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()
def main(infiles=None):
infile = infiles[0]
var = "leadmupt"
bounds = [25, 300]
c1 = ROOT.TCanvas("NLL", "NLL", 1000, 1000)
x = ROOT.RooRealVar(var, var, bounds[0], bounds[1])
aset = ROOT.RooArgSet(x, "aset")
frame = x.frame()
f = ROOT.TFile.Open(infile)
tree = f.Get(f.GetListOfKeys().At(0).GetName())
tree.Print()
nentries = tree.GetEntries()
y = []
dh2 = ROOT.TH1F()
data = ROOT.RooDataSet("Data", "Data", aset)
for n in range(nentries):
tree.GetEntry(n)
y.append(getattr(tree, var))
if y[n] <= bounds[1] and y[n] >= bounds[0]:
x.setVal(y[n])
data.add(aset)
dh2.Fill(y[n])
data.plotOn(frame)
dh = RooDataHist("dh", "dh", RooArgSet(x), data)
nbins = dh2.GetNbinsX()
nbinsy = dh2.GetNbinsX()
print("nbins: ", nbins)
print("nbinsy: ", nbinsy)
for i in range(nbins):
if dh2.GetBinContent(dh2.GetBin(i)) == 0:
print("bin: ", i)
#dh2.SetBinError(bin,0.01)
## CREATE GAUSSIAN MODEL
mx = RooRealVar("mx", "mx", 10, 0, 350)
sx = RooRealVar("sx", "sx", 3, 0, 10)
gx = RooGaussian("gx", "gx", x, mx, sx)
## CREATE EXPONENTIAL MODEL
lambda1 = RooRealVar("lambda1", "slope1", -100, 100)
expo1 = RooExponential("expo1", "exponential PDF 1", x, lambda1)
lambda2 = RooRealVar("lambda2", "slope2", -.03, -1000, 1000)
expo2 = RooExponential("expo2", "exponential PDF 2", x, lambda2)
l1 = RooRealVar("l1", "yield1", 100, 0, 10000)
l2 = RooRealVar("l2", "yield2", 100, 0, 10000)
#sum = RooAddPdf("sum","exp and gauss",RooArgList(expo1,gx),RooArgList(l1,l2))
sum = RooAddPdf("sum", "2 exps", RooArgList(expo1, expo2),
RooArgList(l1, l2))
## Construct binned likelihood
nll = RooNLLVar("nll", "nll", expo1, data, ROOT.RooFit.Extended(True))
## Start Minuit session on NLL
m = RooMinuit(nll)
m.migrad()
m.hesse()
r1 = m.save()
#sum.plotOn(frame,ROOT.RooFit.LineColor(1))
#sum.plotOn(frame,ROOT.RooFit.Components("expo1"),ROOT.RooFit.LineColor(2))
#sum.plotOn(frame,ROOT.RooFit.Components("expo2"),ROOT.RooFit.LineColor(3))
expo1.plotOn(frame)
## Construct Chi2
chi2 = RooChi2Var("chi2", "chi2", expo2, dh)
## Start Minuit session on Chi2
m2 = RooMinuit(chi2)
m2.migrad()
m2.hesse()
r2 = m2.save()
frame.Draw()
c2 = ROOT.TCanvas("Chi2", "Chi2", 1000, 1000)
frame2 = x.frame()
data.plotOn(frame2)
expo2.plotOn(frame2)
#sum.plotOn(frame2,ROOT.RooFit.LineColor(4))
#sum.plotOn(frame2,ROOT.RooFit.Components("expo1"),ROOT.RooFit.LineColor(5))
#sum.plotOn(frame2,ROOT.RooFit.Components("expo2"),ROOT.RooFit.LineColor(6))
## Print results
print("result of likelihood fit")
r1.Print("v")
print("result of chi2 fit")
r2.Print("v")
frame2.Draw()
c1.Draw()
c2.Draw()
rep = ''
while not rep in ['q', 'Q']:
rep = input('enter "q" to quit: ')
if 1 < len(rep):
rep = rep[0]
def doFit(ws, options):
rap_bins = range(1, len(jpsi.pTRange))
pt_bins = None
if options.testBin is not None:
rap_bins = [int(options.testBin.split(',')[0])]
pt_bins = [int(options.testBin.split(',')[1]) - 1]
for rap_bin in rap_bins:
if options.testBin is None:
pt_bins = range(len(jpsi.pTRange[rap_bin]))
for pt_bin in pt_bins:
sigMaxMass = jpsi.polMassJpsi[
rap_bin] + jpsi.nSigMass * jpsi.sigmaMassJpsi[rap_bin]
sigMinMass = jpsi.polMassJpsi[
rap_bin] - jpsi.nSigMass * jpsi.sigmaMassJpsi[rap_bin]
sbHighMass = jpsi.polMassJpsi[
rap_bin] + jpsi.nSigBkgHigh * jpsi.sigmaMassJpsi[rap_bin]
sbLowMass = jpsi.polMassJpsi[
rap_bin] - jpsi.nSigBkgLow * jpsi.sigmaMassJpsi[rap_bin]
jPsiMass = ws.var('JpsiMass')
jPsicTau = ws.var('Jpsict')
jPsiMass.setRange('mlfit_prompt', 2.7, 3.5)
jPsiMass.setRange('mlfit_nonPrompt', 2.7, 3.5)
jPsiMass.setRange('NormalizationRangeFormlfit_prompt', 2.7, 3.5)
jPsiMass.setRange('NormalizationRangeFormlfit_nonPrompt', 2.7, 3.5)
jPsicTau.setRange('mlfit_signal', -1, 2.5)
jPsicTau.setRange('mlfit_leftMassSideBand', -1, 2.5)
jPsicTau.setRange('mlfit_rightMassSideBand', -1, 2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_signal', -1, 2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_leftMassSideBand',
-1, 2.5)
jPsicTau.setRange('NormalizationRangeFormlfit_rightMassSideBand',
-1, 2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_promptSignal',-1,.1)
#jPsicTau.setRange('NormalizationRangeFormlfit_nonPromptSignal',.1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_leftMassSideBand',-1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_rightMassSideBand',-1,2.5)
#jPsicTau.setRange('mlfit_promptSignal',-1,.1)
#jPsicTau.setRange('mlfit_nonPromptSignal',.1,2.5)
#jPsicTau.setRange('mlfit_leftMassSideBand',-1,2.5)
#jPsicTau.setRange('mlfit_rightMassSideBand',-1,2.5)
#reset parameters
ws.var('CBn').setVal(.5)
ws.var('CBalpha').setVal(.5)
ws.var('CBmass').setVal(3.1)
ws.var('CBsigma').setVal(.02)
ws.var('bkgLambda').setVal(0)
ws.var('bkgTauSSDL').setVal(.5)
#ws.var('bkgTauFDL').setVal(.5)
ws.var('bkgTauDSDL').setVal(.5)
ws.var('fBkgSSDL').setVal(.5)
ws.var('fBkgLR').setVal(.5)
ws.var('bkgTauSSDR').setVal(.5)
#ws.var('bkgTauFDR').setVal(.5)
ws.var('bkgTauDSDR').setVal(.5)
ws.var('fBkgSSDR').setVal(.5)
#ws.var('fBkgFDR').setVal(.25)
#ws.var('nPrompt').setVal(5000)
#ws.var('nNonPrompt').setVal(500)
#ws.var('nBackground').setVal(100)
#ws.var('nBackgroundL').setVal(50)
#ws.var('nBackgroundR').setVal(50)
ws.var('nonPromptTau').setVal(.5)
ws.var('promptMean').setVal(0)
ws.var('ctResolution').setVal(1)
LPdf = ws.pdf('LPdf')
MPdf = ws.pdf('MPdf')
data = ws.data('data_rap' + str(rap_bin) + '_pt' + str(pt_bin + 1))
NLLs = RooArgSet()
MassNLL = MPdf.createNLL(
data, ROOT.RooFit.Range('mlfit'), ROOT.RooFit.SplitRange(True),
ROOT.RooFit.ConditionalObservables(
RooArgSet(ws.var('JpsictErr'))), ROOT.RooFit.NumCPU(2))
CTauNLL = LPdf.createNLL(
data, ROOT.RooFit.Range('mlfit'), ROOT.RooFit.SplitRange(True),
ROOT.RooFit.ConditionalObservables(
RooArgSet(ws.var('JpsictErr'))), ROOT.RooFit.NumCPU(2))
NLLs.add(MassNLL)
NLLs.add(CTauNLL)
simNLL = RooAddition('add', 'add', NLLs)
minuit = RooMinuit(simNLL)
minuit.setStrategy(2)
minuit.setPrintEvalErrors(-1)
minuit.simplex()
minuit.migrad()
minuit.migrad()
minuit.hesse()
fitresult = minuit.save('polfitresult_rap' + str(rap_bin) + '_pt' +
str(pt_bin + 1))
getattr(ws, 'import')(fitresult)
ws.saveSnapshot(
'snapshot_rap' + str(rap_bin) + '_pt' + str(pt_bin + 1),
ws.allVars())
