def rooFit601():
print ">>> setup pdf and likelihood..."
x = RooRealVar("x", "x", -20, 20)
mean = RooRealVar("mean", "mean of g1 and g2", 0)
sigma1 = RooRealVar("sigma1", "width of g1", 3)
sigma2 = RooRealVar("sigma2", "width of g2", 4, 3.0,
6.0) # intentional strong correlations
gauss1 = RooGaussian("gauss1", "gauss1", x, mean, sigma1)
gauss2 = RooGaussian("gauss2", "gauss2", x, mean, sigma2)
frac = RooRealVar("frac", "frac", 0.5, 0.0, 1.0)
model = RooAddPdf("model", "model", RooArgList(gauss1, gauss2),
RooArgList(frac))
print ">>> generate to data..."
data = model.generate(RooArgSet(x), 1000) # RooDataSet
print ">>> construct unbinned likelihood of model wrt data..."
nll = model.createNLL(data) # RooAbsReal
print ">>> interactive minimization and error analysis with MINUIT interface object..."
minuit = RooMinuit(nll)
print ">>> set avtive verbosity for logging of MINUIT parameter space stepping..."
minuit.setVerbose(kTRUE)
print ">>> call MIGRAD to minimize the likelihood..."
minuit.migrad()
print "\n>>> parameter values and error estimates that are back propagated from MINUIT:"
model.getParameters(RooArgSet(x)).Print("s")
print "\n>>> disable verbose logging..."
minuit.setVerbose(kFALSE)
print ">>> run HESSE to calculate errors from d2L/dp2..."
minuit.hesse()
print ">>> value of and error on sigma2 (back propagated from MINUIT):"
sigma2.Print()
print "\n>>> run MINOS on sigma2 parameter only..."
minuit.minos(RooArgSet(sigma2))
print "\n>>> value of and error on sigma2 (back propagated from MINUIT after running MINOS):"
sigma2.Print()
print "\n>>> saving results, contour plots..."
# Save a snapshot of the fit result. This object contains the initial
# fit parameters, the final fit parameters, the complete correlation
# matrix, the EDM, the minimized FCN , the last MINUIT status code and
# the number of times the RooFit function object has indicated evaluation
# problems (e.g. zero probabilities during likelihood evaluation)
result = minuit.save() # RooFitResult
# Make contour plot of mx vs sx at 1,2,3 sigma
frame1 = minuit.contour(frac, sigma2, 1, 2, 3) # RooPlot
frame1.SetTitle("RooMinuit contour plot")
# Print the fit result snapshot
result.Print("v")
print "\n>>> change value of \"mean\" parameter..."
mean.setVal(0.3)
# Rerun MIGRAD,HESSE
print ">>> rerun MIGRAD, HESSE..."
minuit.migrad()
minuit.hesse()
print ">>> value on and error of frac:"
frac.Print()
print "\n>>> fix value of \"sigma\" parameter (setConstant)..."
sigma2.setConstant(kTRUE)
print ">>> rerun MIGRAD, HESSE..."
minuit.migrad()
minuit.hesse()
frac.Print()
## Plot Likelihood contours vs scale and resolution
canvases.next("NLL_Contours")
phoRes.setRange(
max(min(phoRes.getVal(), phoEResMC) - 6 * phoRes.getError(), phoRes.getMin()),
min(max(phoRes.getVal(), phoEResMC) + 6 * phoRes.getError(), phoRes.getMax()),
)
phoScale.setRange(
max(min(phoScale.getVal(), 100 * phoEScaleMC) - 10 * phoRes.getError(), phoRes.getMin()),
min(max(phoScale.getVal(), 100 * phoEScaleMC) + 10 * phoRes.getError(), phoRes.getMax()),
)
m = RooMinuit(nll)
m.migrad()
m.minos()
plot = m.contour(phoScale, phoRes, 1, 2, 3)
plot.SetTitle("NLL Countours")
plot.Draw()
mcTruth = ROOT.TMarker(100 * phoEScaleMC, phoEResMC, 2)
mcTruth.DrawClone()
mcTruth.Draw()
## canvases.next('nominal')
## mmgFrame = mmgMass.frame(Range(80,100))
## mmgData.plotOn(mmgFrame)
## #phoScale.setVal(0)
## model.plotOn(mmgFrame)
## # theory.plotOn(mmgFrame, LineStyle(kDashed), LineColor(kRed))
## theoryXphoSmear.plotOn(mmgFrame, LineStyle(kDashed), LineColor(kRed))
## theoryXphoSmear.paramOn(mmgFrame)
def main(options,args):
eta_bins = [[0.0,1.4442]] # try to split material dependence
# [1.560,2.5]]
pt_bins = [[12, 15],
[15, 20]]
#get input workspace
input = TFile.Open(options.inputFile)
ws = input.Get(options.workspaceName).Clone()
input.Close()
#ws.Print("v")
sieie = RooRealVar(options.showerShapeName,options.showerShapeName,0)
pt = RooRealVar(options.ptName,options.ptName,0)
eta = RooRealVar(options.etaName,options.etaName,0)
chIso = RooRealVar(options.isoName,options.isoName,0)
vars = RooArgSet()
vars.add(sieie)
vars.add(pt)
vars.add(eta)
vars.add(chIso)
if options.MCFakeRate:
#chIso = RooRealVar(options.isoName, 'phHIso', 100, 0, 20)
fake = RooRealVar('FakePho','',-0.5,1.5)
vars.add(fake)
data = None
if options.weight is not None:
weight = RooRealVar(options.weight,'',.5)
vars.add(weight)
data = RooDataSet('data','All Input Data',
vars,
RooFit.ImportFromFile(options.dataFile,
options.dataTreeName),
RooFit.WeightVar(options.weight)
)
else:
data = RooDataSet('data','All Input Data',
vars,
RooFit.ImportFromFile(options.dataFile,
options.dataTreeName)
)
data.Print("v")
# put in the raw datasets, no cuts or manipulation
getattr(ws,'import')(data,
RooFit.RenameVariable(options.showerShapeName,'Pho_SigmaIEtaIEta'),
RooFit.RenameVariable(options.ptName,'Pho_Pt'),
RooFit.RenameVariable(options.etaName,'Pho_Eta'))
sieie = ws.var('Pho_SigmaIEtaIEta')
sieie.setRange('barrelSel',0,0.011)
sieie.setRange('endcapSel',0,0.033)
output = TFile.Open(options.outputFile,'RECREATE')
output.cd()
#histograms
fakeRate = []
nFakes = []
nTot = []
fakeRateMC = []
#counters
totBkg = [] #central value in each bin
totBkgUp = [] #upper error
totBkgLo = [] #lower error
ietabin = 0
#setup histograms and counters
for i in range(len(eta_bins)):
fakeRate.append(TGraphAsymmErrors())
fakeRate[i].SetName('fakeRate_etabin%d'%i)
nFakes.append(TGraphAsymmErrors())
nFakes[i].SetName('nFakes_etabin%d'%i)
nTot.append(TGraphAsymmErrors())
nTot[i].SetName('nTot_etabin%d'%i)
if options.MCFakeRate:
fakeRateMC.append(TGraphAsymmErrors())
fakeRateMC[i].SetName('fakeRateMC_etabin%d'%i)
totBkg.append(0)
totBkgUp.append(0)
totBkgLo.append(0)
#loop through bins making all fits
for etabin in eta_bins:
#pt bin number for TGraphs
iptbin = 0
for ptbin in pt_bins:
if 'abs(Pho_Eta) < 1.4442':
phoselsig = ' abs(Pho_Eta) > %f && abs(Pho_Eta) < %f && Pho_Pt > %f && Pho_Pt < %f'%(etabin[0],
etabin[1],
ptbin[0],
ptbin[1])
# if options.MCFakeRate:
phoselbkg = 'phoCHIso > 2 && phoCHIso < 6 && abs(Pho_Eta) > %f && abs(Pho_Eta) < %f && Pho_Pt > %f && Pho_Pt < %f'%(etabin[0],
etabin[1],
ptbin[0],
ptbin[1])
phosel= phoselsig or phoselbkg
#phosel = 'phoselsig || phoselbkg'
#phosel = 'abs(Pho_Eta) > %f && abs(Pho_Eta) < %f && Pho_Pt > %f && Pho_Pt < %f'%(etabin[0],
# etabin[1],
# ptbin[0],
# ptbin[1])
postfix = '_Eta_%.4f_%.4f_Pt_%.2f_%.2f'%(etabin[0],etabin[1],ptbin[0],ptbin[1])
binData = ws.data('data').reduce(RooFit.Cut(phosel),
RooFit.Name('binData'+postfix),
RooFit.Title('Data Events'))
#save it all in the workspace
getattr(ws,'import')(binData)
#create the PDFs we are going to fit!
options.pdfModule.makePdf(ws,etabin,postfix,options.extraInput)
output.cd()
ws.factory('RooExtendPdf::fullPDFExt'+postfix+'('+
'fullPDF'+postfix+',nTot'+postfix+'[2000,1e-8,10000])')
bNLL = ws.pdf('fullPDFExt'+postfix).createNLL(ws.data('binData'+postfix),
RooFit.Extended(True), RooFit.NumCPU(4))
bFit = RooMinuit(bNLL)
#bFit.setVerbose(True)
if ws.data('binData'+postfix).sumEntries() > 0:
bFit.setPrintLevel(-1)
bFit.migrad()
bFit.hesse()
bFit.setPrintLevel(0)
bFit.minos(RooArgSet(ws.var('fBkg'+postfix)))
bRes = bFit.save('binFitResult'+postfix)
getattr(ws,'import')(bRes)
#plot fits on top of data
bFrame = sieie.frame()
bFrame.SetName('binFrame'+postfix)
binData.plotOn(bFrame)
ws.pdf('fullPDFExt'+postfix).plotOn(bFrame, RooFit.LineColor(ROOT.kYellow))
#ws.pdf('fullPDFExt'+postfix).plotOn(bFramea)
ws.pdf('fullPDFExt'+postfix).plotOn(bFrame,
RooFit.Components('bkgTemplatePDF'+postfix),
RooFit.LineColor(ROOT.kBlue))
ws.pdf('fullPDFExt'+postfix).plotOn(bFrame,
RooFit.Components('sigTemplatePDF'+postfix),
RooFit.LineColor(ROOT.kRed))
ws.pdf('fullPDFExt'+postfix).plotOn(bFrame,
RooFit.Components('fullPDF'+postfix),
RooFit.LineColor(ROOT.kGreen))
bFrame.Write()
bSigInt = None
bBkgInt = None
#make plot of fake rate in signal region as a function of pT
#< 0.011 (EB), <0.030 (EE)
if etabin[0] >= 1.560:
bSigInt = ws.pdf('sigTemplatePDF'+
postfix).createIntegral(RooArgSet(sieie),
RooArgSet(sieie),
'endcapSel')
bBkgInt = ws.pdf('bkgTemplatePDF'+
postfix).createIntegral(RooArgSet(sieie),
RooArgSet(sieie),
'endcapSel')
else:
bSigInt = ws.pdf('sigTemplatePDF'+
postfix).createIntegral(RooArgSet(sieie),
RooArgSet(sieie),
'barrelSel')
bBkgInt = ws.pdf('bkgTemplatePDF'+
postfix).createIntegral(RooArgSet(sieie),
RooArgSet(sieie),
'barrelSel')
nEventsB = ws.var('nTot'+postfix).getVal()
nSignalB = (1.0 - ws.var('fBkg'+postfix).getVal())*nEventsB*bSigInt.getVal()
nBkgB = ws.var('fBkg'+postfix).getVal()*nEventsB*bBkgInt.getVal()
nBkgBUp = ws.var('fBkg'+postfix).getErrorHi()*nEventsB*bBkgInt.getVal()
nBkgBLo = -ws.var('fBkg'+postfix).getErrorLo()*nEventsB*bBkgInt.getVal()
print "Events Sig Bkg: %.3f + %.3f - %.3f"%(nEventsB, nSignalB, nBkgB)
trash = (1.0 - ws.var('fBkg'+postfix).getVal())
trash1 = bSigInt.getVal()
trash2 = bBkgInt.getVal()
trash3 = ws.var('fBkg'+postfix).getVal()
print nEventsB
print trash
print trash1
print trash2
print trash3
if nBkgBLo == 0.0: #catch cases when minos dies on lower limits
parb_err = ws.var('fBkg'+postfix).getError()*nEventsB*bBkgInt.getVal()
nBkgBLo = -max(nBkgB - parb_err,-nBkgB)
totBkg[ietabin] += nBkgB
totBkgUp[ietabin] = sqrt(nBkgBUp*nBkgBUp + totBkgUp[ietabin]*totBkgUp[ietabin])
totBkgLo[ietabin] = sqrt(nBkgBLo*nBkgBLo + totBkgLo[ietabin]*totBkgLo[ietabin])
print "Background Events: %.3f + %.3f - %.3f"%(nBkgB,nBkgBUp,nBkgBLo)
fakeRate[ietabin].SetPoint(iptbin,(float(ptbin[1])+float(ptbin[0]))/2,nBkgB/(nSignalB+nBkgB))
fakeRate[ietabin].SetPointError(iptbin,
(float(ptbin[1])+float(ptbin[0]))/2 - float(ptbin[0]),
float(ptbin[1]) - (float(ptbin[1])+float(ptbin[0]))/2,
-ws.var('fBkg'+postfix).getErrorLo(),
ws.var('fBkg'+postfix).getErrorHi())
#nFakes[ietabin].SetPoint(iptbin,(float(ptbin[1])+float(ptbin[0]))/2,nBkgB/(float(ptbin[1]) - float(ptbin[0])))
nFakes[ietabin].SetPoint(iptbin,(float(ptbin[1])+float(ptbin[0]))/2,nBkgB)
nFakes[ietabin].SetPointError(iptbin,
(float(ptbin[1])+float(ptbin[0]))/2 - float(ptbin[0]),
float(ptbin[1]) - (float(ptbin[1])+float(ptbin[0]))/2,
nBkgBLo/(float(ptbin[1]) - float(ptbin[0])),
nBkgBUp/(float(ptbin[1]) - float(ptbin[0])))
#nTot[ietabin].SetPoint(iptbin,(float(ptbin[1])+float(ptbin[0]))/2,nEventsB/(float(ptbin[1]) - float(ptbin[0])))
nTot[ietabin].SetPoint(iptbin,(float(ptbin[1])+float(ptbin[0]))/2,nEventsB)
nTot[ietabin].SetPointError(iptbin,
(float(ptbin[1])+float(ptbin[0]))/2 - float(ptbin[0]),
float(ptbin[1]) - (float(ptbin[1])+float(ptbin[0]))/2,
ws.var('nTot'+postfix).getError()/(float(ptbin[1]) - float(ptbin[0])),
ws.var('nTot'+postfix).getError()/(float(ptbin[1]) - float(ptbin[0])))
if options.MCFakeRate:
fr = 0
if etabin[0] > 1.560:
fr = (ws.data('binData'+postfix).sumEntries('FakePho > 0.5 && Pho_SigmaIEtaIEta < 0.030')/
ws.data('binData'+postfix).sumEntries('FakePho > -1 && Pho_SigmaIEtaIEta < 0.030'))
else:
fr = (ws.data('binData'+postfix).sumEntries('FakePho > 0.5 && Pho_SigmaIEtaIEta < 0.011')/
ws.data('binData'+postfix).sumEntries('FakePho > -1 && Pho_SigmaIEtaIEta < 0.011'))
fakeRateMC[ietabin].SetPoint(iptbin,(float(ptbin[1])+float(ptbin[0]))/2,fr)
fakeRateMC[ietabin].SetPointError(iptbin,
(float(ptbin[1])+float(ptbin[0]))/2 - float(ptbin[0]),
float(ptbin[1]) - (float(ptbin[1])+float(ptbin[0]))/2,
0,
0)
iptbin += 1
ietabin += 1
for i in range(len(eta_bins)):
print " %.4f < |Eta| < %.4f Total Background = %.3f +/- (%.3f,%.3f)"%(eta_bins[i][0],eta_bins[i][1],
totBkg[i],totBkgUp[i],totBkgLo[i])
fakeRate[i].Write()
nFakes[i].Write()
nTot[i].Write()
if options.MCFakeRate:
fakeRateMC[i].Write()
ws.Write()
output.Close()
