def compareFiles(plot, filenames, treename, diff):
tree1 = readTree(filenames[0], treename)
tree2 = readTree(filenames[1], treename)
if diff:
cut1, cut2 = getEventNumberDiff(tree1, tree2)
tree1 = getTreeFriendsWithBooleanVariable(tree1, cut1)
tree2 = getTreeFriendsWithBooleanVariable(tree2, cut2)
h1 = createHistoFromTree(tree1, plot, "x")
h2 = createHistoFromTree(tree2, plot, "x")
else:
h1 = getHisto(tree1, plot, weight="1")
h2 = getHisto(tree2, plot, weight="1")
mini = min(h1.GetBinLowEdge(1), h2.GetBinLowEdge(1))
maxi = max(h2.GetBinLowEdge(h2.GetNbinsX() + 2),
h1.GetBinLowEdge(h1.GetNbinsX() + 2))
h1 = getHisto(tree1, plot, weight="1", firstBin=mini, lastBin=maxi)
h2 = getHisto(tree2, plot, weight="1", firstBin=mini, lastBin=maxi)
h2.SetLineColor(2)
mh = Multihisto()
name1 = getVersion(filenames[0])
name2 = getVersion(filenames[1])
mh.addHisto(h1, name1, draw="hist e")
mh.addHisto(h2, name2, draw="hist e")
mh.Draw()
from myRatio import Ratio
r = Ratio("%s/%s" % (name1, name2), h1, h2)
r.draw()
plot = "test"
ROOT.gPad.GetCanvas().SaveAs("compareFiles_%s.pdf" % plot)
def drawGenComposition2Samples(treeName, sample1, sample2):
histos = {
"W": sample1.plot(treeName, cut="1"),
"W (gen #gamma)": sample1.plot(treeName, cut="photons[0].isGen(0)"),
"W (gen e)": sample1.plot(treeName, cut="photons[0].isGen(1)"),
"W (gen had)": sample1.plot(treeName, cut="photons[0].isGen(3)"),
"#gammaW": sample2.plot(treeName, cut="1"),
"#gammaW (gen #gamma)": sample2.plot(treeName,
cut="photons[0].isGen(0)"),
"#gammaW (gen e)": sample2.plot(treeName, cut="photons[0].isGen(1)"),
"#gammaW (gen had)": sample2.plot(treeName, cut="photons[0].isGen(2)"),
}
mh = Multihisto()
for name, h in histos.iteritems():
if "(gen #gamma)" in name:
h.SetLineStyle(2)
if "(gen e)" in name:
h.SetLineStyle(3)
if "(gen had)" in name:
h.SetLineStyle(4)
mh.addHisto(h, name)
can = ROOT.TCanvas()
can.cd()
mh.Draw()
info = PlotCaption(treeName=treeName)
info.Draw()
allDatasetAbbr = sample1.datasetAbbr + sample2.datasetAbbr
plot = "met"
SaveAs(can,
"composition2samples_%s_%s_%s" % (treeName, plot, allDatasetAbbr))
def drawGenComposition2Samples( treeName, sample1, sample2 ):
histos = {
"W": sample1.plot(treeName, cut="1"),
"W (gen #gamma)": sample1.plot(treeName, cut="photons[0].isGen(0)"),
"W (gen e)": sample1.plot(treeName, cut="photons[0].isGen(1)"),
"W (gen had)": sample1.plot(treeName, cut="photons[0].isGen(3)"),
"#gammaW": sample2.plot(treeName, cut="1"),
"#gammaW (gen #gamma)": sample2.plot(treeName, cut="photons[0].isGen(0)"),
"#gammaW (gen e)": sample2.plot(treeName, cut="photons[0].isGen(1)"),
"#gammaW (gen had)": sample2.plot(treeName, cut="photons[0].isGen(2)"),
}
mh = Multihisto()
for name, h in histos.iteritems():
if "(gen #gamma)" in name:
h.SetLineStyle(2)
if "(gen e)" in name:
h.SetLineStyle(3)
if "(gen had)" in name:
h.SetLineStyle(4)
mh.addHisto( h, name )
can = ROOT.TCanvas()
can.cd()
mh.Draw()
info = PlotCaption(treeName=treeName)
info.Draw()
allDatasetAbbr = sample1.datasetAbbr+sample2.datasetAbbr
plot = "met"
SaveAs( can, "composition2samples_%s_%s_%s"%(treeName, plot,allDatasetAbbr) )
def compareFiles( plot, filenames, treename, diff ): tree1 = readTree( filenames[0], treename ) tree2 = readTree( filenames[1], treename ) if diff: cut1, cut2 = getEventNumberDiff( tree1, tree2 ) tree1 = getTreeFriendsWithBooleanVariable( tree1, cut1) tree2 = getTreeFriendsWithBooleanVariable( tree2, cut2) h1 = createHistoFromTree( tree1, plot, "x" ) h2 = createHistoFromTree( tree2, plot, "x" ) else: h1 = getHisto( tree1, plot, weight="1" ) h2 = getHisto( tree2, plot, weight="1" ) mini = min(h1.GetBinLowEdge(1), h2.GetBinLowEdge(1)) maxi = max(h2.GetBinLowEdge(h2.GetNbinsX()+2), h1.GetBinLowEdge(h1.GetNbinsX()+2)) h1 = getHisto( tree1, plot, weight="1", firstBin=mini, lastBin=maxi ) h2 = getHisto( tree2, plot, weight="1", firstBin=mini, lastBin=maxi ) h2.SetLineColor(2) mh = Multihisto() name1 = getVersion(filenames[0]) name2 = getVersion(filenames[1]) mh.addHisto( h1, name1, draw="hist e" ) mh.addHisto( h2, name2, draw="hist e" ) mh.Draw() from myRatio import Ratio r = Ratio( "%s/%s"%(name1,name2), h1, h2 ) r.draw() plot ="test" ROOT.gPad.GetCanvas().SaveAs( "compareFiles_%s.pdf"%plot )
def closure( filenames, plot, treename="photonJetTree" ): commonCut = "[email protected]() && [email protected]()" #commonCut = "1" leptonPtCut = 15 # 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) totalHist = getHists( filenames, plot, cut=commonCut, treeName=treename ) gGenHist = getHists( filenames, plot, cut="photons[0].isGen(0) && "+commonCut, treeName=treename ) eGenHist = getHists( filenames, plot, cut="photons[0].isGen(1) && "+commonCut, treeName=treename ) gGenHist.SetLineColor(2) eGenHist.SetLineColor(3) gDatasetAbbrs = [getDatasetAbbr(f) for f in filenames ] gDatasetAbbrs = mergeDatasetAbbr( gDatasetAbbrs ) multihisto = Multihisto() multihisto.leg.SetHeader( "/".join([ datasetToLatex(x) for x in gDatasetAbbrs]) ) multihisto.addHisto( totalHist, "Simulation", draw="e0 hist" ) multihisto.addHisto( gGenHist, "gen #gamma", toStack=True, draw="e0 hist" ) multihisto.addHisto( eGenHist, "gen e", toStack=True, draw="e0 hist" ) infoText = ROOT.TLatex(0.03,.96, "CMS Private Work - 8TeV #geq1#gamma,#geq2jets" ) infoText.SetNDC() can = ROOT.TCanvas() can.cd() multihisto.Draw() infoText.Draw() r = Ratio( "Sim./Pred.", totalHist, multihisto.stack.GetStack().Last() ) r.draw(0,2) SaveAs( can, "genMatches_%s_%s"%(getSaveNameFromDatasets(filenames), plot)) ROOT.SetOwnership( can, False ) del can
def split2Din1DMultihist( h2D, useYaxis, uFlow, oFlow ):
"""Creates many TH1 out of one TH2 and put them in a Multihisto.
h2D: TH2 (rebin appropriate if necessary )
useYaxis: The TH2 will be splitted in different values of Y.
If false, the X axis will be used (bool).
uFlow: Take the underFlow into account (bool).
oFlow: Take the overFlow into account (bool).
"""
mh = Multihisto()
mh.setMinimum(1e-8)
style = ROOT.gROOT.GetStyle("tdrStyle") # for nice color gradient
if useYaxis:
axis = h2D.GetYaxis()
else:
axis = h2D.GetXaxis()
variable = axis.GetTitle()
nBins = axis.GetNbins()
binRange = range( nBins + 2 )
if not uFlow:
binRange = binRange[1:]
if not oFlow:
binRange = binRange[:-1]
for iColor, iBin in enumerate(binRange):
if useYaxis:
h = h2D.ProjectionX( "_px%s"%iBin, iBin, iBin )
else:
h = h2D.ProjectionY( "_px%s"%iBin, iBin, iBin )
lowEdge = axis.GetBinLowEdge( iBin )
upEdge = axis.GetBinUpEdge( iBin )
if iBin == 0:
title = " %s < %s"%( variable, upEdge )
elif iBin != nBins+1:
title = "%s #leq %s < %s"%( lowEdge, variable, upEdge )
else:
title = "%s #leq %s"%( lowEdge, variable )
color = style.GetColorPalette( int( 1.*iColor/(len(binRange)-1)*(style.GetNumberOfColors()-1) ) )
h.SetLineColor( color )
h.SetMarkerColor( h.GetLineColor() )
h.SetMarkerSize(0)
h.GetXaxis().SetTitle( h2D.GetXaxis().GetTitle() )
mh.addHisto( h, title,draw="e0" )
return mh
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 drawPileUpHistos(saveTex=False):
inputHistPath = "../TreeWriter/pileUpReweighting/"
data = readHisto(inputHistPath + "nTrueVertexData.root", "pileup")
data.SetLineColor(1)
data.SetMarkerColor(1)
data.SetMarkerStyle(20)
data.Sumw2()
if saveTex:
data.SetMarkerSize(.8)
else:
data.SetMarkerSize(1.2)
mc = readHisto(inputHistPath + "nTrueVertexMC.root", "pileupS10")
mc.SetLineColor(2)
mc.SetMarkerColor(2)
for h in [mc, data]:
h.Scale(1. / h.Integral())
h.SetTitle(";Number of Pile-up Events;Normalized Entries")
if saveTex:
h.SetLineWidth(2)
h.SetTitleSize(0.06311227345609463, "yx")
h.SetLabelSize(0.06311227345609463, "yx")
muhist = Multihisto()
muhist.addHisto(data, "Data", draw="ep")
muhist.addHisto(mc, "Simulation", draw="hist")
muhist.setMinimum(0)
if saveTex:
muhist.leg.SetTextSize(0.063112267888)
texStyle()
pc1 = ROOT.TLatex(0, .96, "CMS Private Work")
pc2 = ROOT.TLatex(.76, .96, "\SI{19.8}{fb^{-1}} #sqrt{s}=\SI{8}{TeV}")
masterPath = "~/master/documents/thesis/plots/"
can = ROOT.TCanvas("", "", 2000, 1000)
can.cd()
can.SetLogy(0)
muhist.Draw()
for pc in [pc1, pc2]:
pc.SetNDC()
if saveTex:
pc.SetTextSize(0.06311227345609463)
pc.Draw()
ending = 'tex' if saveTex else 'pdf'
ROOT.gPad.SaveAs(masterPath + "puDistribution.%s" % ending)
def drawGenComposition( treeName, sample ): mh = Multihisto() mh.addHisto( sample.plot(treeName, cut="photons[0].isGen(0)",color=2), "gen #gamma" ) mh.addHisto( sample.plot(treeName, cut="photons[0].isGen(1)",color=3), "gen e" ) mh.addHisto( sample.plot(treeName, cut="photons[0].isGen(2)",color=4), "gen hadron" ) mh.addHisto( sample.plot(treeName, cut="1",color=1), "inclusive" ) can = ROOT.TCanvas() can.cd() mh.Draw() info = PlotCaption(treeName=treeName) info.Draw() allDatasetAbbr = sample.datasetAbbr plot = "met" SaveAs( can, "composition_%s_%s_%s"%(treeName, plot,allDatasetAbbr) )
def zGammaMixture(plot="met"):
cut = "1"
nunuTree = readTree("slimZGammaNuNu_V03.18_tree.root", "photonTree")
zIncTree = readTree("slimZGamma_V03.18_tree.root", "photonTree")
nBins = range(80, 500,
10) if plot == "photons[0].pt" else readAxisConf(plot)[2]
h_nunu = getHisto(nunuTree,
plot,
cut + "&&photons[0].pt>=130",
nBins=nBins,
color=2)
h_zInc = getHisto(zIncTree,
plot,
cut + "&&photons[0].pt<130",
nBins=nBins,
color=4)
mh = Multihisto()
mh.addHisto(h_nunu,
"#gammaZ#rightarrow#gamma#nu#nu(p_{T}#geq130GeV)",
draw="e",
toStack=True)
mh.addHisto(h_zInc, "#gammaZ(p_{T}<130GeV)", draw="e", toStack=True)
mh.Draw()
if plot == "photons[0].pt":
l = ROOT.TLine(130, 0, 130, 1)
l.Draw()
SavePad("inclusiveAndIsrSample_%s" % plot)
def drawClosure(filenames,
predFilenames,
plot,
commonCut,
infoText,
additionalLabel="",
modifyEmptyBins=True):
infoText = ROOT.TLatex(
0, .96,
"CMS Private Work 19.7fb^{-1} #sqrt{s}=8TeV #geq1#gamma,#geq2jets")
infoText.SetNDC()
infoText.SetTextSize(0.045)
gHist = getHists(filenames, plot, commonCut)
fHist, sysHist = predictionHistos(predFilenames, plot, commonCut,
modifyEmptyBins)
fHist.SetMarkerSize(0)
sysHist.SetFillStyle(3254)
sysHist.SetFillColor(sysHist.GetLineColor())
sysHist.SetLineColor(0)
signalAbbrs = mergeDatasetAbbr([getDatasetAbbr(x) for x in filenames])
muhisto = Multihisto()
muhisto.leg.SetHeader(",".join([datasetToLatex(x) for x in signalAbbrs]))
muhisto.addHisto(gHist, "Simulation", draw="")
muhisto.addHisto(fHist, "Prediction", draw="hist e")
muhisto.addHisto(sysHist, "syst. uncert", draw="e2")
muhisto.addHisto(gHist, "", draw="e0") ## add a second time to draw on top
can = ROOT.TCanvas("", "", 1000, 1200)
can.cd()
muhisto.Draw()
from myRatio import Ratio
r = Ratio("Sim./Pred.", gHist, fHist, sysHist)
r.draw(0., 2)
infoText.Draw()
muhisto.leg.AddEntry(r.totalUncert, "total uncert", "f")
muhisto.leg.Draw()
SaveAs(can,
"qcdClosure_%s_%s" % ("".join(signalAbbrs) + additionalLabel, plot))
# Since root is too stupid to clear the canvas before python is ending, clean
# the canvas yourself
ROOT.SetOwnership(can, False)
del can
def drawBeforeClosure( plot, gTree, foTree, cut, can, info, datasetAbbr, additionalInfo="",norm=False ):
if plot == "met" and cut != "1":
return
# The first attempt to get the histogram is only to get the minimal
# and maximal value on the x-axis, for not predefined binning
h_gamma = getHisto( gTree, plot, cut=cut )
h_fo = getHisto( foTree, plot, cut=cut )
xMin, xMax = getXMinXMax( [ h_gamma, h_fo ] )
h_gamma = getHisto( gTree, plot, cut=cut, color=1, firstBin=xMin,lastBin=xMax )
h_fo = getHisto( foTree, plot, cut=cut, color=46, firstBin=xMin,lastBin=xMax )
if norm:
for h in [h_gamma,h_fo]:
h.Scale(1./h.Integral())
h.GetYaxis().SetTitle("Normed Entries")
muhisto = Multihisto()
muhisto.leg.SetHeader( datasetToLatex( datasetAbbr ) )
muhisto.addHisto( h_gamma, "#gamma", draw="hist e0" )
muhisto.addHisto( h_fo, "#gamma_{jet}", draw="hist e0")
hPad = ROOT.TPad("hPad", "Histogram", 0, 0.2, 1, 1)
hPad.cd()
muhisto.Draw()
ROOT.TGaxis.SetMaxDigits(4)
ratioPad = ROOT.TPad("ratioPad", "Ratio", 0, 0, 1, 0.2)
ratioPad.cd()
ratioPad.SetLogy( False )
ratioGraph = ratios.RatioGraph( h_gamma, h_fo )
ratioGraph.draw(ROOT.gPad, yMin=None, yMax=None, adaptiveBinning=False, errors="yx")
ratioGraph.graph.Draw("same p e0") # draw nice points
ratioGraph.hAxis.SetYTitle( "#gamma/#gamma_{jet}")
can.cd()
hPad.Draw()
ratioPad.Draw()
info.Draw()
SaveAs(can, "qcd_preWeight_%s_%s_%s"%(datasetAbbr+additionalInfo, plot,reweightVar) )
ROOT.SetOwnership( hPad, False )
ROOT.SetOwnership( ratioPad, False )
def compareTrees( plot="photons.pt", filename="slimQCD_V02.28_tree.root" ):
label, unit, binning = readAxisConf( "photons[0].ptJet()" )
#binning = range(0,70,10) + binning
#binning = range(0,1000, 30 )
import array
gH = ROOT.TH1F( randomName(), ";jet_{x};", len(binning)-1, array.array('d', binning) )
fH = gH.Clone( randomName() )
fH.SetLineColor(2)
fH.SetMarkerColor(2)
#cut = "!photons[0].isGen(0)"
#cut = "1"
gTree = readTree( filename, "photonTree" )
fTree = readTree( filename, "photonJetTree" )
#gH = getHisto( gTree, plot, color=1, cut=cut,firstBin=0,lastBin=1400 )
#fH = getHisto( fTree, plot, color=2, cut=cut,firstBin=0,lastBin=1400 )
for h, tree in [(gH, gTree), (fH, fTree)]:
h.Sumw2()
for event in tree:
h.Fill( getFromEvent( event ), event.weight )
for h in [gH, fH]:
h.Scale( 1, "width" )
mh = Multihisto()
mh.addHisto( gH, "#gamma", draw="hist e" )
mh.addHisto( fH, "#gamma_{jet}", draw="hist e" )
can = ROOT.TCanvas()
can.cd()
mh.Draw()
from myRatio import Ratio
r = Ratio( "#gamma/#gamma_{jet}", gH, fH )
r.draw(None,2)
SaveAs( can, "compare_test" )
def compareHists( filenames, tight, fcut, draw=False ):
loose = getHistoFromFiles( "met", "photonJetTree", filenames, fcut )
loose.SetLineColor(2)
if loose.Integral(0,loose.FindBin(99)):
loose.Scale( tight.Integral(0,loose.FindBin(99)) / loose.Integral(0,loose.FindBin(99)) )
option = "uu norm" if filenames[0].startswith("PhotonHad") else "ww"
if draw:
c = ROOT.TCanvas()
c.cd()
mh = Multihisto()
mh.addHisto( tight.Clone( randomName() ), "#gamma_{tight}", draw="hist e")
mh.addHisto( loose, "#gamma_{loose}", draw="hist e")
mh.Draw()
from myRatio import Ratio
r = Ratio( "#gamma_{tight}/#gamma_{loose}", tight, loose )
r.draw(0,2)
c.SaveAs("plots/findFoId_%s_twoDistributions_%s.pdf"%(getSaveNameFromDatasets(filenames),''.join([s for s in fcut if s.isdigit()])) )
return tight.Chi2Test( loose, option )
def compareBinnedSamples(histList1, histList2, plot="met"):
cut = "[email protected]() && [email protected]()"
treeName = "photonTree"
mh = Multihisto()
# if "ZGamma" in histList1[0]:
# mh.setMinimum(0.01)
# sum all histos in histList1
hist1 = None
for fileName in histList1:
datasetAbbr1 = getDatasetAbbr(fileName)
tree = readTree(fileName, treeName)
h = getHisto(tree, plot, cut=cut, color=colors[datasetAbbr1])
if hist1:
hist1.Add(h)
else:
hist1 = h
mh.addHisto(hist1, datasetToLatex(datasetAbbr1), draw="hist e")
# stack histos in histList2
for fileName in histList2:
datasetAbbr = getDatasetAbbr(fileName)
tree = readTree(fileName, treeName)
h = getHisto(tree, plot, cut=cut, color=colors[datasetAbbr])
mh.addHisto(h, datasetToLatex(datasetAbbr), toStack=True)
mh.Draw()
stack = mh.stack.GetStack().Last()
stack.SetMarkerSize(0)
stack.Draw("same e0")
errorHist = mh.stack.GetStack().Last().Clone(randomName())
errorHist.SetMarkerColor(1)
errorHist.Draw("same e")
if plot != "met":
datasetAbbr1 += plot
SavePad("compareBinned" + datasetAbbr1)
def drawPileUpHistos( saveTex=False ):
inputHistPath = "../TreeWriter/pileUpReweighting/"
data = readHisto( inputHistPath + "nTrueVertexData.root", "pileup" )
data.SetLineColor(1)
data.SetMarkerColor(1)
data.SetMarkerStyle(20)
data.Sumw2()
if saveTex:
data.SetMarkerSize(.8)
else:
data.SetMarkerSize(1.2)
mc = readHisto( inputHistPath + "nTrueVertexMC.root", "pileupS10" )
mc.SetLineColor(2)
mc.SetMarkerColor(2)
for h in [mc, data ]:
h.Scale( 1./h.Integral() )
h.SetTitle( ";Number of Pile-up Events;Normalized Entries" )
if saveTex:
h.SetLineWidth(2)
h.SetTitleSize( 0.06311227345609463, "yx" )
h.SetLabelSize( 0.06311227345609463, "yx" )
muhist = Multihisto()
muhist.addHisto( data, "Data", draw="ep" )
muhist.addHisto( mc, "Simulation", draw="hist" )
muhist.setMinimum(0)
if saveTex:
muhist.leg.SetTextSize(0.063112267888)
texStyle()
pc1 = ROOT.TLatex(0,.96, "CMS Private Work")
pc2 = ROOT.TLatex( .76,.96, "\SI{19.8}{fb^{-1}} #sqrt{s}=\SI{8}{TeV}")
masterPath = "~/master/documents/thesis/plots/"
can = ROOT.TCanvas("","",2000,1000)
can.cd()
can.SetLogy(0)
muhist.Draw()
for pc in [pc1, pc2]:
pc.SetNDC()
if saveTex:
pc.SetTextSize(0.06311227345609463)
pc.Draw()
ending = 'tex' if saveTex else 'pdf'
ROOT.gPad.SaveAs(masterPath+"puDistribution.%s"%ending)
def zGammaMixture(plot="met"):
cut = "1"
nunuTree = readTree("slimZGammaNuNu_V03.18_tree.root", "photonTree")
zIncTree = readTree("slimZGamma_V03.18_tree.root", "photonTree")
nBins = range(80, 500, 10) if plot == "photons[0].pt" else readAxisConf(plot)[2]
h_nunu = getHisto(nunuTree, plot, cut + "&&photons[0].pt>=130", nBins=nBins, color=2)
h_zInc = getHisto(zIncTree, plot, cut + "&&photons[0].pt<130", nBins=nBins, color=4)
mh = Multihisto()
mh.addHisto(h_nunu, "#gammaZ#rightarrow#gamma#nu#nu(p_{T}#geq130GeV)", draw="e", toStack=True)
mh.addHisto(h_zInc, "#gammaZ(p_{T}<130GeV)", draw="e", toStack=True)
mh.Draw()
if plot == "photons[0].pt":
l = ROOT.TLine(130, 0, 130, 1)
l.Draw()
SavePad("inclusiveAndIsrSample_%s" % plot)
def compareTrees( plot="photons.pt", filename="slimAllQCD_V02.28_tree.root" ):
label, unit, binning = readAxisConf( "ht" )
import array
fH = ROOT.TH1F( randomName(), ";%s;"%label, len(binning)-1, array.array('d', binning) )
fH.SetLineColor(2)
fH.SetMarkerColor(2)
fH.Sumw2()
#cut = "!photons[0].isGen(0)"
cut = "1"
gTree = readTree( filename, "photonTree" )
fTree = readTree( filename, "photonJetTree" )
gH = getHisto( gTree, "ht", color=1, cut=cut,firstBin=0,lastBin=1400 )
#fH = getHisto( fTree, plot, color=2, cut=cut,firstBin=0,lastBin=1400 )
for event in fTree:
fH.Fill( getFromEvent( event ) )
for h in [gH, fH]:
h.Scale( 1./h.Integral() )
mh = Multihisto()
mh.addHisto( gH, "new", draw="hist e" )
mh.addHisto( fH, "old (recalculated)", draw="hist e" )
can = ROOT.TCanvas()
can.cd()
mh.Draw()
from myRatio import Ratio
r = Ratio( "#gamma/#gamma_{jet}", gH, fH )
r.draw(0,2)
SaveAs( can, "compare_%s_norm"%plot )
def drawTwoHists(gHist, fHist, sHist, saveName, minmax):
for bin in range(sHist.GetNbinsX() + 2):
sHist.SetBinError(bin, sHist.GetBinContent(bin))
sHist.SetBinContent(bin, fHist.GetBinContent(bin))
sHist.SetFillColor(sHist.GetLineColor())
sHist.SetLineColor(sHist.GetLineColor())
sHist.SetFillStyle(3254)
sHist.SetMarkerSize(0)
fHist.SetLineColor(2)
sHist.SetLineColor(2)
for h in gHist, fHist, sHist:
from inheritRoot import H1F
h.__class__ = H1F
h.MergeOverflow()
h.Scale(1., "width")
muhisto = Multihisto()
muhisto.addHisto(gHist, "Simulation", draw="hist e")
muhisto.addHisto(fHist, "Prediction", draw="hist")
muhisto.addHisto(sHist, "#sigma_{w}", draw="e2")
can = ROOT.TCanvas(randomName(), "", 1000, 1200)
can.cd()
muhisto.Draw()
text = ROOT.TLatex(.1, .965,
"%i #leq p_{T^{*}} < %i, %i #leq H_{T} < %i" % minmax)
text.SetNDC()
text.Draw()
from myRatio import Ratio
r = Ratio("Sim./Pred.", gHist, fHist)
r.draw(0, 2)
can.SaveAs(saveName + ".pdf")
ROOT.SetOwnership(can, False)
del can
def split2Din1DMultihist(h2D, useYaxis, uFlow, oFlow):
"""Creates many TH1 out of one TH2 and put them in a Multihisto.
h2D: TH2 (rebin appropriate if necessary )
useYaxis: The TH2 will be splitted in different values of Y.
If false, the X axis will be used (bool).
uFlow: Take the underFlow into account (bool).
oFlow: Take the overFlow into account (bool).
"""
mh = Multihisto()
mh.setMinimum(1e-8)
style = ROOT.gROOT.GetStyle("tdrStyle") # for nice color gradient
if useYaxis:
axis = h2D.GetYaxis()
else:
axis = h2D.GetXaxis()
variable = axis.GetTitle()
nBins = axis.GetNbins()
binRange = range(nBins + 2)
if not uFlow:
binRange = binRange[1:]
if not oFlow:
binRange = binRange[:-1]
for iColor, iBin in enumerate(binRange):
if useYaxis:
h = h2D.ProjectionX("_px%s" % iBin, iBin, iBin)
else:
h = h2D.ProjectionY("_px%s" % iBin, iBin, iBin)
lowEdge = axis.GetBinLowEdge(iBin)
upEdge = axis.GetBinUpEdge(iBin)
if iBin == 0:
title = " %s < %s" % (variable, upEdge)
elif iBin != nBins + 1:
title = "%s #leq %s < %s" % (lowEdge, variable, upEdge)
else:
title = "%s #leq %s" % (lowEdge, variable)
color = style.GetColorPalette(
int(1. * iColor / (len(binRange) - 1) *
(style.GetNumberOfColors() - 1)))
h.SetLineColor(color)
h.SetMarkerColor(h.GetLineColor())
h.SetMarkerSize(0)
h.GetXaxis().SetTitle(h2D.GetXaxis().GetTitle())
mh.addHisto(h, title, draw="e0")
return mh
def plotFakeRate(filenames, opts):
mhisto = Multihisto()
mhisto.legendOption = "lp"
#mhisto.leg.SetHeader("Object matching")
for iColor, filename in enumerate(filenames):
mhisto.addHisto(getFakeRateHisto(filename, opts, iColor + 2),
getDatasetAbbr(filename, slim=False),
draw="")
if opts.plot == "photons.pt":
yuOrig = yutarosHistogramMC(1)
mhisto.addHisto(yuOrig, "DY tag&probe", draw="")
can = ROOT.TCanvas()
can.cd()
can.SetLogy(0)
mhisto.Draw()
saveName = "%s_%s_%s" % ("fakeRate", opts.plot, opts.savePrefix)
can.SaveAs("plots/%s.pdf" % manipulateSaveName(saveName))
def drawClosure( plot, gTree, foTree, cut, can, info, datasetAbbr, additionalInfo="" ):
# The first attempt to get the histogram is only to get the minimal
# and maximal value on the x-axis, for not predefined binning
h_gamma = getHisto( gTree, plot,cut=cut )
h_fo = getHisto( foTree, plot, cut=cut )
xMin, xMax = getXMinXMax( [ h_gamma, h_fo ] )
h_gamma = getHisto( gTree, plot, cut=cut, color=1, firstBin=xMin,lastBin=xMax )
h_fo = getHisto( foTree, plot, cut=cut, weight="weight*w_qcd", color=46, firstBin=xMin,lastBin=xMax )
h_fo_error = getQCDErrorHisto( foTree, plot, cut=cut, firstBin=xMin, lastBin=xMax )
h_fo_error.SetFillColor( h_fo.GetLineColor() )
h_fo_error.SetLineColor( h_fo_error.GetLineColor() )
h_fo_error.SetFillStyle(3254)
h_fo_error.SetMarkerSize(0)
muhisto = Multihisto()
muhisto.leg.SetHeader( datasetToLatex( datasetAbbr ) )
muhisto.addHisto( h_gamma, "#gamma", draw="hist e0" )
muhisto.addHisto( h_fo, "#gamma_{jet}#upointw", draw="hist e0")
muhisto.addHisto( h_fo_error, "#sigma_{w}", draw="e2")
hPad = ROOT.TPad("hPad", "Histogram", 0, 0.2, 1, 1)
hPad.cd()
muhisto.Draw()
ratioPad = ROOT.TPad("ratioPad", "Ratio", 0, 0, 1, 0.2)
ratioPad.cd()
ratioPad.SetLogy( False )
from myRatio import Ratio
r = Ratio( "#gamma/#gamma_{pred}", h_gamma, h_fo )
ratio, sys, one = r.draw(0,2)
ratio.Draw("e1")
sys.Draw("same e2")
one.Draw()
can.cd()
hPad.Draw()
ratioPad.Draw()
info.Draw()
SaveAs(can, "qcd_afterWeighting_%s_%s_%s"%(datasetAbbr+additionalInfo, plot,reweightVar) )
ROOT.SetOwnership( hPad, False )
ROOT.SetOwnership( ratioPad, False )
def compareTrees(plot="photons.pt", filename="slimQCD_V02.28_tree.root"):
label, unit, binning = readAxisConf("photons[0].ptJet()")
#binning = range(0,70,10) + binning
#binning = range(0,1000, 30 )
import array
gH = ROOT.TH1F(randomName(), ";jet_{x};",
len(binning) - 1, array.array('d', binning))
fH = gH.Clone(randomName())
fH.SetLineColor(2)
fH.SetMarkerColor(2)
#cut = "!photons[0].isGen(0)"
#cut = "1"
gTree = readTree(filename, "photonTree")
fTree = readTree(filename, "photonJetTree")
#gH = getHisto( gTree, plot, color=1, cut=cut,firstBin=0,lastBin=1400 )
#fH = getHisto( fTree, plot, color=2, cut=cut,firstBin=0,lastBin=1400 )
for h, tree in [(gH, gTree), (fH, fTree)]:
h.Sumw2()
for event in tree:
h.Fill(getFromEvent(event), event.weight)
for h in [gH, fH]:
h.Scale(1, "width")
mh = Multihisto()
mh.addHisto(gH, "#gamma", draw="hist e")
mh.addHisto(fH, "#gamma_{jet}", draw="hist e")
can = ROOT.TCanvas()
can.cd()
mh.Draw()
from myRatio import Ratio
r = Ratio("#gamma/#gamma_{jet}", gH, fH)
r.draw(None, 2)
SaveAs(can, "compare_test")
def plotFakeRate( filenames, opts ):
mhisto = Multihisto()
mhisto.legendOption = "lp"
#mhisto.leg.SetHeader("Object matching")
for iColor, filename in enumerate(filenames):
mhisto.addHisto( getFakeRateHisto( filename, opts, iColor+2 ), getDatasetAbbr(filename,slim=False), draw="" )
if opts.plot == "photons.pt":
yuOrig = yutarosHistogramMC(1)
mhisto.addHisto( yuOrig, "DY tag&probe", draw="")
can = ROOT.TCanvas()
can.cd()
can.SetLogy(0)
mhisto.Draw()
saveName = "%s_%s_%s"%("fakeRate",opts.plot,opts.savePrefix)
can.SaveAs( "plots/%s.pdf"%manipulateSaveName(saveName) )
def drawClosure( filenames, predFilenames, plot, commonCut, infoText, additionalLabel="", modifyEmptyBins=True ):
infoText = ROOT.TLatex(0,.96, "CMS Private Work 19.7fb^{-1} #sqrt{s}=8TeV #geq1#gamma,#geq2jets" )
infoText.SetNDC()
infoText.SetTextSize(0.045)
gHist = getHists( filenames, plot, commonCut )
fHist, sysHist = predictionHistos( predFilenames, plot, commonCut, modifyEmptyBins )
fHist.SetMarkerSize(0)
sysHist.SetFillStyle(3254)
sysHist.SetFillColor( sysHist.GetLineColor() )
sysHist.SetLineColor(0)
signalAbbrs = mergeDatasetAbbr( [ getDatasetAbbr(x) for x in filenames ] )
muhisto = Multihisto()
muhisto.leg.SetHeader( ",".join( [ datasetToLatex(x) for x in signalAbbrs ] ) )
muhisto.addHisto( gHist, "Simulation", draw="" )
muhisto.addHisto( fHist, "Prediction", draw="hist e")
muhisto.addHisto( sysHist, "syst. uncert", draw="e2")
muhisto.addHisto( gHist, "", draw="e0" ) ## add a second time to draw on top
can = ROOT.TCanvas("", "", 1000, 1200)
can.cd()
muhisto.Draw()
from myRatio import Ratio
r = Ratio( "Sim./Pred.", gHist, fHist, sysHist )
r.draw(0.,2)
infoText.Draw()
muhisto.leg.AddEntry( r.totalUncert, "total uncert", "f" )
muhisto.leg.Draw()
SaveAs(can, "qcdClosure_%s_%s"%("".join(signalAbbrs)+additionalLabel, plot) )
# Since root is too stupid to clear the canvas before python is ending, clean
# the canvas yourself
ROOT.SetOwnership( can, False )
del can
def drawTwoHists( gHist, fHist, sHist, saveName, minmax ):
for bin in range( sHist.GetNbinsX()+2 ):
sHist.SetBinError( bin, sHist.GetBinContent(bin) )
sHist.SetBinContent( bin, fHist.GetBinContent(bin) )
sHist.SetFillColor( sHist.GetLineColor() )
sHist.SetLineColor( sHist.GetLineColor() )
sHist.SetFillStyle(3254)
sHist.SetMarkerSize(0)
fHist.SetLineColor(2)
sHist.SetLineColor(2)
for h in gHist, fHist, sHist:
from inheritRoot import H1F
h.__class__ = H1F
h.MergeOverflow()
h.Scale(1., "width")
muhisto = Multihisto()
muhisto.addHisto( gHist, "Simulation", draw="hist e" )
muhisto.addHisto( fHist, "Prediction", draw="hist")
muhisto.addHisto( sHist, "#sigma_{w}", draw="e2")
can = ROOT.TCanvas(randomName(), "", 1000, 1200)
can.cd()
muhisto.Draw()
text = ROOT.TLatex(.1,.965, "%i #leq p_{T^{*}} < %i, %i #leq H_{T} < %i"%minmax)
text.SetNDC()
text.Draw()
from myRatio import Ratio
r = Ratio( "Sim./Pred.", gHist, fHist )
r.draw(0,2)
can.SaveAs(saveName+".pdf")
ROOT.SetOwnership( can, False )
del can
def compareBinnedSamples(histList1, histList2, plot="met"):
cut = "[email protected]() && [email protected]()"
treeName = "photonTree"
mh = Multihisto()
#if "ZGamma" in histList1[0]:
# mh.setMinimum(0.01)
# sum all histos in histList1
hist1 = None
for fileName in histList1:
datasetAbbr1 = getDatasetAbbr(fileName)
tree = readTree(fileName, treeName)
h = getHisto(tree, plot, cut=cut, color=colors[datasetAbbr1])
if hist1: hist1.Add(h)
else: hist1 = h
mh.addHisto(hist1, datasetToLatex(datasetAbbr1), draw="hist e")
# stack histos in histList2
for fileName in histList2:
datasetAbbr = getDatasetAbbr(fileName)
tree = readTree(fileName, treeName)
h = getHisto(tree, plot, cut=cut, color=colors[datasetAbbr])
mh.addHisto(h, datasetToLatex(datasetAbbr), toStack=True)
mh.Draw()
stack = mh.stack.GetStack().Last()
stack.SetMarkerSize(0)
stack.Draw("same e0")
errorHist = mh.stack.GetStack().Last().Clone(randomName())
errorHist.SetMarkerColor(1)
errorHist.Draw("same e")
if plot != "met":
datasetAbbr1 += plot
SavePad("compareBinned" + datasetAbbr1)
def drawComparison( histName, mc, data ):
hdata = getIsolationHist( data, histName )
hdata.SetLineColor(1)
hmc = getIsolationHist( mc, histName )
hmc.SetLineColor(2)
for h in hmc, hdata:
h.SetLineWidth(2)
hmc.Scale( hdata.Integral() / hmc.Integral() )
mh = Multihisto()
mh.addHisto( hdata, "Data", draw="e0" )
mh.addHisto( hmc, "MC", draw="hist", toStack=True )
c = ROOT.TCanvas()
mh.Draw()
from myRatio import Ratio
r = Ratio( "Data/Bkg", hdata, hmc )
r.draw(0,2)
SavePad("isolationComparison_%s"%histName )
def compareTrees(plot="photons.pt", filename="slimAllQCD_V02.28_tree.root"):
label, unit, binning = readAxisConf("ht")
import array
fH = ROOT.TH1F(randomName(), ";%s;" % label,
len(binning) - 1, array.array('d', binning))
fH.SetLineColor(2)
fH.SetMarkerColor(2)
fH.Sumw2()
#cut = "!photons[0].isGen(0)"
cut = "1"
gTree = readTree(filename, "photonTree")
fTree = readTree(filename, "photonJetTree")
gH = getHisto(gTree, "ht", color=1, cut=cut, firstBin=0, lastBin=1400)
#fH = getHisto( fTree, plot, color=2, cut=cut,firstBin=0,lastBin=1400 )
for event in fTree:
fH.Fill(getFromEvent(event))
for h in [gH, fH]:
h.Scale(1. / h.Integral())
mh = Multihisto()
mh.addHisto(gH, "new", draw="hist e")
mh.addHisto(fH, "old (recalculated)", draw="hist e")
can = ROOT.TCanvas()
can.cd()
mh.Draw()
from myRatio import Ratio
r = Ratio("#gamma/#gamma_{jet}", gH, fH)
r.draw(0, 2)
SaveAs(can, "compare_%s_norm" % plot)
def compareHists(filenames, tight, fcut, draw=False):
loose = getHistoFromFiles("met", "photonJetTree", filenames, fcut)
loose.SetLineColor(2)
if loose.Integral(0, loose.FindBin(99)):
loose.Scale(
tight.Integral(0, loose.FindBin(99)) /
loose.Integral(0, loose.FindBin(99)))
option = "uu norm" if filenames[0].startswith("PhotonHad") else "ww"
if draw:
c = ROOT.TCanvas()
c.cd()
mh = Multihisto()
mh.addHisto(tight.Clone(randomName()), "#gamma_{tight}", draw="hist e")
mh.addHisto(loose, "#gamma_{loose}", draw="hist e")
mh.Draw()
from myRatio import Ratio
r = Ratio("#gamma_{tight}/#gamma_{loose}", tight, loose)
r.draw(0, 2)
c.SaveAs("plots/findFoId_%s_twoDistributions_%s.pdf" %
(getSaveNameFromDatasets(filenames), ''.join(
[s for s in fcut if s.isdigit()])))
return tight.Chi2Test(loose, option)
def drawSignalContamination(filename, xy, split):
import array
label, unit, binning = readAxisConf("met")
metBinning = array.array("d", binning)
gHisto = readHisto(filename, "gMet%s_%s" % xy).Rebin(
len(metBinning) - 1, randomName(), metBinning)
eHisto = readHisto(filename, "eMet%s_%s" % xy).Rebin(
len(metBinning) - 1, randomName(), metBinning)
fHisto = readHisto(filename, "fMet%s_%s" % xy).Rebin(
len(metBinning) - 1, randomName(), metBinning)
eHisto = applyFakeRateEWK(eHisto)
eContamination = divideHistos(eHisto, gHisto)
eContamination.SetLineColor(2)
fContamination = divideHistos(fHisto, gHisto)
fContamination.SetLineColor(4)
totalContamination = divideHistos(addHistos([eHisto, fHisto]), gHisto)
totalContamination.SetLineColor(1)
totalContamination.SetMarkerColor(1)
for h in [totalContamination, fContamination, eContamination]:
h.Scale(100.) # in %
h.GetYaxis().SetTitleOffset(0.9)
h.SetTitle(";%s [#text{%s}];Signal Contamination [%s]" %
("#met", unit, "%"))
h.SetLineWidth(2)
h.SetMarkerSize(0)
h.SetLabelSize(1. / 15.8447, "xy")
h.SetTitleSize(1. / 15.8447, "xy")
mh = Multihisto()
mh.setMinimum(0)
if split:
mh.addHisto(totalContamination, "Total", draw="pe")
mh.addHisto(fContamination,
"b_{#text{signal}}^{#text{QCD}}/s",
draw="hist")
mh.addHisto(eContamination,
"b_{#text{signal}}^{e#rightarrow#gamma}/s",
draw="hist")
else:
mh.addHisto(totalContamination, "", draw="e0")
can = ROOT.TCanvas()
can.cd()
can.SetLogy(False)
mh.Draw()
totalContamination.Draw("same")
info.Draw()
saveName = "signalContamination_%s_%s_%s_%s" % (xy +
(split, filename[0:-4]))
SaveAs(can, saveName)
ROOT.gPad.SaveAs("/home/knut/master/documents/thesis/plots/%stex" %
saveName)
correctTiksPlot("/home/knut/master/documents/thesis/plots/%stex" %
saveName)
def drawClosure(plot,
gTree,
foTree,
cut,
can,
info,
datasetAbbr,
additionalInfo=""):
# The first attempt to get the histogram is only to get the minimal
# and maximal value on the x-axis, for not predefined binning
h_gamma = getHisto(gTree, plot, cut=cut)
h_fo = getHisto(foTree, plot, cut=cut)
xMin, xMax = getXMinXMax([h_gamma, h_fo])
h_gamma = getHisto(gTree,
plot,
cut=cut,
color=1,
firstBin=xMin,
lastBin=xMax)
h_fo = getHisto(foTree,
plot,
cut=cut,
weight="weight*w_qcd",
color=46,
firstBin=xMin,
lastBin=xMax)
h_fo_error = getQCDErrorHisto(foTree,
plot,
cut=cut,
firstBin=xMin,
lastBin=xMax)
h_fo_error.SetFillColor(h_fo.GetLineColor())
h_fo_error.SetLineColor(h_fo_error.GetLineColor())
h_fo_error.SetFillStyle(3254)
h_fo_error.SetMarkerSize(0)
muhisto = Multihisto()
muhisto.leg.SetHeader(datasetToLatex(datasetAbbr))
muhisto.addHisto(h_gamma, "#gamma", draw="hist e0")
muhisto.addHisto(h_fo, "#gamma_{jet}#upointw", draw="hist e0")
muhisto.addHisto(h_fo_error, "#sigma_{w}", draw="e2")
hPad = ROOT.TPad("hPad", "Histogram", 0, 0.2, 1, 1)
hPad.cd()
muhisto.Draw()
ratioPad = ROOT.TPad("ratioPad", "Ratio", 0, 0, 1, 0.2)
ratioPad.cd()
ratioPad.SetLogy(False)
from myRatio import Ratio
r = Ratio("#gamma/#gamma_{pred}", h_gamma, h_fo)
ratio, sys, one = r.draw(0, 2)
ratio.Draw("e1")
sys.Draw("same e2")
one.Draw()
can.cd()
hPad.Draw()
ratioPad.Draw()
info.Draw()
SaveAs(
can, "qcd_afterWeighting_%s_%s_%s" %
(datasetAbbr + additionalInfo, plot, reweightVar))
ROOT.SetOwnership(hPad, False)
ROOT.SetOwnership(ratioPad, False)
def compareTrees(plot="photons.pt",
filenames=["slimAllQCD_V02.28_tree.root"],
drawRatio=False):
cut = "1"
#cut = plot+">110"
#cut = "!photons[0].isGen(0)"
#cut = "ht < 600"
gH = getHistoFromFiles(plot, "photonTree", filenames, cut)
fH = getHistoFromFiles(plot, "photonJetTree", filenames, cut, color=2)
weight2D = getMixedWeigthHisto(filenames,
filenames,
cut,
control=True,
fillEmptyBins=False)
for filename in filenames:
fTree = readTree(filename, "photonJetTree")
writeWeight2DToFile(filename, fTree, weight2D, "foWeightsCompare")
fgammaHist, fgammaWeightError = qcdPredictionHistos(
filenames, plot, cut, True)
for bin in range(fgammaHist.GetNbinsX() + 2):
fgammaHist.SetBinError(
bin,
sqrt(
fgammaHist.GetBinError(bin)**2 +
fgammaWeightError.GetBinContent(bin)**2))
fH = fgammaHist
for h in [gH, fH]:
h.Scale(1. / h.Integral())
h.SetMarkerSize(0)
h.GetXaxis().SetTitleOffset(1.03)
h.GetYaxis().SetTitleOffset(1.5)
h.GetXaxis().SetLabelSize(0.0633790992496425)
h.GetXaxis().SetTitleSize(0.0633790992496425)
h.GetYaxis().SetLabelSize(0.0633790992496425)
h.GetYaxis().SetTitleSize(0.0633790992496425)
h.GetYaxis().SetLabelOffset(0)
if plot == "photons[0].pt":
h.GetXaxis().SetTitle("$p_{T}$ [GeV]")
if plot == "photons[0].ptJet()":
h.GetXaxis().SetTitle("$p_{T^*}$ [GeV]")
mh = Multihisto()
mh.leg.SetX1(0.608)
mh.leg.SetX2(0.95)
mh.addHisto(gH, "#gamma_{#text{tight}}", draw="hist e")
mh.addHisto(fH, "#gamma_{#text{loose}}", draw="hist e")
can = ROOT.TCanvas()
#can.SetBottomMargin(0)
#can.SetTopMargin(0)
#can.SetRightMargin(0)
can.SetLeftMargin(0.19)
can.cd()
mh.Draw()
pc1 = ROOT.TLatex(0, .96, "CMS Private Work")
pc2 = ROOT.TLatex(.51, .96, "\SI{19.8}{fb^{-1}} #sqrt{s}=\SI{8}{TeV}")
for pc in [pc1, pc2]:
pc.SetNDC()
pc.SetTextSize(0.06311227345609463)
pc.Draw()
if drawRatio:
from myRatio import Ratio
r = Ratio("#gamma/#gamma_{jet}", gH, fH)
r.draw()
#can.SetFillColor(ROOT.kGreen)
SavePad("compare2_%s_%s" % (plot, shortName(filenames)))
ROOT.gPad.SaveAs("~/master/documents/thesis/plots/compare_%s_%s.tex" %
(manipulateSaveName(plot), shortName(filenames)))
correctTiksPlot(
"/home/knut/master/documents/thesis/plots/compare_%s_%s.tex" %
(manipulateSaveName(plot), shortName(filenames)))
ROOT.SetOwnership(can, False)
def drawBeforeClosure(plot,
gTree,
foTree,
cut,
can,
info,
datasetAbbr,
additionalInfo="",
norm=False):
if plot == "met" and cut != "1":
return
# The first attempt to get the histogram is only to get the minimal
# and maximal value on the x-axis, for not predefined binning
h_gamma = getHisto(gTree, plot, cut=cut)
h_fo = getHisto(foTree, plot, cut=cut)
xMin, xMax = getXMinXMax([h_gamma, h_fo])
h_gamma = getHisto(gTree,
plot,
cut=cut,
color=1,
firstBin=xMin,
lastBin=xMax)
h_fo = getHisto(foTree,
plot,
cut=cut,
color=46,
firstBin=xMin,
lastBin=xMax)
if norm:
for h in [h_gamma, h_fo]:
h.Scale(1. / h.Integral())
h.GetYaxis().SetTitle("Normed Entries")
muhisto = Multihisto()
muhisto.leg.SetHeader(datasetToLatex(datasetAbbr))
muhisto.addHisto(h_gamma, "#gamma", draw="hist e0")
muhisto.addHisto(h_fo, "#gamma_{jet}", draw="hist e0")
hPad = ROOT.TPad("hPad", "Histogram", 0, 0.2, 1, 1)
hPad.cd()
muhisto.Draw()
ROOT.TGaxis.SetMaxDigits(4)
ratioPad = ROOT.TPad("ratioPad", "Ratio", 0, 0, 1, 0.2)
ratioPad.cd()
ratioPad.SetLogy(False)
ratioGraph = ratios.RatioGraph(h_gamma, h_fo)
ratioGraph.draw(ROOT.gPad,
yMin=None,
yMax=None,
adaptiveBinning=False,
errors="yx")
ratioGraph.graph.Draw("same p e0") # draw nice points
ratioGraph.hAxis.SetYTitle("#gamma/#gamma_{jet}")
can.cd()
hPad.Draw()
ratioPad.Draw()
info.Draw()
SaveAs(
can, "qcd_preWeight_%s_%s_%s" %
(datasetAbbr + additionalInfo, plot, reweightVar))
ROOT.SetOwnership(hPad, False)
ROOT.SetOwnership(ratioPad, False)
def drawSignalContamination( filename, xy, split ):
import array
label, unit, binning = readAxisConf("met")
metBinning = array.array( "d", binning )
gHisto = readHisto( filename, "gMet%s_%s"%xy ).Rebin(len(metBinning)-1, randomName(), metBinning )
eHisto = readHisto( filename, "eMet%s_%s"%xy ).Rebin(len(metBinning)-1, randomName(), metBinning )
fHisto = readHisto( filename, "fMet%s_%s"%xy ).Rebin(len(metBinning)-1, randomName(), metBinning )
eHisto = applyFakeRateEWK( eHisto )
eContamination = divideHistos( eHisto, gHisto )
eContamination.SetLineColor(2)
fContamination = divideHistos( fHisto, gHisto )
fContamination.SetLineColor(4)
totalContamination = divideHistos( addHistos( [eHisto, fHisto] ), gHisto )
totalContamination.SetLineColor(1)
totalContamination.SetMarkerColor(1)
for h in [totalContamination, fContamination, eContamination]:
h.Scale(100.) # in %
h.GetYaxis().SetTitleOffset( 0.9 )
h.SetTitle(";%s [#text{%s}];Signal Contamination [%s]"%("#met",unit, "%"))
h.SetLineWidth(2)
h.SetMarkerSize(0)
h.SetLabelSize(1./15.8447, "xy")
h.SetTitleSize(1./15.8447, "xy")
mh = Multihisto()
mh.setMinimum(0)
if split:
mh.addHisto( totalContamination, "Total", draw="pe" )
mh.addHisto( fContamination, "b_{#text{signal}}^{#text{QCD}}/s", draw="hist" )
mh.addHisto( eContamination, "b_{#text{signal}}^{e#rightarrow#gamma}/s", draw="hist" )
else:
mh.addHisto( totalContamination, "", draw="e0" )
can = ROOT.TCanvas()
can.cd()
can.SetLogy(False)
mh.Draw()
totalContamination.Draw("same")
info.Draw()
saveName = "signalContamination_%s_%s_%s_%s"%(xy+(split,filename[0:-4] ))
SaveAs(can, saveName )
ROOT.gPad.SaveAs("/home/knut/master/documents/thesis/plots/%stex"%saveName )
correctTiksPlot( "/home/knut/master/documents/thesis/plots/%stex"%saveName )
def compareTrees(plot="met",
filenames=["slimAllQCD_V02.28_tree.root"],
drawRatio=False):
#cut = "@electrons.size()==0 && @muons.size()==0"
#cut = plot+">110"
#cut = "!photons[0].isGen(0)"
#cut = "ht < 600"
cut = "1"
gH = getHistoFromFiles(plot, "photonTree", filenames, "1")
fH = getHistoFromFiles(plot, "photonJetTree", filenames, "1", color=2)
for h in [gH, fH]:
#h.Scale( 1./h.Integral() )
h.SetMarkerSize(0)
h.GetXaxis().SetTitleOffset(1.03)
h.GetYaxis().SetTitleOffset(1.5)
h.GetXaxis().SetLabelSize(0.0633790992496425)
h.GetXaxis().SetTitleSize(0.0633790992496425)
h.GetYaxis().SetLabelSize(0.0633790992496425)
h.GetYaxis().SetTitleSize(0.0633790992496425)
h.GetYaxis().SetLabelOffset(0)
if plot == "photons[0].pt":
h.GetXaxis().SetTitle("$p_{T}$ [GeV]")
if plot == "photons[0].ptJet()":
h.GetXaxis().SetTitle("$p_{T^*}$ [GeV]")
ratio = gH.Clone(randomName())
ratio.Divide(fH)
mh = Multihisto()
#mh.leg.SetX1(0.608)
#mh.leg.SetX2(0.95)
mh.addHisto(gH, "#gamma_{#text{tight}}", draw="hist e")
mh.addHisto(fH, "#gamma_{#text{loose}}", draw="hist e")
can = ROOT.TCanvas()
#can.SetBottomMargin(0)
#can.SetTopMargin(0)
#can.SetRightMargin(0)
#can.SetLeftMargin(0.19)
can.cd()
mh.Draw()
pc1 = ROOT.TLatex(0, .96, "CMS Private Work")
pc2 = ROOT.TLatex(.51, .96, "\SI{19.8}{fb^{-1}} #sqrt{s}=\SI{8}{TeV}")
for pc in [pc1, pc2]:
pc.SetNDC()
pc.SetTextSize(0.06311227345609463)
pc.Draw()
if drawRatio:
from myRatio import Ratio
r = Ratio("#gamma/#gamma_{jet}", gH, fH)
r.draw()
#can.SetFillColor(ROOT.kGreen)
SavePad("compare2_%s_%s" % ("test", shortName(filenames)))
#ROOT.gPad.SaveAs("~/master/documents/thesis/plots/compare_%s_%s.tex"%(manipulateSaveName(plot),shortName( filenames )) )
ROOT.SetOwnership(can, False)
def drawPhi( filenames ):
t = readTree( filenames[0], "photonTree" )
for f in filenames[1:]:
t.Add( "%s/photonTree"%f )
absolute = True
label, unit, binning = readAxisConf( "photons[0].phi" )
if absolute:
binning = [ x for x in binning if x >= 0 ]
import array
gH1 = ROOT.TH1F( randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});", len(binning)-1, array.array('d', binning) )
gH2 = ROOT.TH1F( randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});", len(binning)-1, array.array('d', binning) )
gH3 = ROOT.TH1F( randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});", len(binning)-1, array.array('d', binning) )
gH2.SetLineColor(2)
gH3.SetLineColor(3)
for h in gH1, gH2, gH3:
h.Sumw2()
from math import fabs
for e in t:
dPhi = e.photons[0].DeltaPhi( e.metPhi )
if absolute:
dPhi = fabs( dPhi )
if e.met<10:
gH1.Fill( dPhi )
elif e.met < 100:
gH2.Fill( dPhi )
else:
gH3.Fill( dPhi )
ft = readTree( filenames[0], "photonJetTree" )
for f in filenames[1:]:
ft.Add( "%s/photonJetTree"%f )
cutStr = "photons[0].chargedIso/100<2.6 && photons[0].neutralIso/100<3.5+0.04*photons[0].pt && photons[0].photonIso/100<1.3+0.005*photons[0].pt"
cutStr += "&& (photons[0].chargedIso>0 || photons[0].neutralIso>0 || photons[0].photonIso>0)"
ft = ft.CopyTree( cutStr )
fH1 = ROOT.TH1F( randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});", len(binning)-1, array.array('d', binning) )
fH2 = ROOT.TH1F( randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});", len(binning)-1, array.array('d', binning) )
fH3 = ROOT.TH1F( randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});", len(binning)-1, array.array('d', binning) )
fH2.SetLineColor(2)
fH3.SetLineColor(3)
for h in fH1, fH2, fH3:
h.Sumw2()
h.SetLineStyle(3)
for e in ft:
dPhi = e.photons[0].DeltaPhi( e.metPhi )
if absolute:
dPhi = fabs( dPhi )
if e.met<10:
fH1.Fill( dPhi )
elif e.met < 100:
fH2.Fill( dPhi )
else:
fH3.Fill( dPhi )
for h in gH1, gH2, gH3, fH1, fH2, fH3:
if h.Integral():
h.Scale( 1./h.Integral(), "width" )
mh = Multihisto()
mh.addHisto( gH1, "met<10", draw="hist " )
mh.addHisto( fH1, "met<10, loose", draw="hist " )
mh.addHisto( gH2, "10<met<100", draw="hist " )
mh.addHisto( fH2, "10<met<100, loose", draw="hist " )
mh.addHisto( gH3, "100<met", draw="hist " )
mh.addHisto( fH3, "100<met, loose", draw="hist " )
c = ROOT.TCanvas()
c.cd()
mh.Draw()
c.SaveAs("plots/deltaPhi_%s.pdf"%getSaveNameFromDatasets( filenames ))
def inclusiveAndIsrSamples(fList1, fList2, saveAffix=""):
cut = "[email protected]() && [email protected]()"
treeName = "photonTree"
plot = "met"
if saveAffix == "pt130":
cut += " && photons[0].pt>130"
saveAffix = "_" + saveAffix
mh = Multihisto()
h1 = getHists(fList1, plot, cut)
h1gen = getHists(fList1, plot, cut + "&&photons[0].isGen(0)")
h2 = getHists(fList2, plot, cut)
h2gen = getHists(fList2, plot, cut + "&&photons[0].isGen(0)")
for h in h2, h2gen:
h.SetLineColor(2)
for h in h1gen, h2gen:
h.SetLineStyle(2)
for h in [h2, h2gen, h1, h1gen]:
h.SetLabelSize(1.0 / 31.5562 / 0.502113, "xyz")
h.SetTitleSize(1.0 / 31.5562 / 0.502113, "xyz")
h.GetXaxis().SetTitleOffset(1.1)
h.GetYaxis().SetTitleOffset(0.85)
if plot == "met":
h.GetXaxis().SetTitle("#met#text{ [GeV]}")
abbr1 = shortName(fList1)
abbr2 = shortName(fList2)
mh = Multihisto()
if "ZGamma" in fList1[0]:
mh.setMaximum(10)
mh.setMinimum(2e-4)
mh.leg.SetFillStyle(0)
mh.leg.SetX1(0.6)
mh.leg.SetY1(0.6)
mh.addHisto(h1, datasetToLatex(abbr1))
mh.addHisto(h1gen, "#text{match to gen }#gamma")
mh.addHisto(h2, datasetToLatex(abbr2))
mh.addHisto(h2gen, "#text{match to gen }#gamma")
mh.Draw()
info.SetTextSize(1.0 / 31.5562 / 0.502113)
info.Draw()
if "pt130" in saveAffix:
cutInfo = ROOT.TLatex(0.2, 0.8, "p_{T,#gamma}#geq#SI{130}{GeV}")
cutInfo.SetNDC()
info.SetTextSize(1.0 / 31.5562 / 0.502113)
cutInfo.Draw()
SavePad("inclusiveAndIsrSample_%s%s" % (abbr1, saveAffix))
ROOT.gPad.SaveAs("/home/knut/master/documents/thesis/plots/inclusiveAndIsrSample_%s%s.tex" % (abbr1, saveAffix))
correctTiksPlot("/home/knut/master/documents/thesis/plots/inclusiveAndIsrSample_%s%s.tex" % (abbr1, saveAffix))
S10scaledUsingS10 = s10.Clone( randomName() )
S10scaledUsingS10.Divide( s10 )
S10scaledUsingS10.Multiply( data )
S10scaledUsingS10.SetLineStyle( 2 )
S7scaledUsingS7 = s7.Clone( randomName() )
S7scaledUsingS7.Divide( s7 )
S7scaledUsingS7.Multiply( data )
S7scaledUsingS7.SetLineStyle( 3 )
S7scaledUsingS10 = s7.Clone( randomName() )
S7scaledUsingS10.Divide( s10 )
S7scaledUsingS10.Multiply( data )
S7scaledUsingS10.SetLineStyle( 8 )
mh = Multihisto()
mh.setMaximum(0.07)
mh.addHisto( data, "Data", draw="p" )
mh.addHisto( s10, "S10 Scenario", draw="hist" )
mh.addHisto( S10scaledUsingS10, "S10 scaled by data/S10", draw="hist")
mh.addHisto( s7, "S7 Scenario", draw="hist")
mh.addHisto( S7scaledUsingS7, "S7 scaled by data/S7", draw="hist")
mh.addHisto( S7scaledUsingS10, "S7 scaled by data/S10", draw="hist")
mh.Draw()
mh.leg.SetX1( .65 )
mh.leg.SetY1( .7 )
mh.leg.SetX2( 1 )
mh.leg.SetY2( 1 )
ROOT.gPad.SetLogy(0)
ROOT.gPad.SaveAs("plots/pileupScenarios.pdf")
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 )
S10scaledUsingS10 = s10.Clone(randomName())
S10scaledUsingS10.Divide(s10)
S10scaledUsingS10.Multiply(data)
S10scaledUsingS10.SetLineStyle(2)
S7scaledUsingS7 = s7.Clone(randomName())
S7scaledUsingS7.Divide(s7)
S7scaledUsingS7.Multiply(data)
S7scaledUsingS7.SetLineStyle(3)
S7scaledUsingS10 = s7.Clone(randomName())
S7scaledUsingS10.Divide(s10)
S7scaledUsingS10.Multiply(data)
S7scaledUsingS10.SetLineStyle(8)
mh = Multihisto()
mh.setMaximum(0.07)
mh.addHisto(data, "Data", draw="p")
mh.addHisto(s10, "S10 Scenario", draw="hist")
mh.addHisto(S10scaledUsingS10, "S10 scaled by data/S10", draw="hist")
mh.addHisto(s7, "S7 Scenario", draw="hist")
mh.addHisto(S7scaledUsingS7, "S7 scaled by data/S7", draw="hist")
mh.addHisto(S7scaledUsingS10, "S7 scaled by data/S10", draw="hist")
mh.Draw()
mh.leg.SetX1(.65)
mh.leg.SetY1(.7)
mh.leg.SetX2(1)
mh.leg.SetY2(1)
ROOT.gPad.SetLogy(0)
ROOT.gPad.SaveAs("plots/pileupScenarios.pdf")
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)
bkg["zll"] = { "files": ["slimZGammaLL_V02.%s_tree.root"%treeVersion], "title": "#gammaZ#rightarrow#gammall", "color": 2 }
bkg["tt"] = { "files": ["slimTTGamma_V03.%s_tree.root"%treeVersion], "title": "#gammat#bar{t}", "color": ROOT.kBlue }
bkg["wjets"] = { "files": ["slimWJets_250_300_V03.24_tree.root", "slimWJets_300_400_V03.24_tree.root", "slimWJets_400_inf_V03.24_tree.root" ], "title": "W", "color": ROOT.kGreen+4 }
bkg["wgamma"] = { "files": ["slimWGamma_130_inf_V03.24_tree.root", "slimWGamma_50_130_V03.24_tree.root" ], "title": "#gammaW", "color": ROOT.kGreen-4 }
#bkg["qcd"] = { "files": ["slimQCD_1000_inf_V03.24_tree.root", "slimQCD_250_500_V03.24_tree.root", "slimQCD_500_1000_V03.24_tree.root"], "title":"Multijet", "color": ROOT.kCyan+3 }
nestedBkgFiles = [ bkg[a]["files"] for a in bkg.keys()]
bkgFiles = [item for sublist in nestedBkgFiles for item in sublist]
kFactor = getkFactor( dataFiles, bkgFiles, opts.plot, chi2Cut )
#signal = getHists( ["slimW_1700_720_375_V03.24_tree.root" ], opts.plot, cut )
#signal.SetLineColor( ROOT.kGreen )
#signal.SetLineWidth(2)
mh = Multihisto()
mh.setMinimum(0)
mh.addHisto( data, "Data", draw="pe" )
for name, d in bkg.iteritems():
histo = getHists( d["files"], opts.plot, cut )
histo.SetLineColor( d["color"] )
histo.Scale( kFactor )
mh.addHisto( histo, d["title"], True )
#mh.addHisto( signal, "Wino", False )
#combiBkg = getCombinatoricalBkg( dataFiles, opts.plot )
#combiBkg.SetLineWidth(2)
#combiBkg.SetLineColor( ROOT.kBlue )
#combiBkg.Scale( data.Integral(0, data.FindBin(70), "width") / combiBkg.Integral(0,data.FindBin(70),"width"))
#mh.addHisto( combiBkg, "bkg", draw="hist e" )
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 drawGenComposition(treeName, sample):
mh = Multihisto()
mh.addHisto(sample.plot(treeName, cut="photons[0].isGen(0)", color=2),
"gen #gamma")
mh.addHisto(sample.plot(treeName, cut="photons[0].isGen(1)", color=3),
"gen e")
mh.addHisto(sample.plot(treeName, cut="photons[0].isGen(2)", color=4),
"gen hadron")
mh.addHisto(sample.plot(treeName, cut="1", color=1), "inclusive")
can = ROOT.TCanvas()
can.cd()
mh.Draw()
info = PlotCaption(treeName=treeName)
info.Draw()
allDatasetAbbr = sample.datasetAbbr
plot = "met"
SaveAs(can, "composition_%s_%s_%s" % (treeName, plot, allDatasetAbbr))
def inclusiveAndIsrSamples(fList1, fList2, saveAffix=""):
cut = "[email protected]() && [email protected]()"
treeName = "photonTree"
plot = "met"
if saveAffix == "pt130":
cut += " && photons[0].pt>130"
saveAffix = "_" + saveAffix
mh = Multihisto()
h1 = getHists(fList1, plot, cut)
h1gen = getHists(fList1, plot, cut + "&&photons[0].isGen(0)")
h2 = getHists(fList2, plot, cut)
h2gen = getHists(fList2, plot, cut + "&&photons[0].isGen(0)")
for h in h2, h2gen:
h.SetLineColor(2)
for h in h1gen, h2gen:
h.SetLineStyle(2)
for h in [h2, h2gen, h1, h1gen]:
h.SetLabelSize(1. / 31.5562 / 0.502113, "xyz")
h.SetTitleSize(1. / 31.5562 / 0.502113, "xyz")
h.GetXaxis().SetTitleOffset(1.1)
h.GetYaxis().SetTitleOffset(0.85)
if plot == "met":
h.GetXaxis().SetTitle("#met#text{ [GeV]}")
abbr1 = shortName(fList1)
abbr2 = shortName(fList2)
mh = Multihisto()
if "ZGamma" in fList1[0]:
mh.setMaximum(10)
mh.setMinimum(2e-4)
mh.leg.SetFillStyle(0)
mh.leg.SetX1(0.6)
mh.leg.SetY1(0.6)
mh.addHisto(h1, datasetToLatex(abbr1))
mh.addHisto(h1gen, "#text{match to gen }#gamma")
mh.addHisto(h2, datasetToLatex(abbr2))
mh.addHisto(h2gen, "#text{match to gen }#gamma")
mh.Draw()
info.SetTextSize(1. / 31.5562 / 0.502113)
info.Draw()
if "pt130" in saveAffix:
cutInfo = ROOT.TLatex(.2, .8, "p_{T,#gamma}#geq#SI{130}{GeV}")
cutInfo.SetNDC()
info.SetTextSize(1. / 31.5562 / 0.502113)
cutInfo.Draw()
SavePad("inclusiveAndIsrSample_%s%s" % (abbr1, saveAffix))
ROOT.gPad.SaveAs(
"/home/knut/master/documents/thesis/plots/inclusiveAndIsrSample_%s%s.tex"
% (abbr1, saveAffix))
correctTiksPlot(
"/home/knut/master/documents/thesis/plots/inclusiveAndIsrSample_%s%s.tex"
% (abbr1, saveAffix))
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 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 compareTrees( plot="photons.pt", filenames=["slimAllQCD_V02.28_tree.root"], drawRatio=False ):
cut = "1"
#cut = plot+">110"
#cut = "!photons[0].isGen(0)"
#cut = "ht < 600"
gH = getHistoFromFiles( plot, "photonTree", filenames, cut )
fH = getHistoFromFiles( plot, "photonJetTree", filenames, cut, color=2 )
weight2D = getMixedWeigthHisto( filenames, filenames, cut, control=True, fillEmptyBins=False )
for filename in filenames:
fTree = readTree( filename, "photonJetTree" )
writeWeight2DToFile( filename, fTree, weight2D, "foWeightsCompare" )
fgammaHist, fgammaWeightError = qcdPredictionHistos( filenames, plot, cut, True )
for bin in range( fgammaHist.GetNbinsX()+2 ):
fgammaHist.SetBinError( bin, sqrt( fgammaHist.GetBinError(bin)**2 + fgammaWeightError.GetBinContent(bin)**2 ) )
fH = fgammaHist
for h in [gH, fH]:
h.Scale( 1./h.Integral() )
h.SetMarkerSize(0)
h.GetXaxis().SetTitleOffset( 1.03 )
h.GetYaxis().SetTitleOffset( 1.5 )
h.GetXaxis().SetLabelSize( 0.0633790992496425 )
h.GetXaxis().SetTitleSize( 0.0633790992496425 )
h.GetYaxis().SetLabelSize( 0.0633790992496425 )
h.GetYaxis().SetTitleSize( 0.0633790992496425 )
h.GetYaxis().SetLabelOffset(0)
if plot == "photons[0].pt":
h.GetXaxis().SetTitle( "$p_{T}$ [GeV]" )
if plot == "photons[0].ptJet()":
h.GetXaxis().SetTitle( "$p_{T^*}$ [GeV]" )
mh = Multihisto()
mh.leg.SetX1(0.608)
mh.leg.SetX2(0.95)
mh.addHisto( gH, "#gamma_{#text{tight}}", draw="hist e" )
mh.addHisto( fH, "#gamma_{#text{loose}}", draw="hist e" )
can = ROOT.TCanvas()
#can.SetBottomMargin(0)
#can.SetTopMargin(0)
#can.SetRightMargin(0)
can.SetLeftMargin(0.19)
can.cd()
mh.Draw()
pc1 = ROOT.TLatex(0,.96, "CMS Private Work")
pc2 = ROOT.TLatex( .51,.96, "\SI{19.8}{fb^{-1}} #sqrt{s}=\SI{8}{TeV}")
for pc in [pc1, pc2]:
pc.SetNDC()
pc.SetTextSize(0.06311227345609463)
pc.Draw()
if drawRatio:
from myRatio import Ratio
r = Ratio( "#gamma/#gamma_{jet}", gH, fH )
r.draw()
#can.SetFillColor(ROOT.kGreen)
SavePad( "compare2_%s_%s"%(plot,shortName( filenames )) )
ROOT.gPad.SaveAs("~/master/documents/thesis/plots/compare_%s_%s.tex"%(manipulateSaveName(plot),shortName( filenames )) )
correctTiksPlot("/home/knut/master/documents/thesis/plots/compare_%s_%s.tex"%(manipulateSaveName(plot),shortName( filenames )) )
ROOT.SetOwnership( can, False )
"#gammaW",
"color":
ROOT.kGreen - 4
}
#bkg["qcd"] = { "files": ["slimQCD_1000_inf_V03.24_tree.root", "slimQCD_250_500_V03.24_tree.root", "slimQCD_500_1000_V03.24_tree.root"], "title":"Multijet", "color": ROOT.kCyan+3 }
nestedBkgFiles = [bkg[a]["files"] for a in bkg.keys()]
bkgFiles = [item for sublist in nestedBkgFiles for item in sublist]
kFactor = getkFactor(dataFiles, bkgFiles, opts.plot, chi2Cut)
#signal = getHists( ["slimW_1700_720_375_V03.24_tree.root" ], opts.plot, cut )
#signal.SetLineColor( ROOT.kGreen )
#signal.SetLineWidth(2)
mh = Multihisto()
mh.setMinimum(0)
mh.addHisto(data, "Data", draw="pe")
for name, d in bkg.iteritems():
histo = getHists(d["files"], opts.plot, cut)
histo.SetLineColor(d["color"])
histo.Scale(kFactor)
mh.addHisto(histo, d["title"], True)
#mh.addHisto( signal, "Wino", False )
#combiBkg = getCombinatoricalBkg( dataFiles, opts.plot )
#combiBkg.SetLineWidth(2)
#combiBkg.SetLineColor( ROOT.kBlue )
#combiBkg.Scale( data.Integral(0, data.FindBin(70), "width") / combiBkg.Integral(0,data.FindBin(70),"width"))
#mh.addHisto( combiBkg, "bkg", draw="hist e" )
def drawPhi(filenames):
t = readTree(filenames[0], "photonTree")
for f in filenames[1:]:
t.Add("%s/photonTree" % f)
absolute = True
label, unit, binning = readAxisConf("photons[0].phi")
if absolute:
binning = [x for x in binning if x >= 0]
import array
gH1 = ROOT.TH1F(randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});",
len(binning) - 1, array.array('d', binning))
gH2 = ROOT.TH1F(randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});",
len(binning) - 1, array.array('d', binning))
gH3 = ROOT.TH1F(randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});",
len(binning) - 1, array.array('d', binning))
gH2.SetLineColor(2)
gH3.SetLineColor(3)
for h in gH1, gH2, gH3:
h.Sumw2()
from math import fabs
for e in t:
dPhi = e.photons[0].DeltaPhi(e.metPhi)
if absolute:
dPhi = fabs(dPhi)
if e.met < 10:
gH1.Fill(dPhi)
elif e.met < 100:
gH2.Fill(dPhi)
else:
gH3.Fill(dPhi)
ft = readTree(filenames[0], "photonJetTree")
for f in filenames[1:]:
ft.Add("%s/photonJetTree" % f)
cutStr = "photons[0].chargedIso/100<2.6 && photons[0].neutralIso/100<3.5+0.04*photons[0].pt && photons[0].photonIso/100<1.3+0.005*photons[0].pt"
cutStr += "&& (photons[0].chargedIso>0 || photons[0].neutralIso>0 || photons[0].photonIso>0)"
ft = ft.CopyTree(cutStr)
fH1 = ROOT.TH1F(randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});",
len(binning) - 1, array.array('d', binning))
fH2 = ROOT.TH1F(randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});",
len(binning) - 1, array.array('d', binning))
fH3 = ROOT.TH1F(randomName(), ";#Delta#phi(#gamma,#slash{E}_{T});",
len(binning) - 1, array.array('d', binning))
fH2.SetLineColor(2)
fH3.SetLineColor(3)
for h in fH1, fH2, fH3:
h.Sumw2()
h.SetLineStyle(3)
for e in ft:
dPhi = e.photons[0].DeltaPhi(e.metPhi)
if absolute:
dPhi = fabs(dPhi)
if e.met < 10:
fH1.Fill(dPhi)
elif e.met < 100:
fH2.Fill(dPhi)
else:
fH3.Fill(dPhi)
for h in gH1, gH2, gH3, fH1, fH2, fH3:
if h.Integral():
h.Scale(1. / h.Integral(), "width")
mh = Multihisto()
mh.addHisto(gH1, "met<10", draw="hist ")
mh.addHisto(fH1, "met<10, loose", draw="hist ")
mh.addHisto(gH2, "10<met<100", draw="hist ")
mh.addHisto(fH2, "10<met<100, loose", draw="hist ")
mh.addHisto(gH3, "100<met", draw="hist ")
mh.addHisto(fH3, "100<met, loose", draw="hist ")
c = ROOT.TCanvas()
c.cd()
mh.Draw()
c.SaveAs("plots/deltaPhi_%s.pdf" % getSaveNameFromDatasets(filenames))
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 qcdCorrelation( filenames, plot ):
signalCut = "met>=100"
controlCut = "!(%s)"%signalCut
commonCut = " && photons[0].ptJet()>100 && photons[0].ptJet()<120 "
gControlHist = photonHisto( filenames, "photonTree", plot, controlCut+commonCut, True )
fControlHist = photonHisto( filenames, "photonJetTree", plot, controlCut+commonCut, True )
gSignalHist = photonHisto( filenames, "photonTree", plot, signalCut+commonCut, True )
fSignalHist = photonHisto( filenames, "photonJetTree", plot, signalCut+commonCut, True )
gSignalHist.SetLineColor(2)
fSignalHist.SetLineColor(2)
fSignalHist.SetLineStyle(2)
fControlHist.SetLineStyle(2)
for h in [gControlHist, fControlHist, gSignalHist, fSignalHist]:
h.Scale( 1./h.Integral() )
h.SetMarkerSize(0)
pass
mh = Multihisto()
mh.addHisto( gControlHist, "Control #gamma", draw="hist e" )
mh.addHisto( fControlHist, "Control #gamma_{jet}",draw="hist e" )
mh.addHisto( gSignalHist, "Signal #gamma",draw="hist e" )
mh.addHisto( fSignalHist, "Signal #gamma_{jet}",draw="hist e" )
can = ROOT.TCanvas()
can.cd()
mh.Draw()
info = PlotCaption()
info.Draw()
abbrs = mergeDatasetAbbr( [ getDatasetAbbr(x) for x in filenames ] )
SaveAs( can, "correlation_%s_%s"%("".join(abbrs),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 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)