def studyVqqResolution(rootFile):
#get all from file
histos={}
inF=TFile.Open(rootFile)
keys=inF.GetListOfKeys()
for k in keys:
obj=inF.Get(k.GetName())
obj.SetDirectory(0)
histos[k.GetName()]=obj
inF.Close()
#plot
gROOT.SetBatch()
gROOT.SetStyle('Plain')
gStyle.SetOptStat(0)
gStyle.SetOptFit(1111)
gStyle.SetOptTitle(0)
gStyle.SetStatFont(42)
kin=['','30to40','40to50','50to75','75to100','100toInf']
for k in kin:
c=TCanvas('c','c',600,600)
c.cd()
c.SetCanvasSize(1000,500)
c.SetWindowSize(1000,500)
c.Divide(2,1)
c.cd(1)
histos['deta'+k+'barrel'].SetLineWidth(2)
histos['deta'+k+'barrel'].SetTitle('barrel')
histos['deta'+k+'barrel'].Draw('hist')
histos['deta'+k+'endcap'].SetLineWidth(2)
histos['deta'+k+'endcap'].SetLineStyle(7)
histos['deta'+k+'endcap'].SetTitle('endcap')
histos['deta'+k+'endcap'].Draw('histsame')
leg=TLegend(0.6,0.92,0.9,0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['deta'+k+'barrel'],'barrel','f')
leg.AddEntry(histos['deta'+k+'endcap'],'endcap','f')
leg.SetNColumns(2)
leg.Draw()
drawHeader()
c.cd(2)
histos['dphi'+k+'barrel'].SetLineWidth(2)
histos['dphi'+k+'barrel'].SetTitle('barrel')
histos['dphi'+k+'barrel'].Draw('hist')
histos['dphi'+k+'endcap'].SetLineWidth(2)
histos['dphi'+k+'endcap'].SetLineStyle(7)
histos['dphi'+k+'endcap'].SetTitle('endcap')
histos['dphi'+k+'endcap'].Draw('histsame')
c.Modified()
c.Update()
c.SaveAs('dr_%s.png'%k)
labels=[]
responseVars=['dpt','den','dphi','deta','dr']
for r in responseVars:
barrelResponse=TGraphErrors()
barrelResponse.SetName(r+'barrelresponse')
barrelResponse.SetLineWidth(2)
barrelResponse.SetFillStyle(0)
barrelResponse.SetMarkerStyle(20)
barrelCoreResponse=barrelResponse.Clone(r+'barrelcoreresponse')
endcapResponse=TGraphErrors()
endcapResponse.SetName(r+'endcapresponse')
endcapResponse.SetLineWidth(2)
endcapResponse.SetFillStyle(0)
endcapResponse.SetMarkerStyle(24)
endcapCoreResponse=endcapResponse.Clone(r+'endcapresponse')
for k in kin:
c.cd()
c.Clear()
c.SetWindowSize(1000,500)
c.Divide(2,1)
for i in [1,2] :
c.cd(i)
reg='barrel'
if i==2: reg='endcap'
h=histos[r+k+reg]
x=RooRealVar("x", h.GetXaxis().GetTitle(), h.GetXaxis().GetXmin(), h.GetXaxis().GetXmax())
data=RooDataHist("data", "dataset with x", RooArgList(x), h)
frame=x.frame()
RooAbsData.plotOn( data, frame, RooFit.DataError(RooAbsData.SumW2) )
mean1=RooRealVar("mean1","mean1",0,-0.5,0.5);
sigma1=RooRealVar("sigma1","sigma1",0.1,0.01,1.0);
gauss1=RooGaussian("g1","g",x,mean1,sigma1)
if r=='dpt' or r=='den' :
mean2=RooRealVar("mean2","mean2",0,-0.5,0.5);
sigma2=RooRealVar("sigma2","sigma2",0.1,0.01,1.0);
alphacb=RooRealVar("alphacb","alphacb",1,0.1,3);
ncb=RooRealVar("ncb","ncb",4,1,100)
gauss2 = RooCBShape("cb2","cb",x,mean2,sigma2,alphacb,ncb);
else:
mean1.setRange(0,0.5)
mean2=RooRealVar("mean2","mean",0,0,1);
sigma2=RooRealVar("sigma2","sigma",0.1,0.01,1.0);
gauss2=RooGaussian("g2","g",x,mean2,sigma2) ;
frac = RooRealVar("frac","fraction",0.9,0.0,1.0)
if data.sumEntries()<100 :
frac.setVal(1.0)
frac.setConstant(True)
model = RooAddPdf("sum","g1+g2",RooArgList(gauss1,gauss2), RooArgList(frac))
status=model.fitTo(data,RooFit.Save()).status()
if status!=0 : continue
model_cdf=model.createCdf(RooArgSet(x)) ;
cl=0.90
ul=0.5*(1.0+cl)
closestToCL=1.0
closestToUL=-1
closestToMedianCL=1.0
closestToMedian=-1
for ibin in xrange(1,h.GetXaxis().GetNbins()*10):
xval=h.GetXaxis().GetXmin()+(ibin-1)*h.GetXaxis().GetBinWidth(ibin)/10.
x.setVal(xval)
cdfValToCL=math.fabs(model_cdf.getVal()-ul)
if cdfValToCL<closestToCL:
closestToCL=cdfValToCL
closestToUL=xval
cdfValToCL=math.fabs(model_cdf.getVal()-0.5)
if cdfValToCL<closestToMedianCL:
closestToMedianCL=cdfValToCL
closestToMedian=xval
RooAbsPdf.plotOn(model,frame)
frame.Draw()
if i==1: drawHeader()
labels.append( TPaveText(0.6,0.92,0.9,0.98,'brNDC') )
ilab=len(labels)-1
labels[ilab].SetName(r+k+'txt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
kinReg=k.replace('to','-')
kinReg=kinReg.replace('Inf','#infty')
labels[ilab].AddText('['+reg+'] '+kinReg)
labels[ilab].Draw()
resolutionVal=math.fabs(closestToUL-closestToMedian)
responseGr=barrelResponse
responseCoreGr=barrelCoreResponse
coreResolutionVal=sigma1.getVal()
coreResolutionErr=sigma1.getError()
if frac.getVal()<0.7 and (sigma2.getVal()<sigma1.getVal()) :
coreResolutionVal=sigma2.getVal()
coreResolutionErr=sigma2.getError()
if i==2 :
responseGr=endcapResponse
responseCoreGr=endcapCoreResponse
if k!='' :
nrespPts=responseGr.GetN()
kinAvg=150
kinWidth=50
if k=='30to40' :
kinAvg=35
kinWidth=5
if k=='40to50' :
kinAvg=45
kinWidth=5
if k=='50to75' :
kinAvg=62.5
kinWidth=12.5
elif k=='75to100' :
kinAvg=87.5
kinWidth=12.5
responseGr.SetPoint(nrespPts,kinAvg,resolutionVal)
responseCoreGr.SetPoint(nrespPts,kinAvg,coreResolutionVal)
responseCoreGr.SetPointError(nrespPts,kinWidth,coreResolutionErr)
labels.append( TPaveText(0.15,0.7,0.4,0.9,'brNDC') )
ilab=len(labels)-1
labels[ilab].SetName(r+k+'fitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)'%frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean1.getVal(),mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma1.getVal(),sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)'%(1-frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean2.getVal(),mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma2.getVal(),sigma2.getError()))
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs(r+'res_'+k+'.png')
frame=TGraphErrors()
frame.SetPoint(0,0,0)
frame.SetPoint(1,200,0.3)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('frame')
cresp=TCanvas('cresp','cresp',500,500)
cresp.cd()
frame.Draw('ap')
barrelResponse.Draw('pl')
endcapResponse.Draw('pl')
frame.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Resolution %3.2f C.L."%cl )
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg=TLegend(0.6,0.92,0.9,0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelResponse,'barrel','fp')
leg.AddEntry(endcapResponse,'endcap','fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r+'res_evol.png')
frameCore=frame.Clone('framecore')
cresp.Clear()
frameCore.Draw('ap')
barrelCoreResponse.Draw('pl')
endcapCoreResponse.Draw('pl')
frameCore.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frameCore.GetYaxis().SetTitle("Core resolution")
frameCore.GetYaxis().SetTitleOffset(1.4)
frameCore.GetYaxis().SetNdivisions(10)
frameCore.GetYaxis().SetRangeUser(0,0.2)
drawHeader()
leg=TLegend(0.6,0.92,0.9,0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelCoreResponse,'barrel','fp')
leg.AddEntry(endcapCoreResponse,'endcap','fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r+'rescore_evol.png')
bosons=['h','z','w']
kin=['','50','100']
region=['','bb','eb','ee']
for k in kin:
for r in region:
c=TCanvas('c','c',600,600)
c.cd()
histos['mjj'+k+r].Rebin()
histos['mjj'+k+r].Draw()
ic=1
leg=TLegend(0.6,0.92,0.9,0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['mjj'+k+r],'inclusive','f')
for b in bosons:
if histos[b+'mjj'+k+r].Integral()<=0 : continue
ic=ic+1
histos[b+'mjj'+k+r].Rebin()
histos[b+'mjj'+k+r].SetLineColor(ic)
histos[b+'mjj'+k+r].SetLineWidth(2)
histos[b+'mjj'+k+r].SetMarkerColor(ic)
histos[b+'mjj'+k+r].SetMarkerStyle(1)
histos[b+'mjj'+k+r].SetFillStyle(3000+ic)
histos[b+'mjj'+k+r].SetFillColor(ic)
histos[b+'mjj'+k+r].Draw('histsame')
leg.AddEntry(histos[b+'mjj'+k+r],b,"f")
leg.SetNColumns(ic)
leg.Draw()
drawHeader()
labels.append( TPaveText(0.65,0.8,0.9,0.9,'brNDC') )
ilab=len(labels)-1
labels[ilab].SetName(k+r+'mjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle="inclusive"
if r == 'bb' : regionTitle='barrel-barrel'
if r == 'eb' : regionTitle='endcap-barrel'
if r == 'ee' : regionTitle='endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold=30
if k!='' : ptthreshold=float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV'%ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('mjj'+k+r+'.png')
massResolutionGrs=[]
for r in region:
massResolution=TGraphErrors()
massResolution.SetName(r+'dm')
massResolution.SetLineWidth(2)
massResolution.SetFillStyle(0)
massResolution.SetMarkerStyle(20+len(massResolutionGrs))
massResolution.SetMarkerColor(1+len(massResolutionGrs))
massResolution.SetLineColor(1+len(massResolutionGrs))
massResolution.SetFillColor(1+len(massResolutionGrs))
massResolutionGrs.append(massResolution)
for k in kin:
c=TCanvas('c','c',600,600)
c.cd()
h=histos['dmjj'+k+r]
x=RooRealVar("x", h.GetXaxis().GetTitle(), h.GetXaxis().GetXmin(), h.GetXaxis().GetXmax())
data=RooDataHist("data", "dataset with x", RooArgList(x), h)
frame=x.frame()
RooAbsData.plotOn( data, frame, RooFit.DataError(RooAbsData.SumW2) )
mean1=RooRealVar("mean1","mean1",0,-0.5,0.5);
sigma1=RooRealVar("sigma1","sigma1",0.1,0.01,1.0);
gauss1=RooGaussian("g1","g",x,mean1,sigma1)
mean2=RooRealVar("mean2","mean2",0,-0.5,0.5);
sigma2=RooRealVar("sigma2","sigma2",0.1,0.01,1.0);
alphacb=RooRealVar("alphacb","alphacb",1,0.1,3);
ncb=RooRealVar("ncb","ncb",4,1,100)
gauss2 = RooCBShape("cb2","cb",x,mean2,sigma2,alphacb,ncb);
frac = RooRealVar("frac","fraction",0.9,0.0,1.0)
model = RooAddPdf("sum","g1+g2",RooArgList(gauss1,gauss2), RooArgList(frac))
status=model.fitTo(data,RooFit.Save()).status()
if status!=0 : continue
RooAbsPdf.plotOn(model,frame)
frame.Draw()
labels.append( TPaveText(0.6,0.65,0.85,0.9,'brNDC') )
ilab=len(labels)-1
labels[ilab].SetName(r+k+'dmfitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)'%frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean1.getVal(),mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma1.getVal(),sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)'%(1-frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean2.getVal(),mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma2.getVal(),sigma2.getError()))
labels[ilab].Draw()
drawHeader()
labels.append( TPaveText(0.15,0.8,0.4,0.9,'brNDC') )
ilab=len(labels)-1
labels[ilab].SetName(k+r+'dmjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle="inclusive"
if r == 'bb' : regionTitle='barrel-barrel'
if r == 'eb' : regionTitle='endcap-barrel'
if r == 'ee' : regionTitle='endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold=30
if k!='' : ptthreshold=float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV'%ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('dmjj'+k+r+'.png')
massResolution.SetTitle(regionTitle)
ip=massResolution.GetN()
x=40
xerr=10
if k=='50' :
x=75
xerr=25
elif k=='100':
x=150
xerr=50
y=sigma1.getVal()
yerr=sigma1.getError()
if frac.getVal()<0.8:
if sigma2.getVal()<sigma1.getVal():
y=sigma2.getVal()
ey=sigma2.getError()
massResolution.SetPoint(ip,x,y)
massResolution.SetPointError(ip,xerr,yerr)
frame=TGraphErrors()
frame.SetPoint(0,0,0)
frame.SetPoint(1,200,0.2)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('dmframe')
cdmevol=TCanvas('cdmevol','cdmevol',500,500)
cdmevol.cd()
frame.Draw('ap')
leg=TLegend(0.6,0.92,0.9,0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
for dmGr in massResolutionGrs :
dmGr.Draw('pl')
leg.AddEntry(dmGr,dmGr.GetTitle(),'fp')
frame.GetXaxis().SetTitle("Leading quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Core resolution")
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg.SetNColumns(2)
leg.Draw()
cdmevol.Modified()
cdmevol.Update()
cdmevol.SaveAs('dm_evol.png')
c=TCanvas('c','c',600,600)
c.cd()
histos['sel'].Draw('histtext')
drawHeader()
c.Modified()
c.Update()
c.SaveAs('selection.png')
return
genFitter.ws.defineSet("params", genstartpars)
genFitter.ws.saveSnapshot("genInitPars", genstartpars)
print "genPars:"
genFitter.ws.set('params').Print('v')
if opts.extendedGen:
data = genPdf.generate(genFitter.ws.set('obsSet'), RooFit.Name('data_obs'),RooFit.Extended())
else:
data = genPdf.generate(genFitter.ws.set('obsSet'), RooFit.Name('data_obs'))
if fitter.pars.binData:
data = RooDataHist('data_obs', 'data_obs', genFitter.ws.set('obsSet'), data)
data.Print('v')
getattr(fitter.ws, 'import')(data)
print 'Generated Data Events: %.0f' % (data.sumEntries())
elif opts.runMCGenToySim:
print "performing Toy Generation from MC:"
print "Generating ", opts.extdiboson, " diboson events"
dibosonfiles = getattr(pars, 'dibosonFiles')
dibosontoymodels = [-1]
dibosonPdf = fitter.makeComponentPdf('dibosontoy',dibosonfiles,dibosontoymodels)
dibosontoymc = dibosonPdf.generate(fitter.ws.set('obsSet'),opts.extdiboson,RooFit.Name('data_obs'))
dibosontoymc.Print()
gentoymc=dibosontoymc
print "Generating ", opts.extWpJ, " WpJ events"
WpJfiles = getattr(pars, 'WpJFiles')
WpJtoymodels = [-1]
WpJPdf = fitter.makeComponentPdf('WpJtoy',WpJfiles,WpJtoymodels)
WpJtoymc = RooDataSet()
mass) + '_datacard_const_' + name + '.txt'
wsFN = outdir_datacards + 'Qstar' + str(
mass) + '_workspace_const_' + name + '.root'
elif bkgNuisance:
dcFN = outdir_datacards + 'Qstar' + str(mass) + '_datacard_nuisance.txt'
wsFN = outdir_datacards + 'Qstar' + str(mass) + '_workspace_nuisance.root'
#dcFN = outdir_datacards+'Qstar'+str(mass)+'_datacard_nuisance_testForSignificance.txt'
#wsFN = outdir_datacards+'Qstar'+str(mass)+'_workspace_nuisance_testForSignificance.root'
else:
dcFN = outdir_datacards + 'Qstar' + str(
mass) + '_datacard_' + name + '.txt'
wsFN = outdir_datacards + 'Qstar' + str(
mass) + '_workspace_' + name + '.root'
nObs = dataHist_data.sumEntries()
#nObs = roohistBkg.sumEntries();
#nBkg = roohistBkg.sumEntries();
w = RooWorkspace('w', 'workspace')
getattr(w, 'import')(signal)
getattr(w, 'import')(background)
getattr(w, 'import')(background_norm)
getattr(w, 'import')(roohistBkg, RooFit.Rename("data_obs"))
#getattr(w,'import')(dataHist_data,RooFit.Rename("data_obs"))
#getattr(w,'import')(background_f6)
#getattr(w,'import')(background_f6_norm)
w.Print()
w.writeToFile(wsFN)
# -----------------------------------------
parsBkg = background.getParameters(roohistBkg)
parsBkg.setAttribAll('Constant', True)
if fitSig and fitDat:
# -----------------------------------------
# write everything to a workspace to make a datacard
dcFN = 'RS'+str(mass)+'_datacard.txt'
wsFN = 'RS'+str(mass)+'_workspace.root'
if useSub:
dcFN = 'RS'+str(mass)+'_sub_datacard.txt'
wsFN = 'RS'+str(mass)+'_sub_workspace.root'
nObs = roohistBkg.sumEntries();
w = RooWorkspace('w','workspace')
getattr(w,'import')(signal)
getattr(w,'import')(background)
getattr(w,'import')(roohistBkg,RooFit.Rename("data_obs"))
w.Print()
w.writeToFile(wsFN)
# -----------------------------------------
# write a datacard
LUMI = options.lumi
signalCrossSection = options.sigXS
signalEfficiency = options.sigEff
ExpectedSignalRate = signalCrossSection*LUMI*signalEfficiency
def main(options, args):
from ROOT import gSystem, gROOT, gStyle
gROOT.SetBatch()
gSystem.Load("libRooFitCore")
if options.doWebPage:
from lip.Tools.rootutils import loadToolsLib, apply_modifs
loadToolsLib()
from ROOT import TFile, RooFit, RooArgSet, RooDataHist, RooKeysPdf, RooHistPdf, TCanvas, TLegend, TLatex, TArrow, TPaveText, RooAddPdf, RooArgList
from ROOT import kWhite, kBlue, kOpenSquare
if options.doWebPage:
from ROOT import HtmlHelper, HtmlTag, HtmlTable, HtmlPlot
rootglobestyle.setTDRStyle()
gStyle.SetMarkerSize(1.5)
gStyle.SetTitleYOffset(1.5)
gStyle.SetPadLeftMargin(0.16)
gStyle.SetPadRightMargin(0.05)
gStyle.SetPadTopMargin(0.05)
gStyle.SetPadBottomMargin(0.13)
gStyle.SetLabelFont(42, "XYZ")
gStyle.SetLabelOffset(0.007, "XYZ")
gStyle.SetLabelSize(0.05, "XYZ")
gStyle.SetTitleSize(0.06, "XYZ")
gStyle.SetTitleXOffset(0.9)
gStyle.SetTitleYOffset(1.24)
gStyle.SetTitleFont(42, "XYZ")
##
## Read files
##
options.outdir = "%s_m%1.0f" % (options.outdir, options.mH)
if options.fp:
options.outdir += "_fp"
ncat = options.ncat
cats = options.cats
if cats is "":
categories = ["_cat%d" % i for i in range(0, ncat)]
else:
categories = ["_cat%s" % i for i in cats.split(",")]
if options.mva:
clables = {
"_cat0": ("MVA > 0.89", ""),
"_cat1": ("0.74 #leq MVA", "MVA < 0.89"),
"_cat2": ("0.545 #leq MVA", "MVA < 0.74"),
"_cat3": ("0.05 #leq MVA", "MVA < 0.545"),
"_cat4": ("Di-jet", "Tagged"),
"_cat5": ("Di-jet", "Tagged"),
"_combcat": ("All Classes", "Combined")
}
else:
clables = {
"_cat0": ("max(|#eta|<1.5", "min(R_{9})>0.94"),
"_cat1": ("max(|#eta|<1.5", "min(R_{9})<0.94"),
"_cat2": ("max(|#eta|>1.5", "min(R_{9})>0.94"),
"_cat3": ("max(|#eta|>1.5", "min(R_{9})<0.94"),
"_cat4": ("Di-jet", "Tagged"),
"_cat5": ("Di-jet", "Tagged"),
"_combcat": ("All Classes", "Combined")
}
helper = Helper()
fin = TFile.Open(options.infile)
helper.files.append(fin)
ws = fin.Get("cms_hgg_workspace")
mass = ws.var("CMS_hgg_mass")
mass.SetTitle("m_{#gamma#gamma}")
mass.setUnit("GeV")
mass.setRange(100., 150.)
mass.setBins(100, "plot")
mass.setBins(5000)
print ws
aset = RooArgSet(mass)
helper.objs.append(mass)
helper.objs.append(aset)
fitopt = (RooFit.Minimizer("Minuit2", ""), RooFit.Minos(False),
RooFit.SumW2Error(False), RooFit.NumCPU(8))
if not options.binned and not options.refit:
finpdf = TFile.Open(options.infilepdf)
helper.files.append(finpdf)
wspdf = finpdf.Get("wsig")
else:
wspdf = ws
for c in categories:
processes = ["ggh", "vbf", "wzh"]
if options.fp:
processes = ["vbf", "wzh"]
### elif clables[c][0] == "Di-jet":
### processes = [ "vbf", "ggh" ]
dsname = "sig_mass_m%1.0f%s" % (options.mH, c)
print dsname
print ws
ds = ws.data("sig_%s_mass_m%1.0f%s" %
(processes[0], options.mH, c)).Clone(dsname)
for proc in processes[1:]:
ds.append(ws.data("sig_%s_mass_m%1.0f%s" % (proc, options.mH, c)))
helper.dsets.append(ds)
if options.binned:
binned_ds = RooDataHist("binned_%s" % dsname, "binned_%s" % dsname,
aset, ds)
pdf = RooKeysPdf("pdf_%s_%s" % (dsname, f), "pdf_%s" % dsname,
mass, ds)
plot_pdf = RooHistPdf("pdf_%s" % dsname, "pdf_%s" % dsname, aset,
plot_ds)
helper.add(binned_ds, binned_ds.GetName())
else:
if options.refit:
if options.refitall and clables[c][0] != "Di-jet":
rpdfs = []
for proc in processes:
for ngaus in range(1, 4):
pp = build_pdf(ws, "%s_%s" % (c, proc), ngaus,
ngaus == 3)
pp.fitTo(
ws.data("sig_%s_mass_m%1.0f%s" %
(proc, options.mH, c)),
RooFit.Strategy(0), *fitopt)
rpdfs.append(pp)
pdf = RooAddPdf("hggpdfrel%s" % c, "hggpdfrel%s" % c,
RooArgList(*tuple(rpdfs)))
else:
if options.refitall and clables[c][0] == "Di-jet":
for ngaus in range(1, 5):
pdf = build_pdf(ws, c, ngaus, ngaus == 5)
pdf.fitTo(ds, RooFit.Strategy(0), *fitopt)
else:
for ngaus in range(1, 4):
pdf = build_pdf(ws, c, ngaus, ngaus == 3)
pdf.fitTo(ds, RooFit.Strategy(0), *fitopt)
else:
pdfs = (wspdf.pdf("hggpdfrel%s_%s" % (c, p))
for p in processes)
pdf = RooAddPdf("hggpdfrel%s" % c, "hggpdfrel%s" % c,
RooArgList(*pdfs))
helper.add(pdf, pdf.GetName())
plot_pdf = pdf.Clone("pdf_%s" % dsname)
plot_ds = RooDataHist("plot_%s" % dsname, "plot_%s" % dsname, aset,
"plot")
plot_ds.add(ds)
cdf = pdf.createCdf(aset)
hmin, hmax, hm = get_FWHM(mass, pdf, cdf, options.mH - 10.,
options.mH + 10.)
wmin, wmax = get_eff_sigma(mass, pdf, cdf, options.mH - 10.,
options.mH + 10.)
### hmin, hmax, hm = get_FWHM( points )
helper.add(plot_ds, plot_ds.GetName())
helper.add(plot_pdf, plot_pdf.GetName())
helper.add((wmin, wmax), "eff_sigma%s" % c)
helper.add((hmin, hmax, hm), "FWHM%s" % c)
helper.add(ds.sumEntries(),
"sumEntries%s" % c) # signal model integral
# data integral for PAS tables
data = ws.data("data_mass%s" % c)
helper.add(
data.sumEntries("CMS_hgg_mass>=%1.4f && CMS_hgg_mass<=%1.4f" %
(options.mH - 10., options.mH + 10.)),
"data_sumEntries%s" % c)
del cdf
del pdf
dsname = "sig_mass_m%1.0f_combcat" % options.mH
print dsname
combined_ds = helper.dsets[0].Clone(dsname)
for d in helper.dsets[1:]:
combined_ds.append(d)
if options.binned:
binned_ds = RooDataHist("binned_%s" % dsname, "binned_%s" % dsname,
aset, combined_ds)
pdf = RooKeysPdf("pdf_%s" % (dsname), "pdf_%s" % dsname, mass,
combined_ds)
plot_pdf = RooHistPdf("pdf_%s" % dsname, "pdf_%s" % dsname, aset,
plot_ds)
helper.add(binned_ds, binned_ds.GetName())
else:
#### pdf = build_pdf(ws,"_combcat")
#### pdf.fitTo(combined_ds, RooFit.Strategy(0), *fitopt )
#### plot_pdf = pdf.Clone( "pdf_%s" % dsname )
pdf = RooAddPdf(
"pdf_%s" % dsname, "pdf_%s" % dsname,
RooArgList(*(helper.histos["hggpdfrel%s" % c]
for c in categories)))
plot_pdf = pdf
cdf = pdf.createCdf(aset)
plot_ds = RooDataHist("plot_%s" % dsname, "plot_%s" % dsname, aset, "plot")
plot_ds.add(combined_ds)
wmin, wmax = get_eff_sigma(mass, pdf, cdf, options.mH - 10.,
options.mH + 10.)
hmin, hmax, hm = get_FWHM(mass, pdf, cdf, options.mH - 10.,
options.mH + 10.)
helper.add(plot_ds, plot_ds.GetName())
helper.add(plot_pdf, plot_pdf.GetName())
helper.add((wmin, wmax), "eff_sigma_combcat")
helper.add((hmin, hmax, hm), "FWHM_combcat")
helper.add(plot_ds.sumEntries(), "sumEntries_combcat")
mass.setRange("higgsrange", options.mH - 25., options.mH + 15.)
del cdf
del pdf
del helper.dsets
### label = TLatex(0.1812081,0.8618881,"#scale[0.8]{#splitline{CMS preliminary}{Simulation}}")
label = TLatex(0.7, 0.86,
"#scale[0.65]{#splitline{CMS preliminary}{Simulation}}")
label.SetNDC(1)
##
## Make web page with plots
##
if options.doWebPage:
hth = HtmlHelper(options.outdir)
hth.navbar().cell(HtmlTag("a")).firstChild().txt("..").set(
"href", "../?C=M;O=D")
hth.navbar().cell(HtmlTag("a")).firstChild().txt("home").set(
"href", "./")
tab = hth.body().add(HtmlTable())
ip = 0
for c in ["_combcat"] + categories:
### for c in categories:
if options.doWebPage and ip % 4 == 0:
row = tab.row()
ip = ip + 1
dsname = "sig_mass_m%1.0f%s" % (options.mH, c)
canv = TCanvas(dsname, dsname, 600, 600)
helper.objs.append(canv)
### leg = TLegend(0.4345638,0.6835664,0.9362416,0.9178322)
leg = TLegend(0.2, 0.96, 0.5, 0.55)
#apply_modifs( leg, [("SetLineColor",kWhite),("SetFillColor",kWhite),("SetFillStyle",0),("SetLineStyle",0)] )
hplotcompint = mass.frame(RooFit.Bins(250), RooFit.Range("higgsrange"))
helper.objs.append(hplotcompint)
helper.objs.append(leg)
plot_ds = helper.histos["plot_%s" % dsname]
plot_pdf = helper.histos["pdf_%s" % dsname]
wmin, wmax = helper.histos["eff_sigma%s" % c]
hmin, hmax, hm = helper.histos["FWHM%s" % c]
print hmin, hmax, hm
style = (RooFit.LineColor(kBlue), RooFit.LineWidth(2),
RooFit.FillStyle(0))
style_seff = (
RooFit.LineWidth(2),
RooFit.FillStyle(1001),
RooFit.VLines(),
RooFit.LineColor(15),
)
style_ds = (RooFit.MarkerStyle(kOpenSquare), )
plot_ds.plotOn(hplotcompint, RooFit.Invisible())
plot_pdf.plotOn(hplotcompint, RooFit.NormRange("higgsrange"),
RooFit.Range(wmin, wmax), RooFit.FillColor(19),
RooFit.DrawOption("F"), *style_seff)
seffleg = hplotcompint.getObject(int(hplotcompint.numItems() - 1))
plot_pdf.plotOn(hplotcompint, RooFit.NormRange("higgsrange"),
RooFit.Range(wmin, wmax), RooFit.LineColor(15),
*style_seff)
plot_pdf.plotOn(hplotcompint, RooFit.NormRange("higgsrange"),
RooFit.Range("higgsrange"), *style)
pdfleg = hplotcompint.getObject(int(hplotcompint.numItems() - 1))
plot_ds.plotOn(hplotcompint, *style_ds)
pointsleg = hplotcompint.getObject(int(hplotcompint.numItems() - 1))
iob = int(hplotcompint.numItems() - 1)
leg.AddEntry(pointsleg, "Simulation", "pe")
leg.AddEntry(pdfleg, "Parametric model", "l")
leg.AddEntry(seffleg,
"#sigma_{eff} = %1.2f GeV " % (0.5 * (wmax - wmin)), "fl")
clabel = TLatex(0.74, 0.65,
"#scale[0.65]{#splitline{%s}{%s}}" % clables[c])
clabel.SetNDC(1)
helper.objs.append(clabel)
hm = hplotcompint.GetMaximum() * 0.5 * 0.9
### hm = pdfleg.GetMaximum()*0.5
fwhmarrow = TArrow(hmin, hm, hmax, hm)
fwhmarrow.SetArrowSize(0.03)
helper.objs.append(fwhmarrow)
fwhmlabel = TPaveText(0.20, 0.58, 0.56, 0.48, "brNDC")
fwhmlabel.SetFillStyle(0)
fwhmlabel.SetLineColor(kWhite)
reducedFWHM = (hmax - hmin) / 2.3548200
fwhmlabel.AddText("FWHM/2.35 = %1.2f GeV" % reducedFWHM)
helper.objs.append(fwhmlabel)
hplotcompint.SetTitle("")
hplotcompint.GetXaxis().SetNoExponent(True)
hplotcompint.GetXaxis().SetTitle("m_{#gamma#gamma} (GeV)")
hplotcompint.GetXaxis().SetNdivisions(509)
## hplotcompint.GetYaxis().SetTitle("A.U.");
## hplotcompint.GetYaxis().SetRangeUser(0.,hplotcompint.GetMaximum()*1.4);
hplotcompint.Draw()
leg.Draw("same")
label.Draw("same")
clabel.Draw("same")
fwhmarrow.Draw("<>")
fwhmlabel.Draw("same")
plot_ds.sumEntries()
if options.doWebPage:
hpl = HtmlPlot(canv, False, "", True, True, True)
hpl.caption("<i>%s</i>" % canv.GetTitle())
row.cell(hpl)
else:
if os.path.isdir(options.outdir) is False:
os.mkdir(options.outdir)
for ext in "C", "png", "pdf":
canv.SaveAs(
os.path.join(options.outdir,
"%s.%s" % (canv.GetName(), ext)))
if "comb" in c:
ip = 0
if options.doWebPage:
print "Creating pages..."
hth.dump()
for f in helper.files:
f.Close()
gROOT.Reset()
from pprint import pprint
pprint(helper)
print 'Summary statistics per event class'
print 'Cat\tSignal\t\tData/GeV (in %3.1f+/-10)\tsigEff\tFWHM/2.35' % options.mH
sigTotal = 0.
dataTotal = 0.
for c in categories:
sigVal = helper.histos["sumEntries%s" % c]
datVal = helper.histos["data_sumEntries%s" % c]
sigTotal += sigVal
dataTotal += datVal
for c in categories:
sigVal = helper.histos["sumEntries%s" % c]
datVal = helper.histos["data_sumEntries%s" % c]
effSig = 0.5 * (helper.histos["eff_sigma%s" % c][1] -
helper.histos["eff_sigma%s" % c][0])
fwhm = (helper.histos["FWHM%s" % c][1] -
helper.histos["FWHM%s" % c][0]) / 2.3548200
print c, '\t%3.1f (%3.1f%%)\t%3.1f (%3.1f%%)\t\t\t%2.2f\t%2.2f' % (
sigVal, 100. * sigVal / sigTotal, datVal /
(10. + 10.), 100. * datVal / dataTotal, effSig, fwhm)
print "Done."
if bkgConst:
dcFN = outdir_datacards+'Qstar'+str(mass)+'_datacard_const_'+name+'.txt'
wsFN = outdir_datacards+'Qstar'+str(mass)+'_workspace_const_'+name+'.root'
elif bkgNuisance:
dcFN = outdir_datacards+'Qstar'+str(mass)+'_datacard_nuisance.txt'
wsFN = outdir_datacards+'Qstar'+str(mass)+'_workspace_nuisance.root'
#dcFN = outdir_datacards+'Qstar'+str(mass)+'_datacard_nuisance_testForSignificance.txt'
#wsFN = outdir_datacards+'Qstar'+str(mass)+'_workspace_nuisance_testForSignificance.root'
else:
dcFN = outdir_datacards+'Qstar'+str(mass)+'_datacard_'+name+'.txt'
wsFN = outdir_datacards+'Qstar'+str(mass)+'_workspace_'+name+'.root'
nObs = dataHist_data.sumEntries();
#nObs = roohistBkg.sumEntries();
#nBkg = roohistBkg.sumEntries();
w = RooWorkspace('w','workspace')
getattr(w,'import')(signal)
getattr(w,'import')(background)
#getattr(w,'import')(roohistBkg,RooFit.Rename("data_obs"))
getattr(w,'import')(dataHist_data,RooFit.Rename("data_obs"))
getattr(w,'import')(background_norm)
w.Print()
w.writeToFile(wsFN)
# -----------------------------------------
# write a datacard
genFitter.ws.set('params').Print('v')
if opts.extendedGen:
data = genPdf.generate(genFitter.ws.set('obsSet'),
RooFit.Name('data_obs'), RooFit.Extended())
else:
data = genPdf.generate(genFitter.ws.set('obsSet'),
RooFit.Name('data_obs'))
if fitter.pars.binData:
data = RooDataHist('data_obs', 'data_obs', genFitter.ws.set('obsSet'),
data)
data.Print('v')
getattr(fitter.ws, 'import')(data)
print 'Generated Data Events: %.0f' % (data.sumEntries())
elif opts.runMCGenToySim:
print "performing ToyMC Generation:"
print "Generating ", opts.extdiboson, " diboson events"
dibosonfiles = getattr(pars, 'dibosonFiles')
dibosontoymodels = [-1]
dibosonPdf = fitter.makeComponentPdf('dibosontoy', dibosonfiles,
dibosontoymodels)
dibosontoymc = dibosonPdf.generate(fitter.ws.set('obsSet'),
opts.extdiboson,
RooFit.Name('data_obs'))
dibosontoymc.Print()
gentoymc = dibosontoymc
print "Generating ", opts.extWpJ, " WpJ events"
WpJfiles = getattr(pars, 'WpJFiles')
def main():
# usage description
usage = "Example: ./scripts/createDatacards.py --inputData inputs/rawhistV7_Run2015D_scoutingPFHT_UNBLINDED_649_838_JEC_HLTplusV7_Mjj_cor_smooth.root --dataHistname mjj_mjjcor_gev --inputSig inputs/ResonanceShapes_gg_13TeV_Scouting_Spring15.root -f gg -o datacards -l 1866 --lumiUnc 0.027 --massrange 1000 1500 50 --runFit --p1 5 --p2 7 --p3 0.4 --massMin 838 --massMax 2037 --fitStrategy 2"
# input parameters
parser = ArgumentParser(
description=
'Script that creates combine datacards and corresponding RooFit workspaces',
epilog=usage)
parser.add_argument("--inputData",
dest="inputData",
required=True,
help="Input data spectrum",
metavar="INPUT_DATA")
parser.add_argument("--dataHistname",
dest="dataHistname",
required=True,
help="Data histogram name",
metavar="DATA_HISTNAME")
parser.add_argument("--inputSig",
dest="inputSig",
required=True,
help="Input signal shapes",
metavar="INPUT_SIGNAL")
parser.add_argument("-f",
"--final_state",
dest="final_state",
required=True,
help="Final state (e.g. qq, qg, gg)",
metavar="FINAL_STATE")
parser.add_argument("-f2",
"--type",
dest="atype",
required=True,
help="Type (e.g. hG, lG, hR, lR)")
parser.add_argument(
"-o",
"--output_path",
dest="output_path",
required=True,
help="Output path where datacards and workspaces will be stored",
metavar="OUTPUT_PATH")
parser.add_argument(
"-l",
"--lumi",
dest="lumi",
required=True,
default=1000.,
type=float,
help="Integrated luminosity in pb-1 (default: %(default).1f)",
metavar="LUMI")
parser.add_argument(
"--massMin",
dest="massMin",
default=500,
type=int,
help=
"Lower bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MIN")
parser.add_argument(
"--massMax",
dest="massMax",
default=1200,
type=int,
help=
"Upper bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MAX")
parser.add_argument(
"--p1",
dest="p1",
default=5.0000e-03,
type=float,
help="Fit function p1 parameter (default: %(default)e)",
metavar="P1")
parser.add_argument(
"--p2",
dest="p2",
default=9.1000e+00,
type=float,
help="Fit function p2 parameter (default: %(default)e)",
metavar="P2")
parser.add_argument(
"--p3",
dest="p3",
default=5.0000e-01,
type=float,
help="Fit function p3 parameter (default: %(default)e)",
metavar="P3")
parser.add_argument(
"--lumiUnc",
dest="lumiUnc",
required=True,
type=float,
help="Relative uncertainty in the integrated luminosity",
metavar="LUMI_UNC")
parser.add_argument("--jesUnc",
dest="jesUnc",
type=float,
help="Relative uncertainty in the jet energy scale",
metavar="JES_UNC")
parser.add_argument(
"--jerUnc",
dest="jerUnc",
type=float,
help="Relative uncertainty in the jet energy resolution",
metavar="JER_UNC")
parser.add_argument(
"--sqrtS",
dest="sqrtS",
default=13000.,
type=float,
help="Collision center-of-mass energy (default: %(default).1f)",
metavar="SQRTS")
parser.add_argument("--fixP3",
dest="fixP3",
default=False,
action="store_true",
help="Fix the fit function p3 parameter")
parser.add_argument("--runFit",
dest="runFit",
default=False,
action="store_true",
help="Run the fit")
parser.add_argument("--fitBonly",
dest="fitBonly",
default=False,
action="store_true",
help="Run B-only fit")
parser.add_argument("--fixBkg",
dest="fixBkg",
default=False,
action="store_true",
help="Fix all background parameters")
parser.add_argument("--decoBkg",
dest="decoBkg",
default=False,
action="store_true",
help="Decorrelate background parameters")
parser.add_argument("--fitStrategy",
dest="fitStrategy",
type=int,
default=1,
help="Fit strategy (default: %(default).1f)")
parser.add_argument("--debug",
dest="debug",
default=False,
action="store_true",
help="Debug printout")
parser.add_argument(
"--postfix",
dest="postfix",
default='',
help="Postfix for the output file names (default: %(default)s)")
parser.add_argument("--pyes",
dest="pyes",
default=False,
action="store_true",
help="Make files for plots")
parser.add_argument("--jyes",
dest="jyes",
default=False,
action="store_true",
help="Make files for JES/JER plots")
parser.add_argument(
"--pdir",
dest="pdir",
default='testarea',
help="Name a directory for the plots (default: %(default)s)")
parser.add_argument("--chi2",
dest="chi2",
default=False,
action="store_true",
help="Compute chi squared")
parser.add_argument("--widefit",
dest="widefit",
default=False,
action="store_true",
help="Fit with wide bin hist")
mass_group = parser.add_mutually_exclusive_group(required=True)
mass_group.add_argument(
"--mass",
type=int,
nargs='*',
default=1000,
help=
"Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
)
mass_group.add_argument(
"--massrange",
type=int,
nargs=3,
help="Define a range of masses to be produced. Format: min max step",
metavar=('MIN', 'MAX', 'STEP'))
mass_group.add_argument("--masslist",
help="List containing mass information")
args = parser.parse_args()
if args.atype == 'hG':
fstr = "bbhGGBB"
in2 = 'bcorrbin/binmodh.root'
elif args.atype == 'hR':
fstr = "bbhRS"
in2 = 'bcorrbin/binmodh.root'
elif args.atype == 'lG':
fstr = "bblGGBB"
in2 = 'bcorrbin/binmodl.root'
else:
fstr = "bblRS"
in2 = 'bcorrbin/binmodl.root'
# check if the output directory exists
if not os.path.isdir(os.path.join(os.getcwd(), args.output_path)):
os.mkdir(os.path.join(os.getcwd(), args.output_path))
# mass points for which resonance shapes will be produced
masses = []
if args.massrange != None:
MIN, MAX, STEP = args.massrange
masses = range(MIN, MAX + STEP, STEP)
elif args.masslist != None:
# A mass list was provided
print "Will create mass list according to", args.masslist
masslist = __import__(args.masslist.replace(".py", ""))
masses = masslist.masses
else:
masses = args.mass
# sort masses
masses.sort()
# import ROOT stuff
from ROOT import gStyle, TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE, TCanvas, TLegend, TPad, TLine
from ROOT import RooHist, RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, RooExtendPdf
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0, kFALSE)
RooMsgService.instance().setStreamStatus(1, kFALSE)
# input data file
inputData = TFile(args.inputData)
# input data histogram
hData = inputData.Get(args.dataHistname)
inData2 = TFile(in2)
hData2 = inData2.Get('h_data')
# input sig file
inputSig = TFile(args.inputSig)
sqrtS = args.sqrtS
# mass variable
mjj = RooRealVar('mjj', 'mjj', float(args.massMin), float(args.massMax))
# integrated luminosity and signal cross section
lumi = args.lumi
signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
for mass in masses:
print ">> Creating datacard and workspace for %s resonance with m = %i GeV..." % (
args.final_state, int(mass))
# get signal shape
hSig = inputSig.Get("h_" + args.final_state + "_" + str(int(mass)))
# normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
hSig.Scale(
signalCrossSection * lumi / hSig.Integral()
) # divide by a number that provides roughly an r value of 1-10
rooSigHist = RooDataHist('rooSigHist', 'rooSigHist', RooArgList(mjj),
hSig)
print 'Signal acceptance:', (rooSigHist.sumEntries() / hSig.Integral())
signal = RooHistPdf('signal', 'signal', RooArgSet(mjj), rooSigHist)
signal_norm = RooRealVar('signal_norm', 'signal_norm', 0, -1e+05,
1e+05)
signal_norm2 = RooRealVar('signal_norm2', 'signal_norm2', 0, -1e+05,
1e+05)
signal_norm3 = RooRealVar('signal_norm3', 'signal_norm3', 0, -1e+05,
1e+05)
signal_norm4 = RooRealVar('signal_norm4', 'signal_norm4', 0, -1e+05,
1e+05)
signal_norm5 = RooRealVar('signal_norm5', 'signal_norm5', 0, -1e+05,
1e+05)
if args.fitBonly:
signal_norm.setConstant()
signal_norm2.setConstant()
signal_norm3.setConstant()
signal_norm4.setConstant()
signal_norm5.setConstant()
p1 = RooRealVar('p1', 'p1', args.p1, 0., 100.)
p2 = RooRealVar('p2', 'p2', args.p2, 0., 60.)
p3 = RooRealVar('p3', 'p3', args.p3, -10., 10.)
p4 = RooRealVar('p4', 'p4', 5.6, -50., 50.)
p5 = RooRealVar('p5', 'p5', 10., -50., 50.)
p6 = RooRealVar('p6', 'p6', .016, -50., 50.)
p7 = RooRealVar('p7', 'p7', 8., -50., 50.)
p8 = RooRealVar('p8', 'p8', .22, -50., 50.)
p9 = RooRealVar('p9', 'p9', 14.1, -50., 50.)
p10 = RooRealVar('p10', 'p10', 8., -50., 50.)
p11 = RooRealVar('p11', 'p11', 4.8, -50., 50.)
p12 = RooRealVar('p12', 'p12', 7., -50., 50.)
p13 = RooRealVar('p13', 'p13', 7., -50., 50.)
p14 = RooRealVar('p14', 'p14', 7., -50., 50.)
p15 = RooRealVar('p15', 'p15', 1., -50., 50.)
p16 = RooRealVar('p16', 'p16', 9., -50., 50.)
p17 = RooRealVar('p17', 'p17', 0.6, -50., 50.)
if args.fixP3: p3.setConstant()
background = RooGenericPdf(
'background',
'(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))' %
(sqrtS, sqrtS, sqrtS), RooArgList(mjj, p1, p2, p3))
dataInt = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norm = RooRealVar('background_norm', 'background_norm',
dataInt, 0., 1e+08)
background2 = RooGenericPdf(
'background2',
'(pow(@0/%.1f,-@1)*pow(1-@0/%.1f,@2))' % (sqrtS, sqrtS),
RooArgList(mjj, p4, p5))
dataInt2 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background2_norm = RooRealVar('background2_norm', 'background2_norm',
dataInt2, 0., 1e+08)
background3 = RooGenericPdf('background3',
'(1/pow(@1+@0/%.1f,@2))' % (sqrtS),
RooArgList(mjj, p6, p7))
dataInt3 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background3_norm = RooRealVar('background3_norm', 'background3_norm',
dataInt3, 0., 1e+08)
background4 = RooGenericPdf(
'background4',
'(1/pow(@1+@2*@0/%.1f+pow(@0/%.1f,2),@3))' % (sqrtS, sqrtS),
RooArgList(mjj, p8, p9, p10))
dataInt4 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background4_norm = RooRealVar('background4_norm', 'background4_norm',
dataInt4, 0., 1e+08)
background5 = RooGenericPdf(
'background5',
'(pow(@0/%.1f,-@1)*pow(1-pow(@0/%.1f,1/3),@2))' % (sqrtS, sqrtS),
RooArgList(mjj, p11, p12))
dataInt5 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background5_norm = RooRealVar('background5_norm', 'background5_norm',
dataInt5, 0., 1e+08)
background6 = RooGenericPdf(
'background6', '(pow(@0/%.1f,2)+@1*@0/%.1f+@2)' % (sqrtS, sqrtS),
RooArgList(mjj, p13, p14))
dataInt6 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norm6 = RooRealVar('background_norm6', 'background_norm6',
dataInt6, 0., 1e+08)
background7 = RooGenericPdf(
'background7',
'((-1+@1*@0/%.1f)*pow(@0/%.1f,@2+@3*log(@0/%.1f)))' %
(sqrtS, sqrtS, sqrtS), RooArgList(mjj, p15, p16, p17))
dataInt7 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norm7 = RooRealVar('background_norm7', 'background_norm7',
dataInt7, 0., 1e+08)
#Extend PDFs
exts = RooExtendPdf('extsignal', 'Extended Signal Pdf', signal,
signal_norm)
extb = RooExtendPdf('extbackground', 'Extended Background Pdf',
background, background_norm)
exts2 = RooExtendPdf('extsignal2', 'Extended Signal Pdf2', signal,
signal_norm2)
extb2 = RooExtendPdf('extbackground2', 'Extended Background Pdf2',
background2, background2_norm)
exts3 = RooExtendPdf('extsignal3', 'Extended Signal Pdf3', signal,
signal_norm3)
extb3 = RooExtendPdf('extbackground3', 'Extended Background Pdf3',
background3, background3_norm)
exts4 = RooExtendPdf('extsignal4', 'Extended Signal Pdf4', signal,
signal_norm4)
extb4 = RooExtendPdf('extbackground4', 'Extended Background Pdf4',
background4, background4_norm)
exts5 = RooExtendPdf('extsignal5', 'Extended Signal Pdf5', signal,
signal_norm5)
extb5 = RooExtendPdf('extbackground5', 'Extended Background Pdf5',
background5, background5_norm)
# S+B model
model = RooAddPdf("model", "s+b", RooArgList(extb, exts))
model2 = RooAddPdf("model2", "s+b2", RooArgList(extb2, exts2))
model3 = RooAddPdf("model3", "s+b3", RooArgList(extb3, exts3))
model4 = RooAddPdf("model4", "s+b4", RooArgList(extb4, exts4))
model5 = RooAddPdf("model5", "s+b5", RooArgList(extb5, exts5))
#model6 = RooAddPdf("model6","s+b6",RooArgList(background6,signal),RooArgList(background_norm6,signal_norm))
#model7 = RooAddPdf("model7","s+b7",RooArgList(background7,signal),RooArgList(background_norm7,signal_norm))
rooDataHist = RooDataHist('rooDatahist', 'rooDathist', RooArgList(mjj),
hData)
if args.runFit:
mframe = mjj.frame()
rooDataHist.plotOn(mframe, ROOT.RooFit.Name("setonedata"))
res = model.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
model.plotOn(mframe, ROOT.RooFit.Name("model1"),
ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
res2 = model2.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model2.plotOn(mframe, ROOT.RooFit.Name("model2"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kOrange))
res3 = model3.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model3.plotOn(mframe, ROOT.RooFit.Name("model3"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
res4 = model4.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model4.plotOn(mframe, ROOT.RooFit.Name("model4"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
res5 = model5.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model5.plotOn(mframe, ROOT.RooFit.Name("model5"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kViolet))
# res6 = model6.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
# model6.plotOn(mframe, ROOT.RooFit.Name("model6"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kPink))
# res7 = model7.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
# model7.plotOn(mframe, ROOT.RooFit.Name("model7"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kAzure))
rooDataHist2 = RooDataHist('rooDatahist2', 'rooDathist2',
RooArgList(mjj), hData2)
# rooDataHist2.plotOn(mframe, ROOT.RooFit.Name("data"))
if args.pyes:
c = TCanvas("c", "c", 800, 800)
mframe.SetAxisRange(300., 1300.)
c.SetLogy()
# mframe.SetMaximum(10)
# mframe.SetMinimum(1)
mframe.Draw()
fitname = args.pdir + '/5funcfit_m' + str(mass) + fstr + '.pdf'
c.SaveAs(fitname)
# cpull = TCanvas("cpull","cpull",800,800)
# pulls = mframe.pullHist("data","model1")
# pulls.Draw("ABX")
# pullname = args.pdir+'/pull_m'+str(mass)+fstr+'.pdf'
# cpull.SaveAs(pullname)
# cpull2 = TCanvas("cpull2","cpull2",800,800)
# pulls2 = mframe.pullHist("setonedata","model1")
# pulls2.Draw("ABX")
# pull2name = args.pdir+'/pull2_m'+str(mass)+fstr+'.pdf'
# cpull2.SaveAs(pull2name)
if args.widefit:
mframew = mjj.frame()
rooDataHist2.plotOn(mframew, ROOT.RooFit.Name("data"))
res6 = model.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model.plotOn(mframew, ROOT.RooFit.Name("model1"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
res7 = model2.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model2.plotOn(mframew, ROOT.RooFit.Name("model2"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kOrange))
res8 = model3.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model3.plotOn(mframew, ROOT.RooFit.Name("model3"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
res9 = model4.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model4.plotOn(mframew, ROOT.RooFit.Name("model4"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
res10 = model5.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model5.plotOn(mframew, ROOT.RooFit.Name("model5"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kViolet))
if args.pyes:
c = TCanvas("c", "c", 800, 800)
mframew.SetAxisRange(300., 1300.)
c.SetLogy()
# mframew.SetMaximum(10)
# mframew.SetMinimum(1)
mframew.Draw()
fitname = args.pdir + '/5funcfittowide_m' + str(
mass) + fstr + '.pdf'
c.SaveAs(fitname)
cpull = TCanvas("cpull", "cpull", 800, 800)
pulls = mframew.pullHist("data", "model1")
pulls.Draw("ABX")
pullname = args.pdir + '/pullwidefit_m' + str(
mass) + fstr + '.pdf'
cpull.SaveAs(pullname)
if args.chi2:
fullInt = model.createIntegral(RooArgSet(mjj))
norm = dataInt / fullInt.getVal()
chi1 = 0.
fullInt2 = model2.createIntegral(RooArgSet(mjj))
norm2 = dataInt2 / fullInt2.getVal()
chi2 = 0.
fullInt3 = model3.createIntegral(RooArgSet(mjj))
norm3 = dataInt3 / fullInt3.getVal()
chi3 = 0.
fullInt4 = model4.createIntegral(RooArgSet(mjj))
norm4 = dataInt4 / fullInt4.getVal()
chi4 = 0.
fullInt5 = model5.createIntegral(RooArgSet(mjj))
norm5 = dataInt5 / fullInt5.getVal()
chi5 = 0.
for i in range(args.massMin, args.massMax):
new = 0
new2 = 0
new3 = 0
new4 = 0
new5 = 0
height = hData.GetBinContent(i)
xLow = hData.GetXaxis().GetBinLowEdge(i)
xUp = hData.GetXaxis().GetBinLowEdge(i + 1)
obs = height * (xUp - xLow)
mjj.setRange("intrange", xLow, xUp)
integ = model.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp = integ.getVal() * norm
new = pow(exp - obs, 2) / exp
chi1 = chi1 + new
integ2 = model2.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp2 = integ2.getVal() * norm2
new2 = pow(exp2 - obs, 2) / exp2
chi2 = chi2 + new2
integ3 = model3.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp3 = integ3.getVal() * norm3
new3 = pow(exp3 - obs, 2) / exp3
chi3 = chi3 + new3
integ4 = model4.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp4 = integ4.getVal() * norm4
if exp4 != 0:
new4 = pow(exp4 - obs, 2) / exp4
else:
new4 = 0
chi4 = chi4 + new4
integ5 = model5.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp5 = integ5.getVal() * norm5
new5 = pow(exp5 - obs, 2) / exp5
chi5 = chi5 + new5
print "chi1 %d " % (chi1)
print "chi2 %d " % (chi2)
print "chi3 %d " % (chi3)
print "chi4 %d " % (chi4)
print "chi5 %d " % (chi5)
if not args.decoBkg:
print " "
res.Print()
res2.Print()
res3.Print()
res4.Print()
res5.Print()
# res6.Print()
# res7.Print()
# decorrelated background parameters for Bayesian limits
if args.decoBkg:
signal_norm.setConstant()
res = model.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
res.Print()
## temp workspace for the PDF diagonalizer
w_tmp = RooWorkspace("w_tmp")
deco = PdfDiagonalizer("deco", w_tmp, res)
# here diagonalizing only the shape parameters since the overall normalization is already decorrelated
background_deco = deco.diagonalize(background)
print "##################### workspace for decorrelation"
w_tmp.Print("v")
print "##################### original parameters"
background.getParameters(rooDataHist).Print("v")
print "##################### decorrelated parameters"
# needed if want to evaluate limits without background systematics
if args.fixBkg:
w_tmp.var("deco_eig1").setConstant()
w_tmp.var("deco_eig2").setConstant()
if not args.fixP3: w_tmp.var("deco_eig3").setConstant()
background_deco.getParameters(rooDataHist).Print("v")
print "##################### original pdf"
background.Print()
print "##################### decorrelated pdf"
background_deco.Print()
# release signal normalization
signal_norm.setConstant(kFALSE)
# set the background normalization range to +/- 5 sigma
bkg_val = background_norm.getVal()
bkg_error = background_norm.getError()
background_norm.setMin(bkg_val - 5 * bkg_error)
background_norm.setMax(bkg_val + 5 * bkg_error)
background_norm.Print()
# change background PDF names
background.SetName("background_old")
background_deco.SetName("background")
# needed if want to evaluate limits without background systematics
if args.fixBkg:
background_norm.setConstant()
p1.setConstant()
p2.setConstant()
p3.setConstant()
# -----------------------------------------
# dictionaries holding systematic variations of the signal shape
hSig_Syst = {}
hSig_Syst_DataHist = {}
sigCDF = TGraph(hSig.GetNbinsX() + 1)
# JES and JER uncertainties
if args.jesUnc != None or args.jerUnc != None:
sigCDF.SetPoint(0, 0., 0.)
integral = 0.
for i in range(1, hSig.GetNbinsX() + 1):
x = hSig.GetXaxis().GetBinLowEdge(i + 1)
integral = integral + hSig.GetBinContent(i)
sigCDF.SetPoint(i, x, integral)
if args.jesUnc != None:
hSig_Syst['JESUp'] = copy.deepcopy(hSig)
hSig_Syst['JESDown'] = copy.deepcopy(hSig)
if args.jerUnc != None:
hSig_Syst['JERUp'] = copy.deepcopy(hSig)
hSig_Syst['JERDown'] = copy.deepcopy(hSig)
# reset signal histograms
for key in hSig_Syst.keys():
hSig_Syst[key].Reset()
hSig_Syst[key].SetName(hSig_Syst[key].GetName() + '_' + key)
# produce JES signal shapes
if args.jesUnc != None:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jes = 1. - args.jesUnc
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jes = 1. + args.jesUnc
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JESUp'] = RooDataHist('hSig_JESUp',
'hSig_JESUp',
RooArgList(mjj),
hSig_Syst['JESUp'])
hSig_Syst_DataHist['JESDown'] = RooDataHist(
'hSig_JESDown', 'hSig_JESDown', RooArgList(mjj),
hSig_Syst['JESDown'])
if args.jyes:
c2 = TCanvas("c2", "c2", 800, 800)
mframe2 = mjj.frame(ROOT.RooFit.Title("JES One Sigma Shifts"))
mframe2.SetAxisRange(args.massMin, args.massMax)
hSig_Syst_DataHist['JESUp'].plotOn(
mframe2, ROOT.RooFit.Name("JESUP"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kRed),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
hSig_Syst_DataHist['JESDown'].plotOn(
mframe2, ROOT.RooFit.Name("JESDOWN"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kBlue),
ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
rooSigHist.plotOn(mframe2, ROOT.RooFit.DataError(2),
ROOT.RooFit.Name("SIG"),
ROOT.RooFit.DrawOption("L"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kGreen),
ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
mframe2.Draw()
mframe2.GetXaxis().SetTitle("Dijet Mass (GeV)")
leg = TLegend(0.7, 0.8, 0.9, 0.9)
leg.SetFillColor(0)
leg.AddEntry(mframe2.findObject("SIG"), "Signal Model", "l")
leg.AddEntry(mframe2.findObject("JESUP"), "+1 Sigma", "l")
leg.AddEntry(mframe2.findObject("JESDOWN"), "-1 Sigma", "l")
leg.Draw()
jesname = args.pdir + '/jes_m' + str(mass) + fstr + '.pdf'
c2.SaveAs(jesname)
# produce JER signal shapes
if args.jesUnc != None:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jer = 1. - args.jerUnc
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jer = 1. + args.jerUnc
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JERUp'] = RooDataHist('hSig_JERUp',
'hSig_JERUp',
RooArgList(mjj),
hSig_Syst['JERUp'])
hSig_Syst_DataHist['JERDown'] = RooDataHist(
'hSig_JERDown', 'hSig_JERDown', RooArgList(mjj),
hSig_Syst['JERDown'])
if args.jyes:
c3 = TCanvas("c3", "c3", 800, 800)
mframe3 = mjj.frame(ROOT.RooFit.Title("JER One Sigma Shifts"))
mframe3.SetAxisRange(args.massMin, args.massMax)
hSig_Syst_DataHist['JERUp'].plotOn(
mframe3, ROOT.RooFit.Name("JERUP"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kRed),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
hSig_Syst_DataHist['JERDown'].plotOn(
mframe3, ROOT.RooFit.Name("JERDOWN"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kBlue),
ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
rooSigHist.plotOn(mframe3, ROOT.RooFit.DrawOption("L"),
ROOT.RooFit.Name("SIG"),
ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kGreen),
ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
mframe3.Draw()
mframe3.GetXaxis().SetTitle("Dijet Mass (GeV)")
leg = TLegend(0.7, 0.8, 0.9, 0.9)
leg.SetFillColor(0)
leg.AddEntry(mframe3.findObject("SIG"), "Signal Model", "l")
leg.AddEntry(mframe3.findObject("JERUP"), "+1 Sigma", "l")
leg.AddEntry(mframe3.findObject("JERDOWN"), "-1 Sigma", "l")
leg.Draw()
jername = args.pdir + '/jer_m' + str(mass) + fstr + '.pdf'
c3.SaveAs(jername)
# -----------------------------------------
# create a datacard and corresponding workspace
postfix = (('_' + args.postfix) if args.postfix != '' else '')
dcName = 'datacard_' + args.final_state + '_m' + str(
mass) + postfix + '.txt'
wsName = 'workspace_' + args.final_state + '_m' + str(
mass) + postfix + '.root'
w = RooWorkspace('w', 'workspace')
getattr(w, 'import')(rooSigHist, RooFit.Rename("signal"))
if args.jesUnc != None:
getattr(w, 'import')(hSig_Syst_DataHist['JESUp'],
RooFit.Rename("signal__JESUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JESDown'],
RooFit.Rename("signal__JESDown"))
if args.jerUnc != None:
getattr(w, 'import')(hSig_Syst_DataHist['JERUp'],
RooFit.Rename("signal__JERUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JERDown'],
RooFit.Rename("signal__JERDown"))
if args.decoBkg:
getattr(w, 'import')(background_deco, ROOT.RooCmdArg())
else:
getattr(w, 'import')(background, ROOT.RooCmdArg(),
RooFit.Rename("background"))
getattr(w, 'import')(background2, ROOT.RooCmdArg(),
RooFit.Rename("background2"))
getattr(w, 'import')(background3, ROOT.RooCmdArg(),
RooFit.Rename("background3"))
getattr(w, 'import')(background4, ROOT.RooCmdArg(),
RooFit.Rename("background4"))
getattr(w, 'import')(background5, ROOT.RooCmdArg(),
RooFit.Rename("background5"))
getattr(w, 'import')(background_norm, ROOT.RooCmdArg(),
RooFit.Rename("background_norm"))
getattr(w, 'import')(background2_norm, ROOT.RooCmdArg(),
RooFit.Rename("background2_norm"))
getattr(w, 'import')(background3_norm, ROOT.RooCmdArg(),
RooFit.Rename("background3_norm"))
getattr(w, 'import')(background4_norm, ROOT.RooCmdArg(),
RooFit.Rename("background4_norm"))
getattr(w, 'import')(background5_norm, ROOT.RooCmdArg(),
RooFit.Rename("background5_norm"))
getattr(w, 'import')(res)
getattr(w, 'import')(res2)
getattr(w, 'import')(res3)
getattr(w, 'import')(res4)
getattr(w, 'import')(res5)
getattr(w, 'import')(background_norm, ROOT.RooCmdArg())
getattr(w, 'import')(signal_norm, ROOT.RooCmdArg())
getattr(w, 'import')(rooDataHist, RooFit.Rename("data_obs"))
w.Print()
w.writeToFile(os.path.join(args.output_path, wsName))
beffUnc = 0.3
boffUnc = 0.06
datacard = open(os.path.join(args.output_path, dcName), 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if args.jesUnc != None or args.jerUnc != None:
datacard.write('shapes * * ' + wsName +
' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write('shapes * * ' + wsName + ' w:$PROCESS\n')
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background\n')
datacard.write('process 0 1\n')
datacard.write('rate -1 1\n')
datacard.write('------------------------------\n')
datacard.write('lumi lnN %f -\n' % (1. + args.lumiUnc))
datacard.write('beff lnN %f -\n' % (1. + beffUnc))
datacard.write('boff lnN %f -\n' % (1. + boffUnc))
datacard.write('bkg lnN - 1.03\n')
if args.jesUnc != None:
datacard.write('JES shape 1 -\n')
if args.jerUnc != None:
datacard.write('JER shape 1 -\n')
# flat parameters --- flat prior
datacard.write('background_norm flatParam\n')
if args.decoBkg:
datacard.write('deco_eig1 flatParam\n')
datacard.write('deco_eig2 flatParam\n')
if not args.fixP3: datacard.write('deco_eig3 flatParam\n')
else:
datacard.write('p1 flatParam\n')
datacard.write('p2 flatParam\n')
if not args.fixP3: datacard.write('p3 flatParam\n')
datacard.close()
print '>> Datacards and workspaces created and stored in %s/' % (
os.path.join(os.getcwd(), args.output_path))
def main():
# usage description
usage = "Example: ./scripts/createDatacards.py --inputData inputs/rawhistV7_Run2015D_scoutingPFHT_UNBLINDED_649_838_JEC_HLTplusV7_Mjj_cor_smooth.root --dataHistname mjj_mjjcor_gev --inputSig inputs/ResonanceShapes_gg_13TeV_Scouting_Spring15.root -f gg -o datacards -l 1866 --lumiUnc 0.027 --massrange 1000 1500 50 --runFit --p1 5 --p2 7 --p3 0.4 --massMin 838 --massMax 2037 --fitStrategy 2"
# input parameters
parser = ArgumentParser(description='Script that creates combine datacards and corresponding RooFit workspaces',epilog=usage)
parser.add_argument("--inputData", dest="inputData", required=True,
help="Input data spectrum",
metavar="INPUT_DATA")
parser.add_argument("--dataHistname", dest="dataHistname", required=True,
help="Data histogram name",
metavar="DATA_HISTNAME")
parser.add_argument("--inputSig", dest="inputSig", required=True,
help="Input signal shapes",
metavar="INPUT_SIGNAL")
parser.add_argument("-f", "--final_state", dest="final_state", required=True,
help="Final state (e.g. qq, qg, gg)",
metavar="FINAL_STATE")
parser.add_argument("-f2", "--type", dest="atype", required=True, help="Type (e.g. hG, lG, hR, lR)")
parser.add_argument("-o", "--output_path", dest="output_path", required=True,
help="Output path where datacards and workspaces will be stored",
metavar="OUTPUT_PATH")
parser.add_argument("-l", "--lumi", dest="lumi", required=True,
default=1000., type=float,
help="Integrated luminosity in pb-1 (default: %(default).1f)",
metavar="LUMI")
parser.add_argument("--massMin", dest="massMin",
default=500, type=int,
help="Lower bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MIN")
parser.add_argument("--massMax", dest="massMax",
default=1200, type=int,
help="Upper bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MAX")
parser.add_argument("--p1", dest="p1",
default=5.0000e-03, type=float,
help="Fit function p1 parameter (default: %(default)e)",
metavar="P1")
parser.add_argument("--p2", dest="p2",
default=9.1000e+00, type=float,
help="Fit function p2 parameter (default: %(default)e)",
metavar="P2")
parser.add_argument("--p3", dest="p3",
default=5.0000e-01, type=float,
help="Fit function p3 parameter (default: %(default)e)",
metavar="P3")
parser.add_argument("--lumiUnc", dest="lumiUnc",
required=True, type=float,
help="Relative uncertainty in the integrated luminosity",
metavar="LUMI_UNC")
parser.add_argument("--jesUnc", dest="jesUnc",
type=float,
help="Relative uncertainty in the jet energy scale",
metavar="JES_UNC")
parser.add_argument("--jerUnc", dest="jerUnc",
type=float,
help="Relative uncertainty in the jet energy resolution",
metavar="JER_UNC")
parser.add_argument("--sqrtS", dest="sqrtS",
default=13000., type=float,
help="Collision center-of-mass energy (default: %(default).1f)",
metavar="SQRTS")
parser.add_argument("--fixP3", dest="fixP3", default=False, action="store_true", help="Fix the fit function p3 parameter")
parser.add_argument("--runFit", dest="runFit", default=False, action="store_true", help="Run the fit")
parser.add_argument("--fitBonly", dest="fitBonly", default=False, action="store_true", help="Run B-only fit")
parser.add_argument("--fixBkg", dest="fixBkg", default=False, action="store_true", help="Fix all background parameters")
parser.add_argument("--decoBkg", dest="decoBkg", default=False, action="store_true", help="Decorrelate background parameters")
parser.add_argument("--fitStrategy", dest="fitStrategy", type=int, default=1, help="Fit strategy (default: %(default).1f)")
parser.add_argument("--debug", dest="debug", default=False, action="store_true", help="Debug printout")
parser.add_argument("--postfix", dest="postfix", default='', help="Postfix for the output file names (default: %(default)s)")
parser.add_argument("--pyes", dest="pyes", default=False, action="store_true", help="Make files for plots")
parser.add_argument("--jyes", dest="jyes", default=False, action="store_true", help="Make files for JES/JER plots")
parser.add_argument("--pdir", dest="pdir", default='testarea', help="Name a directory for the plots (default: %(default)s)")
parser.add_argument("--chi2", dest="chi2", default=False, action="store_true", help="Compute chi squared")
parser.add_argument("--widefit", dest="widefit", default=False, action="store_true", help="Fit with wide bin hist")
mass_group = parser.add_mutually_exclusive_group(required=True)
mass_group.add_argument("--mass",
type=int,
nargs = '*',
default = 1000,
help="Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
)
mass_group.add_argument("--massrange",
type=int,
nargs = 3,
help="Define a range of masses to be produced. Format: min max step",
metavar = ('MIN', 'MAX', 'STEP')
)
mass_group.add_argument("--masslist",
help = "List containing mass information"
)
args = parser.parse_args()
if args.atype == 'hG':
fstr = "bbhGGBB"
in2 = 'bcorrbin/binmodh.root'
elif args.atype == 'hR':
fstr = "bbhRS"
in2 = 'bcorrbin/binmodh.root'
elif args.atype == 'lG':
fstr = "bblGGBB"
in2 = 'bcorrbin/binmodl.root'
else:
fstr = "bblRS"
in2 = 'bcorrbin/binmodl.root'
# check if the output directory exists
if not os.path.isdir( os.path.join(os.getcwd(),args.output_path) ):
os.mkdir( os.path.join(os.getcwd(),args.output_path) )
# mass points for which resonance shapes will be produced
masses = []
if args.massrange != None:
MIN, MAX, STEP = args.massrange
masses = range(MIN, MAX+STEP, STEP)
elif args.masslist != None:
# A mass list was provided
print "Will create mass list according to", args.masslist
masslist = __import__(args.masslist.replace(".py",""))
masses = masslist.masses
else:
masses = args.mass
# sort masses
masses.sort()
# import ROOT stuff
from ROOT import gStyle, TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE, TCanvas, TLegend, TPad, TLine
from ROOT import RooHist, RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0,kFALSE)
RooMsgService.instance().setStreamStatus(1,kFALSE)
# input data file
inputData = TFile(args.inputData)
# input data histogram
hData = inputData.Get(args.dataHistname)
inData2 = TFile(in2)
hData2 = inData2.Get('h_data')
# input sig file
inputSig = TFile(args.inputSig)
sqrtS = args.sqrtS
# mass variable
mjj = RooRealVar('mjj','mjj',float(args.massMin),float(args.massMax))
# integrated luminosity and signal cross section
lumi = args.lumi
signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
for mass in masses:
print ">> Creating datacard and workspace for %s resonance with m = %i GeV..."%(args.final_state, int(mass))
# get signal shape
hSig = inputSig.Get( "h_" + args.final_state + "_" + str(int(mass)) )
# normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
hSig.Scale(signalCrossSection*lumi/hSig.Integral()) # divide by a number that provides roughly an r value of 1-10
rooSigHist = RooDataHist('rooSigHist','rooSigHist',RooArgList(mjj),hSig)
print 'Signal acceptance:', (rooSigHist.sumEntries()/hSig.Integral())
signal = RooHistPdf('signal','signal',RooArgSet(mjj),rooSigHist)
signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+05,1e+05)
if args.fitBonly: signal_norm.setConstant()
p1 = RooRealVar('p1','p1',args.p1,0.,100.)
p2 = RooRealVar('p2','p2',args.p2,0.,60.)
p3 = RooRealVar('p3','p3',args.p3,-10.,10.)
p4 = RooRealVar('p4','p4',5.6,-50.,50.)
p5 = RooRealVar('p5','p5',10.,-50.,50.)
p6 = RooRealVar('p6','p6',.016,-50.,50.)
p7 = RooRealVar('p7','p7',8.,-50.,50.)
p8 = RooRealVar('p8','p8',.22,-50.,50.)
p9 = RooRealVar('p9','p9',14.1,-50.,50.)
p10 = RooRealVar('p10','p10',8.,-50.,50.)
p11 = RooRealVar('p11','p11',4.8,-50.,50.)
p12 = RooRealVar('p12','p12',7.,-50.,50.)
p13 = RooRealVar('p13','p13',7.,-50.,50.)
p14 = RooRealVar('p14','p14',7.,-50.,50.)
p15 = RooRealVar('p15','p15',1.,-50.,50.)
p16 = RooRealVar('p16','p16',9.,-50.,50.)
p17 = RooRealVar('p17','p17',0.6,-50.,50.)
if args.fixP3: p3.setConstant()
background = RooGenericPdf('background','(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(sqrtS,sqrtS,sqrtS),RooArgList(mjj,p1,p2,p3))
dataInt = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
background_norm = RooRealVar('background_norm','background_norm',dataInt,0.,1e+08)
background2 = RooGenericPdf('background2','(pow(@0/%.1f,-@1)*pow(1-@0/%.1f,@2))'%(sqrtS,sqrtS),RooArgList(mjj,p4,p5))
dataInt2 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
background_norm2 = RooRealVar('background_norm2','background_norm2',dataInt2,0.,1e+08)
background3 = RooGenericPdf('background3','(1/pow(@1+@0/%.1f,@2))'%(sqrtS),RooArgList(mjj,p6,p7))
dataInt3 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
background_norm3 = RooRealVar('background_norm3','background_norm3',dataInt3,0.,1e+08)
background4 = RooGenericPdf('background4','(1/pow(@1+@2*@0/%.1f+pow(@0/%.1f,2),@3))'%(sqrtS,sqrtS),RooArgList(mjj,p8,p9,p10))
dataInt4 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
background_norm4 = RooRealVar('background_norm4','background_norm4',dataInt4,0.,1e+08)
background5 = RooGenericPdf('background5','(pow(@0/%.1f,-@1)*pow(1-pow(@0/%.1f,1/3),@2))'%(sqrtS,sqrtS),RooArgList(mjj,p11,p12))
dataInt5 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
background_norm5 = RooRealVar('background_norm5','background_norm5',dataInt5,0.,1e+08)
background6 = RooGenericPdf('background6','(pow(@0/%.1f,2)+@1*@0/%.1f+@2)'%(sqrtS,sqrtS),RooArgList(mjj,p13,p14))
dataInt6 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
background_norm6 = RooRealVar('background_norm6','background_norm6',dataInt6,0.,1e+08)
background7 = RooGenericPdf('background7','((-1+@1*@0/%.1f)*pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(sqrtS,sqrtS,sqrtS),RooArgList(mjj,p15,p16,p17))
dataInt7 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
background_norm7 = RooRealVar('background_norm7','background_norm7',dataInt7,0.,1e+08)
# S+B model
model = RooAddPdf("model","s+b",RooArgList(background,signal),RooArgList(background_norm,signal_norm))
model2 = RooAddPdf("model2","s+b2",RooArgList(background2,signal),RooArgList(background_norm2,signal_norm))
model3 = RooAddPdf("model3","s+b3",RooArgList(background3,signal),RooArgList(background_norm3,signal_norm))
model4 = RooAddPdf("model4","s+b4",RooArgList(background4,signal),RooArgList(background_norm4,signal_norm))
model5 = RooAddPdf("model5","s+b5",RooArgList(background5,signal),RooArgList(background_norm5,signal_norm))
model6 = RooAddPdf("model6","s+b6",RooArgList(background6,signal),RooArgList(background_norm6,signal_norm))
model7 = RooAddPdf("model7","s+b7",RooArgList(background7,signal),RooArgList(background_norm7,signal_norm))
rooDataHist = RooDataHist('rooDatahist','rooDathist',RooArgList(mjj),hData)
if args.runFit:
mframe = mjj.frame()
rooDataHist.plotOn(mframe, ROOT.RooFit.Name("setonedata"), ROOT.RooFit.Invisible())
res = model.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
model.plotOn(mframe, ROOT.RooFit.Name("model1"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kRed))
res2 = model2.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
model2.plotOn(mframe, ROOT.RooFit.Name("model2"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kOrange))
res3 = model3.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
model3.plotOn(mframe, ROOT.RooFit.Name("model3"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
res4 = model4.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
model4.plotOn(mframe, ROOT.RooFit.Name("model4"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
res5 = model5.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
model5.plotOn(mframe, ROOT.RooFit.Name("model5"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kViolet))
res6 = model6.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
# model6.plotOn(mframe, ROOT.RooFit.Name("model6"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kPink))
res7 = model7.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
# model7.plotOn(mframe, ROOT.RooFit.Name("model7"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kAzure))
rooDataHist2 = RooDataHist('rooDatahist2','rooDathist2',RooArgList(mjj),hData2)
rooDataHist2.plotOn(mframe, ROOT.RooFit.Name("data"))
canvas = TCanvas("cdouble", "cdouble", 800, 1000)
gStyle.SetOptStat(0);
gStyle.SetOptTitle(0);
top = TPad("top", "top", 0., 0.5, 1., 1.)
top.SetBottomMargin(0.03)
top.Draw()
top.SetLogy()
bottom = TPad("bottom", "bottom", 0., 0., 1., 0.5)
bottom.SetTopMargin(0.02)
bottom.SetBottomMargin(0.2)
bottom.Draw()
top.cd()
frame_top = TH1D("frame_top", "frame_top", 100, 526, 1500)
frame_top.GetXaxis().SetTitleSize(0)
frame_top.GetXaxis().SetLabelSize(0)
frame_top.GetYaxis().SetLabelSize(0.04)
frame_top.GetYaxis().SetTitleSize(0.04)
frame_top.GetYaxis().SetTitle("Events")
frame_top.SetMaximum(1000.)
frame_top.SetMinimum(0.1)
frame_top.Draw("axis")
mframe.Draw("p e1 same")
bottom.cd()
frame_bottom = TH1D("frame_bottom", "frame_bottom", 100, 526, 1500)
frame_bottom.GetXaxis().SetTitle("m_{jj} [GeV]")
frame_bottom.GetYaxis().SetTitle("Pull")
frame_bottom.GetXaxis().SetLabelSize(0.04)
frame_bottom.GetXaxis().SetTitleSize(0.06)
frame_bottom.GetXaxis().SetLabelOffset(0.01)
frame_bottom.GetXaxis().SetTitleOffset(1.1)
frame_bottom.GetYaxis().SetLabelSize(0.04)
frame_bottom.GetYaxis().SetTitleSize(0.04)
frame_bottom.GetYaxis().SetTitleOffset(0.85)
frame_bottom.SetMaximum(4.)
frame_bottom.SetMinimum(-3.)
frame_bottom.Draw("axis")
zero = TLine(526., 0., 1500., 0.)
zero.SetLineColor(ROOT.EColor.kBlack)
zero.SetLineStyle(1)
zero.SetLineWidth(2)
zero.Draw("same")
# Ratio histogram with no errors (not so well defined, since this isn't a well-defined efficiency)
newHist = mframe.getHist("data")
curve = mframe.getObject(1)
hresid = newHist.makePullHist(curve,kTRUE)
resframe = mjj.frame()
mframe.SetAxisRange(526.,1500.)
resframe.addPlotable(hresid,"B X")
resframe.Draw("same")
canvas.cd()
canvas.SaveAs("testdouble.pdf")
if args.pyes:
c = TCanvas("c","c",800,800)
mframe.SetAxisRange(300.,1300.)
c.SetLogy()
# mframe.SetMaximum(10)
# mframe.SetMinimum(1)
mframe.Draw()
fitname = args.pdir+'/5funcfit_m'+str(mass)+fstr+'.pdf'
c.SaveAs(fitname)
cpull = TCanvas("cpull","cpull",800,800)
pulls = mframe.pullHist("data","model3")
pulls.Draw("ABX")
pullname = args.pdir+'/pull_m'+str(mass)+fstr+'.pdf'
cpull.SaveAs(pullname)
cpull2 = TCanvas("cpull2","cpull2",800,800)
pulls2 = mframe.pullHist("setonedata","model1")
pulls2.Draw("ABX")
pull2name = args.pdir+'/pull2_m'+str(mass)+fstr+'.pdf'
cpull2.SaveAs(pull2name)
if args.widefit:
mframew = mjj.frame()
rooDataHist2.plotOn(mframew, ROOT.RooFit.Name("data"))
res6 = model.fitTo(rooDataHist2, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
model.plotOn(mframew, ROOT.RooFit.Name("model1"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kRed))
res7 = model2.fitTo(rooDataHist2, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
model2.plotOn(mframew, ROOT.RooFit.Name("model2"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kOrange))
res8 = model3.fitTo(rooDataHist2, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
model3.plotOn(mframew, ROOT.RooFit.Name("model3"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
res9 = model4.fitTo(rooDataHist2, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
model4.plotOn(mframew, ROOT.RooFit.Name("model4"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
res10 = model5.fitTo(rooDataHist2, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
model5.plotOn(mframew, ROOT.RooFit.Name("model5"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kViolet))
if args.pyes:
c = TCanvas("c","c",800,800)
mframew.SetAxisRange(300.,1300.)
c.SetLogy()
# mframew.SetMaximum(10)
# mframew.SetMinimum(1)
mframew.Draw()
fitname = args.pdir+'/5funcfittowide_m'+str(mass)+fstr+'.pdf'
c.SaveAs(fitname)
cpull = TCanvas("cpull","cpull",800,800)
pulls = mframew.pullHist("data","model1")
pulls.Draw("ABX")
pullname = args.pdir+'/pullwidefit_m'+str(mass)+fstr+'.pdf'
cpull.SaveAs(pullname)
if args.chi2:
fullInt = model.createIntegral(RooArgSet(mjj))
norm = dataInt/fullInt.getVal()
chi1 = 0.
fullInt2 = model2.createIntegral(RooArgSet(mjj))
norm2 = dataInt2/fullInt2.getVal()
chi2 = 0.
fullInt3 = model3.createIntegral(RooArgSet(mjj))
norm3 = dataInt3/fullInt3.getVal()
chi3 = 0.
fullInt4 = model4.createIntegral(RooArgSet(mjj))
norm4 = dataInt4/fullInt4.getVal()
chi4 = 0.
fullInt5 = model5.createIntegral(RooArgSet(mjj))
norm5 = dataInt5/fullInt5.getVal()
chi5 = 0.
for i in range(args.massMin, args.massMax):
new = 0
new2 = 0
new3 = 0
new4 = 0
new5 = 0
height = hData.GetBinContent(i)
xLow = hData.GetXaxis().GetBinLowEdge(i)
xUp = hData.GetXaxis().GetBinLowEdge(i+1)
obs = height*(xUp-xLow)
mjj.setRange("intrange",xLow,xUp)
integ = model.createIntegral(RooArgSet(mjj),ROOT.RooFit.NormSet(RooArgSet(mjj)),ROOT.RooFit.Range("intrange"))
exp = integ.getVal()*norm
new = pow(exp-obs,2)/exp
chi1 = chi1 + new
integ2 = model2.createIntegral(RooArgSet(mjj),ROOT.RooFit.NormSet(RooArgSet(mjj)),ROOT.RooFit.Range("intrange"))
exp2 = integ2.getVal()*norm2
new2 = pow(exp2-obs,2)/exp2
chi2 = chi2 + new2
integ3 = model3.createIntegral(RooArgSet(mjj),ROOT.RooFit.NormSet(RooArgSet(mjj)),ROOT.RooFit.Range("intrange"))
exp3 = integ3.getVal()*norm3
new3 = pow(exp3-obs,2)/exp3
chi3 = chi3 + new3
integ4 = model4.createIntegral(RooArgSet(mjj),ROOT.RooFit.NormSet(RooArgSet(mjj)),ROOT.RooFit.Range("intrange"))
exp4 = integ4.getVal()*norm4
if exp4 != 0:
new4 = pow(exp4-obs,2)/exp4
else:
new4 = 0
chi4 = chi4 + new4
integ5 = model5.createIntegral(RooArgSet(mjj),ROOT.RooFit.NormSet(RooArgSet(mjj)),ROOT.RooFit.Range("intrange"))
exp5 = integ5.getVal()*norm5
new5 = pow(exp5-obs,2)/exp5
chi5 = chi5 + new5
print "chi1 %d "%(chi1)
print "chi2 %d "%(chi2)
print "chi3 %d "%(chi3)
print "chi4 %d "%(chi4)
print "chi5 %d "%(chi5)
if not args.decoBkg:
print " "
res.Print()
# res2.Print()
# res3.Print()
# res4.Print()
# res5.Print()
# res6.Print()
# res7.Print()
# decorrelated background parameters for Bayesian limits
if args.decoBkg:
signal_norm.setConstant()
res = model.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
res.Print()
## temp workspace for the PDF diagonalizer
w_tmp = RooWorkspace("w_tmp")
deco = PdfDiagonalizer("deco",w_tmp,res)
# here diagonalizing only the shape parameters since the overall normalization is already decorrelated
background_deco = deco.diagonalize(background)
print "##################### workspace for decorrelation"
w_tmp.Print("v")
print "##################### original parameters"
background.getParameters(rooDataHist).Print("v")
print "##################### decorrelated parameters"
# needed if want to evaluate limits without background systematics
if args.fixBkg:
w_tmp.var("deco_eig1").setConstant()
w_tmp.var("deco_eig2").setConstant()
if not args.fixP3: w_tmp.var("deco_eig3").setConstant()
background_deco.getParameters(rooDataHist).Print("v")
print "##################### original pdf"
background.Print()
print "##################### decorrelated pdf"
background_deco.Print()
# release signal normalization
signal_norm.setConstant(kFALSE)
# set the background normalization range to +/- 5 sigma
bkg_val = background_norm.getVal()
bkg_error = background_norm.getError()
background_norm.setMin(bkg_val-5*bkg_error)
background_norm.setMax(bkg_val+5*bkg_error)
background_norm.Print()
# change background PDF names
background.SetName("background_old")
background_deco.SetName("background")
# needed if want to evaluate limits without background systematics
if args.fixBkg:
background_norm.setConstant()
p1.setConstant()
p2.setConstant()
p3.setConstant()
# -----------------------------------------
# dictionaries holding systematic variations of the signal shape
hSig_Syst = {}
hSig_Syst_DataHist = {}
sigCDF = TGraph(hSig.GetNbinsX()+1)
# JES and JER uncertainties
if args.jesUnc != None or args.jerUnc != None:
sigCDF.SetPoint(0,0.,0.)
integral = 0.
for i in range(1, hSig.GetNbinsX()+1):
x = hSig.GetXaxis().GetBinLowEdge(i+1)
integral = integral + hSig.GetBinContent(i)
sigCDF.SetPoint(i,x,integral)
if args.jesUnc != None:
hSig_Syst['JESUp'] = copy.deepcopy(hSig)
hSig_Syst['JESDown'] = copy.deepcopy(hSig)
if args.jerUnc != None:
hSig_Syst['JERUp'] = copy.deepcopy(hSig)
hSig_Syst['JERDown'] = copy.deepcopy(hSig)
# reset signal histograms
for key in hSig_Syst.keys():
hSig_Syst[key].Reset()
hSig_Syst[key].SetName(hSig_Syst[key].GetName() + '_' + key)
# produce JES signal shapes
if args.jesUnc != None:
for i in range(1, hSig.GetNbinsX()+1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i+1)
jes = 1. - args.jesUnc
xLowPrime = jes*xLow
xUpPrime = jes*xUp
hSig_Syst['JESUp'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jes = 1. + args.jesUnc
xLowPrime = jes*xLow
xUpPrime = jes*xUp
hSig_Syst['JESDown'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JESUp'] = RooDataHist('hSig_JESUp','hSig_JESUp',RooArgList(mjj),hSig_Syst['JESUp'])
hSig_Syst_DataHist['JESDown'] = RooDataHist('hSig_JESDown','hSig_JESDown',RooArgList(mjj),hSig_Syst['JESDown'])
if args.jyes:
c2 = TCanvas("c2","c2",800,800)
mframe2 = mjj.frame(ROOT.RooFit.Title("JES One Sigma Shifts"))
mframe2.SetAxisRange(525.,1200.)
hSig_Syst_DataHist['JESUp'].plotOn(mframe2, ROOT.RooFit.Name("JESUP"),ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kRed), ROOT.RooFit.LineColor(ROOT.EColor.kRed))
hSig_Syst_DataHist['JESDown'].plotOn(mframe2,ROOT.RooFit.Name("JESDOWN"),ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kBlue), ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
rooSigHist.plotOn(mframe2, ROOT.RooFit.DataError(2),ROOT.RooFit.Name("SIG"),ROOT.RooFit.DrawOption("L"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kGreen), ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
mframe2.Draw()
mframe2.GetXaxis().SetTitle("Dijet Mass (GeV)")
leg = TLegend(0.7,0.8,0.9,0.9)
leg.AddEntry(mframe2.findObject("SIG"),"Signal Model","l")
leg.AddEntry(mframe2.findObject("JESUP"),"+1 Sigma","l")
leg.AddEntry(mframe2.findObject("JESDOWN"),"-1 Sigma","l")
leg.Draw()
jesname = args.pdir+'/jes_m'+str(mass)+fstr+'.pdf'
c2.SaveAs(jesname)
# produce JER signal shapes
if args.jesUnc != None:
for i in range(1, hSig.GetNbinsX()+1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i+1)
jer = 1. - args.jerUnc
xLowPrime = jer*(xLow-float(mass))+float(mass)
xUpPrime = jer*(xUp-float(mass))+float(mass)
hSig_Syst['JERUp'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jer = 1. + args.jerUnc
xLowPrime = jer*(xLow-float(mass))+float(mass)
xUpPrime = jer*(xUp-float(mass))+float(mass)
hSig_Syst['JERDown'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JERUp'] = RooDataHist('hSig_JERUp','hSig_JERUp',RooArgList(mjj),hSig_Syst['JERUp'])
hSig_Syst_DataHist['JERDown'] = RooDataHist('hSig_JERDown','hSig_JERDown',RooArgList(mjj),hSig_Syst['JERDown'])
if args.jyes:
c3 = TCanvas("c3","c3",800,800)
mframe3 = mjj.frame(ROOT.RooFit.Title("JER One Sigma Shifts"))
mframe3.SetAxisRange(525.,1200.)
hSig_Syst_DataHist['JERUp'].plotOn(mframe3,ROOT.RooFit.Name("JERUP"),ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kRed), ROOT.RooFit.LineColor(ROOT.EColor.kRed))
hSig_Syst_DataHist['JERDown'].plotOn(mframe3,ROOT.RooFit.Name("JERDOWN"),ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kBlue), ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
rooSigHist.plotOn(mframe3,ROOT.RooFit.DrawOption("L"),ROOT.RooFit.Name("SIG"), ROOT.RooFit.DataError(2), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kGreen), ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
mframe3.Draw()
mframe3.GetXaxis().SetTitle("Dijet Mass (GeV)")
leg = TLegend(0.7,0.8,0.9,0.9)
leg.AddEntry(mframe3.findObject("SIG"),"Signal Model","l")
leg.AddEntry(mframe3.findObject("JERUP"),"+1 Sigma","l")
leg.AddEntry(mframe3.findObject("JERDOWN"),"-1 Sigma","l")
leg.Draw()
jername = args.pdir+'/jer_m'+str(mass)+fstr+'.pdf'
c3.SaveAs(jername)
# -----------------------------------------
# create a datacard and corresponding workspace
postfix = (('_' + args.postfix) if args.postfix != '' else '')
dcName = 'datacard_' + args.final_state + '_m' + str(mass) + postfix + '.txt'
wsName = 'workspace_' + args.final_state + '_m' + str(mass) + postfix + '.root'
w = RooWorkspace('w','workspace')
getattr(w,'import')(rooSigHist,RooFit.Rename("signal"))
if args.jesUnc != None:
getattr(w,'import')(hSig_Syst_DataHist['JESUp'],RooFit.Rename("signal__JESUp"))
getattr(w,'import')(hSig_Syst_DataHist['JESDown'],RooFit.Rename("signal__JESDown"))
if args.jerUnc != None:
getattr(w,'import')(hSig_Syst_DataHist['JERUp'],RooFit.Rename("signal__JERUp"))
getattr(w,'import')(hSig_Syst_DataHist['JERDown'],RooFit.Rename("signal__JERDown"))
if args.decoBkg:
getattr(w,'import')(background_deco,ROOT.RooCmdArg())
else:
getattr(w,'import')(background,ROOT.RooCmdArg(),RooFit.Rename("background"))
#if use different fits for shape uncertainties
#getattr(w,'import')(,ROOT.RooCmdArg(),RooFit.Rename("background__bkgUp"))
#getattr(w,'import')(,ROOT.RooCmdArg(),RooFit.Rename("background__bkgDown"))
getattr(w,'import')(background_norm,ROOT.RooCmdArg())
getattr(w,'import')(rooDataHist,RooFit.Rename("data_obs"))
w.Print()
w.writeToFile(os.path.join(args.output_path,wsName))
beffUnc = 0.3
boffUnc = 0.06
datacard = open(os.path.join(args.output_path,dcName),'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if args.jesUnc != None or args.jerUnc != None:
datacard.write('shapes * * '+wsName+' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write('shapes * * '+wsName+' w:$PROCESS\n')
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background\n')
datacard.write('process 0 1\n')
datacard.write('rate -1 1\n')
datacard.write('------------------------------\n')
datacard.write('lumi lnN %f -\n'%(1.+args.lumiUnc))
datacard.write('beff lnN %f -\n'%(1.+beffUnc))
datacard.write('boff lnN %f -\n'%(1.+boffUnc))
datacard.write('bkg lnN - 1.03\n')
if args.jesUnc != None:
datacard.write('JES shape 1 -\n')
if args.jerUnc != None:
datacard.write('JER shape 1 -\n')
# flat parameters --- flat prior
datacard.write('background_norm flatParam\n')
if args.decoBkg:
datacard.write('deco_eig1 flatParam\n')
datacard.write('deco_eig2 flatParam\n')
if not args.fixP3: datacard.write('deco_eig3 flatParam\n')
else:
datacard.write('p1 flatParam\n')
datacard.write('p2 flatParam\n')
if not args.fixP3: datacard.write('p3 flatParam\n')
datacard.close()
print '>> Datacards and workspaces created and stored in %s/'%( os.path.join(os.getcwd(),args.output_path) )
def main():
# usage description
usage = "Example: ./scripts/createDatacards.py --inputData inputs/rawhistV7_Run2015D_scoutingPFHT_UNBLINDED_649_838_JEC_HLTplusV7_Mjj_cor_smooth.root --dataHistname mjj_mjjcor_gev --inputSig inputs/ResonanceShapes_gg_13TeV_Scouting_Spring15.root -f gg -o datacards -l 1866 --lumiUnc 0.027 --massrange 1000 1500 50 --runFit --p1 5 --p2 7 --p3 0.4 --massMin 838 --massMax 2037 --fitStrategy 2"
# input parameters
parser = ArgumentParser(description='Script that creates combine datacards and corresponding RooFit workspaces',epilog=usage)
parser.add_argument("analysis", type=str, help="Analysis name")
parser.add_argument("model", type=str, help="Model (Hbb, RSG)")
#parser.add_argument("--inputData", dest="inputData", required=True,
# help="Input data spectrum",
# metavar="INPUT_DATA")
parser.add_argument("--dataHistname", dest="dataHistname", type=str, default="h_data",
help="Data histogram name",
metavar="DATA_HISTNAME")
#parser.add_argument("--inputSig", dest="inputSig", required=True,
# help="Input signal shapes",
# metavar="INPUT_SIGNAL")
parser.add_argument("-f", "--final_state", dest="final_state", default="qq",
help="Final state (e.g. qq, qg, gg)",
metavar="FINAL_STATE")
parser.add_argument("--fit_functions", dest="fit_functions", default="f1,f2,f3,f4,f5", help="List of fit functions")
#parser.add_argument("-f2", "--type", dest="atype", required=True, help="Type (e.g. hG, lG, hR, lR)")
parser.add_argument("-o", "--output_path", dest="output_path",
help="Output path where datacards and workspaces will be stored. If not specified, this is derived from limit_configuration.",
metavar="OUTPUT_PATH")
parser.add_argument("--correctTrigger", dest="correctTrigger",
action='store_true',
help="Include trigger correction in PDF")
parser.add_argument("-l", "--lumi", dest="lumi",
default=19700., type=float,
help="Integrated luminosity in pb-1 (default: %(default).1f)",
metavar="LUMI")
parser.add_argument("--massMin", dest="massMin",
default=500, type=int,
help="Lower bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MIN")
parser.add_argument("--massMax", dest="massMax",
default=1200, type=int,
help="Upper bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MAX")
parser.add_argument("--fitSignal", action="store_true", help="Use signal fitted shapes (CB+Voigtian) instead of histogram templates")
#parser.add_argument("--lumiUnc", dest="lumiUnc",
# required=True, type=float,
# help="Relative uncertainty in the integrated luminosity",
# metavar="LUMI_UNC")
#parser.add_argument("--jesUnc", dest="jesUnc",
# type=float,
# help="Relative uncertainty in the jet energy scale",
# metavar="JES_UNC")
#parser.add_argument("--jerUnc", dest="jerUnc",
# type=float,
# help="Relative uncertainty in the jet energy resolution",
# metavar="JER_UNC")
parser.add_argument("--sqrtS", dest="sqrtS",
default=8000., type=float,
help="Collision center-of-mass energy (default: %(default).1f)",
metavar="SQRTS")
parser.add_argument("--fixP3", dest="fixP3", default=False, action="store_true", help="Fix the fit function p3 parameter")
parser.add_argument("--runFit", dest="runFit", default=False, action="store_true", help="Run the fit")
parser.add_argument("--fitBonly", dest="fitBonly", default=False, action="store_true", help="Run B-only fit")
parser.add_argument("--fixBkg", dest="fixBkg", default=False, action="store_true", help="Fix all background parameters")
parser.add_argument("--decoBkg", dest="decoBkg", default=False, action="store_true", help="Decorrelate background parameters")
parser.add_argument("--fitStrategy", dest="fitStrategy", type=int, default=1, help="Fit strategy (default: %(default).1f)")
parser.add_argument("--debug", dest="debug", default=False, action="store_true", help="Debug printout")
parser.add_argument("--postfix", dest="postfix", default='', help="Postfix for the output file names (default: %(default)s)")
parser.add_argument("--pyes", dest="pyes", default=False, action="store_true", help="Make files for plots")
parser.add_argument("--jyes", dest="jyes", default=False, action="store_true", help="Make files for JES/JER plots")
parser.add_argument("--pdir", dest="pdir", default='testarea', help="Name a directory for the plots (default: %(default)s)")
parser.add_argument("--chi2", dest="chi2", default=False, action="store_true", help="Compute chi squared")
parser.add_argument("--widefit", dest="widefit", default=False, action="store_true", help="Fit with wide bin hist")
mass_group = parser.add_mutually_exclusive_group(required=True)
mass_group.add_argument("--mass",
type=int,
nargs = '*',
default = 1000,
help="Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
)
mass_group.add_argument("--massrange",
type=int,
nargs = 3,
help="Define a range of masses to be produced. Format: min max step",
metavar = ('MIN', 'MAX', 'STEP')
)
mass_group.add_argument("--masslist",
help = "List containing mass information"
)
args = parser.parse_args()
fit_functions = args.fit_functions.split(",")
# mass points for which resonance shapes will be produced
masses = []
if args.fitBonly:
masses.append(750)
else:
if args.massrange != None:
MIN, MAX, STEP = args.massrange
masses = range(MIN, MAX+STEP, STEP)
elif args.masslist != None:
# A mass list was provided
print "Will create mass list according to", args.masslist
masslist = __import__(args.masslist.replace(".py",""))
masses = masslist.masses
else:
masses = args.mass
# sort masses
masses.sort()
# import ROOT stuff
from ROOT import gStyle, TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE, TCanvas, TLegend, TPad, TLine
from ROOT import RooHist, RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooProdPdf, RooEffProd, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, RooExtendPdf
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0,kFALSE)
RooMsgService.instance().setStreamStatus(1,kFALSE)
# input data file
#inputData = TFile(limit_config.get_data_input(args.analysis))
# input data histogram
#hData = inputData.Get(args.dataHistname)
#hData.SetDirectory(0)
data_file = TFile(analysis_config.get_b_histogram_filename(args.analysis, "BJetPlusX_2012"))
hData = data_file.Get("BHistograms/h_pfjet_mjj")
hData.SetDirectory(0)
# input sig file
if not args.fitSignal:
print "[create_datacards] INFO : Opening resonance shapes file at " + limit_config.get_resonance_shapes(args.analysis, args.model)
inputSig = TFile(limit_config.get_resonance_shapes(args.analysis, args.model), "READ")
sqrtS = args.sqrtS
# mass variable
mjj = RooRealVar('mjj','mjj',float(args.massMin),float(args.massMax))
# integrated luminosity and signal cross section
lumi = args.lumi
signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
if args.correctTrigger:
trigger_efficiency_pdf = trigger_efficiency.get_pdf(args.analysis, mjj)
trigger_efficiency_formula = trigger_efficiency.get_formula(args.analysis, mjj)
else:
trigger_efficiency_pdf = trigger_efficiency.get_trivial_pdf(mjj)
trigger_efficiency_formula = trigger_efficiency.get_trivial_formula(mjj)
for mass in masses:
print ">> Creating datacard and workspace for %s resonance with m = %i GeV..."%(args.final_state, int(mass))
rooDataHist = RooDataHist('rooDatahist','rooDathist',RooArgList(mjj),hData)
if not args.fitSignal:
hSig = inputSig.Get( "h_" + args.final_state + "_" + str(int(mass)) )
if not hSig:
raise Exception("Couldn't find histogram " + "h_" + args.final_state + "_" + str(int(mass)) + " in file " + limit_config.get_resonance_shapes(args.analysis, args.model))
# normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
hSig.Scale(signalCrossSection*lumi/hSig.Integral()) # divide by a number that provides roughly an r value of 1-10
rooSigHist = RooDataHist('rooSigHist','rooSigHist',RooArgList(mjj),hSig)
print 'Signal acceptance:', (rooSigHist.sumEntries()/hSig.Integral())
# If using fitted signal shapes, load the signal PDF
if args.fitSignal:
print "[create_datacards] Loading fitted signal PDFs from " + analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses)), "READ")
w_signal = f_signal_pdfs.Get("w_signal")
input_parameters = signal_fits.get_parameters(w_signal.pdf("signal"))
# Make a new PDF with nuisance parameters
signal_pdf_notrig, signal_vars = signal_fits.make_signal_pdf_systematic("bukin", mjj, mass=mass)
signal_pdf_name = signal_pdf_notrig.GetName()
signal_pdf_notrig.SetName(signal_pdf_name + "_notrig")
#signal_pdf = RooProdPdf(signal_pdf_name, signal_pdf_name, signal_pdf_notrig, trigger_efficiency_pdf)
signal_pdf = RooEffProd(signal_pdf_name, signal_pdf_name, signal_pdf_notrig, trigger_efficiency_formula)
# Copy input parameter values
signal_vars["xp_0"].setVal(input_parameters["xp"][0])
signal_vars["xp_0"].setError(input_parameters["xp"][1])
signal_vars["xp_0"].setConstant()
signal_vars["sigp_0"].setVal(input_parameters["sigp"][0])
signal_vars["sigp_0"].setError(input_parameters["sigp"][1])
signal_vars["sigp_0"].setConstant()
signal_vars["xi_0"].setVal(input_parameters["xi"][0])
signal_vars["xi_0"].setError(input_parameters["xi"][1])
signal_vars["xi_0"].setConstant()
signal_vars["rho1_0"].setVal(input_parameters["rho1"][0])
signal_vars["rho1_0"].setError(input_parameters["rho1"][1])
signal_vars["rho1_0"].setConstant()
signal_vars["rho2_0"].setVal(input_parameters["rho2"][0])
signal_vars["rho2_0"].setError(input_parameters["rho2"][1])
signal_vars["rho2_0"].setConstant()
f_signal_pdfs.Close()
signal_parameters = {}
signal_pdfs_notrig = {}
signal_pdfs = {}
signal_norms = {}
background_pdfs = {}
background_pdfs_notrig = {}
background_parameters = {}
background_norms = {}
signal_epdfs = {}
background_epdfs = {}
models = {}
fit_results = {}
for fit_function in fit_functions:
print "[create_datacards] INFO : On fit function {}".format(fit_function)
if args.fitSignal:
# Make a copy of the signal PDF, so that each fitTo call uses its own copy.
# The copy should have all variables set constant.
#signal_pdfs[fit_function], signal_parameters[fit_function] = signal_fits.copy_signal_pdf("bukin", signal_pdf, mjj, tag=fit_function, include_systematics=True)
signal_pdfs_notrig[fit_function] = ROOT.RooBukinPdf(signal_pdf_notrig, signal_pdf_notrig.GetName() + "_" + fit_function)
signal_pdfs[fit_function] = RooEffProd(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigger_efficiency_formula)
#signal_pdfs[fit_function] = RooProdPdf(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigger_efficiency_pdf)
iterator = signal_pdfs_notrig[fit_function].getVariables().createIterator()
this_parameter = iterator.Next()
while this_parameter:
this_parameter.setConstant()
this_parameter = iterator.Next()
else:
signal_pdfs[fit_function] = RooHistPdf('signal_' + fit_function,'signal_' + fit_function, RooArgSet(mjj), rooSigHist)
signal_norms[fit_function] = RooRealVar('signal_norm_' + fit_function, 'signal_norm_' + fit_function, 0., 0., 1e+05)
if args.fitBonly:
signal_norms[fit_function].setConstant()
background_pdfs_notrig[fit_function], background_parameters[fit_function] = make_background_pdf(fit_function, mjj, collision_energy=8000.)
background_pdf_name = background_pdfs_notrig[fit_function].GetName()
background_pdfs_notrig[fit_function].SetName(background_pdf_name + "_notrig")
background_pdfs[fit_function] = RooEffProd(background_pdf_name, background_pdf_name, background_pdfs_notrig[fit_function], trigger_efficiency_formula)
#background_pdfs[fit_function] = RooProdPdf(background_pdf_name, background_pdf_name, background_pdfs_notrig[fit_function], trigger_efficiency_pdf)
#background_pdfs[fit_function] = background_pdfs_notrig[fit_function]
#background_pdfs[fit_function].SetName(background_pdf_name)
# Initial values
if "trigbbh" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(55.)
background_parameters[fit_function]["p1"].setMin(20.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(28.)
background_parameters[fit_function]["p2"].setVal(-22.)
background_parameters[fit_function]["p3"].setVal(10.)
elif "trigbbl" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(82.)
background_parameters[fit_function]["p1"].setMin(60.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(41.)
background_parameters[fit_function]["p2"].setVal(-45.)
background_parameters[fit_function]["p3"].setVal(10.)
data_integral = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
background_norms[fit_function] = RooRealVar('background_' + fit_function + '_norm', 'background_' + fit_function + '_norm', data_integral, 0., 1.e8)
signal_epdfs[fit_function] = RooExtendPdf('esignal_' + fit_function, 'esignal_' + fit_function, signal_pdfs[fit_function], signal_norms[fit_function])
background_epdfs[fit_function] = RooExtendPdf('ebackground_' + fit_function, 'ebackground_' + fit_function, background_pdfs[fit_function], background_norms[fit_function])
models[fit_function] = RooAddPdf('model_' + fit_function, 's+b', RooArgList(background_epdfs[fit_function], signal_epdfs[fit_function]))
if args.runFit:
print "[create_datacards] INFO : Starting fit with function {}".format(fit_function)
fit_results[fit_function] = models[fit_function].fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Extended(kTRUE), RooFit.Strategy(args.fitStrategy), RooFit.Verbose(0))
print "[create_datacards] INFO : Done with fit {}. Printing results.".format(fit_function)
fit_results[fit_function].Print()
print "[create_datacards] DEBUG : End args.runFit if block."
# needed if want to evaluate limits without background systematics
if args.fixBkg:
background_norms[fit_function].setConstant()
for par_name, par in background_parameters[fit_function].iteritems():
par.setConstant()
# -----------------------------------------
#signal_pdfs_syst = {}
# JES and JER uncertainties
if args.fitSignal:
print "[create_datacards] INFO : Getting signal PDFs from " + analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses)))
w_signal = f_signal_pdfs.Get("w_signal")
if "jes" in systematics:
xp_central = signal_vars["xp_0"].getVal()
#print w_signal.pdf("signal__JESUp")
#print signal_fits.get_parameters(w_signal.pdf("signal__JESUp"))
xp_up = signal_fits.get_parameters(w_signal.pdf("signal__JESUp"))["xpJESUp"][0]
xp_down = signal_fits.get_parameters(w_signal.pdf("signal__JESDown"))["xpJESDown"][0]
signal_vars["dxp"].setVal(max(abs(xp_up - xp_central), abs(xp_down - xp_central)))
if signal_vars["dxp"].getVal() > 2 * mass * 0.1:
print "[create_datacards] WARNING : Large dxp value. dxp = {}, xp_down = {}, xp_central = {}, xp_up = {}".format(signal_vars["dxp"].getVal(), xp_down, xp_central, xp_up)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant(False)
else:
signal_vars["dxp"].setVal(0.)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant()
signal_vars["dxp"].setError(0.)
signal_vars["dxp"].setConstant()
if "jer" in systematics:
sigp_central = signal_vars["sigp_0"].getVal()
sigp_up = signal_fits.get_parameters(w_signal.pdf("signal__JERUp"))["sigpJERUp"][0]
sigp_down = signal_fits.get_parameters(w_signal.pdf("signal__JERDown"))["sigpJERDown"][0]
signal_vars["dsigp"].setVal(max(abs(sigp_up - sigp_central), abs(sigp_down - sigp_central)))
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant(False)
else:
signal_vars["dsigp"].setVal(0.)
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant()
signal_vars["dsigp"].setError(0.)
signal_vars["dsigp"].setConstant()
#for variation in ["JERUp", "JERDown"]:
# signal_pdfs_syst[variation] = w_signal.pdf("signal__" + variation)
#for variation, pdf in signal_pdfs_syst.iteritems():
# signal_parameters = pdf.getVariables()
# iter = signal_parameters.createIterator()
# var = iter.Next()
# while var:
# var.setConstant()
# var = iter.Next()
f_signal_pdfs.Close()
else:
# dictionaries holding systematic variations of the signal shape
hSig_Syst = {}
hSig_Syst_DataHist = {}
sigCDF = TGraph(hSig.GetNbinsX()+1)
if "jes" in systematics or "jer" in systematics:
sigCDF.SetPoint(0,0.,0.)
integral = 0.
for i in range(1, hSig.GetNbinsX()+1):
x = hSig.GetXaxis().GetBinLowEdge(i+1)
integral = integral + hSig.GetBinContent(i)
sigCDF.SetPoint(i,x,integral)
if "jes" in systematics:
hSig_Syst['JESUp'] = copy.deepcopy(hSig)
hSig_Syst['JESDown'] = copy.deepcopy(hSig)
if "jer" in systematics:
hSig_Syst['JERUp'] = copy.deepcopy(hSig)
hSig_Syst['JERDown'] = copy.deepcopy(hSig)
# reset signal histograms
for key in hSig_Syst.keys():
hSig_Syst[key].Reset()
hSig_Syst[key].SetName(hSig_Syst[key].GetName() + '_' + key)
# produce JES signal shapes
if "jes" in systematics:
for i in range(1, hSig.GetNbinsX()+1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i+1)
jes = 1. - systematics["jes"]
xLowPrime = jes*xLow
xUpPrime = jes*xUp
hSig_Syst['JESUp'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jes = 1. + systematics["jes"]
xLowPrime = jes*xLow
xUpPrime = jes*xUp
hSig_Syst['JESDown'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JESUp'] = RooDataHist('hSig_JESUp','hSig_JESUp',RooArgList(mjj),hSig_Syst['JESUp'])
hSig_Syst_DataHist['JESDown'] = RooDataHist('hSig_JESDown','hSig_JESDown',RooArgList(mjj),hSig_Syst['JESDown'])
# produce JER signal shapes
if "jer" in systematics:
for i in range(1, hSig.GetNbinsX()+1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i+1)
jer = 1. - systematics["jer"]
xLowPrime = jer*(xLow-float(mass))+float(mass)
xUpPrime = jer*(xUp-float(mass))+float(mass)
hSig_Syst['JERUp'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jer = 1. + systematics["jer"]
xLowPrime = jer*(xLow-float(mass))+float(mass)
xUpPrime = jer*(xUp-float(mass))+float(mass)
hSig_Syst['JERDown'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JERUp'] = RooDataHist('hSig_JERUp','hSig_JERUp',RooArgList(mjj),hSig_Syst['JERUp'])
hSig_Syst_DataHist['JERDown'] = RooDataHist('hSig_JERDown','hSig_JERDown',RooArgList(mjj),hSig_Syst['JERDown'])
# -----------------------------------------
# create a datacard and corresponding workspace
postfix = (('_' + args.postfix) if args.postfix != '' else '')
wsName = 'workspace_' + args.final_state + '_m' + str(mass) + postfix + '.root'
w = RooWorkspace('w','workspace')
if args.fitSignal:
signal_pdf.SetName("signal")
getattr(w,'import')(signal_pdf,RooFit.Rename("signal"))
# Create a norm variable "signal_norm" which normalizes the PDF to unity.
norm = args.lumi
#signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", 1. / norm, 0.1 / norm, 10. / norm)
#if args.analysis == "trigbbh_CSVTM" and mass >= 1100:
signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", norm/100., norm/100. / 10., norm * 10.)
#else:
# signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", norm, norm / 10., norm * 10.)
print "[create_datacards] INFO : Set signal norm to {}".format(signal_norm.getVal())
signal_norm.setConstant()
getattr(w,'import')(signal_norm,ROOT.RooCmdArg())
#if "jes" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JESUp'],RooFit.Rename("signal__JESUp"))
# getattr(w,'import')(signal_pdfs_syst['JESDown'],RooFit.Rename("signal__JESDown"))
#if "jer" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JERUp'],RooFit.Rename("signal__JERUp"))
# getattr(w,'import')(signal_pdfs_syst['JERDown'],RooFit.Rename("signal__JERDown"))
else:
getattr(w,'import')(rooSigHist,RooFit.Rename("signal"))
if "jes" in systematics:
getattr(w,'import')(hSig_Syst_DataHist['JESUp'],RooFit.Rename("signal__JESUp"))
getattr(w,'import')(hSig_Syst_DataHist['JESDown'],RooFit.Rename("signal__JESDown"))
if "jer" in systematics:
getattr(w,'import')(hSig_Syst_DataHist['JERUp'],RooFit.Rename("signal__JERUp"))
getattr(w,'import')(hSig_Syst_DataHist['JERDown'],RooFit.Rename("signal__JERDown"))
if args.decoBkg:
getattr(w,'import')(background_deco,ROOT.RooCmdArg())
else:
for fit_function in fit_functions:
print "Importing background PDF"
print background_pdfs[fit_function]
background_pdfs[fit_function].Print()
getattr(w,'import')(background_pdfs[fit_function],ROOT.RooCmdArg(),RooFit.Rename("background_" + fit_function), RooFit.RecycleConflictNodes())
w.pdf("background_" + fit_function).Print()
getattr(w,'import')(background_norms[fit_function],ROOT.RooCmdArg(),RooFit.Rename("background_" + fit_function + "_norm"))
getattr(w,'import')(fit_results[fit_function])
getattr(w,'import')(signal_norms[fit_function],ROOT.RooCmdArg())
if args.fitBonly:
getattr(w,'import')(models[fit_function],ROOT.RooCmdArg(),RooFit.RecycleConflictNodes())
getattr(w,'import')(rooDataHist,RooFit.Rename("data_obs"))
w.Print()
print "Starting save"
if args.output_path:
if not os.path.isdir( os.path.join(os.getcwd(),args.output_path) ):
os.mkdir( os.path.join(os.getcwd(),args.output_path) )
print "[create_datacards] INFO : Writing workspace to file {}".format(os.path.join(args.output_path,wsName))
w.writeToFile(os.path.join(args.output_path,wsName))
else:
print "[create_datacards] INFO : Writing workspace to file {}".format(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitBonly=args.fitBonly, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))
w.writeToFile(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitBonly=args.fitBonly, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))
# Clean up
for name, obj in signal_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in fit_results.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, dict_l2 in background_parameters.iteritems():
for name2, obj in dict_l2.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in models.iteritems():
if obj:
obj.IsA().Destructor(obj)
rooDataHist.IsA().Destructor(rooDataHist)
w.IsA().Destructor(w)
# Make datacards only if S+B fitted
if not args.fitBonly:
beffUnc = 0.3
boffUnc = 0.06
for fit_function in fit_functions:
if args.output_path:
dcName = 'datacard_' + args.final_state + '_m' + str(mass) + postfix + '_' + fit_function + '.txt'
print "[create_datacards] INFO : Writing datacard to file {}".format(os.path.join(args.output_path,dcName))
datacard = open(os.path.join(args.output_path,dcName),'w')
else:
print "[create_datacards] INFO : Writing datacard to file {}".format(limit_config.get_datacard_filename(args.analysis, args.model, mass, fit_function, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))
datacard = open(limit_config.get_datacard_filename(args.analysis, args.model, mass, fit_function, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger), 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if ("jes" in systematics or "jer" in systematics) and not args.fitSignal:
if args.output_path:
datacard.write('shapes * * '+wsName+' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write('shapes * * '+os.path.basename(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))+' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
if args.output_path:
datacard.write('shapes * * '+wsName+' w:$PROCESS\n')
else:
datacard.write('shapes * * '+os.path.basename(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))+' w:$PROCESS\n')
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background_' + fit_function + '\n')
datacard.write('process 0 1\n')
if args.fitSignal:
datacard.write('rate 1 1\n')
else:
datacard.write('rate -1 1\n')
datacard.write('------------------------------\n')
datacard.write('lumi lnN %f -\n'%(1.+systematics["luminosity"]))
datacard.write('beff lnN %f -\n'%(1.+beffUnc))
datacard.write('boff lnN %f -\n'%(1.+boffUnc))
#datacard.write('bkg lnN - 1.03\n')
if args.fitSignal:
if "jes" in systematics:
datacard.write("alpha_jes param 0.0 1.0\n")
if "jer" in systematics:
datacard.write("alpha_jer param 0.0 1.0\n")
else:
if "jes" in systematics:
datacard.write('JES shape 1 -\n')
if "jer" in systematics:
datacard.write('JER shape 1 -\n')
# flat parameters --- flat prior
datacard.write('background_' + fit_function + '_norm flatParam\n')
if args.decoBkg:
datacard.write('deco_eig1 flatParam\n')
datacard.write('deco_eig2 flatParam\n')
else:
for par_name, par in background_parameters[fit_function].iteritems():
datacard.write(fit_function + "_" + par_name + ' flatParam\n')
datacard.close()
print "[create_datacards] INFO : Done with this datacard"
#print '>> Datacards and workspaces created and stored in %s/'%( os.path.join(os.getcwd(),args.output_path) )
print "All done."
def studyVqqResolution(rootFile):
#get all from file
histos = {}
inF = TFile.Open(rootFile)
keys = inF.GetListOfKeys()
for k in keys:
obj = inF.Get(k.GetName())
obj.SetDirectory(0)
histos[k.GetName()] = obj
inF.Close()
#plot
gROOT.SetBatch()
gROOT.SetStyle('Plain')
gStyle.SetOptStat(0)
gStyle.SetOptFit(1111)
gStyle.SetOptTitle(0)
gStyle.SetStatFont(42)
kin = ['', '30to40', '40to50', '50to75', '75to100', '100toInf']
for k in kin:
c = TCanvas('c', 'c', 600, 600)
c.cd()
c.SetCanvasSize(1000, 500)
c.SetWindowSize(1000, 500)
c.Divide(2, 1)
c.cd(1)
histos['deta' + k + 'barrel'].SetLineWidth(2)
histos['deta' + k + 'barrel'].SetTitle('barrel')
histos['deta' + k + 'barrel'].Draw('hist')
histos['deta' + k + 'endcap'].SetLineWidth(2)
histos['deta' + k + 'endcap'].SetLineStyle(7)
histos['deta' + k + 'endcap'].SetTitle('endcap')
histos['deta' + k + 'endcap'].Draw('histsame')
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['deta' + k + 'barrel'], 'barrel', 'f')
leg.AddEntry(histos['deta' + k + 'endcap'], 'endcap', 'f')
leg.SetNColumns(2)
leg.Draw()
drawHeader()
c.cd(2)
histos['dphi' + k + 'barrel'].SetLineWidth(2)
histos['dphi' + k + 'barrel'].SetTitle('barrel')
histos['dphi' + k + 'barrel'].Draw('hist')
histos['dphi' + k + 'endcap'].SetLineWidth(2)
histos['dphi' + k + 'endcap'].SetLineStyle(7)
histos['dphi' + k + 'endcap'].SetTitle('endcap')
histos['dphi' + k + 'endcap'].Draw('histsame')
c.Modified()
c.Update()
c.SaveAs('dr_%s.png' % k)
labels = []
responseVars = ['dpt', 'den', 'dphi', 'deta', 'dr']
for r in responseVars:
barrelResponse = TGraphErrors()
barrelResponse.SetName(r + 'barrelresponse')
barrelResponse.SetLineWidth(2)
barrelResponse.SetFillStyle(0)
barrelResponse.SetMarkerStyle(20)
barrelCoreResponse = barrelResponse.Clone(r + 'barrelcoreresponse')
endcapResponse = TGraphErrors()
endcapResponse.SetName(r + 'endcapresponse')
endcapResponse.SetLineWidth(2)
endcapResponse.SetFillStyle(0)
endcapResponse.SetMarkerStyle(24)
endcapCoreResponse = endcapResponse.Clone(r + 'endcapresponse')
for k in kin:
c.cd()
c.Clear()
c.SetWindowSize(1000, 500)
c.Divide(2, 1)
for i in [1, 2]:
c.cd(i)
reg = 'barrel'
if i == 2: reg = 'endcap'
h = histos[r + k + reg]
x = RooRealVar("x",
h.GetXaxis().GetTitle(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
data = RooDataHist("data", "dataset with x", RooArgList(x), h)
frame = x.frame()
RooAbsData.plotOn(data, frame,
RooFit.DataError(RooAbsData.SumW2))
mean1 = RooRealVar("mean1", "mean1", 0, -0.5, 0.5)
sigma1 = RooRealVar("sigma1", "sigma1", 0.1, 0.01, 1.0)
gauss1 = RooGaussian("g1", "g", x, mean1, sigma1)
if r == 'dpt' or r == 'den':
mean2 = RooRealVar("mean2", "mean2", 0, -0.5, 0.5)
sigma2 = RooRealVar("sigma2", "sigma2", 0.1, 0.01, 1.0)
alphacb = RooRealVar("alphacb", "alphacb", 1, 0.1, 3)
ncb = RooRealVar("ncb", "ncb", 4, 1, 100)
gauss2 = RooCBShape("cb2", "cb", x, mean2, sigma2, alphacb,
ncb)
else:
mean1.setRange(0, 0.5)
mean2 = RooRealVar("mean2", "mean", 0, 0, 1)
sigma2 = RooRealVar("sigma2", "sigma", 0.1, 0.01, 1.0)
gauss2 = RooGaussian("g2", "g", x, mean2, sigma2)
frac = RooRealVar("frac", "fraction", 0.9, 0.0, 1.0)
if data.sumEntries() < 100:
frac.setVal(1.0)
frac.setConstant(True)
model = RooAddPdf("sum", "g1+g2", RooArgList(gauss1, gauss2),
RooArgList(frac))
status = model.fitTo(data, RooFit.Save()).status()
if status != 0: continue
model_cdf = model.createCdf(RooArgSet(x))
cl = 0.90
ul = 0.5 * (1.0 + cl)
closestToCL = 1.0
closestToUL = -1
closestToMedianCL = 1.0
closestToMedian = -1
for ibin in xrange(1, h.GetXaxis().GetNbins() * 10):
xval = h.GetXaxis().GetXmin() + (
ibin - 1) * h.GetXaxis().GetBinWidth(ibin) / 10.
x.setVal(xval)
cdfValToCL = math.fabs(model_cdf.getVal() - ul)
if cdfValToCL < closestToCL:
closestToCL = cdfValToCL
closestToUL = xval
cdfValToCL = math.fabs(model_cdf.getVal() - 0.5)
if cdfValToCL < closestToMedianCL:
closestToMedianCL = cdfValToCL
closestToMedian = xval
RooAbsPdf.plotOn(model, frame)
frame.Draw()
if i == 1: drawHeader()
labels.append(TPaveText(0.6, 0.92, 0.9, 0.98, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'txt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
kinReg = k.replace('to', '-')
kinReg = kinReg.replace('Inf', '#infty')
labels[ilab].AddText('[' + reg + '] ' + kinReg)
labels[ilab].Draw()
resolutionVal = math.fabs(closestToUL - closestToMedian)
responseGr = barrelResponse
responseCoreGr = barrelCoreResponse
coreResolutionVal = sigma1.getVal()
coreResolutionErr = sigma1.getError()
if frac.getVal() < 0.7 and (sigma2.getVal() < sigma1.getVal()):
coreResolutionVal = sigma2.getVal()
coreResolutionErr = sigma2.getError()
if i == 2:
responseGr = endcapResponse
responseCoreGr = endcapCoreResponse
if k != '':
nrespPts = responseGr.GetN()
kinAvg = 150
kinWidth = 50
if k == '30to40':
kinAvg = 35
kinWidth = 5
if k == '40to50':
kinAvg = 45
kinWidth = 5
if k == '50to75':
kinAvg = 62.5
kinWidth = 12.5
elif k == '75to100':
kinAvg = 87.5
kinWidth = 12.5
responseGr.SetPoint(nrespPts, kinAvg, resolutionVal)
responseCoreGr.SetPoint(nrespPts, kinAvg,
coreResolutionVal)
responseCoreGr.SetPointError(nrespPts, kinWidth,
coreResolutionErr)
labels.append(TPaveText(0.15, 0.7, 0.4, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'fitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)' % frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean1.getVal(), mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma1.getVal(), sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)' %
(1 - frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean2.getVal(), mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma2.getVal(), sigma2.getError()))
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs(r + 'res_' + k + '.png')
frame = TGraphErrors()
frame.SetPoint(0, 0, 0)
frame.SetPoint(1, 200, 0.3)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('frame')
cresp = TCanvas('cresp', 'cresp', 500, 500)
cresp.cd()
frame.Draw('ap')
barrelResponse.Draw('pl')
endcapResponse.Draw('pl')
frame.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Resolution %3.2f C.L." % cl)
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelResponse, 'barrel', 'fp')
leg.AddEntry(endcapResponse, 'endcap', 'fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r + 'res_evol.png')
frameCore = frame.Clone('framecore')
cresp.Clear()
frameCore.Draw('ap')
barrelCoreResponse.Draw('pl')
endcapCoreResponse.Draw('pl')
frameCore.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frameCore.GetYaxis().SetTitle("Core resolution")
frameCore.GetYaxis().SetTitleOffset(1.4)
frameCore.GetYaxis().SetNdivisions(10)
frameCore.GetYaxis().SetRangeUser(0, 0.2)
drawHeader()
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelCoreResponse, 'barrel', 'fp')
leg.AddEntry(endcapCoreResponse, 'endcap', 'fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r + 'rescore_evol.png')
bosons = ['h', 'z', 'w']
kin = ['', '50', '100']
region = ['', 'bb', 'eb', 'ee']
for k in kin:
for r in region:
c = TCanvas('c', 'c', 600, 600)
c.cd()
histos['mjj' + k + r].Rebin()
histos['mjj' + k + r].Draw()
ic = 1
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['mjj' + k + r], 'inclusive', 'f')
for b in bosons:
if histos[b + 'mjj' + k + r].Integral() <= 0: continue
ic = ic + 1
histos[b + 'mjj' + k + r].Rebin()
histos[b + 'mjj' + k + r].SetLineColor(ic)
histos[b + 'mjj' + k + r].SetLineWidth(2)
histos[b + 'mjj' + k + r].SetMarkerColor(ic)
histos[b + 'mjj' + k + r].SetMarkerStyle(1)
histos[b + 'mjj' + k + r].SetFillStyle(3000 + ic)
histos[b + 'mjj' + k + r].SetFillColor(ic)
histos[b + 'mjj' + k + r].Draw('histsame')
leg.AddEntry(histos[b + 'mjj' + k + r], b, "f")
leg.SetNColumns(ic)
leg.Draw()
drawHeader()
labels.append(TPaveText(0.65, 0.8, 0.9, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(k + r + 'mjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle = "inclusive"
if r == 'bb': regionTitle = 'barrel-barrel'
if r == 'eb': regionTitle = 'endcap-barrel'
if r == 'ee': regionTitle = 'endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold = 30
if k != '': ptthreshold = float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV' % ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('mjj' + k + r + '.png')
massResolutionGrs = []
for r in region:
massResolution = TGraphErrors()
massResolution.SetName(r + 'dm')
massResolution.SetLineWidth(2)
massResolution.SetFillStyle(0)
massResolution.SetMarkerStyle(20 + len(massResolutionGrs))
massResolution.SetMarkerColor(1 + len(massResolutionGrs))
massResolution.SetLineColor(1 + len(massResolutionGrs))
massResolution.SetFillColor(1 + len(massResolutionGrs))
massResolutionGrs.append(massResolution)
for k in kin:
c = TCanvas('c', 'c', 600, 600)
c.cd()
h = histos['dmjj' + k + r]
x = RooRealVar("x",
h.GetXaxis().GetTitle(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
data = RooDataHist("data", "dataset with x", RooArgList(x), h)
frame = x.frame()
RooAbsData.plotOn(data, frame, RooFit.DataError(RooAbsData.SumW2))
mean1 = RooRealVar("mean1", "mean1", 0, -0.5, 0.5)
sigma1 = RooRealVar("sigma1", "sigma1", 0.1, 0.01, 1.0)
gauss1 = RooGaussian("g1", "g", x, mean1, sigma1)
mean2 = RooRealVar("mean2", "mean2", 0, -0.5, 0.5)
sigma2 = RooRealVar("sigma2", "sigma2", 0.1, 0.01, 1.0)
alphacb = RooRealVar("alphacb", "alphacb", 1, 0.1, 3)
ncb = RooRealVar("ncb", "ncb", 4, 1, 100)
gauss2 = RooCBShape("cb2", "cb", x, mean2, sigma2, alphacb, ncb)
frac = RooRealVar("frac", "fraction", 0.9, 0.0, 1.0)
model = RooAddPdf("sum", "g1+g2", RooArgList(gauss1, gauss2),
RooArgList(frac))
status = model.fitTo(data, RooFit.Save()).status()
if status != 0: continue
RooAbsPdf.plotOn(model, frame)
frame.Draw()
labels.append(TPaveText(0.6, 0.65, 0.85, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'dmfitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)' % frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean1.getVal(), mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma1.getVal(), sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)' % (1 - frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean2.getVal(), mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma2.getVal(), sigma2.getError()))
labels[ilab].Draw()
drawHeader()
labels.append(TPaveText(0.15, 0.8, 0.4, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(k + r + 'dmjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle = "inclusive"
if r == 'bb': regionTitle = 'barrel-barrel'
if r == 'eb': regionTitle = 'endcap-barrel'
if r == 'ee': regionTitle = 'endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold = 30
if k != '': ptthreshold = float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV' % ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('dmjj' + k + r + '.png')
massResolution.SetTitle(regionTitle)
ip = massResolution.GetN()
x = 40
xerr = 10
if k == '50':
x = 75
xerr = 25
elif k == '100':
x = 150
xerr = 50
y = sigma1.getVal()
yerr = sigma1.getError()
if frac.getVal() < 0.8:
if sigma2.getVal() < sigma1.getVal():
y = sigma2.getVal()
ey = sigma2.getError()
massResolution.SetPoint(ip, x, y)
massResolution.SetPointError(ip, xerr, yerr)
frame = TGraphErrors()
frame.SetPoint(0, 0, 0)
frame.SetPoint(1, 200, 0.2)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('dmframe')
cdmevol = TCanvas('cdmevol', 'cdmevol', 500, 500)
cdmevol.cd()
frame.Draw('ap')
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
for dmGr in massResolutionGrs:
dmGr.Draw('pl')
leg.AddEntry(dmGr, dmGr.GetTitle(), 'fp')
frame.GetXaxis().SetTitle("Leading quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Core resolution")
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg.SetNColumns(2)
leg.Draw()
cdmevol.Modified()
cdmevol.Update()
cdmevol.SaveAs('dm_evol.png')
c = TCanvas('c', 'c', 600, 600)
c.cd()
histos['sel'].Draw('histtext')
drawHeader()
c.Modified()
c.Update()
c.SaveAs('selection.png')
return
def main():
# usage description
usage = "Example: ./scripts/createDatacards.py --inputData inputs/rawhistV7_Run2015D_scoutingPFHT_UNBLINDED_649_838_JEC_HLTplusV7_Mjj_cor_smooth.root --dataHistname mjj_mjjcor_gev --inputSig inputs/ResonanceShapes_gg_13TeV_Scouting_Spring15.root -f gg -o datacards -l 1866 --lumiUnc 0.027 --massrange 1000 1500 50 --runFit --p1 5 --p2 7 --p3 0.4 --massMin 838 --massMax 2037 --fitStrategy 2"
# input parameters
parser = ArgumentParser(
description=
'Script that creates combine datacards and corresponding RooFit workspaces',
epilog=usage)
parser.add_argument("analysis", type=str, help="Analysis name")
parser.add_argument("model", type=str, help="Model (Hbb, RSG)")
#parser.add_argument("--inputData", dest="inputData", required=True,
# help="Input data spectrum",
# metavar="INPUT_DATA")
parser.add_argument("--dataHistname",
dest="dataHistname",
type=str,
default="h_data",
help="Data histogram name",
metavar="DATA_HISTNAME")
#parser.add_argument("--inputSig", dest="inputSig", required=True,
# help="Input signal shapes",
# metavar="INPUT_SIGNAL")
parser.add_argument("-f",
"--final_state",
dest="final_state",
default="qq",
help="Final state (e.g. qq, qg, gg)",
metavar="FINAL_STATE")
parser.add_argument("--fit_functions",
dest="fit_functions",
default="f1,f2,f3,f4,f5",
help="List of fit functions")
#parser.add_argument("-f2", "--type", dest="atype", required=True, help="Type (e.g. hG, lG, hR, lR)")
parser.add_argument(
"-o",
"--output_path",
dest="output_path",
help=
"Output path where datacards and workspaces will be stored. If not specified, this is derived from limit_configuration.",
metavar="OUTPUT_PATH")
parser.add_argument("--correctTrigger",
dest="correctTrigger",
action='store_true',
help="Include trigger correction in PDF")
parser.add_argument(
"-l",
"--lumi",
dest="lumi",
default=19700.,
type=float,
help="Integrated luminosity in pb-1 (default: %(default).1f)",
metavar="LUMI")
parser.add_argument(
"--massMin",
dest="massMin",
default=500,
type=int,
help=
"Lower bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MIN")
parser.add_argument(
"--massMax",
dest="massMax",
default=1200,
type=int,
help=
"Upper bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MAX")
parser.add_argument(
"--fitSignal",
action="store_true",
help=
"Use signal fitted shapes (CB+Voigtian) instead of histogram templates"
)
#parser.add_argument("--lumiUnc", dest="lumiUnc",
# required=True, type=float,
# help="Relative uncertainty in the integrated luminosity",
# metavar="LUMI_UNC")
#parser.add_argument("--jesUnc", dest="jesUnc",
# type=float,
# help="Relative uncertainty in the jet energy scale",
# metavar="JES_UNC")
#parser.add_argument("--jerUnc", dest="jerUnc",
# type=float,
# help="Relative uncertainty in the jet energy resolution",
# metavar="JER_UNC")
parser.add_argument(
"--sqrtS",
dest="sqrtS",
default=8000.,
type=float,
help="Collision center-of-mass energy (default: %(default).1f)",
metavar="SQRTS")
parser.add_argument("--fixP3",
dest="fixP3",
default=False,
action="store_true",
help="Fix the fit function p3 parameter")
parser.add_argument("--runFit",
dest="runFit",
default=False,
action="store_true",
help="Run the fit")
parser.add_argument("--fitBonly",
dest="fitBonly",
default=False,
action="store_true",
help="Run B-only fit")
parser.add_argument("--fixBkg",
dest="fixBkg",
default=False,
action="store_true",
help="Fix all background parameters")
parser.add_argument("--decoBkg",
dest="decoBkg",
default=False,
action="store_true",
help="Decorrelate background parameters")
parser.add_argument("--fitStrategy",
dest="fitStrategy",
type=int,
default=1,
help="Fit strategy (default: %(default).1f)")
parser.add_argument("--debug",
dest="debug",
default=False,
action="store_true",
help="Debug printout")
parser.add_argument(
"--postfix",
dest="postfix",
default='',
help="Postfix for the output file names (default: %(default)s)")
parser.add_argument("--pyes",
dest="pyes",
default=False,
action="store_true",
help="Make files for plots")
parser.add_argument("--jyes",
dest="jyes",
default=False,
action="store_true",
help="Make files for JES/JER plots")
parser.add_argument(
"--pdir",
dest="pdir",
default='testarea',
help="Name a directory for the plots (default: %(default)s)")
parser.add_argument("--chi2",
dest="chi2",
default=False,
action="store_true",
help="Compute chi squared")
parser.add_argument("--widefit",
dest="widefit",
default=False,
action="store_true",
help="Fit with wide bin hist")
mass_group = parser.add_mutually_exclusive_group(required=True)
mass_group.add_argument(
"--mass",
type=int,
nargs='*',
default=1000,
help=
"Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
)
mass_group.add_argument(
"--massrange",
type=int,
nargs=3,
help="Define a range of masses to be produced. Format: min max step",
metavar=('MIN', 'MAX', 'STEP'))
mass_group.add_argument("--masslist",
help="List containing mass information")
args = parser.parse_args()
fit_functions = args.fit_functions.split(",")
# mass points for which resonance shapes will be produced
masses = []
if args.fitBonly:
masses.append(750)
else:
if args.massrange != None:
MIN, MAX, STEP = args.massrange
masses = range(MIN, MAX + STEP, STEP)
elif args.masslist != None:
# A mass list was provided
print "Will create mass list according to", args.masslist
masslist = __import__(args.masslist.replace(".py", ""))
masses = masslist.masses
else:
masses = args.mass
# sort masses
masses.sort()
# import ROOT stuff
from ROOT import gStyle, TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE, TCanvas, TLegend, TPad, TLine
from ROOT import RooHist, RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooProdPdf, RooEffProd, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, RooExtendPdf
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0, kFALSE)
RooMsgService.instance().setStreamStatus(1, kFALSE)
# input data file
#inputData = TFile(limit_config.get_data_input(args.analysis))
# input data histogram
#hData = inputData.Get(args.dataHistname)
#hData.SetDirectory(0)
data_file = TFile(
analysis_config.get_b_histogram_filename(args.analysis,
"BJetPlusX_2012"))
hData = data_file.Get("BHistograms/h_pfjet_mjj")
hData.SetDirectory(0)
# input sig file
if not args.fitSignal:
print "[create_datacards] INFO : Opening resonance shapes file at " + limit_config.get_resonance_shapes(
args.analysis, args.model)
inputSig = TFile(
limit_config.get_resonance_shapes(args.analysis, args.model),
"READ")
sqrtS = args.sqrtS
# mass variable
mjj = RooRealVar('mjj', 'mjj', float(args.massMin), float(args.massMax))
# integrated luminosity and signal cross section
lumi = args.lumi
signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
if args.correctTrigger:
trigger_efficiency_pdf = trigger_efficiency.get_pdf(args.analysis, mjj)
trigger_efficiency_formula = trigger_efficiency.get_formula(
args.analysis, mjj)
else:
trigger_efficiency_pdf = trigger_efficiency.get_trivial_pdf(mjj)
trigger_efficiency_formula = trigger_efficiency.get_trivial_formula(
mjj)
for mass in masses:
print ">> Creating datacard and workspace for %s resonance with m = %i GeV..." % (
args.final_state, int(mass))
rooDataHist = RooDataHist('rooDatahist', 'rooDathist', RooArgList(mjj),
hData)
if not args.fitSignal:
hSig = inputSig.Get("h_" + args.final_state + "_" + str(int(mass)))
if not hSig:
raise Exception("Couldn't find histogram " + "h_" +
args.final_state + "_" + str(int(mass)) +
" in file " +
limit_config.get_resonance_shapes(
args.analysis, args.model))
# normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
hSig.Scale(
signalCrossSection * lumi / hSig.Integral()
) # divide by a number that provides roughly an r value of 1-10
rooSigHist = RooDataHist('rooSigHist', 'rooSigHist',
RooArgList(mjj), hSig)
print 'Signal acceptance:', (rooSigHist.sumEntries() /
hSig.Integral())
# If using fitted signal shapes, load the signal PDF
if args.fitSignal:
print "[create_datacards] Loading fitted signal PDFs from " + analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(not mass
in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(
analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(
not mass
in analysis_config.simulation.simulated_masses)),
"READ")
w_signal = f_signal_pdfs.Get("w_signal")
input_parameters = signal_fits.get_parameters(
w_signal.pdf("signal"))
# Make a new PDF with nuisance parameters
signal_pdf_notrig, signal_vars = signal_fits.make_signal_pdf_systematic(
"bukin", mjj, mass=mass)
signal_pdf_name = signal_pdf_notrig.GetName()
signal_pdf_notrig.SetName(signal_pdf_name + "_notrig")
#signal_pdf = RooProdPdf(signal_pdf_name, signal_pdf_name, signal_pdf_notrig, trigger_efficiency_pdf)
signal_pdf = RooEffProd(signal_pdf_name, signal_pdf_name,
signal_pdf_notrig,
trigger_efficiency_formula)
# Copy input parameter values
signal_vars["xp_0"].setVal(input_parameters["xp"][0])
signal_vars["xp_0"].setError(input_parameters["xp"][1])
signal_vars["xp_0"].setConstant()
signal_vars["sigp_0"].setVal(input_parameters["sigp"][0])
signal_vars["sigp_0"].setError(input_parameters["sigp"][1])
signal_vars["sigp_0"].setConstant()
signal_vars["xi_0"].setVal(input_parameters["xi"][0])
signal_vars["xi_0"].setError(input_parameters["xi"][1])
signal_vars["xi_0"].setConstant()
signal_vars["rho1_0"].setVal(input_parameters["rho1"][0])
signal_vars["rho1_0"].setError(input_parameters["rho1"][1])
signal_vars["rho1_0"].setConstant()
signal_vars["rho2_0"].setVal(input_parameters["rho2"][0])
signal_vars["rho2_0"].setError(input_parameters["rho2"][1])
signal_vars["rho2_0"].setConstant()
f_signal_pdfs.Close()
signal_parameters = {}
signal_pdfs_notrig = {}
signal_pdfs = {}
signal_norms = {}
background_pdfs = {}
background_pdfs_notrig = {}
background_parameters = {}
background_norms = {}
signal_epdfs = {}
background_epdfs = {}
models = {}
fit_results = {}
for fit_function in fit_functions:
print "[create_datacards] INFO : On fit function {}".format(
fit_function)
if args.fitSignal:
# Make a copy of the signal PDF, so that each fitTo call uses its own copy.
# The copy should have all variables set constant.
#signal_pdfs[fit_function], signal_parameters[fit_function] = signal_fits.copy_signal_pdf("bukin", signal_pdf, mjj, tag=fit_function, include_systematics=True)
signal_pdfs_notrig[fit_function] = ROOT.RooBukinPdf(
signal_pdf_notrig,
signal_pdf_notrig.GetName() + "_" + fit_function)
signal_pdfs[fit_function] = RooEffProd(
signal_pdf.GetName() + "_" + fit_function,
signal_pdf.GetName() + "_" + fit_function,
signal_pdfs_notrig[fit_function],
trigger_efficiency_formula)
#signal_pdfs[fit_function] = RooProdPdf(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigger_efficiency_pdf)
iterator = signal_pdfs_notrig[fit_function].getVariables(
).createIterator()
this_parameter = iterator.Next()
while this_parameter:
this_parameter.setConstant()
this_parameter = iterator.Next()
else:
signal_pdfs[fit_function] = RooHistPdf(
'signal_' + fit_function, 'signal_' + fit_function,
RooArgSet(mjj), rooSigHist)
signal_norms[fit_function] = RooRealVar(
'signal_norm_' + fit_function, 'signal_norm_' + fit_function,
0., 0., 1e+05)
if args.fitBonly:
signal_norms[fit_function].setConstant()
background_pdfs_notrig[fit_function], background_parameters[
fit_function] = make_background_pdf(fit_function,
mjj,
collision_energy=8000.)
background_pdf_name = background_pdfs_notrig[fit_function].GetName(
)
background_pdfs_notrig[fit_function].SetName(background_pdf_name +
"_notrig")
background_pdfs[fit_function] = RooEffProd(
background_pdf_name, background_pdf_name,
background_pdfs_notrig[fit_function],
trigger_efficiency_formula)
#background_pdfs[fit_function] = RooProdPdf(background_pdf_name, background_pdf_name, background_pdfs_notrig[fit_function], trigger_efficiency_pdf)
#background_pdfs[fit_function] = background_pdfs_notrig[fit_function]
#background_pdfs[fit_function].SetName(background_pdf_name)
# Initial values
if "trigbbh" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(55.)
background_parameters[fit_function]["p1"].setMin(20.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(28.)
background_parameters[fit_function]["p2"].setVal(-22.)
background_parameters[fit_function]["p3"].setVal(10.)
elif "trigbbl" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(82.)
background_parameters[fit_function]["p1"].setMin(60.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(41.)
background_parameters[fit_function]["p2"].setVal(-45.)
background_parameters[fit_function]["p3"].setVal(10.)
data_integral = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norms[fit_function] = RooRealVar(
'background_' + fit_function + '_norm',
'background_' + fit_function + '_norm', data_integral, 0.,
1.e8)
signal_epdfs[fit_function] = RooExtendPdf(
'esignal_' + fit_function, 'esignal_' + fit_function,
signal_pdfs[fit_function], signal_norms[fit_function])
background_epdfs[fit_function] = RooExtendPdf(
'ebackground_' + fit_function, 'ebackground_' + fit_function,
background_pdfs[fit_function], background_norms[fit_function])
models[fit_function] = RooAddPdf(
'model_' + fit_function, 's+b',
RooArgList(background_epdfs[fit_function],
signal_epdfs[fit_function]))
if args.runFit:
print "[create_datacards] INFO : Starting fit with function {}".format(
fit_function)
fit_results[fit_function] = models[fit_function].fitTo(
rooDataHist, RooFit.Save(kTRUE), RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy), RooFit.Verbose(0))
print "[create_datacards] INFO : Done with fit {}. Printing results.".format(
fit_function)
fit_results[fit_function].Print()
print "[create_datacards] DEBUG : End args.runFit if block."
# needed if want to evaluate limits without background systematics
if args.fixBkg:
background_norms[fit_function].setConstant()
for par_name, par in background_parameters[
fit_function].iteritems():
par.setConstant()
# -----------------------------------------
#signal_pdfs_syst = {}
# JES and JER uncertainties
if args.fitSignal:
print "[create_datacards] INFO : Getting signal PDFs from " + analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(not mass
in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(
analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(
not mass
in analysis_config.simulation.simulated_masses)))
w_signal = f_signal_pdfs.Get("w_signal")
if "jes" in systematics:
xp_central = signal_vars["xp_0"].getVal()
#print w_signal.pdf("signal__JESUp")
#print signal_fits.get_parameters(w_signal.pdf("signal__JESUp"))
xp_up = signal_fits.get_parameters(
w_signal.pdf("signal__JESUp"))["xpJESUp"][0]
xp_down = signal_fits.get_parameters(
w_signal.pdf("signal__JESDown"))["xpJESDown"][0]
signal_vars["dxp"].setVal(
max(abs(xp_up - xp_central), abs(xp_down - xp_central)))
if signal_vars["dxp"].getVal() > 2 * mass * 0.1:
print "[create_datacards] WARNING : Large dxp value. dxp = {}, xp_down = {}, xp_central = {}, xp_up = {}".format(
signal_vars["dxp"].getVal(), xp_down, xp_central,
xp_up)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant(False)
else:
signal_vars["dxp"].setVal(0.)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant()
signal_vars["dxp"].setError(0.)
signal_vars["dxp"].setConstant()
if "jer" in systematics:
sigp_central = signal_vars["sigp_0"].getVal()
sigp_up = signal_fits.get_parameters(
w_signal.pdf("signal__JERUp"))["sigpJERUp"][0]
sigp_down = signal_fits.get_parameters(
w_signal.pdf("signal__JERDown"))["sigpJERDown"][0]
signal_vars["dsigp"].setVal(
max(abs(sigp_up - sigp_central),
abs(sigp_down - sigp_central)))
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant(False)
else:
signal_vars["dsigp"].setVal(0.)
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant()
signal_vars["dsigp"].setError(0.)
signal_vars["dsigp"].setConstant()
#for variation in ["JERUp", "JERDown"]:
# signal_pdfs_syst[variation] = w_signal.pdf("signal__" + variation)
#for variation, pdf in signal_pdfs_syst.iteritems():
# signal_parameters = pdf.getVariables()
# iter = signal_parameters.createIterator()
# var = iter.Next()
# while var:
# var.setConstant()
# var = iter.Next()
f_signal_pdfs.Close()
else:
# dictionaries holding systematic variations of the signal shape
hSig_Syst = {}
hSig_Syst_DataHist = {}
sigCDF = TGraph(hSig.GetNbinsX() + 1)
if "jes" in systematics or "jer" in systematics:
sigCDF.SetPoint(0, 0., 0.)
integral = 0.
for i in range(1, hSig.GetNbinsX() + 1):
x = hSig.GetXaxis().GetBinLowEdge(i + 1)
integral = integral + hSig.GetBinContent(i)
sigCDF.SetPoint(i, x, integral)
if "jes" in systematics:
hSig_Syst['JESUp'] = copy.deepcopy(hSig)
hSig_Syst['JESDown'] = copy.deepcopy(hSig)
if "jer" in systematics:
hSig_Syst['JERUp'] = copy.deepcopy(hSig)
hSig_Syst['JERDown'] = copy.deepcopy(hSig)
# reset signal histograms
for key in hSig_Syst.keys():
hSig_Syst[key].Reset()
hSig_Syst[key].SetName(hSig_Syst[key].GetName() + '_' + key)
# produce JES signal shapes
if "jes" in systematics:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jes = 1. - systematics["jes"]
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jes = 1. + systematics["jes"]
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JESUp'] = RooDataHist(
'hSig_JESUp', 'hSig_JESUp', RooArgList(mjj),
hSig_Syst['JESUp'])
hSig_Syst_DataHist['JESDown'] = RooDataHist(
'hSig_JESDown', 'hSig_JESDown', RooArgList(mjj),
hSig_Syst['JESDown'])
# produce JER signal shapes
if "jer" in systematics:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jer = 1. - systematics["jer"]
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jer = 1. + systematics["jer"]
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JERUp'] = RooDataHist(
'hSig_JERUp', 'hSig_JERUp', RooArgList(mjj),
hSig_Syst['JERUp'])
hSig_Syst_DataHist['JERDown'] = RooDataHist(
'hSig_JERDown', 'hSig_JERDown', RooArgList(mjj),
hSig_Syst['JERDown'])
# -----------------------------------------
# create a datacard and corresponding workspace
postfix = (('_' + args.postfix) if args.postfix != '' else '')
wsName = 'workspace_' + args.final_state + '_m' + str(
mass) + postfix + '.root'
w = RooWorkspace('w', 'workspace')
if args.fitSignal:
signal_pdf.SetName("signal")
getattr(w, 'import')(signal_pdf, RooFit.Rename("signal"))
# Create a norm variable "signal_norm" which normalizes the PDF to unity.
norm = args.lumi
#signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", 1. / norm, 0.1 / norm, 10. / norm)
#if args.analysis == "trigbbh_CSVTM" and mass >= 1100:
signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm",
norm / 100., norm / 100. / 10.,
norm * 10.)
#else:
# signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", norm, norm / 10., norm * 10.)
print "[create_datacards] INFO : Set signal norm to {}".format(
signal_norm.getVal())
signal_norm.setConstant()
getattr(w, 'import')(signal_norm, ROOT.RooCmdArg())
#if "jes" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JESUp'],RooFit.Rename("signal__JESUp"))
# getattr(w,'import')(signal_pdfs_syst['JESDown'],RooFit.Rename("signal__JESDown"))
#if "jer" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JERUp'],RooFit.Rename("signal__JERUp"))
# getattr(w,'import')(signal_pdfs_syst['JERDown'],RooFit.Rename("signal__JERDown"))
else:
getattr(w, 'import')(rooSigHist, RooFit.Rename("signal"))
if "jes" in systematics:
getattr(w, 'import')(hSig_Syst_DataHist['JESUp'],
RooFit.Rename("signal__JESUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JESDown'],
RooFit.Rename("signal__JESDown"))
if "jer" in systematics:
getattr(w, 'import')(hSig_Syst_DataHist['JERUp'],
RooFit.Rename("signal__JERUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JERDown'],
RooFit.Rename("signal__JERDown"))
if args.decoBkg:
getattr(w, 'import')(background_deco, ROOT.RooCmdArg())
else:
for fit_function in fit_functions:
print "Importing background PDF"
print background_pdfs[fit_function]
background_pdfs[fit_function].Print()
getattr(w,
'import')(background_pdfs[fit_function],
ROOT.RooCmdArg(),
RooFit.Rename("background_" + fit_function),
RooFit.RecycleConflictNodes())
w.pdf("background_" + fit_function).Print()
getattr(w, 'import')(background_norms[fit_function],
ROOT.RooCmdArg(),
RooFit.Rename("background_" +
fit_function + "_norm"))
getattr(w, 'import')(fit_results[fit_function])
getattr(w, 'import')(signal_norms[fit_function],
ROOT.RooCmdArg())
if args.fitBonly:
getattr(w, 'import')(models[fit_function],
ROOT.RooCmdArg(),
RooFit.RecycleConflictNodes())
getattr(w, 'import')(rooDataHist, RooFit.Rename("data_obs"))
w.Print()
print "Starting save"
if args.output_path:
if not os.path.isdir(os.path.join(os.getcwd(), args.output_path)):
os.mkdir(os.path.join(os.getcwd(), args.output_path))
print "[create_datacards] INFO : Writing workspace to file {}".format(
os.path.join(args.output_path, wsName))
w.writeToFile(os.path.join(args.output_path, wsName))
else:
print "[create_datacards] INFO : Writing workspace to file {}".format(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitBonly=args.fitBonly,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger))
w.writeToFile(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitBonly=args.fitBonly,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger))
# Clean up
for name, obj in signal_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in fit_results.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, dict_l2 in background_parameters.iteritems():
for name2, obj in dict_l2.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in models.iteritems():
if obj:
obj.IsA().Destructor(obj)
rooDataHist.IsA().Destructor(rooDataHist)
w.IsA().Destructor(w)
# Make datacards only if S+B fitted
if not args.fitBonly:
beffUnc = 0.3
boffUnc = 0.06
for fit_function in fit_functions:
if args.output_path:
dcName = 'datacard_' + args.final_state + '_m' + str(
mass) + postfix + '_' + fit_function + '.txt'
print "[create_datacards] INFO : Writing datacard to file {}".format(
os.path.join(args.output_path, dcName))
datacard = open(os.path.join(args.output_path, dcName),
'w')
else:
print "[create_datacards] INFO : Writing datacard to file {}".format(
limit_config.get_datacard_filename(
args.analysis,
args.model,
mass,
fit_function,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger))
datacard = open(
limit_config.get_datacard_filename(
args.analysis,
args.model,
mass,
fit_function,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger), 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if ("jes" in systematics
or "jer" in systematics) and not args.fitSignal:
if args.output_path:
datacard.write('shapes * * ' + wsName +
' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write('shapes * * ' + os.path.basename(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger)) +
' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
if args.output_path:
datacard.write('shapes * * ' + wsName +
' w:$PROCESS\n')
else:
datacard.write('shapes * * ' + os.path.basename(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger)) +
' w:$PROCESS\n')
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background_' +
fit_function + '\n')
datacard.write('process 0 1\n')
if args.fitSignal:
datacard.write('rate 1 1\n')
else:
datacard.write('rate -1 1\n')
datacard.write('------------------------------\n')
datacard.write('lumi lnN %f -\n' %
(1. + systematics["luminosity"]))
datacard.write('beff lnN %f -\n' % (1. + beffUnc))
datacard.write('boff lnN %f -\n' % (1. + boffUnc))
#datacard.write('bkg lnN - 1.03\n')
if args.fitSignal:
if "jes" in systematics:
datacard.write("alpha_jes param 0.0 1.0\n")
if "jer" in systematics:
datacard.write("alpha_jer param 0.0 1.0\n")
else:
if "jes" in systematics:
datacard.write('JES shape 1 -\n')
if "jer" in systematics:
datacard.write('JER shape 1 -\n')
# flat parameters --- flat prior
datacard.write('background_' + fit_function +
'_norm flatParam\n')
if args.decoBkg:
datacard.write('deco_eig1 flatParam\n')
datacard.write('deco_eig2 flatParam\n')
else:
for par_name, par in background_parameters[
fit_function].iteritems():
datacard.write(fit_function + "_" + par_name +
' flatParam\n')
datacard.close()
print "[create_datacards] INFO : Done with this datacard"
#print '>> Datacards and workspaces created and stored in %s/'%( os.path.join(os.getcwd(),args.output_path) )
print "All done."
def shapeCards(datahistosFile, histosFile, signalFile, signalSample, hist,
signalMass, minMass, maxMass, outputName, outputFileTheta):
"""function to run Roofit and save workspace for RooStats"""
warnings.filterwarnings(
action='ignore',
category=RuntimeWarning,
message='.*class stack<RooAbsArg\*,deque<RooAbsArg\*> >')
############################################################# DATA
#hData = histosFile.Get('massAve_deltaEtaDijet_QCDPtAll')
#hData = datahistosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
dataFile = TFile(datahistosFile)
hData = dataFile.Get(hist + '_DATA')
hData.Rebin(args.reBin)
#hData = hData.Rebin( len( boostedMassAveBins )-1, hData.GetName(), boostedMassAveBins )
#hData = histosFile.Get(hist+'_QCDPtAll_A')
#hData.Add(htmpSignal)
#hData.Scale(1/hData.Integral())
#maxMass = boostedMassAveBins[ hData.FindLastBinAbove( 0, 1) ]
#minMass = signalMass-30
#maxMass = signalMass+30
massAve = RooRealVar('massAve', 'massAve', minMass, maxMass)
#massAveData = RooRealVar( 'massAveData', 'massAveData', minMass, maxMass )
rooDataHist = RooDataHist('rooDatahist', 'rooDatahist',
RooArgList(massAve),
hData) # if isData else hPseudo )
rooDataHist.Print()
############################################################################################
####################### Signal
if 'gaus' in args.job:
hSignal = TH1F('massAve_RPVStop', 'massAve_RPVStop',
maxMass / args.reBin, minMass, maxMass)
for q in range(hSignal.GetNbinsX() + 1):
gausEval = signalFile.Eval(hSignal.GetXaxis().GetBinCenter(q))
hSignal.SetBinContent(q, gausEval)
#meanSig = RooRealVar( 'meanSig', 'mean of signal', sigGaus.GetParameter( 1 ) )
#sigmaSig = RooRealVar( 'sigmaSig', 'sigma of signal', sigGaus.GetParameter( 2 ) )
#signalPdf = RooGaussian( 'signal', 'signal', massAve, meanSig, sigmaSig )
#signalPdf.Print()
else:
signalHistosFile = TFile(signalFile)
hSignal = signalHistosFile.Get(hist + '_' + signalSample)
hSignal.Rebin(args.reBin)
hSignal.Scale(twoProngSF)
signalXS = search(dictXS, 'RPVStopStopToJets_UDD312_M-' + str(signalMass))
rooSigHist = RooDataHist('rooSigHist', 'rooSigHist', RooArgList(massAve),
hSignal)
sigAcc = rooSigHist.sumEntries(
) #round(hSignal.Integral( hSignal.GetXaxis().FindBin( minMass ), hSignal.GetXaxis().FindBin( maxMass )), 2)
rooSigHist.Print()
#signal = RooHistPdf('signal','signal',RooArgSet(massAve),rooSigHist)
#signal.Print()
#signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+04,1e+04)
#if args.fitBonly: signal_norm.setConstant()
#signal_norm.Print()
#####################################################################
hSigSyst = signalUnc(hSignal, signalMass)
hSigSystDataHist = {}
if args.jesUnc:
hSigSystDataHist['JESUp'] = RooDataHist('hSignalJESUp', 'hSignalJESUp',
RooArgList(massAve),
hSigSyst['JESUp'])
hSigSystDataHist['JESDown'] = RooDataHist('hSignalJESDown',
'hSignalJESDown',
RooArgList(massAve),
hSigSyst['JESDown'])
if args.jerUnc:
hSigSystDataHist['JERUp'] = RooDataHist('hSignalJERUp', 'hSignalJERUp',
RooArgList(massAve),
hSigSyst['JERUp'])
hSigSystDataHist['JERDown'] = RooDataHist('hSignalJERDown',
'hSignalJERDown',
RooArgList(massAve),
hSigSyst['JERDown'])
#################################### Background
if args.altBkg:
newBkgHistoFile = datahistosFile.replace('DATA', 'DATA_ABCDBkg')
newBkgFile = TFile(newBkgHistoFile)
htmpBkg = newBkgFile.Get(
'massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
if (htmpBkg.GetBinWidth(1) != args.reBin):
print '|----- Bin size in DATA_C histogram is different than rest.'
sys.exit(0)
else:
htmpBkg = dataFile.Get(
'massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
htmpBkg.Rebin(args.reBin)
#hBkg = histosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_QCDPtAll_ABCDProj')
#hBkg = histosFile.Get(hist+'_QCDPtAll_BCD')
#htmpBkg = htmpBkg.Rebin( len( boostedMassAveBins )-1, htmpBkg.GetName(), boostedMassAveBins )
hBkg = htmpBkg.Clone()
hBkg.Reset()
for ibin in range(htmpBkg.GetNbinsX()):
binCont = htmpBkg.GetBinContent(ibin)
binErr = htmpBkg.GetBinError(ibin)
if binCont == 0:
hBkg.SetBinContent(ibin, 0)
hBkg.SetBinError(ibin, 1.8)
else:
hBkg.SetBinContent(ibin, binCont)
hBkg.SetBinError(ibin, binErr)
#hBkg.Scale(1/hBkg.Integral())
hPseudo = createPseudoExperiment(hBkg, bkgAcc)
###### Adding statistical uncertanty
hBkgStatUncUp = hBkg.Clone()
hBkgStatUncUp.Reset()
hBkgStatUncDown = hBkg.Clone()
hBkgStatUncDown.Reset()
for i in range(hBkg.GetNbinsX() + 1):
cont = hBkg.GetBinContent(i)
contErr = hBkg.GetBinError(i)
hBkgStatUncUp.SetBinContent(i, cont + (1 * contErr))
hBkgStatUncDown.SetBinContent(i, cont - (1 * contErr))
hBkgStatUncUpDataHist = RooDataHist('hBkgStatUncUp', 'hBkgStatUncUp',
RooArgList(massAve), hBkgStatUncUp)
hBkgStatUncDownDataHist = RooDataHist('hBkgStatUncDown', 'hBkgStatUncDown',
RooArgList(massAve), hBkgStatUncDown)
if 'template' in args.job:
rooBkgHist = RooDataHist('rooBkgHist', 'rooBkgHist',
RooArgList(massAve), hBkg)
bkgAcc = rooBkgHist.sumEntries()
rooBkgHist.Print()
background = RooHistPdf('background', 'background', RooArgSet(massAve),
rooBkgHist)
background.Print()
else:
massAveBkg = RooRealVar('massAveBkg', 'massAveBkg', minMass, maxMass)
p1 = RooRealVar('p1', 'p1', 1, 0., 100.)
p2 = RooRealVar('p2', 'p2', 1, 0., 60.)
p3 = RooRealVar('p3', 'p3', 1, -10., 10.)
bkgAcc = round(
hBkg.Integral(hBkg.GetXaxis().FindBin(minMass),
hBkg.GetXaxis().FindBin(maxMass)), 2)
background = RooGenericPdf(
'background',
'(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))' %
(1300, 1300, 1300), RooArgList(massAveBkg, p1, p2, p3))
background.Print()
hBkgSyst = {}
hBkgSystDataHist = {}
if args.bkgUnc:
print ' |---> Adding bkg unc'
hBkgSyst['BkgUncUp'] = hBkg.Clone()
hBkgSyst['BkgUncDown'] = hBkg.Clone()
for key in hBkgSyst:
hBkgSyst[key].Reset()
hBkgSyst[key].SetName(hBkgSyst[key].GetName() + '_' + key)
for q in range(0, hBkg.GetNbinsX()):
binCont = hBkg.GetBinContent(q)
bkgUncUp = 1. + (args.bkgUncValue / 100.)
hBkgSyst['BkgUncUp'].SetBinContent(q, binCont * bkgUncUp)
bkgUncDown = 1. - (args.bkgUncValue / 100.)
hBkgSyst['BkgUncDown'].SetBinContent(q, binCont * bkgUncDown)
hBkgSystDataHist['BkgUncUp'] = RooDataHist('hBkgBkgUncUp',
'hBkgBkgUncUp',
RooArgList(massAve),
hBkgSyst['BkgUncUp'])
hBkgSystDataHist['BkgUncDown'] = RooDataHist('hBkgBkgUncDown',
'hBkgBkgUncDown',
RooArgList(massAve),
hBkgSyst['BkgUncDown'])
############################################################################################
#model = RooAddPdf("model","s+b",RooArgList(background,signal),RooArgList(background_norm,signal_norm))
#res = model.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(0))
#res.Print()
############################ Create Workspace
myWS = RooWorkspace("myWS")
getattr(myWS, 'import')(rooBkgHist, RooFit.Rename("background"))
#getattr(myWS,'import')(background,RooFit.Rename("background"))
#getattr(myWS,'import')(signal_norm)
#getattr(myWS,'import')(background_norm)
'''
if 'gaus' in args.job:
getattr(myWS,'import')(signalPdf,RooFit.Rename("signal"))
if args.jesUnc:
getattr(myWS,'import')(signalPdfJESUp,RooFit.Rename("signal__JESUp"))
getattr(myWS,'import')(signalPdfJESDown,RooFit.Rename("signal__JESDown"))
else:
'''
getattr(myWS, 'import')(rooSigHist, RooFit.Rename("signal"))
if args.jesUnc:
getattr(myWS, 'import')(hSigSystDataHist['JESUp'],
RooFit.Rename("signal__JESUp"))
getattr(myWS, 'import')(hSigSystDataHist['JESDown'],
RooFit.Rename("signal__JESDown"))
if args.jerUnc:
getattr(myWS, 'import')(hSigSystDataHist['JERUp'],
RooFit.Rename("signal__JERUp"))
getattr(myWS, 'import')(hSigSystDataHist['JERDown'],
RooFit.Rename("signal__JERDown"))
if args.bkgUnc:
getattr(myWS, 'import')(hBkgSystDataHist['BkgUncUp'],
RooFit.Rename("background__BkgUncUp"))
getattr(myWS, 'import')(hBkgSystDataHist['BkgUncDown'],
RooFit.Rename("background__BkgUncDown"))
getattr(myWS, 'import')(hBkgStatUncUpDataHist,
RooFit.Rename("background__BkgStatUncUp"))
getattr(myWS, 'import')(hBkgStatUncDownDataHist,
RooFit.Rename("background__BkgStatUncDown"))
getattr(myWS, 'import')(rooDataHist, RooFit.Rename("data_obs"))
myWS.Print()
outputRootFile = currentDir + '/Rootfiles/workspace_' + outputName + '.root'
myWS.writeToFile(outputRootFile, True)
print ' |----> Workspace created in root file:\n', outputRootFile
'''
c1 = TCanvas('c1', 'c1', 10, 10, 750, 500 )
# c1.SetLogy()
xframe = myWS.var("massAve").frame()
signalPdf.plotOn( xframe )
xframe.Draw()
c1.SaveAs('test.png')
del c1
'''
############################################################################################
######################### write a datacard
dataCardName = currentDir + '/Datacards/datacard_' + outputName + '.txt'
datacard = open(dataCardName, 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if args.jesUnc or args.jerUnc or args.lumiUnc or args.bkgUnc or args.unc:
datacard.write('shapes * * ' + outputRootFile +
' myWS:$PROCESS myWS:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write("shapes * * " + outputRootFile + " myWS:$PROCESS \n")
datacard.write('---------------\n')
datacard.write('bin ' + signalSample + '\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin ' + signalSample + ' ' +
signalSample + '\n')
datacard.write('process signal background\n')
datacard.write('process 0 1\n')
#datacard.write('rate -1 -1\n')
datacard.write('rate ' + str(sigAcc) + ' ' + str(bkgAcc) +
'\n')
datacard.write('------------------------------\n')
if args.lumiUnc:
datacard.write('lumi lnN %f -\n' % (lumiValue))
if args.puUnc: datacard.write('pu lnN %f -\n' % (puValue))
if args.jesUnc: datacard.write('JES shape 1 -\n')
if args.jerUnc: datacard.write('JER shape 1 -\n')
#flat parameters --- flat prior
#if args.bkgUnc: datacard.write('BkgUnc shape - '+str( round( 1/ (args.bkgUncValue/34.1), 2 ) )+'\n')
if args.bkgUnc: datacard.write('BkgUnc shape - 1 \n')
#NcombineUnc = ( 1 / TMath.Sqrt( args.bkgUncValue / 100. ) ) - 1
#datacard.write('background_norm gmN '+str(int(round(NcombineUnc)))+' - '+str( round(bkgAcc/NcombineUnc,2) )+'\n')
#datacard.write('p1 flatParam\n')
datacard.write('BkgStatUnc shape - 1 \n')
datacard.close()
print ' |----> Datacard created:\n', dataCardName
############################################################################################
########## Theta
if args.theta:
print ' |----> Creating Theta file\n', outputFileTheta
outFile = TFile(outputFileTheta, 'update')
tmpName = 'rpvstopjj' + str(signalMass)
hSignal.SetName('massAve__' + tmpName)
hSignal.Write()
hSigSyst['JESDown'].SetName('massAve__' + tmpName + '__jes__down')
hSigSyst['JESDown'].Write()
hSigSyst['JESUp'].SetName('massAve__' + tmpName + '__jes__up')
hSigSyst['JESUp'].Write()
hSigSyst['JERDown'].SetName('massAve__' + tmpName + '__jer__down')
hSigSyst['JERDown'].Write()
hSigSyst['JERUp'].SetName('massAve__' + tmpName + '__jer__up')
hSigSyst['JERUp'].Write()
if (signalMass == 100): #or (signalMass == 170):
hBkg.SetName('massAve__background')
hBkg.Write()
hBkgSyst['BkgUncDown'].SetName('massAve__background__unc__down')
hBkgSyst['BkgUncDown'].Write()
hBkgSyst['BkgUncUp'].SetName('massAve__background__unc__up')
hBkgSyst['BkgUncUp'].Write()
hData.SetName('massAve__DATA')
hData.Write()
outFile.Close()
def main(options,args):
from ROOT import gSystem, gROOT, gStyle
gROOT.SetBatch()
gSystem.Load("libRooFitCore")
if options.doWebPage:
from lip.Tools.rootutils import loadToolsLib, apply_modifs
loadToolsLib()
from ROOT import TFile, RooFit, RooArgSet, RooDataHist, RooKeysPdf, RooHistPdf, TCanvas, TLegend, TLatex, TArrow, TPaveText, RooAddPdf, RooArgList
from ROOT import kWhite, kBlue, kOpenSquare
if options.doWebPage:
from ROOT import HtmlHelper, HtmlTag, HtmlTable, HtmlPlot
rootglobestyle.setTDRStyle()
gStyle.SetMarkerSize(1.5)
gStyle.SetTitleYOffset(1.5)
gStyle.SetPadLeftMargin(0.16)
gStyle.SetPadRightMargin(0.05)
gStyle.SetPadTopMargin(0.05)
gStyle.SetPadBottomMargin(0.13)
gStyle.SetLabelFont(42,"XYZ")
gStyle.SetLabelOffset(0.007, "XYZ")
gStyle.SetLabelSize(0.05,"XYZ")
gStyle.SetTitleSize(0.06,"XYZ")
gStyle.SetTitleXOffset(0.9)
gStyle.SetTitleYOffset(1.24)
gStyle.SetTitleFont(42,"XYZ")
##
## Read files
##
options.outdir = "%s_m%1.0f" % ( options.outdir, options.mH )
if options.fp:
options.outdir += "_fp"
ncat=options.ncat
cats=options.cats
if cats is "":
categories =[ "_cat%d" % i for i in range(0,ncat) ]
else:
categories =[ "_cat%s" % i for i in cats.split(",") ]
if options.mva:
clables = { "_cat0" : ("MVA > 0.89",""),
"_cat1" : ("0.74 #leq MVA","MVA < 0.89"),
"_cat2" : ("0.545 #leq MVA","MVA < 0.74"),
"_cat3" : ("0.05 #leq MVA","MVA < 0.545"),
"_cat4" : ("Di-jet","Tagged"),
"_cat5" : ("Di-jet","Tagged"),
"_combcat" : ("All Classes","Combined")
}
else:
clables = { "_cat0" : ("max(|#eta|<1.5","min(R_{9})>0.94"),
"_cat1" : ("max(|#eta|<1.5","min(R_{9})<0.94"),
"_cat2" : ("max(|#eta|>1.5","min(R_{9})>0.94"),
"_cat3" : ("max(|#eta|>1.5","min(R_{9})<0.94"),
"_cat4" : ("Di-jet","Tagged"),
"_cat5" : ("Di-jet","Tagged"),
"_combcat" : ("All Classes","Combined")
}
helper = Helper()
fin = TFile.Open(options.infile)
helper.files.append(fin)
ws = fin.Get("cms_hgg_workspace")
mass = ws.var("CMS_hgg_mass")
mass.SetTitle("m_{#gamma#gamma}");
mass.setUnit("GeV");
mass.setRange(100.,150.)
mass.setBins(100,"plot")
mass.setBins(5000)
print ws
aset = RooArgSet(mass)
helper.objs.append( mass )
helper.objs.append( aset )
fitopt = ( RooFit.Minimizer("Minuit2", ""), RooFit.Minos(False), RooFit.SumW2Error(False), RooFit.NumCPU(8) )
if not options.binned and not options.refit:
finpdf = TFile.Open(options.infilepdf)
helper.files.append(finpdf)
wspdf = finpdf.Get("wsig")
else:
wspdf = ws
for c in categories:
processes = [ "ggh", "vbf", "wzh" ]
if options.fp:
processes = [ "vbf", "wzh" ]
### elif clables[c][0] == "Di-jet":
### processes = [ "vbf", "ggh" ]
dsname = "sig_mass_m%1.0f%s" % (options.mH,c)
print dsname
print ws
ds = ws.data( "sig_%s_mass_m%1.0f%s" % (processes[0],options.mH,c) ).Clone(dsname)
for proc in processes[1:]:
ds.append( ws.data( "sig_%s_mass_m%1.0f%s" % (proc,options.mH,c) ) )
helper.dsets.append( ds )
if options.binned:
binned_ds = RooDataHist( "binned_%s" % dsname,"binned_%s" % dsname,aset, ds)
pdf = RooKeysPdf( "pdf_%s_%s" % (dsname, f), "pdf_%s" % dsname, mass, ds )
plot_pdf = RooHistPdf( "pdf_%s" % dsname, "pdf_%s" % dsname, aset, plot_ds )
helper.add( binned_ds, binned_ds.GetName() )
else:
if options.refit:
if options.refitall and clables[c][0] != "Di-jet":
rpdfs = []
for proc in processes:
for ngaus in range(1,4):
pp = build_pdf(ws,"%s_%s" % (c,proc),ngaus,ngaus==3 )
pp.fitTo( ws.data( "sig_%s_mass_m%1.0f%s" % (proc,options.mH,c)), RooFit.Strategy(0), *fitopt )
rpdfs.append(pp)
pdf = RooAddPdf("hggpdfrel%s" % c, "hggpdfrel%s" % c, RooArgList(*tuple(rpdfs) ))
else:
if options.refitall and clables[c][0] == "Di-jet":
for ngaus in range(1,5):
pdf = build_pdf(ws,c,ngaus,ngaus==5)
pdf.fitTo(ds, RooFit.Strategy(0), *fitopt )
else:
for ngaus in range(1,4):
pdf = build_pdf(ws,c,ngaus,ngaus==3)
pdf.fitTo(ds, RooFit.Strategy(0), *fitopt )
else:
pdfs = (wspdf.pdf( "hggpdfrel%s_%s" % (c, p)) for p in processes )
pdf = RooAddPdf("hggpdfrel%s" % c, "hggpdfrel%s" % c, RooArgList(*pdfs ))
helper.add(pdf,pdf.GetName())
plot_pdf = pdf.Clone("pdf_%s" % dsname)
plot_ds = RooDataHist( "plot_%s" % dsname,"plot_%s" % dsname, aset, "plot")
plot_ds.add( ds )
cdf = pdf.createCdf(aset)
hmin, hmax, hm = get_FWHM( mass, pdf, cdf, options.mH-10., options.mH+10. )
wmin, wmax = get_eff_sigma( mass, pdf, cdf, options.mH-10., options.mH+10. )
### hmin, hmax, hm = get_FWHM( points )
helper.add( plot_ds, plot_ds.GetName() )
helper.add( plot_pdf, plot_pdf.GetName() )
helper.add( (wmin,wmax), "eff_sigma%s" % c )
helper.add( (hmin, hmax, hm), "FWHM%s" % c )
helper.add( ds.sumEntries(), "sumEntries%s" %c ) # signal model integral
# data integral for PAS tables
data = ws.data( "data_mass%s"%c)
helper.add( data.sumEntries("CMS_hgg_mass>=%1.4f && CMS_hgg_mass<=%1.4f"%(options.mH-10.,options.mH+10.)),"data_sumEntries%s"%c)
del cdf
del pdf
dsname = "sig_mass_m%1.0f_combcat" % options.mH
print dsname
combined_ds = helper.dsets[0].Clone(dsname)
for d in helper.dsets[1:]:
combined_ds.append(d)
if options.binned:
binned_ds = RooDataHist( "binned_%s" % dsname,"binned_%s" % dsname,aset, combined_ds)
pdf = RooKeysPdf( "pdf_%s" % (dsname), "pdf_%s" % dsname, mass, combined_ds )
plot_pdf = RooHistPdf( "pdf_%s" % dsname, "pdf_%s" % dsname, aset, plot_ds )
helper.add( binned_ds, binned_ds.GetName() )
else:
#### pdf = build_pdf(ws,"_combcat")
#### pdf.fitTo(combined_ds, RooFit.Strategy(0), *fitopt )
#### plot_pdf = pdf.Clone( "pdf_%s" % dsname )
pdf = RooAddPdf( "pdf_%s" % dsname, "pdf_%s" % dsname, RooArgList( *(helper.histos["hggpdfrel%s" % c] for c in categories) ) )
plot_pdf = pdf
cdf = pdf.createCdf(aset)
plot_ds = RooDataHist( "plot_%s" % dsname,"plot_%s" % dsname, aset, "plot")
plot_ds.add( combined_ds )
wmin, wmax = get_eff_sigma( mass, pdf, cdf, options.mH-10., options.mH+10. )
hmin, hmax, hm = get_FWHM( mass, pdf, cdf, options.mH-10., options.mH+10. )
helper.add( plot_ds, plot_ds.GetName() )
helper.add( plot_pdf, plot_pdf.GetName() )
helper.add( (wmin,wmax), "eff_sigma_combcat" )
helper.add( (hmin, hmax, hm), "FWHM_combcat" )
helper.add( plot_ds.sumEntries(), "sumEntries_combcat" )
mass.setRange("higgsrange",options.mH-25.,options.mH+15.);
del cdf
del pdf
del helper.dsets
### label = TLatex(0.1812081,0.8618881,"#scale[0.8]{#splitline{CMS preliminary}{Simulation}}")
label = TLatex(0.7,0.86,"#scale[0.65]{#splitline{CMS preliminary}{Simulation}}")
label.SetNDC(1)
##
## Make web page with plots
##
if options.doWebPage:
hth = HtmlHelper(options.outdir)
hth.navbar().cell( HtmlTag("a") ).firstChild().txt("..").set("href","../?C=M;O=D")
hth.navbar().cell( HtmlTag("a") ).firstChild().txt("home").set("href","./")
tab = hth.body().add( HtmlTable() )
ip = 0
for c in ["_combcat"]+categories:
### for c in categories:
if options.doWebPage and ip % 4 == 0:
row = tab.row()
ip = ip + 1
dsname = "sig_mass_m%1.0f%s" % (options.mH,c)
canv = TCanvas(dsname,dsname,600,600)
helper.objs.append(canv)
### leg = TLegend(0.4345638,0.6835664,0.9362416,0.9178322)
leg = TLegend(0.2,0.96,0.5,0.55)
#apply_modifs( leg, [("SetLineColor",kWhite),("SetFillColor",kWhite),("SetFillStyle",0),("SetLineStyle",0)] )
hplotcompint = mass.frame(RooFit.Bins(250),RooFit.Range("higgsrange"))
helper.objs.append(hplotcompint)
helper.objs.append(leg)
plot_ds =helper.histos["plot_%s" % dsname ]
plot_pdf =helper.histos["pdf_%s" % dsname ]
wmin,wmax = helper.histos["eff_sigma%s" % c ]
hmin, hmax, hm = helper.histos["FWHM%s" % c ]
print hmin, hmax, hm
style = ( RooFit.LineColor(kBlue), RooFit.LineWidth(2), RooFit.FillStyle(0) )
style_seff = ( RooFit.LineWidth(2), RooFit.FillStyle(1001), RooFit.VLines(), RooFit.LineColor(15), )
style_ds = ( RooFit.MarkerStyle(kOpenSquare), )
plot_ds.plotOn(hplotcompint,RooFit.Invisible())
plot_pdf.plotOn(hplotcompint,RooFit.NormRange("higgsrange"),RooFit.Range(wmin,wmax), RooFit.FillColor(19),
RooFit.DrawOption("F"), *style_seff)
seffleg = hplotcompint.getObject(int(hplotcompint.numItems()-1))
plot_pdf.plotOn(hplotcompint,RooFit.NormRange("higgsrange"),RooFit.Range(wmin,wmax), RooFit.LineColor(15), *style_seff)
plot_pdf.plotOn(hplotcompint,RooFit.NormRange("higgsrange"),RooFit.Range("higgsrange"),*style)
pdfleg = hplotcompint.getObject(int(hplotcompint.numItems()-1))
plot_ds.plotOn(hplotcompint,*style_ds)
pointsleg = hplotcompint.getObject(int(hplotcompint.numItems()-1))
iob = int( hplotcompint.numItems() - 1 )
leg.AddEntry( pointsleg, "Simulation", "pe" )
leg.AddEntry( pdfleg, "Parametric model", "l" )
leg.AddEntry( seffleg, "#sigma_{eff} = %1.2f GeV " % ( 0.5*(wmax-wmin) ), "fl" )
clabel = TLatex(0.74,0.65,"#scale[0.65]{#splitline{%s}{%s}}" % clables[c])
clabel.SetNDC(1)
helper.objs.append(clabel)
hm = hplotcompint.GetMaximum()*0.5*0.9
### hm = pdfleg.GetMaximum()*0.5
fwhmarrow = TArrow(hmin,hm,hmax,hm)
fwhmarrow.SetArrowSize(0.03)
helper.objs.append(fwhmarrow)
fwhmlabel = TPaveText(0.20,0.58,0.56,0.48,"brNDC")
fwhmlabel.SetFillStyle(0)
fwhmlabel.SetLineColor(kWhite)
reducedFWHM = (hmax-hmin)/2.3548200
fwhmlabel.AddText("FWHM/2.35 = %1.2f GeV" % reducedFWHM)
helper.objs.append(fwhmlabel)
hplotcompint.SetTitle("");
hplotcompint.GetXaxis().SetNoExponent(True);
hplotcompint.GetXaxis().SetTitle("m_{#gamma#gamma} (GeV)");
hplotcompint.GetXaxis().SetNdivisions(509);
## hplotcompint.GetYaxis().SetTitle("A.U.");
## hplotcompint.GetYaxis().SetRangeUser(0.,hplotcompint.GetMaximum()*1.4);
hplotcompint.Draw();
leg.Draw("same")
label.Draw("same")
clabel.Draw("same")
fwhmarrow.Draw("<>")
fwhmlabel.Draw("same")
plot_ds.sumEntries()
if options.doWebPage:
hpl = HtmlPlot(canv,False,"",True,True,True)
hpl.caption("<i>%s</i>" % canv.GetTitle())
row.cell( hpl )
else:
if os.path.isdir(options.outdir) is False:
os.mkdir(options.outdir)
for ext in "C","png","pdf":
canv.SaveAs( os.path.join(options.outdir,"%s.%s" % (canv.GetName(), ext)) )
if "comb" in c:
ip = 0
if options.doWebPage:
print "Creating pages..."
hth.dump()
for f in helper.files:
f.Close()
gROOT.Reset()
from pprint import pprint
pprint(helper)
print 'Summary statistics per event class'
print 'Cat\tSignal\t\tData/GeV (in %3.1f+/-10)\tsigEff\tFWHM/2.35'%options.mH
sigTotal=0.
dataTotal=0.
for c in categories:
sigVal = helper.histos["sumEntries%s"%c]
datVal = helper.histos["data_sumEntries%s"%c]
sigTotal+=sigVal
dataTotal+=datVal
for c in categories:
sigVal = helper.histos["sumEntries%s"%c]
datVal = helper.histos["data_sumEntries%s"%c]
effSig = 0.5*(helper.histos["eff_sigma%s"%c][1]-helper.histos["eff_sigma%s"%c][0])
fwhm = (helper.histos["FWHM%s"%c][1]-helper.histos["FWHM%s"%c][0]) / 2.3548200
print c, '\t%3.1f (%3.1f%%)\t%3.1f (%3.1f%%)\t\t\t%2.2f\t%2.2f'%(sigVal,100.*sigVal/sigTotal,datVal/(10.+10.),100.*datVal/dataTotal,effSig,fwhm)
print "Done."
