def NEvtsCalc( uPars, uEPars):
Pars = {}
Pars[0] = RooRealVar('c1', 'Exponential constant', -1, -10, 0)
Pars[1] = RooRealVar('ExpY', 'Background Yield', 100, 0, 10000000)
Pars[2] = RooRealVar('Mean', 'Voigtian Mean' , 90.0, 20, 180.0)
Pars[3] = RooRealVar('Width', 'Voigtian Width' , 5.0, 0.5, 40.0)
Pars[4] = RooRealVar('Sigma', 'Voigtian Sigma' , 5.0, 0.5, 40.0)
Pars[5] = RooRealVar('VoY', 'Signal Yield', 100, 0, 10000000)
if len(Pars) != len(uPars):
print 'The input array has a weird number of entries...'
return 0
if len(uPars) != len(uEPars):
print 'The input arrays are of different sizes...'
return 0
for x in xrange(0, 6):
Pars[x].setVal(uPars[x])
Pars[x].setError(uEPars[x])
v = RooRealVar('v', 'Invariant Mass (GeV)', 60, 120)
Voigt = RooVoigtian('Voigt', 'Voigtian - Signal', v, Pars[2], Pars[3], Pars[4])
## Calculate integral from -2sigma to 2sigma
VStDev_r = Voigt.sigma(v)
VStDev = VStDev_r.getVal()
v.setRange("sobRange", Pars[2].getVal() - 2.*VStDev, Pars[2].getVal() + 2.*VStDev)
integralSig = Voigt.createIntegral(RooArgSet(v), RooFit.NormSet(RooArgSet(v)), RooFit.Range("sobRange"))
FinalNumber = integralSig.getVal()*Pars[5].getVal()
return FinalNumber
a1 = RooRealVar("p_1", "p_1", 0.001, -10., 10.)
a2 = RooRealVar("p_2", "p_2", -0.00001, -10., 10.)
a3 = RooRealVar("p_3", "p_3", -0.000001, -10., 10.)
a4 = RooRealVar("p_4", "p_4", -0.000001, -10., 10.)
a5 = RooRealVar("p_5", "p_5", -0.000001, -10., 10.)
poliset = RooArgList(a0, a1, a2, a3, a4)
# gaussFrac = RooRealVar("s","fraction of component 1 in kkSig",0.3,0.0,1.0)
nSigKK = RooRealVar("nSig", "nSig",
theData.numEntries() * 0.3, 0.0,
theData.numEntries() * 1.5)
nBkgKK = RooRealVar("nBkg", "nBkg",
theData.numEntries() * 0.7, 0.0,
theData.numEntries() * 1.5)
kkSig = RooVoigtian("kkSig", "kkSig", tt_mass, kkMean, kkGamma, kkSigma)
#kkSig = RooGaussian("kkSig","kkSig",tt_mass,kkMean,kkGamma)#,kkSigma)
#kkBkg = RooBernstein("kkBkg" , "kkBkg", tt_mass, RooArgList(B_1, B_2,B_3,B_4))#,B_5) )#,B_6))
kkBkg = RooChebychev("kkBkg", "Background", tt_mass, poliset)
kkTot = RooAddPdf("kkTot", "kkTot", RooArgList(kkSig, kkBkg),
RooArgList(nSigKK, nBkgKK))
nfit = 0
#kkfit = kkTot.fitTo(traKFitData,Range(fitphimin+0.005,fitphimax-0.005),RooFit.PrintLevel(-1), RooFit.NumCPU(7),RooFit.Save())
#nfit +=1
if debugging:
kkGamma.setConstant(kTRUE)
kkMean.setConstant(kTRUE)
kkSigma.setConstant(kTRUE)
def RooFitHist(inputhist, title='title', path='.'):
# RooFit.gErrorIgnoreLevel = RooFit.kInfo # <6.02
# RooFit.SetSilentMode()# >6.02
# RooMsgService().instance().SetSilentMode(kTRUE)
fitbinning = array('d')
binwidth = 200
#NBins=(14000/binwidth) - ( (1040/binwidth) + 1 )
NBins = (14000 / binwidth) - ((1040 / binwidth) + 1)
for i in range(NBins + 1):
fitbinning.append(1050 + i * binwidth)
# print(fitbinning)
hist = inputhist.Rebin(NBins, "fit parameter", fitbinning)
meanstart = hist.GetBinCenter(hist.GetMaximumBin())
sigmastart = hist.GetRMS()
print('meanstart:', meanstart, 'sigmastart:', sigmastart)
# inputhist.Draw()
# hist.Draw()
# hold=raw_input('press enter to exit.')
gStyle.SetOptFit(1100)
gStyle.SetOptTitle(0)
RooFit.SumW2Error(kTRUE)
mjj = RooRealVar('mjj', 'M_{jj-AK8}', fitbinning[0],
fitbinning[len(fitbinning) - 1], 'GeV')
mjjral = RooArgList(mjj)
dh = RooDataHist('dh', 'dh', mjjral, RooFit.Import(hist))
shapes = {}
#Gaussian
gaussmean = RooRealVar('#mu_{gauss}', 'mass mean value', meanstart, 0,
2 * meanstart)
gausssigma = RooRealVar('#sigma_{gauss}', 'mass resolution', sigmastart, 0,
2 * sigmastart)
gauss = RooGaussian('gauss', 'gauss', mjj, gaussmean, gausssigma)
shapes.update({'Gauss': gauss})
#CrystalBall
mean = RooRealVar('#mu', 'mean', meanstart, 0, 2 * meanstart)
sigma = RooRealVar('#sigma', 'sigma', sigmastart, 0, 2 * sigmastart)
alpha = RooRealVar('#alpha', 'Gaussian tail', -1000, 0)
n = RooRealVar('n', 'Normalization', -1000, 1000)
cbshape = RooCBShape('cbshape', 'crystalball PDF', mjj, mean, sigma, alpha,
n)
shapes.update({'CrystalBall': cbshape})
#Voigt
voigtmean = RooRealVar('#mu', 'mass mean value', meanstart, 0,
2 * meanstart)
voigtwidth = RooRealVar('#gamma', 'width of voigt', 0, 5000)
voigtsigma = RooRealVar('#sigma', 'mass resolution', sigmastart, 0,
2 * sigmastart)
voigt = RooVoigtian('voigt', 'voigt', mjj, voigtmean, voigtwidth,
voigtsigma)
shapes.update({'Voigt': voigt})
#BreitWigner
bwmean = RooRealVar('#mu', 'mass mean value', meanstart, 0, 2 * meanstart)
bwwidth = RooRealVar('#sigma', 'width of bw', sigmastart, 0,
2 * sigmastart)
bw = RooBreitWigner('bw', 'bw', mjj, bwmean, bwwidth)
shapes.update({'BreitWigner': bw})
#Landau
landaumean = RooRealVar('#mu_{landau}', 'mean landau', meanstart, 0,
2 * meanstart)
landausigma = RooRealVar('#sigma_{landau}', 'mass resolution', sigmastart,
0, 2 * sigmastart)
landau = RooLandau('landau', 'landau', mjj, landaumean, landausigma)
shapes.update({'Landau': landau})
#LandauGauss Convolution
landaugauss = RooFFTConvPdf('landaugauss', 'landau x gauss', mjj, landau,
gauss)
shapes.update({'LandauGauss': landaugauss})
#Logistics
# logisticsmean=RooRealVar('#mu_{logistics}','mean logistics',meanstart,0,2*meanstart)
# logisticssigma= RooRealVar('#sigma_{logistics}','mass resolution',sigmastart,0,2*sigmastart)
# logistics=RooLogistics('logistics','logistics',mjj,logisticsmean,logisticssigma)
# shapes.update({'Logistics':logistics})
#ExpAndGauss
# expgaussmean=RooRealVar('#mu_{expgauss}','mean expgauss',meanstart,0,2*meanstart)
# expgausssigma= RooRealVar('#sigma_{expgauss}','mass resolution',sigmastart,0,2*sigmastart)
# expgausstrans= RooRealVar('trans','trans',0,100)
# expgauss=RooExpAndGauss('expgauss','expgauss',mjj,expgaussmean,expgausssigma,expgausstrans)
# shapes.update({'ExpAndGauss':expgauss})
#BifurGauss
BifurGaussmean = RooRealVar('#mu_{BifurGauss}', 'mean BifurGauss',
meanstart, 0, 2 * meanstart)
BifurGausslsigma = RooRealVar('#sigma_{left}', 'mass resolution',
sigmastart, 0, 2 * sigmastart)
BifurGaussrsigma = RooRealVar('#sigma_{right}', 'mass resolution',
sigmastart, 0, 2 * sigmastart)
BifurGauss = RooBifurGauss('BifurGauss', 'BifurGauss', mjj, BifurGaussmean,
BifurGausslsigma, BifurGaussrsigma)
shapes.update({'BifurGauss': BifurGauss})
#Chebychev
Chebychev1 = RooRealVar('c0', 'Chebychev0', -1000, 1000)
Chebychev2 = RooRealVar('c1', 'Chebychev1', -1000, 1000)
Chebychev3 = RooRealVar('c2', 'Chebychev2', 2, -1000, 1000)
Chebychev = RooChebychev('Chebychev', 'Chebychev', mjj,
RooArgList(Chebychev1, Chebychev2, Chebychev3))
shapes.update({'Chebychev': Chebychev})
#Polynomial
Polynomial1 = RooRealVar('Polynomial1', 'Polynomial1', 100, 0, 1000)
Polynomial2 = RooRealVar('Polynomial2', 'Polynomial2', 100, 0, 1000)
Polynomial = RooPolynomial('Polynomial', 'Polynomial', mjj,
RooArgList(Polynomial1, Polynomial2))
shapes.update({'Polynomial': Polynomial})
# mjj.setRange("FitRange",1050.,14000.)
# for fname in ['Gauss','Logistics','BifurGauss']:
for fname in ['BifurGauss']:
plottitle = '%s Fit of %s' % (fname, title)
shape = shapes[fname]
# shape.fitTo(dh,RooFit.Range("FitRange"),RooFit.SumW2Error(True))
shape.fitTo(dh, RooFit.SumW2Error(True))
frame = mjj.frame(RooFit.Title(plottitle))
frame.GetYaxis().SetTitleOffset(2)
dh.plotOn(frame, RooFit.MarkerStyle(4))
shape.plotOn(frame, RooFit.LineColor(2))
ndof = dh.numEntries() - 3
#chiSquare legend
chi2 = frame.chiSquare()
probChi2 = TMath.Prob(chi2 * ndof, ndof)
chi2 = round(chi2, 2)
probChi2 = round(probChi2, 2)
leg = TLegend(0.5, 0.5, 0.9, 0.65)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
shape.paramOn(frame, RooFit.Layout(0.5, 0.9, 0.9))
leg.AddEntry(0, '#chi^{2} =' + str(chi2), '')
leg.AddEntry(0, 'Prob #chi^{2} = ' + str(probChi2), '')
leg.SetTextSize(0.04)
frame.addObject(leg)
canv = TCanvas(plottitle, plottitle, 700, 700)
canv.SetLogy()
canv.SetLeftMargin(0.20)
canv.cd()
frame.SetMinimum(10**(-3))
frame.Draw()
canv.Print(path + '/%s__%s.eps' % (title, fname))
return chi2
def voigtian(mean, width_, sigma_, tagged_mass, w):
sigmaV = RooRealVar ("#sigma_{V}" , "sigmaV" , sigma_ , 0, 10 )
widthV = RooRealVar ("widthV" , "widthV" , width_ , 0, 5 )
vgshape = RooVoigtian ("vgshape" , "vgshape" , tagged_mass, mean, widthV, sigmaV)
_import(w,vgshape)
model.SetParameter(0, hist.Integral())
model.SetParameter(1, 0.0)
model.SetParameter(2, 0.01)
hist.Fit(model)
"""
x = RooRealVar("x", "x", -1000.0, 1000.0)
roodata = RooDataHist("roodata", "equity dataset", RooArgList(x), hist)
frame = x.frame()
mass = RooRealVar("mass", "mass", 0.0, -10.0, 10.0)
width = RooRealVar("width", "width", 0.01, -10.0, 10.0)
s = RooRealVar("s", "s", 0.005, -10.0, 10.0)
#model = RooGaussian("gaus", "gaus", x, mass, width)
#model = RooBreitWigner("bw", "bw", x, mass, width)
model = RooVoigtian("voigt", "voigt", x, mass, width, s)
fit_result = model.fitTo(roodata, RooFit.Save())
roodata.plotOn(frame)
#roodata.statOn(frame)
model.plotOn(frame)
model.paramOn(frame, roodata)
frame.Draw()
chisq = frame.chiSquare()
ndf = n_bins
print "------> Fit Quality:", TMath.Prob(chisq, ndf)
canv.Update()
canv.SaveAs("%s.png" % name[i])
c.SaveAs("testmass.png")
# In[13]:
phimean = 1.019
sigma = RooRealVar("sigma", "width of gaussian", 0.0013)
gamma = RooRealVar("gamma", "gamma of bw", 0.004253, 0.001, 0.01)
mean = RooRealVar("mean", "mean of gaussian", phimean, phimean - 0.005,
phimean + 0.005)
nSig = RooRealVar("nSig", "nSig", 1E6, 0., 5.0E6)
nBkg = RooRealVar("nBkg", "nBkg", 5E5, 0., 5.0E6)
#cheb = RooChebychev("cheb","Background",masskk,aset)
#gauss = RooGaussian("gauss","gaussian PDF ",mass,mean,sigma)
signal = RooVoigtian("signal", "signal", masskk, mean, gamma, sigma)
B_1 = RooRealVar("B_1", "B_1 ", 0.3, -20, 100)
B_2 = RooRealVar("B_2", "B_2", 0.3, -20, 100)
B_3 = RooRealVar("B_3", "B_3", 0.3, -20, 100)
B_4 = RooRealVar("B_4", "B_4", 0.3, -20, 100)
bkg = RooBernstein("pdfB", "pdfB", masskk, RooArgList(B_1, B_2, B_3, B_4))
tot = RooAddPdf("tot", "g+cheb", RooArgList(signal, bkg),
RooArgList(nSig, nBkg))
mean.setVal(phimean)
gamma.setConstant(ROOT.kTRUE)
mean.setConstant(ROOT.kTRUE)
7 a_{3} -2.42999e-02 6.27522e-04 3.86717e-04 -4.86190e-02
8 n_{Bkg} 1.87466e+07 1.36843e+04 3.48814e-04 8.52813e-01
9 n_{Sig} 1.92959e+06 1.30579e+04 3.90748e-04 3.83492e+00
'''
sigma_1 = RooRealVar("#sigma_{1}","width of gaussian",1.6002e-03,0.001,0.004)
sigma_2 = RooRealVar("#sigma_{2}","width of gaussian",1.6002e-03,0.001,0.004)
rFrac = RooRealVar("f_{1}","f1",0.5,0.0,1.0)
gamma = RooRealVar("#Gamma","gamma of bw",4.41369e-03,0.004,0.005)
mean = RooRealVar("m","mean of gaussian",1.01959e+00,phimean-0.005,phimean+0.005);
nSig = RooRealVar("n_{Sig}","nSig",float(nentries)*0.01,0.,float(nentries)*0.5)
nBkg = RooRealVar("n_{Bkg}","nBkg",float(nentries)*0.9,0.,float(nentries))
cheb = RooChebychev("cheb","Background",masskk,aset)
res = RooGaussian("gauss","gaussian PDF ",masskk,mean,sigma_1)
peak = RooVoigtian("peak","peak",masskk,mean,gamma,sigma_2)
signal = RooAddPdf("signal","signal",res,peak,rFrac)
B_1 = RooRealVar ( "B_{1}" , "B_1 " , 0.3 , -20 , 100 )
B_2 = RooRealVar ( "B_{2}" , "B_2" , 0.3 , -20 , 100 )
B_3 = RooRealVar ( "B_{3}" , "B_3" , 0.3 , -20 , 100 )
B_4 = RooRealVar ( "B_{4}" , "B_4" , 0.3 , -20 , 100 )
bkg = RooChebychev("pdfB" , "pdfB" , masskk , RooArgList(aset))
tot = RooAddPdf("tot","g+cheb",RooArgList(signal,bkg),RooArgList(nSig,nBkg))
nfits = 0
mean.setConstant(True)
gamma.setConstant(True)
def fitChicSpectrum(dataset, binname):
""" Fit chic spectrum"""
x = RooRealVar('s', 's', -2, 2)
x.setBins(200)
#signal model
q_chi1 = RooRealVar('qchi1', 'q_{#chi 1}', 0.414, 0.2, 0.6)
q_chi2 = RooRealVar('qchi2', 'q_{#chi 2}', 0.430, 0.2, 0.6)
delta_chi10 = RooRealVar('delta_chi10', 'delta_chi10', 0.09591)
q_chi0 = RooFormulaVar('q_chi0', '@0 - @1',
RooArgList(q_chi1, delta_chi10))
alphacb_chi1 = RooRealVar('alphacb_chi1', '#alpha^{CB}_{#chi 1}', 0.6, 0,
2)
alphacb_chi2 = RooRealVar('alphacb_chi2', '#alpha^{CB}_{#chi 2}', 0.4, 0,
2)
sigmacb_chi1 = RooRealVar('sigmacb_chi1', '#sigma^{CB}_{#chi 1}', 0.005, 0,
1)
sigmacb_chi2 = RooRealVar('sigmacb_chi2', '#sigma^{CB}_{#chi 2}', 0.005, 0,
1)
n_cb = RooRealVar('ncb', 'n^{CB}', 3.0, 0., 5.)
gamma_chi0 = RooRealVar('gamma_chi0', 'gamma_chi0', 0.0104)
sigmacb_chi0 = RooRealVar('sigmacb_chi0', '#sigma^{CB}_{#chi 0}', 0.005)
chi0_sig = RooVoigtian('chi0sig', 'chi0sig,', x, q_chi0, sigmacb_chi0,
gamma_chi0)
chi1_sig = RooCBShape('chi1sig', 'chi1sig', x, q_chi1, sigmacb_chi1,
alphacb_chi1, n_cb)
chi2_sig = RooCBShape('chi2sig', 'chi2sig', x, q_chi2, sigmacb_chi2,
alphacb_chi2, n_cb)
fchi0 = RooRealVar('fchi0', 'f_{#chi 0}', 0.01, 0, 1)
fchi1 = RooRealVar('fchi1', 'f_{#chi 1}', 0.5, 0, 1)
fchi2 = RooFormulaVar('fchi2', '1-@0-@1', RooArgList(fchi0, fchi1))
fbck = RooRealVar('fbck', 'f_{bck}', 0.2, 0, 1)
sigmodel = RooAddPdf('sigm', 'sigm',
RooArgList(chi0_sig, chi1_sig, chi2_sig),
RooArgList(fchi0, fchi1, fchi2))
#background model
q0Start = 0.0
a_bck = RooRealVar('a_bck', 'a_{bck}', 0.5, -5, 5)
b_bck = RooRealVar('b_bck', 'b_{bck}', -2.5, -7., 0.)
q0 = RooRealVar('q0', 'q0', q0Start)
delta = RooFormulaVar('delta', 'TMath::Abs(@0-@1)', RooArgList(x, q0))
bfun = RooFormulaVar('bfun', '@0*(@1-@2)', RooArgList(b_bck, x, q0))
signum = RooFormulaVar('signum', '( TMath::Sign( -1.,@0-@1 )+1 )/2.',
RooArgList(x, q0))
background = RooGenericPdf('background', 'Background',
'signum*pow(delta,a_bck)*exp(bfun)',
RooArgList(signum, delta, a_bck, bfun))
modelPdf = RooAddPdf('chicmodel', 'chicmodel',
RooArgList(sigmodel, background), RooArgList(fbck))
frame = x.frame(RooFit.Title('Q'))
range = x.setRange('range', 0, 2)
# result = modelPdf.fitTo(dataset,RooFit.Save(),RooFit.Range('range'))
dataset.plotOn(frame, RooFit.MarkerSize(0.7))
modelPdf.plotOn(frame, RooFit.LineWidth(2))
#plotting
canvas = TCanvas('fit', "", 1400, 700)
canvas.Divide(1)
canvas.cd(1)
gPad.SetRightMargin(0.3)
gPad.SetFillColor(10)
modelPdf.paramOn(frame, RooFit.Layout(0.725, 0.9875, 0.9))
frame.Draw()
canvas.SaveAs('out-' + binname + '.png')
canvas.SaveAs('out-' + binname + '.root')
def RooFitHist(inputhist,title='title',path='.'):
# RooFit.gErrorIgnoreLevel = RooFit.kInfo # <6.02
# RooFit.SetSilentMode()# >6.02
# RooMsgService().instance().SetSilentMode(kTRUE)
fitbinning=array('d')
binwidth=200
#NBins=(14000/binwidth) - ( (1040/binwidth) + 1 ) Mjj
NBins=(8000/binwidth) - ( (200/binwidth)+1 )#pT
for i in range(NBins+1):
fitbinning.append(210+i*binwidth)# Mjj 1050
# print(fitbinning)
#import numpy as np
#fitbinning=np.linspace(0,8000,81)
hist=inputhist.Rebin(NBins,"fit parameter",fitbinning)
#hist=inputhist.Rebin(80,"fit parameter", fitbinning)
#meanstart=hist.GetBinCenter(2000)
meanstart=hist.GetBinCenter(hist.GetMaximumBin())#maximum
sigmastart=hist.GetRMS()
#sigmastart=3000
print('meanstart:',meanstart,'sigmastart:',sigmastart)
# inputhist.Draw()
# hist.Draw()
# hold=raw_input('press enter to exit.')
gStyle.SetOptFit(1111)
gStyle.SetOptTitle(0)
RooFit.SumW2Error(kTRUE)
RooFit.Extended(kTRUE)
# RooDataHist::adjustBinning(dh): fit range of variable mjj expanded to nearest bin boundaries: [1050,13850] --> [1050,13850]
mjj=RooRealVar('mjj','P_{T-AK8}',fitbinning[0],fitbinning[len(fitbinning)-1],'GeV')
mjjral=RooArgList(mjj)
dh=RooDataHist('dh','dh',mjjral,RooFit.Import(hist))
#shape.fitTo(dh,RooFit.Range("FitRange"),RooFit.Extended(True),RooFit.SumW2Error(False))
shapes={}
#Gaussian not really
# 3rd, 4th and 5th arguments are: (starting value, minimum possible value, maximum possible value) -- not goot
gaussmean = RooRealVar('#mu_{gauss}','mass mean value',meanstart,0,2*meanstart)
gausssigma= RooRealVar('#sigma_{gauss}','mass resolution',sigmastart,0,2*meanstart)
gauss=RooGaussian('gauss','gauss',mjj,gaussmean,gausssigma)
shapes.update({'Gauss':gauss})
#poisson
poissonmean = RooRealVar('#mu_{poisson}', 'pT mean value', meanstart,0,2*meanstart)
poissonsigma = RooRealVar('#sigma_{poisson]', 'pT resolution', sigmastart, 0, 2*sigmastart)
poisson = RooPoisson('poisson', 'poisson', mjj, poissonmean, False)
shapes.update({'Poisson': poisson})
#Landau -- not good
landaumean=RooRealVar('#mu_{landau}','mean landau',meanstart,0,2*meanstart)
landausigma= RooRealVar('#sigma_{landau}','mass resolution',sigmastart,0,2*sigmastart)#bzw8
landau=RooLandau('landau','landau',mjj,landaumean,landausigma)
shapes.update({'Landau':landau})
#CrystalBall -> this is close to be good but matrix error :(
mean = RooRealVar('#mu','mean',meanstart,0,2*meanstart)
sigma= RooRealVar('#sigma','sigma',sigmastart,0,2*sigmastart)
alpha=RooRealVar('#alpha','Gaussian tail',-1000,0)
n=RooRealVar('n','Normalization',-1000,1000)
cbshape=RooCBShape('cbshape','crystalball PDF',mjj,mean,sigma,alpha,n)
shapes.update({'CrystalBall':cbshape})
#Voigt ---pff
voigtmean = RooRealVar('#mu','mass mean value',meanstart,0,2*meanstart)
voigtwidth = RooRealVar('#gamma','width of voigt',0,100)
voigtsigma= RooRealVar('#sigma','mass resolution',sigmastart,0,150)
voigt=RooVoigtian('voigt','voigt',mjj,voigtmean,voigtwidth,voigtsigma)
shapes.update({'Voigt':voigt})
#BreitWigner--worst
bwmean = RooRealVar('#mu','mass mean value',meanstart,0,2*meanstart)
bwwidth = RooRealVar('#sigma','width of bw',sigmastart,100, 150)
bw=RooBreitWigner('bw','bw',mjj,bwmean,bwwidth)
shapes.update({'BreitWigner':bw})
#Logistics
#logisticsmean=RooRealVar('#mu_{logistics}','mean logistics',meanstart,0,2*meanstart)
#logisticssigma= RooRealVar('#sigma_{logistics}','mass resolution',sigmastart,0,2*sigmastart)
#logistics=RooLogistics('logistics','logistics',mjj,logisticsmean,logisticssigma)
#shapes.update({'Logistics':logistics})
#ExpAndGauss
expgaussmean=RooRealVar('#mu_{expgauss}','mean expgauss',meanstart,490,900)
expgausssigma= RooRealVar('#sigma_{expgauss}','mass resolution',sigmastart,20,3*sigmastart)
expgausstrans= RooRealVar('trans','trans',0,1000)
ExpAndGauss=RooExpAndGauss('expgauss','expgauss',mjj,expgaussmean,expgausssigma,expgausstrans)
shapes.update({'ExpAndGauss':ExpAndGauss})
#Exp
#expmean=RooRealVar('')
#BifurGauss -bad
BifurGaussmean=RooRealVar('#mu_{BifurGauss}','mean BifurGauss',meanstart,0,2*meanstart)
BifurGausslsigma= RooRealVar('#sigma_{left}','mass resolution',sigmastart,200,2*sigmastart)#2*sigmastart
BifurGaussrsigma= RooRealVar('#sigma_{right}','mass resolution',sigmastart,200,2*sigmastart)
BifurGauss=RooBifurGauss('BifurGauss','BifurGauss',mjj,BifurGaussmean,BifurGausslsigma,BifurGaussrsigma)
shapes.update({'BifurGauss':BifurGauss})
#Chebychev -nope
Chebychev1=RooRealVar('c0','Chebychev0',-1000,1000)
Chebychev2= RooRealVar('c1','Chebychev1',-1000,1000)
Chebychev3= RooRealVar('c2','Chebychev2',2,-1000,1000)
Chebychev=RooChebychev('Chebychev','Chebychev',mjj,RooArgList(Chebychev1,Chebychev2,Chebychev3))
shapes.update({'Chebychev':Chebychev})
#Polynomial -nope
Polynomial1=RooRealVar('Polynomial1','Polynomial1',100,0,1000)
Polynomial2= RooRealVar('Polynomial2','Polynomial2',100,0,1000)
Polynomial=RooPolynomial('Polynomial','Polynomial',mjj,RooArgList(Polynomial1,Polynomial2))
shapes.update({'Polynomial':Polynomial})
#pareto
#___________________________________________________________We will see
#Convolutions
#-> use bin > 1000 for better accuracy -----cyclic trouble
#-> Convolution depends on order!!! RooFFTConvPdf the first p.d.f. is the theory model and that the second p.d.f. is the resolution model
#
#LandauGauss Convolution - really bad
landaugauss=RooFFTConvPdf('landaugauss','landau x gauss',mjj,landau, gauss)
#landaugauss.setBufferFraction(126)
shapes.update({'LandauGauss':landaugauss})
#GaussLandau Convolution -> Better but NOT posdef. ...but for 100 it is
gausslandau=RooFFTConvPdf('gausslandau','gauss x landau ',mjj,gauss,landau)
#gausslandau.setBufferFraction(126)
shapes.update({'GaussLandau':gausslandau})
#CrystalBallLandau Convolution cbshape x landau looks better -> status failed
crystlandau=RooFFTConvPdf('crystallandau','cbshape x landau', mjj, landau, cbshape)
crystlandau.setBufferFraction(39)
shapes.update({'CrystLandau': crystlandau})
#BifurGaussLandau Convolution -> Better in look, NO matrix error for Binwidth 200
BifurGaussLandau=RooFFTConvPdf('bifurgausslandau','landau x bifurgauss',mjj,landau, BifurGauss)
BifurGaussLandau.setBufferFraction(39) #against over cycling
shapes.update({'BifurGaussLandau':BifurGaussLandau})
#CrystalGauss Convolution looks better -> status failed
crystgauss=RooFFTConvPdf('crystalgauss','cbshape x gauss', mjj, cbshape, gauss)
#crystgauss.setBufferFraction(39)
shapes.update({'CrystGauss': crystgauss})
#BreitWignerLandau Convolution (Breitwigner = Lorentz)-> status OK....
BreitWignerLandau=RooFFTConvPdf('breitwignerlandau','breitwigner x landau',mjj,landau,bw,3)
#BreitWignerLandau.setBufferFraction(48) #setBufferFraction(fraction of the sampling array size) ->cyclic behaviour fix
#crystgauss.setShift(0,0)
#s1 and s2 are the amounts by which the sampling ranges for pdf's are shifted respectively
#(0,-(xmin+xmax)/2) replicates the default behavior
#(0,0) disables the shifting feature altogether
shapes.update({'BreitWignerLandau':BreitWignerLandau})
for fname in ['ExpAndGauss']:
plottitle='%s Fit of %s'%(fname,title)
shape=shapes[fname]
# shape.fitTo(dh,RooFit.Range("FitRange"),RooFit.SumW2Error(True))
#shape.fitTo(dh,RooFit.Extended(True),RooFit.SumW2Error(True))
mjj.setRange("FitRange",500,4000)#tried
shape.fitTo(dh,RooFit.Range("FitRange"),RooFit.Extended(False),RooFit.SumW2Error(True))
frame=mjj.frame(RooFit.Title(plottitle))
#frame.GetYaxis().SetTitleOffset(2)
dh.plotOn(frame,RooFit.MarkerStyle(4))
shape.plotOn(frame,RooFit.LineColor(2))
ndof=dh.numEntries()-3
print ('ndof', ndof)
#chiSquare legend
chi2 = frame.chiSquare()#there are 2 chiSquare. the 2cond returns chi2/ndf /// \return \f$ \chi^2 / \mathrm{ndf} \f$
print ('chi2', chi2)
probChi2 = TMath.Prob(chi2*ndof, ndof)# why chi2*ndof ?! makes no sense to me
#Double_t Prob(Double_t chi2, Int_t ndf)
#Computation of the probability for a certain Chi-squared (chi2)
#and number of degrees of freedom (ndf).
#P(a,x) represents the probability that the observed Chi-squared
#for a correct model should be less than the value chi2.
#The returned probability corresponds to 1-P(a,x), !!!!
#which denotes the probability that an observed Chi-squared exceeds
#the value chi2 by chance, even for a correct model.
#--- NvE 14-nov-1998 UU-SAP Utrecht
#probChi2=TMath.Prob(chi2, ndof)
chi2 = round(chi2,2)
#probChi2 = round(probChi2,2)
leg = TLegend(0.5,0.5,0.5,0.65)#0.9
leg.SetBorderSize(0)
leg.SetFillStyle(0)
shape.paramOn(frame, RooFit.Layout(0.5,0.9,0.9))
leg.AddEntry(0,'#chi^{2} ='+str(chi2),'')
leg.AddEntry(0,'Prob #chi^{2} = '+str(probChi2),'')
leg.SetTextSize(0.04)
frame.addObject(leg)
canv=TCanvas(plottitle,plottitle,700,700)
canv.SetLogy()
canv.SetLeftMargin(0.20)
canv.cd()
frame.SetMaximum(10**(1))
frame.SetMinimum(10**(-11))#from -3 -> -6
frame.Draw()
#canv.Print(path+'/%s__%sS2MoreLessY.pdf'%(title,fname))
raw_input('press enter to continue')
return chi2
def buildPdf(ws, p):
mass = RooRealVar("mass", "mass", p.minMass, p.maxMass)
# ws.import(mass)
getattr(ws,'import')(mass)
# Construct signal pdf
mean = RooRealVar("mean", "mean", 60, 0, 130)
width = RooRealVar("width", "width", 10, 1, 40)
# alpha = RooRealVar("alpha", "#alpha", -0.1, -10.0, -0.1)
# npow = RooRealVar("npow", "n_{CB}", 2.3, 0.1, 10.)
# width.setConstant(ROOT.kTRUE)
sigma = RooRealVar("sigma", "sigma", 1.2, 0.2, 40)
# minusMass = RooFormulaVar("minusMass", "@0*-1", RooArgList(mass))
# signalAll = RooCBShape("signalAll", "signalAll", mass, mean, width, alpha, npow);
signalAll = RooVoigtian("signalAll", "signalAll", mass, mean, width, sigma)
# Construct background pdf
# a0_all = RooRealVar("a0_all","a0_all",-0.1,-1,1)
# a1_all = RooRealVar("a1_all","a1_all",0.004,-1,1)
# backgroundAll = RooChebychev("backgroundAll","backgroundAll",mass,RooArgList(a0_all,a1_all))
turnOnAll = RooRealVar("turnOnAll","turnOnAll", 80., 40., 150.)
widthAll_bkg = RooRealVar("widthAll","widthAll", 2., 0., 50.)
decayAll_bkg = RooRealVar("decayAll","decayAll", 80., 20., 150.)
meanB = RooRealVar("meanB", "meanB", 90, 60, 130)
sigmaB = RooRealVar("sigmaB", "sigmaB", 10, 1, 20)
bkg_a1 = RooRealVar("bkg_a1", "bkg_a1", 0., -2., 2.)
bkg_a2 = RooRealVar("bkg_a2", "bkg_a2", 0., -2., 2.)
#backgroundAll = RooChebychev("backgroundAll", "backgroundAll", mass, RooArgList(bkg_a1, bkg_a2))
#backgroundAll = RooGenericPdf("backgroundAll","backgroundAll", "exp(-@0/@3)*(TMath::Erf((@0-@1)/@2)+1)", RooArgList(mass, turnOnAll, widthAll_bkg, decayAll_bkg))
backgroundAll = RooGenericPdf("backgroundAll","backgroundAll","exp(-@0/@1)",RooArgList(mass, decayAll_bkg))
#backgroundAll = RooGaussian("backgroundAll", "backgroundAll", mass, meanB, sigmaB)
# Construct composite pdf
sigAll = RooRealVar("sigAll", "sigAll", 2000, 0, 10000000)
bkgAll = RooRealVar("bkgAll", "bkgAll", 100, 0, 1000000)
modelAll = RooAddPdf("modelAll", "modelAll", RooArgList(signalAll, backgroundAll), RooArgList(sigAll, bkgAll))
if p.NoBkgd:
modelAll = RooAddPdf("modelAll", "modelAll", RooArgList(signalAll), RooArgList(sigAll))
# Define pdf for all probes
# Construct signal pdf.
# NOTE that sigma is shared with the signal sample model
signalPass = RooVoigtian("signalPass","signalPass",mass,mean,width,sigma)
# signalPass = RooCBShape("signalPass", "signalPass", mass, mean, width, alpha, npow);
# Construct the background pdf
# a0_pass = RooRealVar("a0_pass","a0_pass",-0.1,-1,1)
# a1_pass = RooRealVar("a1_pass","a1_pass",0.5,-0.1,1)
# backgroundPass = RooChebychev("backgroundPass","backgroundPass",mass,RooArgList(a0_pass,a1_pass))
# turnOnPass = RooRealVar("turnOnPass","turnOnPass", 80., 50., 150.)
# widthPass_bkg = RooRealVar("widthPass","widthPass", 2., 0., 50.)
decayPass_bkg = RooRealVar("decayPass","decayPass", 80., 20., 150.)
#backgroundPass = RooChebychev("backgroundPass", "backgroundPass", mass, RooArgList(bkg_a1, bkg_a2))
#backgroundPass = RooGenericPdf("backgroundPass","backgroundPass", "exp(-@0/@3)*(TMath::Erf((@0-@1)/@2)+1)", RooArgList(mass, turnOnPass, widthPass_bkg, decayPass_bkg));
#backgroundPass = RooGenericPdf("backgroundPass","backgroundPass", "exp(-@0/@3)*(TMath::Erf((@0-@1)/@2)+1)", RooArgList(mass, turnOnAll, widthAll_bkg, decayAll_bkg));
backgroundPass = RooGenericPdf("backgroundPass","backgroundPass","exp(-@0/@1)",RooArgList(mass, decayPass_bkg))
#backgroundPass = RooGaussian("backgroundPass", "backgroundPass", mass, meanB, sigmaB)
# Construct the composite model
efficiency = RooRealVar("efficiency","efficiency",0.9,0.1,1.)
# Use it only analyzing the data prescaling value set to be 20
sigPass = RooFormulaVar("sigPass", "@0*@1*"+str(p.scaleFactor), RooArgList(sigAll, efficiency))
bkgPass = RooRealVar("bkgPass", "bkgPass", 100, 0, 1000000)
# bkgPass = RooFormulaVar("bkgPass", "@0*@1", RooArgList(bkgAll, efficiency))
modelPass = RooAddPdf("modelPass", "modelPass", RooArgList(signalPass, backgroundPass), RooArgList(sigPass, bkgPass))
if p.NoBkgd:
modelPass = RooAddPdf("modelPass", "modelPass", RooArgList(signalPass), RooArgList(sigPass))
frac = RooRealVar("frac", "frac", 0.8, 0., 1.)
# Define combined pdf for simultaneous fit
# Define category to distinguish physics and control samples events
sample = RooCategory("sample","sample")
sample.defineType("all")
sample.defineType("pass")
simPdf = RooSimultaneous("simPdf","simultaneous pdf",sample)
# Associate model with the physics state and model_ctl with the control state
simPdf.addPdf(modelAll,"all")
simPdf.addPdf(modelPass,"pass")
# ws.import(simPdf)
getattr(ws,'import')(simPdf)
# B_5 = RooRealVar ( "B_5" , "B_5" , 0.3 , -20 , 100 )
# B_6 = RooRealVar ( "B_6" , "B_6" , 0.3 , -20 , 100 )
a0 = RooRealVar("p0", "p0", 0.001, -2., 2.)
a1 = RooRealVar("p1", "p1", 0.001, -2., 2.)
a2 = RooRealVar("p2", "p2", -0.00001, -2., 2.)
a3 = RooRealVar("p3", "p3", -0.000001, -2, 2.)
a4 = RooRealVar("p4", "p4", -0.000001, -2., 2.)
# a5 = RooRealVar("a5","a5",-0.000001,-2.,2.)
poliset = RooArgList(a0, a1, a2, a3, a4)
# gaussFrac = RooRealVar("s","fraction of component 1 in kkSig",0.3,0.0,1.0)
nSigKK = RooRealVar("nSig", "nSig", 1E5, 0., 10E6)
nBkgKK = RooRealVar("nBkg", "nBkg", 5E5, 0., 10E6)
kkSig = RooVoigtian("kkSig", "kkSig", tt_mass, kkMean, kkGamma, kkSigma)
#kkBkg = RooBernstein("kkBkg" , "kkBkg", tt_mass, RooArgList(B_1, B_2,B_3,B_4))#,B_5) )#,B_6))
kkBkg = RooChebychev("kkBkg", "Background", tt_mass, poliset)
kkTot = RooAddPdf("kkTot", "kkTot", RooArgList(kkSig, kkBkg),
RooArgList(nSigKK, nBkgKK))
kkGamma.setConstant(ROOT.kTRUE)
kkMean.setConstant(ROOT.kTRUE)
nfit = 0
# kkfit = kkTot.fitTo(traKFitData,Range(fitphimin+0.005,fitphimax-0.005),RooFit.PrintLevel(-1), RooFit.NumCPU(7),RooFit.Save())
# nfit +=1
if not debugging:
def buildPdf(ws, p):
mass = RooRealVar("mass", "mass", p.minMass, p.maxMass)
getattr(ws, 'import')(mass)
# Construct signal pdf
mean = RooRealVar("mean", "mean", 90, 85, 95)
width = RooRealVar("width", "width", 2.4952, 1, 3)
width.setConstant(ROOT.kTRUE)
sigma = RooRealVar("sigma", "sigma", 1.2, 0.2, 10)
signalAll = RooVoigtian("signalAll", "signalAll", mass, mean, width, sigma)
turnOnAll = RooRealVar("turnOnAll", "turnOnAll", 80., 40., 150.)
widthAll_bkg = RooRealVar("widthAll", "widthAll", 2., 0., 50.)
decayAll_bkg = RooRealVar("decayAll", "decayAll", 80., 20., 150.)
meanB = RooRealVar("meanB", "meanB", 90, 60, 130)
sigmaB = RooRealVar("sigmaB", "sigmaB", 10, 1, 20)
bkg_a1 = RooRealVar("bkg_a1", "bkg_a1", 0., -2., 2.)
bkg_a2 = RooRealVar("bkg_a2", "bkg_a2", 0., -2., 2.)
backgroundAll = RooGaussian("backgroundAll", "backgroundAll", mass, meanB,
sigmaB)
# Construct composite pdf
sigAll = RooRealVar("sigAll", "sigAll", 2000, 0, 100000)
bkgAll = RooRealVar("bkgAll", "bkgAll", 100, 0, 10000)
modelAll = RooAddPdf("modelAll", "modelAll",
RooArgList(signalAll, backgroundAll),
RooArgList(sigAll, bkgAll))
if p.NoBkgd:
modelAll = RooAddPdf("modelAll", "modelAll", RooArgList(signalAll),
RooArgList(sigAll))
# Define pdf for all probes
# Construct signal pdf.
# NOTE that sigma is shared with the signal sample model
signalPass = RooVoigtian("signalPass", "signalPass", mass, mean, width,
sigma)
# Construct the background pdf
backgroundPass = RooGaussian("backgroundPass", "backgroundPass", mass,
meanB, sigmaB)
# Construct the composite model
efficiency = RooRealVar("efficiency", "efficiency", 0.9, 0.3, 1.)
sigPass = RooFormulaVar("sigPass", "@0*@1", RooArgList(sigAll, efficiency))
bkgPass = RooRealVar("bkgPass", "bkgPass", 100, 0, 10000)
modelPass = RooAddPdf("modelPass", "modelPass",
RooArgList(signalPass, backgroundPass),
RooArgList(sigPass, bkgPass))
if p.NoBkgd:
modelPass = RooAddPdf("modelPass", "modelPass", RooArgList(signalPass),
RooArgList(sigPass))
frac = RooRealVar("frac", "frac", 0.8, 0., 1.)
# Define combined pdf for simultaneous fit
# Define category to distinguish physics and control samples events
sample = RooCategory("sample", "sample")
sample.defineType("all")
sample.defineType("pass")
simPdf = RooSimultaneous("simPdf", "simultaneous pdf", sample)
# Associate model with the physics state and model_ctl with the control state
simPdf.addPdf(modelAll, "all")
simPdf.addPdf(modelPass, "pass")
# ws.import(simPdf)
getattr(ws, 'import')(simPdf)