColor(ROOT.kBlue + i)
#signal.SetLineStyle(5+i)
# prettify histograms
fgammaHist.SetLineColor(7)
egammaHist.SetLineColor(3)
fsrZ.SetLineColor(ROOT.kRed - 7)
fsrW.SetLineColor(ROOT.kRed - 9)
fsrT.SetLineColor(ROOT.kRed)
mh = Multihisto()
mh.orderByIntegral = False
mh.addHisto(fsrT, "#gamma t#bar{t}", True)
mh.addHisto(fsrW, "#gamma W", True)
mh.addHisto(fsrZ, "#gamma Z", True)
mh.addHisto(egammaHist, "e#rightarrow#gamma", True)
mh.addHisto(fgammaHist, "Multijet", True)
dataLegName = "Data"
mh.addHisto(dataHist, dataLegName, draw="pe")
mh.addHisto(signal2, "Bino-like #chi_{1}^{0}", draw="hist")
mh.addHisto(signal1, "Wino-like #chi_{1}^{0}", draw="hist")
# get all SYSTEMATICAL uncertainties:
systematicUncertHistStack = ROOT.THStack()
systematicUncertHistStack.Add(fgammaWeightError)
systematicUncertHistStack.Add(egammaHistsys)
#systematicUncertHistStack.Add( fsrTsys )
#systematicUncertHistStack.Add( fsrWsys )
#systematicUncertHistStack.Add( fsrZsys )
systematicUncertHistStack.Add(fsrSys)
#if plot == "met":
# writeDataCard( treeVersion, dataHist, fgammaHist, fgammaWeightError,
# egammaHist, egammaHistsys,
# fsr, fsrSys )
# draw stuff
luminosity = 19.7
infoText = ROOT.TLatex(
0, .96,
"CMS Private Work - %sfb^{-1} #sqrt{s}=8TeV #geq1#gamma_{tight},#geq2jets"
% luminosity)
infoText.SetNDC()
infoText.SetTextSize(.04)
can = ROOT.TCanvas()
mh.Draw()
errorBand = mh.stack.GetStack().Last().Clone(randomName())
sysUncert = systematicUncertHistStack.GetStack().Last()
for bin in range(errorBand.GetNbinsX() + 2):
errorBand.SetBinError(
bin,
errorBand.GetBinError(bin) | qPlus | sysUncert.GetBinError(bin))
errorBand.SetFillStyle(3002)
errorBand.SetMarkerSize(0)
errorBand.SetFillColor(1)
errorBand.Draw("e2 same")
from myRatio import Ratio
r = Ratio("Data / Bkg", dataHist,
mh.stack.GetStack().Last(),
systematicUncertHistStack.GetStack().Last())
r.draw(0.5, 1.5)
infoText.Draw()
SaveAs(can, "finalDistributionData_%s" % plot)
def drawStackedBackground( plot, treeName, listOfFiles, sumBinned, order=False ): tightCut = " photons[0].sigmaIetaIeta<0.011 && photons[0].chargedIso<0.7 && photons[0].neutralIso < 0.4+0.04*photons[0].pt && photons[0].photonIso < 0.5+0.005*photons[0].pt && photons[0].r9<0.9" cut = "[email protected]() && [email protected]()" #cut = cut +"&&" + tightCut cut = "std::abs(photons[0].eta)>1.4442" # if a data histogram is present, the mc integral will scaled to data mcHists = [] dataHists = [] for fileName in listOfFiles: datasetAbbr = getDatasetAbbr( fileName ) try: color = colors[datasetAbbr] except: color = 1 tree = readTree( fileName, treeName ) histo = getHisto( tree, plot, color=color, cut=cut ) if "PhotonHad" in fileName: dataHists.append( (datasetAbbr, histo ) ) else: mcHists.append( ( datasetAbbr, histo ) ) # merge datahists if there dataHist = addHistos( [histo for datasetAbbr, histo in dataHists] ) if dataHists else None scale = 1.*dataHist.Integral()/addHistos( [histo for datasetAbbr, histo in mcHists] ).Integral() if dataHists else 1 scale = 1. # combine MC datasets if sumBinned: for combiAbbr, abbrList in combinedDatasets.iteritems(): if set(abbrList).issubset( set( [ a for a, h in mcHists ] ) ): indices = [] for a,b in mcHists: if a in abbrList: indices.append(mcHists.index((a,b))) #print "a valid combination was found" histosToAdd = [ h for a,h in mcHists if a in abbrList ] thisSum = addHistos( histosToAdd ) mcHists = [ (a,h) for a,h in mcHists if a not in abbrList] mcHists.insert(min(indices),(combiAbbr,thisSum)) mh = Multihisto() mh.setMinimum(0.01) mh.leg.SetFillStyle(0) mh.leg.SetNColumns(2) mh.orderByIntegral = order if dataHist: mh.addHisto( dataHist, "Data", draw="ep" ) for abbr, hist in mcHists: hist.Scale( scale ) mh.addHisto( hist, datasetToLatex(abbr), toStack=True, draw="hist" ) egammaHist = True if egammaHist: egammaHist = multiDimFakeRate( [ filename for filename in listOfFiles if "PhotonHad" in filename ], plot, cut ) egammaHist.SetLineColor(ROOT.kGreen ) mh.addHisto( egammaHist, "e#rightarrow#gamma", toStack=True ) #totalBG = addHistos( [ h for abbr, h in mcHists ] ) #totalBG.SetLineColor(2) #mh.addHisto( totalBG, "SM Simulation", toStack=False, draw="hist" ) # add signal histos #signalFiles = ["slimW_1000_1020_375_V02.44_tree.root", "slimW_1200_1120_375_V02.44_tree.root"] signalFiles = ["slimW_1200_1120_375_V02.44_tree.root"] signalFiles = ["slimW_1700_720_375_V03.22_tree.root"] for iColor, sf in enumerate(signalFiles): tree = readTree( sf, treeName ) histo = getHisto( tree, plot, color=ROOT.kBlue+iColor, cut=cut ) #mh.addHisto( histo, datasetToLatex(getDatasetAbbr(sf)), draw="hist" ) mh.addHisto( histo, "Signal", draw="hist" ) infoText = ROOT.TLatex(0,.97, "#text{CMS Private Work }#geq1#gamma_{#text{pixel}},#geq2#text{jets}" ) infoText.SetNDC() infoText.SetTextSize(0.05) can = ROOT.TCanvas() can.cd() mh.Draw() infoText.Draw() #if dataHist: # from myRatio import Ratio # den = totalBG #den = mh.stack.GetStack().Last().Clone( randomName() ) # den.SetLineColor(2) # r = Ratio( "Data/Sim.", dataHist, den ) # r.draw(0,2) allDatasetAbbr = getSaveNameFromDatasets( listOfFiles ) SaveAs( can, "stackedHisto_%s_%s_%s"%(treeName, plot,allDatasetAbbr) )
def finalDistributionData( plot ): # Sample names treeVersion = "31" wg = [ "slimWGamma_50_130_V03.%s_tree.root"%treeVersion, \ "slimWGamma_130_inf_V03.%s_tree.root"%treeVersion ] tg = [ "slimTTGamma_V03.%s_tree.root"%treeVersion ] zgn = [ "slimZGammaNuNu_V03.%s_tree.root"%treeVersion ] zgll = [ "slimZGammaLL_V02.19b_tree.root" ] data = [ "PhotonHad%s_V03.%s_tree.root"%(x,treeVersion) for x in ["A","B","C","D" ] ] # additional ISR uncertainty ewkUncertainty = 0.11 isrUncertaintyZ = 0.5 isrUncertaintyW = 0.5 isrUncertaintyT = 0.5 isrUncertainty = 0.5 leptonPtCut = 25 # only larger than 15 make sense here, since this is the reprocessing cut #commonCut = "([email protected]() || Max$(electrons.pt)<{0}) && ([email protected]() || Max$(muons.pt)<{0})".format(leptonPtCut) commonCut = "[email protected]() && [email protected]() && thisPt>0 && recoilChr > 0" # Compute the weights: weight2D = getMixedWeigthHisto( data, data, commonCut ) attachWeightsToFiles( data, weight2D, "foWeights" ) drawWeightHisto( weight2D, "Data", writeWeightFile=True ) # Get Histograms dataHist = getHists( data, plot, commonCut ) fgammaHist, fgammaWeightError = predictionHistos( data, plot, commonCut, modifyEmptyBins=False ) egammaHist = multiDimFakeRate( data, plot, commonCut ) egammaHistsys = setRelativeUncertainty( egammaHist.Clone(randomName()), ewkUncertainty ) fsrZ = getHists( zgn, plot, commonCut+"&&genPhotons[0].pt>130" ) fsrZll = getHists( zgll, plot, commonCut ) if plot=="met": fsrZ2 = getHists( zgll, plot+"LL", commonCut+"&&genPhotons[0].pt<130" ) else: fsrZ2 = getHists( zgll, plot, commonCut+"&&0" ) fsrZ2.Scale( 20./(2.*3.363) ) fsrW = getHists( wg, plot, commonCut ) fsrT = getHists( tg, plot, commonCut ) # apply common scale factor for h in fsrW, fsrT, fsrZ, fsrZ2, fsrZll: h.Scale(1.5) fsr = addHistos( [fsrT, fsrW, fsrZ, fsrZ2,fsrZll ] ) fsr.SetLineColor(ROOT.kRed) fsrSys = setRelativeUncertainty( fsr, isrUncertainty ) #signal1 = getMetHisto( "W", 900, 1720 ) #signal2 = getMetHisto( "B", 1700, 1120 ) signal1 = getHists( ["slimW_1700_720_375_V03.06_tree.root"], plot, commonCut ) signal2 = getHists( ["slimW_900_1720_375_V03.06_tree.root"], plot, commonCut ) signal3 = getHists( ["slimB_1300_1720_375_V03.06_tree.root"], plot, commonCut ) signal4 = getHists( ["slimB_1700_1120_375_V03.06_tree.root"], plot, commonCut ) for i, signal in enumerate([signal1, signal2, signal3, signal4]): signal.SetLineColor( ROOT.kGreen + i ) signal.SetLineColor( ROOT.kBlue + i ) #signal.SetLineStyle(5+i) # prettify histograms fgammaHist.SetLineColor(7) egammaHist.SetLineColor( 3 ) fsrZ.SetLineColor( ROOT.kRed-7 ) fsrW.SetLineColor( ROOT.kRed-9 ) fsrT.SetLineColor( ROOT.kRed ) mh = Multihisto() mh.setMinimum(0.2) mh.orderByIntegral = False mh.addHisto( fsr, "ISR", True ) mh.addHisto( egammaHist, "e#rightarrow#gamma", True ) mh.addHisto( fgammaHist, "Multijet", True ) dataLegName = "Data" mh.addHisto( dataHist, dataLegName, draw="pe x0" ) mh.addHisto( signal2, "Bino-like #chi_{1}^{0}", draw="hist" ) mh.addHisto( signal1, "Wino-like #chi_{1}^{0}", draw="hist" ) # get all SYSTEMATICAL uncertainties: systematicUncertHistStack = ROOT.THStack() systematicUncertHistStack.Add( fgammaWeightError ) systematicUncertHistStack.Add( egammaHistsys ) systematicUncertHistStack.Add( fsrSys ) if plot == "met": writeDataCard( treeVersion, dataHist, fgammaHist, fgammaWeightError, egammaHist, egammaHistsys, fsr, fsrSys ) # draw stuff luminosity = 19.7 infoText = ROOT.TLatex(0,.96, "CMS Private Work - %sfb^{-1} #sqrt{s}=8TeV #geq1#gamma_{tight},#geq2jets"%luminosity ) infoText.SetNDC() infoText.SetTextSize(.04) can = ROOT.TCanvas() mh.Draw() statUncert = mh.stack.GetStack().Last().Clone( randomName() ) systUncert = systematicUncertHistStack.GetStack().Last().Clone( randomName() ) totalUncert = statUncert.Clone( randomName() ) for bin in range( totalUncert.GetNbinsX()+2 ): totalUncert.SetBinError( bin, statUncert.GetBinError(bin) |qPlus| systUncert.GetBinError(bin) ) for h in statUncert, systUncert, totalUncert: h.SetMarkerSize(0) totalUncert.SetFillStyle(3002) totalUncert.SetFillColor(1) totalUncert.Draw("same e2") systUncert.SetFillStyle(3254) systUncert.SetFillColor(2) systUncert.Draw("same e2") statUncert.SetLineWidth(3) statUncert.SetLineColor(2) statUncert.Draw("same e x0") dataHist.Draw("same pe x0") from myRatio import Ratio r = Ratio( "Data / Bkg", dataHist, mh.stack.GetStack().Last(), systematicUncertHistStack.GetStack().Last() ) r.draw(.5,1.5) infoText.Draw() SaveAs( can, "finalDistributionData_%s"%plot )
