def rooFit207():
print ">>> setup model signal components: gaussians..."
x = RooRealVar("x","x",0,10)
mean = RooRealVar("mean","mean of gaussians",5)
sigma = RooRealVar("sigma","width of gaussians",0.5)
sig = RooGaussian("sig","Signal",x,mean,sigma)
print ">>> setup model background components: Chebychev polynomial plus exponential..."
a0 = RooRealVar("a0","a0",0.5,0.,1.)
a1 = RooRealVar("a1","a1",-0.2,0.,1.)
bkg1 = RooChebychev("bkg1","Background 1",x,RooArgList(a0,a1))
alpha = RooRealVar("alpha","alpha",-1)
bkg2 = RooExponential("bkg2","Background 2",x,alpha)
bkg1frac = RooRealVar("bkg1frac","fraction of component 1 in background",0.2,0.,1.)
bkg = RooAddPdf("bkg","Signal",RooArgList(bkg1,bkg2),RooArgList(bkg1frac))
print ">>> sum signal and background component..."
bkgfrac = RooRealVar("bkgfrac","fraction of background",0.5,0.,1.)
model = RooAddPdf("model","g1+g2+a",RooArgList(bkg,sig),RooArgList(bkgfrac))
# Create dummy dataset that has more observables than the above pdf
y = RooRealVar("y","y",-10,10)
data = RooDataSet("data","data",RooArgSet(x,y))
# Basic information requests:"
print ">>> get list of observables of pdf in context of a dataset..."
# Observables are define each context as the variables
# shared between a model and a dataset. In this case
# that is the variable 'x'
model_obs = model.getObservables(data) # RooArgSet
model_obs.Print('v')
print "\n>>> get list of parameters..."
# Get list of parameters, given list of observables
model_params = model.getParameters(RooArgSet(x)) # RooArgSet
print ">>> model_params.getStringValue(\"a0\") = %s" % (model_params.getStringValue("a0"))
print ">>> model_params.getRealValue(\"a0\") = %s" % (model_params.getRealValue("a0"))
print ">>> model_params.find(\"a0\").GetName() = %s" % (model_params.find("a0").GetName())
print ">>> model_params.find(\"a0\").getVal() = %s" % (model_params.find("a0").getVal())
# print ">>> for param in model_params:"
# for param in model_params.():
# print ">>> %s"%(model_params.first())
# print ">>> %s"%(model_params.first())
# model_params.selectByName("a*").Print('v')
model_params.Print('v')
print "\n>>> get list of parameters of a dataset..."
# Gives identical results to operation above
model_params2 = model.getParameters(data) # RooArgSet
model_params2.Print()
print "\n>>> get list of components..."
# Get list of component objects, including top-level node
model_comps = model.getComponents() # RooArgSet
model_comps.Print('v')
print "\n>>> modifications to structure of composites..."
sigma2 = RooRealVar("sigma2","width of gaussians",1)
sig2 = RooGaussian("sig2","Signal component 1",x,mean,sigma2)
sig1frac = RooRealVar("sig1frac","fraction of component 1 in signal",0.8,0.,1.)
sigsum = RooAddPdf("sigsum","sig+sig2",RooArgList(sig,sig2),RooArgList(sig1frac))
print ">>> construct a customizer utility to customize model..."
cust = RooCustomizer(model,"cust")
print ">>> instruct the customizer to replace node 'sig' with node 'sigsum'..."
cust.replaceArg(sig,sigsum)
# Build a clone of the input pdf according to the above customization
# instructions. Each node that requires modified is clone so that the
# original pdf remained untouched. The name of each cloned node is that
# of the original node suffixed by the name of the customizer object
#
# The returned head node own all nodes that were cloned as part of
# the build process so when cust_clone is deleted so will all other
# nodes that were created in the process.
cust_clone = cust.build(kTRUE) # RooAbsPdf
# Print structure of clone of model with sig->sigsum replacement.
cust_clone.Print("t")
# delete clone
del cust_clone
canS = TCanvas('Template_TT','Template_TT',900,600)
#gPad.SetLogy()
res = signal.fitTo(roohisto[0],RooFit.Save())
res.Print()
frame = x.frame()
roohisto[0].plotOn(frame)
signal.plotOn(frame)
signal.plotOn(frame,RooFit.Components('ttbar_gaus1'),RooFit.LineColor(ROOT.kRed),RooFit.LineWidth(2),RooFit.LineStyle(ROOT.kDashed))
signal.plotOn(frame,RooFit.Components('ttbar_gaus2'),RooFit.LineColor(ROOT.kGreen+1),RooFit.LineWidth(2),RooFit.LineStyle(ROOT.kDashed))
#signal.plotOn(frame,RooFit.Components('sig3'),RooFit.LineColor(ROOT.kMagenta),RooFit.LineWidth(2),RooFit.LineStyle(ROOT.kDashed))
frame.GetXaxis().SetTitle('m_{t} (GeV)')
frame.Draw()
gPad.Update()
canS.Print(canS.GetName()+".pdf")
parsSig = signal.getParameters(roohisto[0])
parsSig.setAttribAll('Constant', True)
# -----------------------------------------
# fit qcd
m1QCD = RooRealVar('qcd_mean1' ,'qcd_mean1',200,130,350)
s1QCD = RooRealVar('qcd_sigma1','qcd_sigma1',50,20,200)
qcd1 = RooGaussian('qcd_gaus1','qcd_gaus1',x,m1QCD,s1QCD)
m2QCD = RooRealVar('qcd_mean2' ,'qcd_mean2',140,130,300)
s2QCD = RooRealVar('qcd_sigma2','qcd_sigma2',50,10,200)
qcd2 = RooGaussian('qcd_gaus2' ,'qcd_gaus2',x,m2QCD,s2QCD)
fqcd = RooRealVar('qcd_f1','qcd_f1',0.5,0,1)
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()
roohistSig = RooDataHist('roohist', 'roohist', RooArgList(x), hSig)
roohistSig.Print()
res_sig = signal.fitTo(roohistSig, RooFit.Save(ROOT.kTRUE))
res_sig.Print()
frame = x.frame()
roohistSig.plotOn(frame, RooFit.Binning(166))
signal.plotOn(frame)
signal.plotOn(frame, RooFit.Components('bkg'), RooFit.LineColor(ROOT.kRed),
RooFit.LineWidth(2), RooFit.LineStyle(ROOT.kDashed))
#frame.GetXaxis().SetRangeUser(1118,6099)
frame.GetXaxis().SetRangeUser(minX_mass, maxX_mass)
frame.GetXaxis().SetTitle('m_{jj} (GeV)')
frame.Draw()
parsSig = signal.getParameters(roohistSig)
parsSig.setAttribAll('Constant', True)
if histpdfSig:
# -----------------------------------------
# hist pdf signal
canSname = 'can_Mjj' + str(mass)
canS = TCanvas(canSname, canSname, 900, 600)
gPad.SetLogy()
roohistSig = RooDataHist('roohist', 'roohist', RooArgList(x), hSig)
roohistSig.Print()
signal = RooHistPdf('signal', 'signal', RooArgSet(x), roohistSig)
signal.Print()
frame = x.frame()
l1=RooRealVar("l1","l1",1.,0.,1000.)
l2=RooRealVar("l2","l2",1.,0.,1000.)
#sigbkg=RooPolynomial("sigbkg"+str(cut),"bkg",mass,RooArgList(l0,l1,l2))
#sigbkg=RooChebychev("sigbkg"+str(cut),"bkg",mass,RooArgList(l0,l1))
sigbkg=RooLogistics("sigbkg"+str(cut),"bkg",mass,l0,l1)
#sigbkg=RooExponential("sigbkg"+str(cut),"sigbkg",mass,l0)
nsigref=RooRealVar("nsigref","number of signal events",100,0,1e10)
sigmodel=RooAddPdf("sigmodel"+str(cut),"sig+bkg",RooArgList(sigbkg,sig),RooArgList(nbkg,nsigref))
s0.setConstant(False)
s1.setConstant(False)
sigmodel.fitTo(signal,RooFit.SumW2Error(True)) #,RooFit.Range("signal")
sigmodel.fitTo(signal,RooFit.SumW2Error(True)) #,RooFit.Range("signal")
sigmodel.fitTo(signal,RooFit.SumW2Error(True)) #,RooFit.Range("signal")
chi2=RooChi2Var("chi2","chi2",sigmodel,signal,RooFit.DataError(RooAbsData.SumW2))
nbins=data.numEntries()
nfree=sigmodel.getParameters(data).selectByAttrib("Constant",False).getSize()
s0.setConstant(True)
s1.setConstant(True)
if cut>=1:
fullintegral=sumsighist.Integral()
else:
fullintegral=sighist.Integral()
print "SIGNAL FRACTION",nsigref.getValV()/(nsigref.getValV()+nbkg.getValV())
if nsigref.getValV()==0: continue
if fit=="data":
xframe=mass.frame(RooFit.Title("signal fraction in peak ="+str(int(nsigref.getValV()/(nsigref.getValV()+nbkg.getValV())*1000.)/1000.)+"+-"+str(int(nsigref.getError()/(nsigref.getValV()+nbkg.getValV())*1000.)/1000.)+", #chi^{2}/DOF = "+str(int((chi2.getVal()/(nbins-nfree))*10.)/10.)))
signal.plotOn(xframe,RooFit.DataError(RooAbsData.SumW2))
sigmodel.plotOn(xframe,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
sigmodel.plotOn(xframe,RooFit.Components("sigbkg"+str(cut)),RooFit.LineStyle(kDashed),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
canSname = 'can_Sub_Mjj'+str(mass)
canS = TCanvas(canSname,canSname,900,600)
#gPad.SetLogy()
roohistSig = RooDataHist('roohist','roohist',RooArgList(x),hSig)
signal.fitTo(roohistSig)
frame = x.frame()
roohistSig.plotOn(frame)
signal.plotOn(frame)
signal.plotOn(frame,RooFit.Components('bkg'),RooFit.LineColor(ROOT.kRed),RooFit.LineWidth(2),RooFit.LineStyle(ROOT.kDashed))
frame.GetXaxis().SetRangeUser(900,4500)
frame.GetXaxis().SetTitle('m_{jj} (GeV)')
frame.Draw()
parsSig = signal.getParameters(roohistSig)
parsSig.setAttribAll('Constant', True)
if fitDat:
# -----------------------------------------
# define parameters for background
NBINS = 180
p1 = RooRealVar('p1','p1',7,1,10)
p2 = RooRealVar('p2','p2',5,1,10)
p3 = RooRealVar('p3','p3',0.03,0.01,0.07)
background = RooGenericPdf('background','(pow(1-@0/8000,@1)/pow(@0/8000,@2+@3*log(@0/8000)))',RooArgList(x,p1,p2,p3))
roohistBkg = RooDataHist('roohist','roohist',RooArgList(x),hDat)
res = background.fitTo(roohistBkg)
def fitMC(fulldata, correctTag, ibin):
print 'now fitting: ', ibin, ' for ', correctTag * 'correctTag ', (
1 - correctTag) * 'wrongTag'
cut = cut_base + '&& (mumuMass*mumuMass > %s && mumuMass*mumuMass < %s)' % (
q2binning[ibin], q2binning[ibin + 1])
data = fulldata.reduce(RooArgSet(thevarsMC), cut)
pol_c1 = RooRealVar("p1", "coeff x^0 term", -0.5, -10, 10)
bkg_pol = RooChebychev("bkg_pol", "bkg_pol", tagged_mass,
RooArgList(pol_c1))
signalFunction = bkg_pol ### just a placeholder
nsig = RooRealVar("Yield", "nsig", 10000, 0, 1000000)
nbkg = RooRealVar("nbkg", "nbkg", 10, 0, 100000)
doextended = False
fitrange = "mcrange"
nbins = 70
if correctTag:
doubleG(B0Mass_, initial_sigma1, initial_sigma2, 0.8, tagged_mass, w,
"RT%s" % ibin) ## (mean_ , sigma1_, sigma2_, f1_)
signalFunction = w.pdf("doublegaus_RT%s" % ibin)
fitFunction = RooAddPdf("fitfunction", "fit function",
RooArgList(signalFunction, bkg_pol),
RooArgList(nsig, nbkg))
doextended = True
fitrange = "full"
nbins = 60
else:
mean = RooRealVar("mean^{WT%s}" % ibin, "massWT", B0Mass_, 5, 6, "GeV")
sigmaCB = RooRealVar("#sigma_{CB}^{WT%s}" % ibin, "sigmaCB", 0.03, 0,
1)
alpha1 = RooRealVar("#alpha_{1}^{WT%s}" % ibin, "#alpha_{1}", 0.5, 0,
10)
alpha2 = RooRealVar("#alpha_{2}^{WT%s}" % ibin, "#alpha_{2}", 0.5, 0,
10)
n1 = RooRealVar("n_{1}^{WT%s}" % ibin, "n_1", 2, 0, 90)
n2 = RooRealVar("n_{2}^{WT%s}" % ibin, "n_2", 1, 0, 90)
doublecb = ROOT.RooDoubleCBFast("doublecb_%s" % ibin, "doublecb",
tagged_mass, mean, sigmaCB, alpha1, n1,
alpha2, n2)
# getattr(w, 'import')(doublecb)
signalFunction = doublecb
fitFunction = doublecb
getattr(w, "import")(signalFunction)
r = fitFunction.fitTo(data, RooFit.Extended(doextended), RooFit.Save(),
RooFit.Range(fitrange))
print 'fit status: ', r.status(), r.covQual()
if not _goodFit(r):
r = fitFunction.fitTo(data, RooFit.Extended(doextended), RooFit.Save(),
RooFit.Range(fitrange))
print 'fit status (redo): ', r.status(), r.covQual()
if not _goodFit(r) and correctTag:
r = fitFunction.fitTo(data, RooFit.Extended(doextended), RooFit.Save(),
RooFit.Range(fitrange))
print 'fit status (redo2): ', r.status(), r.covQual()
params = signalFunction.getParameters(RooArgSet(tagged_mass))
w.saveSnapshot(
"reference_fit_%s_%s" % ('RT' * correctTag + 'WT' *
(1 - correctTag), ibin), params, ROOT.kTRUE)
frame = tagged_mass.frame(RooFit.Range(fitrange))
data.plotOn(frame, RooFit.Binning(nbins), RooFit.MarkerSize(.7))
drawPdfComponents(fitFunction,
frame,
ROOT.kGreen if correctTag else ROOT.kViolet,
RooFit.NormRange(fitrange),
RooFit.Range(fitrange),
isData=False)
fitFunction.plotOn(frame, RooFit.NormRange(fitrange),
RooFit.Range(fitrange))
fitFunction.paramOn(frame, RooFit.Layout(0.62, 0.86, 0.88))
frame.Draw()
niceFrame(frame, '')
frame.addObject(_writeFitStatus(r))
## evaluate sort of chi2 and save number of RT/WT events
observables = RooArgSet(tagged_mass)
flparams = fitFunction.getParameters(observables)
nparam = int(flparams.selectByAttrib("Constant", ROOT.kFALSE).getSize())
if correctTag:
frame.addObject(
_writeChi2(
frame.chiSquare(
"fitfunction_Norm[tagged_mass]_Range[full]_NormRange[full]",
"h_fullmc", nparam)))
dict_s_rt[ibin] = _getFittedVar(nsig)
nRT = RooRealVar("nRT_%s" % ibin, "yield of RT signal", 0, 1.E6)
nRT.setVal(dict_s_rt[ibin].n)
nRT.setError(dict_s_rt[ibin].s)
getattr(w, "import")(nRT)
else:
frame.addObject(
_writeChi2(
frame.chiSquare(
"doublecb_%s_Norm[tagged_mass]_Comp[doublecb_%s]_Range[mcrange]_NormRange[mcrange]"
% (ibin, ibin), "h_fullmc", nparam)))
dict_s_wt[ibin] = ufloat(data.sumEntries(),
math.sqrt(data.sumEntries()))
nWT = RooRealVar("nWT_%s" % ibin, "yield of WT signal", 0, 1.E6)
nWT.setVal(dict_s_wt[ibin].n)
nWT.setError(dict_s_wt[ibin].s)
getattr(w, "import")(nWT)
# chi2 = frame.chiSquare("doublecb_%s_Norm[tagged_mass]_Comp[doublecb_%s]_Range[mcrange]_NormRange[mcrange]"%(ibin,ibin), "h_fullmc", nparam)
# if chi2 == -1:
# chi2 = frame.chiSquare("gauscb_%s_Norm[tagged_mass]_Comp[gauscb_%s]_Range[mcrange]_NormRange[mcrange]"%(ibin,ibin), "h_fullmc", nparam)
# frame. addObject(_writeChi2( chi2 ) )
frame.Draw()
frame.SetTitle('correctly' * correctTag + 'wrongly' * (1 - correctTag) +
' tagged events')
# c1.SetLogy()
c1.SaveAs('fit_results_mass/save_fit_mc_%s_%s_%sT_newCB.pdf' %
(ibin, args.year, "R" * correctTag + "W" * (1 - correctTag)))
out_f.cd()
r.Write('results_%s_%s' % (correctTag * 'RT' +
(1 - correctTag) * 'WT', ibin))
def fitData(fulldata, ibin, n_bkg, w):
cut = cut_base + '&& (mumuMass*mumuMass > %s && mumuMass*mumuMass < %s)' % (
q2binning[ibin], q2binning[ibin + 1])
fulldata_v2 = fulldata.reduce(
RooArgSet(tagged_mass, mumuMass, mumuMassE, randVar), cut)
## reduce to data-like statistics
nDataEntries = fulldata_v2.sumEntries()
nDesired = n_bin[ibin] / nDataEntries
cut = 'rand < %f' % nDesired
signaldata = fulldata_v2.reduce(
RooArgSet(tagged_mass, mumuMass, mumuMassE), cut)
n_realsignal = signaldata.sumEntries()
nrt_mc = _getFittedVar("nRT_%s" % ibin, w)
nwt_mc = _getFittedVar("nWT_%s" % ibin, w)
fraction = nrt_mc / (nrt_mc + nwt_mc)
### creating RT component
w.loadSnapshot("reference_fit_RT_%s" % ibin)
meanrt = w.var("mean^{RT%s}" % ibin)
sigmart = RooRealVar()
sigmart1 = RooRealVar()
sigmart2 = RooRealVar()
alphart1 = RooRealVar()
alphart2 = RooRealVar()
nrt1 = RooRealVar()
nrt2 = RooRealVar()
## double cb fast
if ibin < 5:
sigmart = w.var("#sigma_{CB}^{RT%s}" % ibin)
alphart1 = w.var("#alpha_{1}^{RT%s}" % ibin)
alphart2 = w.var("#alpha_{2}^{RT%s}" % ibin)
nrt1 = w.var("n_{1}^{RT%s}" % ibin)
nrt2 = w.var("n_{2}^{RT%s}" % ibin)
## double cb old
else:
sigmart1 = w.var("#sigma_{CBRT0}^{%s}" % ibin)
sigmart2 = w.var("#sigma_{CBRT1}^{%s}" % ibin)
alphart1 = w.var("#alpha_{RT0}^{%s}" % ibin)
alphart2 = w.var("#alpha_{RT1}^{%s}" % ibin)
nrt1 = w.var("n_{RT0}^{%s}" % ibin)
nrt2 = w.var("n_{RT1}^{%s}" % ibin)
pars_init_vals = {}
theRTgauss = w.pdf("doublecb_RT%s" % ibin)
if ibin < 5:
c_sigma_rt = _constrainVar(sigmart, 1, pars_init_vals)
else:
c_sigma_rt1 = _constrainVar(sigmart1, 1, pars_init_vals)
c_sigma_rt2 = _constrainVar(sigmart2, 1, pars_init_vals)
c_alpha_rt1 = _constrainVar(alphart1, 1, pars_init_vals)
c_alpha_rt2 = _constrainVar(alphart2, 1, pars_init_vals)
c_n_rt1 = _constrainVar(nrt1, 1, pars_init_vals)
c_n_rt2 = _constrainVar(nrt2, 1, pars_init_vals)
### creating WT component
w.loadSnapshot("reference_fit_WT_%s" % ibin)
meanwt = w.var("mean^{WT%s}" % ibin)
sigmawt = w.var("#sigma_{CB}^{WT%s}" % ibin)
alphawt1 = w.var("#alpha_{1}^{WT%s}" % ibin)
alphawt2 = w.var("#alpha_{2}^{WT%s}" % ibin)
nwt1 = w.var("n_{1}^{WT%s}" % ibin)
nwt2 = w.var("n_{2}^{WT%s}" % ibin)
theWTgauss = w.pdf("doublecb_%s" % ibin)
c_sigma_wt = _constrainVar(sigmawt, 1, pars_init_vals)
c_alpha_wt1 = _constrainVar(alphawt1, 1, pars_init_vals)
c_alpha_wt2 = _constrainVar(alphawt2, 1, pars_init_vals)
c_n_wt1 = _constrainVar(nwt1, 1, pars_init_vals)
c_n_wt2 = _constrainVar(nwt2, 1, pars_init_vals)
### creating constraints for the RT component
c_vars = RooArgSet()
if ibin < 5:
c_RTgauss = RooProdPdf(
"c_RTgauss", "c_RTgauss",
RooArgList(theRTgauss, c_alpha_rt1, c_n_rt1, c_sigma_rt,
c_alpha_rt2, c_n_rt2))
c_vars = RooArgSet(c_sigma_rt, c_alpha_rt1, c_alpha_rt2, c_n_rt1,
c_n_rt2)
else:
c_RTgauss = RooProdPdf(
"c_RTgauss", "c_RTgauss",
RooArgList(theRTgauss, c_alpha_rt1, c_n_rt1, c_sigma_rt1,
c_sigma_rt2, c_alpha_rt2, c_n_rt2))
c_vars = RooArgSet(c_sigma_rt1, c_sigma_rt2, c_alpha_rt1, c_alpha_rt2,
c_n_rt1, c_n_rt2)
### creating constraints for the WT component
c_WTgauss = RooProdPdf(
"c_WTgauss", "c_WTgauss",
RooArgList(theWTgauss, c_alpha_wt1, c_n_wt1, c_sigma_wt, c_alpha_wt2,
c_n_wt2))
c_vars.add(c_sigma_wt)
c_vars.add(c_alpha_wt1)
c_vars.add(c_alpha_wt2)
c_vars.add(c_n_wt1)
c_vars.add(c_n_wt2)
frt = RooRealVar("F_{RT}", "frt", fraction.n, 0, 1)
signalFunction = RooAddPdf("sumgaus", "rt+wt",
RooArgList(c_RTgauss, c_WTgauss),
RooArgList(frt))
c_frt = RooGaussian("c_frt", "c_frt", frt,
ROOT.RooFit.RooConst(fraction.n),
ROOT.RooFit.RooConst(fraction.s))
### creating constraints for the difference between the two peaks
deltaPeaks = RooFormulaVar("deltaPeaks", "@0 - @1",
RooArgList(meanrt, meanwt))
c_deltaPeaks = RooGaussian(
"c_deltaPeaks",
"c_deltaPeaks",
deltaPeaks,
ROOT.RooFit.RooConst(deltaPeaks.getVal()),
ROOT.RooFit.RooConst(0.0005) ## value to be checked
)
c_signalFunction = RooProdPdf(
"c_signalFunction", "c_signalFunction",
RooArgList(signalFunction, c_frt, c_deltaPeaks))
c_vars.add(frt)
c_vars.add(deltaPeaks)
### now create background parametrization
slope = RooRealVar("slope", "slope", 0.5, -10, 10)
bkg_exp = RooExponential("bkg_exp", "exponential", slope, tagged_mass)
pol_c1 = RooRealVar("p1", "coeff x^0 term", 0.5, -10, 10)
pol_c2 = RooRealVar("p2", "coeff x^1 term", 0.5, -10, 10)
bkg_pol = RooChebychev("bkg_pol", "2nd order pol", tagged_mass,
RooArgList(pol_c1, pol_c2))
nsig = RooRealVar("Yield", "signal frac", nrt_mc.n + nwt_mc.n, 0, 1000000)
nbkg = RooRealVar("nbkg", "bkg fraction", 1000, 0, 550000)
print nsig.getVal()
fitFunction = RooAddPdf("fitfunction", "fit function",
RooArgList(c_signalFunction, bkg_pol),
RooArgList(nsig, nbkg))
pars_to_tune = [
sigmawt, alphawt1, alphawt2, nwt1, nwt2, alphart1, alphart2, nrt1, nrt2
]
if ibin < 5:
pars_to_tune.append(sigmart)
else:
pars_to_tune.append(sigmart1)
pars_to_tune.append(sigmart2)
## add toy bkg
for itoy in range(args.ntoys):
data = deepcopy(signaldata)
toy_bkg = generateBkg(tagged_mass, ibin, n_bkg)
data.append(toy_bkg)
print 'toy number', itoy
for ipar in pars_to_tune:
ipar.setVal(pars_init_vals[ipar.GetName()])
# r = fitFunction.fitTo(data,
# RooFit.Extended(True),
# RooFit.Range("full"),
# ROOT.RooFit.Constrain(c_vars),
# # ROOT.RooFit.Minimizer("Minuit2","migrad"),
# ROOT.RooFit.Hesse(True),
# ROOT.RooFit.Strategy(2),
# ROOT.RooFit.Minos(False),
# )
# # print 'fit with Hesse strategy 2 done, now Minos'
r = fitFunction.fitTo(
data,
RooFit.Extended(True),
RooFit.Save(),
RooFit.Range("full"),
RooFit.Verbose(False),
ROOT.RooFit.Constrain(c_vars),
# ROOT.RooFit.Minimizer("Minuit2","migrad"),
# ROOT.RooFit.Hesse(True),
ROOT.RooFit.Strategy(2),
ROOT.RooFit.Minos(True),
)
# # r.Print()
# # r.correlationMatrix().Print()
fitStats['data%s_itoy%s' % (ibin, itoy)] = r.status()
covStats['data%s_itoy%s' % (ibin, itoy)] = r.covQual()
frame = tagged_mass.frame(RooFit.Range("full"))
data.plotOn(frame, RooFit.Binning(nbins), RooFit.MarkerSize(.7))
fitFunction.plotOn(frame, RooFit.NormRange("full"),
RooFit.Range("full"))
## evaluate sort of chi2 and save number of RT/WT events
observables = RooArgSet(tagged_mass)
flparams = fitFunction.getParameters(observables)
nparam = int(
flparams.selectByAttrib("Constant", ROOT.kFALSE).getSize())
pdfstring = "fitfunction_Norm[tagged_mass]_Range[full]_NormRange[full]"
chi2s['data%s_itoy%s' % (ibin, itoy)] = frame.chiSquare(
pdfstring, "h_fulldata", nparam)
frame.addObject(_writeChi2(chi2s['data%s_itoy%s' % (ibin, itoy)]))
## save plot only if 1 toy is run
if args.ntoys == 1:
drawPdfComponents(fitFunction,
frame,
ROOT.kAzure,
RooFit.NormRange("full"),
RooFit.Range("full"),
isData=True)
fitFunction.paramOn(frame, RooFit.Layout(0.62, 0.86, 0.89))
# parList = RooArgSet (nsig, nbkg, meanrt, meanwt, alphart1, alphart2, meanwt, sigmawt)
# if ibin < 5 :
# parList.add(sigmart)
# else:
# parList.add(sigmart1)
# parList.add(sigmart2)
# parList.add(alphawt1)
# parList.add(alphawt2)
# parList.add(nwt1)
# parList.add(nwt2)
# parList.add(frt)
# fitFunction.paramOn(frame, RooFit.Parameters(parList), RooFit.Layout(0.62,0.86,0.89))
frame.Draw()
niceFrame(frame, '')
frame.addObject(_writeFitStatus(r))
c1 = ROOT.TCanvas()
upperPad = ROOT.TPad('upperPad', 'upperPad', 0., 0.35, 1., 1.)
lowerPad = ROOT.TPad('lowerPad', 'lowerPad', 0., 0.0, 1., 0.345)
upperPad.SetBottomMargin(0.012)
lowerPad.SetTopMargin(0)
lowerPad.SetBottomMargin(0.2)
upperPad.Draw()
lowerPad.Draw()
upperPad.cd()
frame.Draw()
if not args.year == 'test':
writeCMS(frame, args.year,
[q2binning[ibin], q2binning[ibin + 1]], 1)
frame.Draw()
## add plot of pulls
lowerPad.cd()
hpull = frame.pullHist("h_fulldata", pdfstring)
frame2 = tagged_mass.frame(RooFit.Range("full"), RooFit.Title(''))
frame2.addPlotable(hpull, "P")
niceFrameLowerPad(frame2, 'pull')
frame2.Draw()
line = ROOT.TLine(5.0, 1, 5.6, 1)
line.SetLineColor(ROOT.kGreen + 3)
line.Draw()
for ilog in [True, False]:
upperPad.SetLogy(ilog)
c1.SaveAs(
'fit_results_mass_checkOnMC/toybkg/save_fit_data_%s_%s_nbkg%s_LMNR_Final%s_%s_update_pol2bkg.pdf'
% (ibin, args.year, n_bkg, '_logScale' * ilog, itoy))
out_f.cd()
# r.Write('results_data_%s_ntoy%s'%(ibin,itoy))
## compare nkbg fitted w/ original value
nbkgs['data%s_itoy%s' %
(ibin, itoy)] = (toy_bkg.sumEntries() -
nbkg.getVal()) / toy_bkg.sumEntries()
nsigs['data%s_itoy%s' %
(ibin, itoy)] = (n_realsignal - nsig.getVal()) / n_realsignal
def fitData(fulldata, ibin, w):
cut = cut_base + '&& (mumuMass*mumuMass > %s && mumuMass*mumuMass < %s)'%(q2binning[ibin], q2binning[ibin+1])
data = fulldata.reduce(RooArgSet(tagged_mass,mumuMass,mumuMassE), cut)
nrt_mc = _getFittedVar("nRT_%s"%ibin, w)
nwt_mc = _getFittedVar("nWT_%s"%ibin, w)
fraction = nrt_mc / (nrt_mc + nwt_mc)
print 'mistag fraction on MC for bin ', ibin , ' : ' , fraction.n , '+/-', fraction.s
### creating RT component
w.loadSnapshot("reference_fit_RT_%s"%ibin)
mean_rt = w.var("mean^{RT%s}"%ibin)
sigma_rt1 = w.var("#sigma_{RT1}^{%s}"%ibin)
sigma_rt2 = RooRealVar()
alpha_rt1 = RooRealVar()
alpha_rt2 = RooRealVar()
n_rt1 = RooRealVar()
n_rt2 = RooRealVar()
f1rt = RooRealVar()
## double cb fast
if ibin < 4:
alpha_rt1 = w.var("#alpha_{RT1}^{%s}"%ibin)
alpha_rt2 = w.var("#alpha_{RT2}^{%s}"%ibin)
n_rt1 = w.var("n_{RT1}^{%s}"%ibin)
n_rt2 = w.var("n_{RT2}^{%s}"%ibin)
## double cb old
else:
sigma_rt2 = w.var("#sigma_{RT2}^{%s}"%ibin)
alpha_rt1 = w.var("#alpha_{RT1}^{%s}"%ibin)
alpha_rt2 = w.var("#alpha_{RT2}^{%s}"%ibin)
n_rt1 = w.var("n_{RT1}^{%s}"%ibin)
n_rt2 = w.var("n_{RT2}^{%s}"%ibin)
f1rt = w.var("f^{RT%s}"%ibin)
theRTgauss = w.pdf("doublecb_RT%s"%ibin)
### creating WT component
w.loadSnapshot("reference_fit_WT_%s"%ibin)
mean_wt = w.var("mean_{WT}^{%s}"%ibin)
sigma_wt = w.var("#sigma_{WT1}^{%s}"%ibin)
alpha_wt1 = w.var("#alpha_{WT1}^{%s}"%ibin)
alpha_wt2 = w.var("#alpha_{WT2}^{%s}"%ibin)
n_wt1 = w.var("n_{WT1}^{%s}"%ibin)
n_wt2 = w.var("n_{WT2}^{%s}"%ibin)
theWTgauss = w.pdf("doublecb_%s"%ibin)
### creating variable for the difference between the two peaks
deltaPeaks = RooFormulaVar("deltaPeaks%s"%ibin, "@0 - @1", RooArgList(mean_rt, mean_wt))
frt = RooRealVar ("F_{RT}%s"%ibin , "frt" , fraction.n , 0, 1)
signalFunction = RooAddPdf ("sumgaus%s"%ibin , "rt+wt" , RooArgList(theRTgauss,theWTgauss), RooArgList(frt))
### now create background parametrization
slope = RooRealVar ("slope_%s"%ibin , "slope" , 0.5, -10, 10);
bkg_exp = RooExponential("bkg_exp%s"%ibin , "exponential" , slope, tagged_mass );
pol_c1 = RooRealVar ("p1_%s"%ibin , "coeff x^0 term" , 0.5, -10, 10);
pol_c2 = RooRealVar ("p2_%s"%ibin , "coeff x^1 term" , 0.5, -10, 10);
bkg_pol = RooPolynomial ("bkg_pol%s"%ibin , "2nd order pol" , tagged_mass, RooArgList(pol_c1, pol_c2));
fsig = RooRealVar("fsig%s"%ibin , "fsig" , 0.9, 0, 1);
# nsig = RooRealVar("Yield%s"%ibin , "signal frac" , 1000, 0, 10000);
# nbkg = RooRealVar("nbkg%s"%ibin , "bkg fraction" , 1000, 0, 500000);
nsig = RooRealVar("Yield" , "signal frac" , 600000, 0, 5000000);
nbkg = RooRealVar("nbkg" , "bkg fraction" , 100000, 0, 2000000);
# if ibin==4:
# nsig.setRange(500000,1500000)
# nsig.setVal(900000)
# nbkg.setRange(80000,1000000)
# nbkg.setVal(100000)
### creating constraints
c_vars = RooArgSet()
c_pdfs = RooArgSet()
c_sigma_rt1 = _constrainVar(sigma_rt1, 1)
c_alpha_rt1 = _constrainVar(alpha_rt1, 1)
c_alpha_rt2 = _constrainVar(alpha_rt2, 1)
c_n_rt1 = _constrainVar(n_rt1, 1)
c_n_rt2 = _constrainVar(n_rt2, 1)
c_sigma_wt = _constrainVar(sigma_wt, 1)
c_alpha_wt1 = _constrainVar(alpha_wt1, 1)
c_alpha_wt2 = _constrainVar(alpha_wt2, 1)
c_n_wt1 = _constrainVar(n_wt1, 1)
c_n_wt2 = _constrainVar(n_wt2, 1)
if ibin < 4:
c_pdfs = RooArgSet(c_sigma_rt1, c_alpha_rt1, c_alpha_rt2, c_n_rt1, c_n_rt2)
c_vars = RooArgSet(sigma_rt1, alpha_rt1, alpha_rt2, n_rt1, n_rt2)
else:
c_sigma_rt2 = _constrainVar(sigma_rt2, 1)
c_pdfs = RooArgSet(c_sigma_rt1, c_sigma_rt2, c_alpha_rt1, c_alpha_rt2, c_n_rt1, c_n_rt2)
c_vars = RooArgSet( sigma_rt1, sigma_rt2, alpha_rt1, alpha_rt2, n_rt1, n_rt2)
c_pdfs.add(c_sigma_wt); c_vars.add(sigma_wt)
c_pdfs.add(c_alpha_wt1); c_vars.add(alpha_wt1)
c_pdfs.add(c_alpha_wt2); c_vars.add(alpha_wt2)
c_pdfs.add(c_n_wt1); c_vars.add(n_wt1)
c_pdfs.add(c_n_wt2); c_vars.add(n_wt2)
c_deltaPeaks = RooGaussian("c_deltaPeaks%s"%ibin , "c_deltaPeaks", deltaPeaks, ROOT.RooFit.RooConst( deltaPeaks.getVal() ),
ROOT.RooFit.RooConst( 0.0005 ) ## value to be checked
)
c_pdfs.add(c_deltaPeaks)
c_vars.add(deltaPeaks)
c_frt = RooGaussian("c_frt%s"%ibin , "c_frt" , frt, ROOT.RooFit.RooConst(fraction.n) , ROOT.RooFit.RooConst(frt_sigma[ibin]) )
c_pdfs.add(c_frt)
c_vars.add(frt)
constr_list = RooArgList(c_pdfs)
constr_list.add(signalFunction)
c_signalFunction = RooProdPdf ("c_signalFunction", "c_signalFunction", constr_list)
# mean = RooRealVar ("mass" , "mean" , B0Mass_, 3, 7, "GeV")
# sigma = RooRealVar ("#sigma_{1}" , "sigma" , 0.028, 0, 10, "GeV")
# signalGauss = RooGaussian("signalGauss" , "signal gauss" , tagged_mass, mean,sigma)
#
# sigma2 = RooRealVar ("#sigma_{2}" , "sigma2" , 0.048, 0, 0.07, "GeV")
# signalGauss2 = RooGaussian("signalGauss2" , "signal gauss2" , tagged_mass, mean,sigma2)
# f1 = RooRealVar ("f1" , "f1" , 0.8 , 0., 1.)
# gaus = RooAddPdf ("gaus" , "gaus1+gaus2" , RooArgList(signalGauss,signalGauss2), RooArgList(f1))
# pol_c1 = RooRealVar ("p1" , "coeff x^0 term", 0.5, -10, 10);
# pol_c2 = RooRealVar ("p2" , "coeff x^1 term", 0.5, -10, 10);
# pol_c3 = RooRealVar ("p3" , "coeff x^2 term", 0.5, -10, 10);
# slope = RooRealVar ("slope" , "slope" , 0.5, -10, 10);
# bkg_exp = RooExponential("bkg_exp" , "exponential" , slope, tagged_mass );
# bkg_pol = RooChebychev("bkg_pol" , "2nd order pol" , tagged_mass, RooArgList(pol_c1,pol_c2));
fitFunction = RooAddPdf ("fitfunction%s"%ibin , "fit function" , RooArgList(c_signalFunction, bkg_exp), RooArgList(nsig, nbkg))
# r = fitFunction.fitTo(data,
# # RooFit.Extended(True),
# RooFit.Range("full"),
# ROOT.RooFit.Constrain(c_vars),
# ROOT.RooFit.Minimizer("Minuit2","migrad"),
# ROOT.RooFit.Hesse(True),
# ROOT.RooFit.Strategy(2),
# ROOT.RooFit.Minos(False),
# )
print 'fit with Hesse strategy 2 done, now Minos'
r = fitFunction.fitTo(data,
RooFit.Extended(True),
RooFit.Save(),
RooFit.Range("full"),
RooFit.Verbose(False),
ROOT.RooFit.Constrain(c_vars),
# ROOT.RooFit.Minimizer("Minuit2","migrad"),
# ROOT.RooFit.Hesse(True),
# ROOT.RooFit.Strategy(2),
# ROOT.RooFit.Minos(False),
)
r.Print()
r.correlationMatrix().Print()
fitStats['data%s'%(ibin)] = r.status()
covStats['data%s'%(ibin)] = r.covQual()
frame = tagged_mass.frame( RooFit.Range("full") )
data.plotOn(frame, RooFit.Binning(nbins), RooFit.MarkerSize(.7))
fitFunction.plotOn(frame, RooFit.NormRange("full"), RooFit.Range("full"))
## evaluate sort of chi2 and save number of RT/WT events
observables = RooArgSet(tagged_mass)
flparams = fitFunction.getParameters(observables)
nparam = int(flparams.selectByAttrib("Constant",ROOT.kFALSE).getSize())
pdfstring = "fitfunction%s_Norm[tagged_mass]_Range[full]_NormRange[full]"%ibin
chi2s['data%s'%ibin] = frame.chiSquare(pdfstring, "h_fulldata", nparam)
frame. addObject(_writeChi2( chi2s['data%s'%ibin] ))
drawPdfComponents(fitFunction, frame, ROOT.kAzure, RooFit.NormRange("full"), RooFit.Range("full"), isData = True)
# fitFunction.paramOn(frame, RooFit.Layout(0.62,0.86,0.89))
parList = RooArgSet (nsig, mean_rt, sigma_rt, alpha_rt1, alpha_rt2, n_rt1, n_rt2, mean_wt, sigma_wt)
# parList.add(alphawt1)
# parList.add(alphawt2)
# parList.add(nwt1)
# parList.add(nwt2)
parList.add(frt)
fitFunction.paramOn(frame, RooFit.Parameters(parList), RooFit.Layout(0.62,0.86,0.89))
frame.Draw()
niceFrame(frame, '')
frame. addObject(_writeFitStatus(r))
c1 = ROOT.TCanvas()
upperPad = ROOT.TPad('upperPad' , 'upperPad' , 0., 0.35 , 1., 1. )
lowerPad = ROOT.TPad('lowerPad' , 'lowerPad' , 0., 0.0 , 1., 0.345 )
upperPad.SetBottomMargin(0.012)
lowerPad.SetTopMargin(0)
lowerPad.SetBottomMargin(0.2)
upperPad.Draw(); lowerPad.Draw()
upperPad.cd()
frame.Draw()
if not args.year=='test': writeCMS(frame, args.year, [ q2binning[ibin], q2binning[ibin+1] ], 0)
frame.Draw()
## add plot of pulls
lowerPad.cd()
hpull = frame.pullHist("h_fulldata", pdfstring)
frame2 = tagged_mass.frame(RooFit.Range("full"), RooFit.Title(''))
frame2.addPlotable(hpull,"P")
niceFrameLowerPad(frame2, 'pull')
frame2.Draw()
line = ROOT.TLine(5.0,1,5.6,1)
line.SetLineColor(ROOT.kGreen+3)
line.Draw()
for ilog in [True,False]:
upperPad.SetLogy(ilog)
c1.SaveAs('fit_results_mass/save_fit_data_%s_%s_LMNR_Update%s_newSigmaFRT_pars_Jpsi.pdf'%(ibin, args.year, '_logScale'*ilog))
out_f.cd()
r.Write('results_data_%s'%(ibin))
params = fitFunction.getParameters(RooArgSet(tagged_mass))
out_w.saveSnapshot("reference_fit_data_%s"%(ibin),params,ROOT.kTRUE)
getattr(out_w, 'import')(fitFunction)
0, 1e10)
sigmodel = RooAddPdf("sigmodel" + str(cut), "sig+bkg",
RooArgList(sigbkg, sig),
RooArgList(nbkg, nsigref))
s0.setConstant(False)
s1.setConstant(False)
sigmodel.fitTo(
signal, RooFit.SumW2Error(True)) #,RooFit.Range("signal")
sigmodel.fitTo(
signal, RooFit.SumW2Error(True)) #,RooFit.Range("signal")
sigmodel.fitTo(
signal, RooFit.SumW2Error(True)) #,RooFit.Range("signal")
chi2 = RooChi2Var("chi2", "chi2", sigmodel, signal,
RooFit.DataError(RooAbsData.SumW2))
nbins = data.numEntries()
nfree = sigmodel.getParameters(data).selectByAttrib(
"Constant", False).getSize()
s0.setConstant(True)
s1.setConstant(True)
if cut >= 1:
fullintegral = sumsighist.Integral()
else:
fullintegral = sighist.Integral()
print "SIGNAL FRACTION", nsigref.getValV() / (
nsigref.getValV() + nbkg.getValV())
if nsigref.getValV() == 0: continue
if fit == "data":
xframe = mass.frame(
RooFit.Title("signal fraction in peak =" + str(
int(nsigref.getValV() /
def fillWorkspace(workspace):
print ">>> setup model components..."
x = RooRealVar("x", "x", 0, 10)
mean = RooRealVar("mean", "mean of gaussians", 5, 0, 10)
sigma1 = RooRealVar("sigma1", "width of gaussians", 0.5)
sigma2 = RooRealVar("sigma2", "width of gaussians", 1)
sig1 = RooGaussian("sig1", "Signal component 1", x, mean, sigma1)
sig2 = RooGaussian("sig2", "Signal component 2", x, mean, sigma2)
a0 = RooRealVar("a0", "a0", 0.5, 0., 1.)
a1 = RooRealVar("a1", "a1", -0.2, 0., 1.)
bkg = RooChebychev("bkg", "Background", x, RooArgList(a0, a1))
print ">>> sum model components..."
sig1frac = RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8,
0., 1.)
sig = RooAddPdf("sig", "Signal", RooArgList(sig1, sig2),
RooArgList(sig1frac))
bkgfrac = RooRealVar("bkgfrac", "fraction of background", 0.5, 0., 1.)
model = RooAddPdf("model", "g1+g2+a", RooArgList(bkg, sig),
RooArgList(bkgfrac))
print ">>> import model into workspace..."
getattr(workspace, 'import')(model) # import model and all its components
#workspace.import(model) # causes synthax error in python
print "\n>>> encode definition of parameters and observables in workspace..."
# Define named sets "parameters" and "observables", which list which variables should
# be considered parameters and observables by the users convention
#
# Variables appearing in sets _must_ live in the workspace already, or the autoImport
# flag of defineSet must be set to import them on the fly. Named sets contain only
# references to the original variables, therefore the value of observables in named
# sets already reflect their 'current' value
params = model.getParameters(RooArgSet(x)) # RooArgSet
workspace.defineSet("parameters", RooArgSet(params))
workspace.defineSet("observables", RooArgSet(x))
# Encode reference value for parameters in workspace:
# Define a parameter 'snapshot' in the pdf
# Unlike a named set, a parameter snapshot stores an independent set of values for
# a given set of variables in the workspace. The values can be stored and reloaded
# into the workspace variable objects using the loadSnapshot() and saveSnapshot()
# methods. A snapshot saves the value of each variable, any errors that are stored
# with it as well as the 'Constant' flag that is used in fits to determine if a
# parameter is kept fixed or not.
print ">>> generate and fit data..."
# Do a dummy fit to a (supposedly) reference dataset here and store the results
# of that fit into a snapshot
refData = model.generate(RooArgSet(x), 10000) # RooDataSet
model.fitTo(refData, PrintLevel(-1))
print "\n>>> save fit results into a snapshot in the workspace..."
# The kTRUE flag imports the values of the objects in (*params) into the workspace
# If not set, the present values of the workspace parameters objects are stored
workspace.saveSnapshot("reference_fit", params, kTRUE)
print ">>> make another fit with the signal component forced to zero..."
bkgfrac.setVal(1)
bkgfrac.setConstant(kTRUE)
bkgfrac.removeError()
model.fitTo(refData, PrintLevel(-1))
print "\n>>> save new fit parameters in different snapshot..."
workspace.saveSnapshot("reference_fit_bkgonly", params, kTRUE)
