def fitNBkg(ibdt, fullbkg):
slope = RooRealVar("slope", "slope", 0.5, -10, 10)
bkg_exp = RooExponential("bkg_exp", "exponential", slope, theBMass)
cut = cut_base + '&& bdt_prob > %s' % (ibdt)
theBMass.setRange('sigRangeMC', B0Mass_ - 3 * dict_sigma[ibdt],
B0Mass_ + 3 * dict_sigma[ibdt])
databkg = fullbkg.reduce(RooArgSet(theBMass, mumuMass, mumuMassE), cut)
r = bkg_exp.fitTo(databkg, RooFit.Save(), ROOT.RooFit.Range('left,right'))
frame = theBMass.frame()
databkg.plotOn(frame, RooFit.Binning(70), RooFit.MarkerSize(.7))
bkg_exp.plotOn(frame, )
# bkg_exp.fixCoefRange('left,right')
nbkg = RooRealVar('nbkg', 'bkg n', 1000, 0, 550000)
ebkg = RooExtendPdf('ebkg', 'ebkg', bkg_exp, nbkg, 'sigRangeMC')
ebkg.fitTo(databkg, ROOT.RooFit.Range('left,right'))
ebkg.plotOn(frame, RooFit.LineStyle(ROOT.kDashed),
RooFit.LineColor(ROOT.kGreen + 1), RooFit.Range(4.9, 5.6))
frame.Draw()
dict_b_v1[ibdt] = [nbkg.getVal(), nbkg.getError()]
def fitNBkg(ibdt, fullbkg, isample):
slope = RooRealVar("slope", "slope", 0.5, -10, 10)
bkg_exp = RooExponential("bkg_exp", "exponential", slope, theBMass)
cut = cut_base + '&& bdt_prob > %s' % (ibdt)
theBMass.setRange('sigRangeMC', B0Mass_ - 3 * dict_sigma[ibdt],
B0Mass_ + 3 * dict_sigma[ibdt])
databkg = fullbkg.reduce(RooArgSet(theBMass, mumuMass, mumuMassE), cut)
r = bkg_exp.fitTo(databkg, RooFit.Save(), ROOT.RooFit.Range('left,right'),
RooFit.PrintLevel(-1))
frame = theBMass.frame()
databkg.plotOn(frame, RooFit.Binning(70), RooFit.MarkerSize(.7))
bkg_exp.plotOn(frame, )
canv = ROOT.TCanvas()
frame.Draw()
nbkg = RooRealVar('nbkg', 'bkg n', 1000, 0, 550000)
ebkg = RooExtendPdf(
'ebkg', 'ebkg', bkg_exp, nbkg,
'sigRangeMC') ## here imposing the range to calculate bkg yield
ebkg.fitTo(databkg, ROOT.RooFit.Range('left,right'), RooFit.PrintLevel(-1))
ebkg.plotOn(frame, RooFit.LineStyle(ROOT.kDashed),
RooFit.LineColor(ROOT.kGreen + 1), RooFit.Range(4.9, 5.6))
frame.Draw()
# canv.SaveAs('bkg_fit_bdt%f_sample%i.pdf'%(ibdt,isample))
dict_b_v1[ibdt] = [nbkg.getVal(), nbkg.getError()]
def RooFitSig(mbbarray, bdtarray, weightarray, TC_mass, binstart, binend):
fitstart = 40
fitend = 150
mbbarray = range(200)
bdtarray = range(200)
weightarray = range(200)
mass = RooRealVar("X", "m(bb)[GeV]", fitstart, fitend)
BDT = RooRealVar("BDT", "BDT", -1, 100)
weight = RooRealVar("weight", "weight", -100, 200)
branchnames = ["X", "BDT", "weight"]
dtype = np.dtype([(branchnames[idx], np.float64)
for idx in range(len(branchnames))])
treearray = np.array([(mbbarray[idx], bdtarray[idx], weightarray[idx])
for idx in range(len(mbbarray))], dtype)
tree = rnp.array2tree(treearray)
m0 = RooRealVar("m0", "m0", TC_mass * 1., TC_mass * 1. - 60.,
TC_mass * 1. + 60.)
m02 = RooRealVar("m02", "m02", TC_mass * 1., TC_mass * 1. - 60.,
TC_mass * 1. + 60.)
alpha = RooRealVar("alpha", "alpha", 1.295, 1.0, 1.6)
sigma2 = RooRealVar("sigma2", "sigma2", 35, 8., 100)
n = RooRealVar("n", "n", 5, 1, 35)
mean = RooRealVar("mean", "mean of gaussian", 750, 0, 6000)
sigma = RooRealVar("sigma", "width of gaussian", 90, 38, 300)
gauss = RooGaussian("gauss", "gaussian PDF", mass, m0, sigma)
gauss2 = RooGaussian("gauss2", "gaussian PDF", mass, m02, sigma2)
CBshape = RooCBShape("CBshape", "Crystal Ball PDF", mass, m0, sigma2,
alpha, n)
##PDF normalization
num1 = RooRealVar("num1", "number of events", 400, 0, 5000)
##relative weight of 2 PDFs
f = RooRealVar("f", "f", 0.95, 0.6, 1)
sigPdf = RooAddPdf("sigPdf", "Signal PDF", RooArgList(CBshape, gauss),
RooArgList(f))
extPdf = RooExtendPdf("extPdf", "extPdf", sigPdf, num1)
data = RooDataSet("data", "data", tree, RooArgSet(mass, BDT, weight),
"BDT>0", "weight")
xframe = mass.frame()
mass.setBins(20)
data.plotOn(xframe)
extPdf.plotOn(
xframe) #,Normalization(1.0,RooAbsReal.RelativeExpected),LineColor(1))
hist = extPdf.createHistogram("X", fitend - fitstart)
hist.SetAxisRange(binstart, binend)
return deepcopy(hist)
xArg = RooArgSet(x)
N_times_experiments = 1000
N_number_of_events = 500
# gauss 16
mean16 = RooRealVar("mean16","mean of gaussian",-1,-10,10)
sigma16 = RooRealVar("sigma16","width of gaussian",2,0.1,10)
gauss16 = RooGaussian("gauss16","gaussian PDF",x,mean16,sigma16)
nGauss16 = RooRealVar("nGauss16", "nGauss16", N_number_of_events , 0. ,1000 )
gauss16_ext = RooExtendPdf("gauss16_ext", "extended p.d.f", gauss16, nGauss16)
gauss16_ext.plotOn( xframe10 )
# RooMCStudy
mgr1 = RooMCStudy(gauss16_ext , gauss16_ext , xArg )
mgr1.generateAndFit( N_times_experiments , N_number_of_events )
# plot
list_par = [mean16, sigma16, nGauss16]
list_frame1 = RooMCStudy_box( mgr1 , list_par )
# -------------------------------------------
# plot and save
print "for gauss3 before fit" print "mean3: ", mean3.Print()," sigma3: ",sigma3.Print() print "nGauss3: ", nGauss3.Print() # fit gauss3_ext.fitTo(data1) # plot norm3 = nGauss3.getVal() #gauss3_ext.plotOn(xframe2, RooFit.LineColor(RooFit.kRed)) #gauss3_ext.plotOn(xframe2, RooFit.Normalization(norm3, RooAbsReal.NumEvent), RooFit.LineColor(RooFit.kRed)) gauss3_ext.plotOn(xframe2, RooFit.Normalization(1.0, RooAbsReal.RelativeExpected), RooFit.LineColor(RooFit.kRed)) #gauss3.plotOn(xframe2, RooFit.Normalization(norm3, RooAbsReal.NumEvent), RooFit.LineColor(RooFit.kRed)) data1.plotOn(xframe2) gauss1.plotOn(xframe2,RooFit.LineColor(RooFit.kBlue)) print "for data1" print "number of events: ", data1.sumEntries() print "for gauss3 after fit" print "mean3: ", mean3.Print()," sigma3: ",sigma3.Print() print "nGauss3: ", nGauss3.Print() # ------------------------------------------- # 3. Add PDF = gauss4 + gauss5
print "iLeft: ", iLeft.getVal()
print "iTotal: ", iTotal.getVal()
#iLeft = gauss11_ext.createIntegral(xArg, RooFit.Range("left_gaussian_range"))
#iTotal = gauss11_ext.createIntegral(xArg, RooFit.Range("whole_range"))
#print "without NormSet"
#print "iLeft: ", iLeft.getVal()
#print "iTotal: ", iTotal.getVal()
n_total_evaluate = nGauss11.getVal() / iLeft.getVal()
print "n_total_evaluate: ", n_total_evaluate
#gauss11_ext.plotOn(xframe7,RooFit.Normalization( n_total_evaluate , RooAbsReal.NumEvent), RooFit.Range("whole_range"), RooFit.LineColor(RooFit.kRed))
#gauss11_ext.plotOn(xframe7,RooFit.Normalization( nGauss11.getVal() , RooAbsReal.NumEvent), RooFit.Range("whole_range"), RooFit.LineColor(RooFit.kRed))
gauss11_ext.plotOn(xframe7,RooFit.Normalization( nGauss11.getVal() , RooAbsReal.NumEvent), RooFit.Range("left_gaussian_range"), RooFit.LineColor(RooFit.kRed))
#gauss11_ext.plotOn(xframe7,RooFit.Normalization( nGauss11.getVal() , RooAbsReal.NumEvent), RooFit.Range("whole_range"), RooFit.LineColor(RooFit.kRed),RooFit.DrawOption("F"), RooFit.FillColor(RooFit.kRed), RooFit.FillStyle(1001) )
#data3.plotOn(xframe7)
data3_half.plotOn(xframe7)
#gauss11.plotOn(xframe7,RooFit.LineColor(RooFit.kRed))
# -------------------------------------------
# 8. use combined PDF to fit SB only and test fixAddCoefRange
xframe8 = x.frame(RooFit.Title("8. use combined PDF to fit SB only and test fixAddCoefRange"))
# gauss 12
norm.Print()
mass_var = multipdf.getVariables().first()
mass_var.setRange("HiggsMassWindow5",120,130)
mass_var.setRange("HiggsMassWindow2",123,127)
mass_var.setRange("HiggsMassWindow10",115,135)
mass_var.setRange("WholeRange",100,180)
frame = mass_var.frame()
option = "AL"
pdf_information = {}
multipdf.plotOn( frame )
for pdf_index in range(0,multipdf.getNumPdfs()):
pdf_ = multipdf.getPdf( pdf_index )
print pdf_.GetName() + "_Normalized" , pdf_.GetTitle()
pdf = RooExtendPdf( pdf_.GetName() + "_Normalized" , pdf_.GetTitle() , pdf_ , norm )
pdf.plotOn( frame , RooFit.DrawOption(option) , RooFit.LineColor( colors[pdf_index] ) , RooFit.LineStyle(0) , RooFit.LineWidth(3) , RooFit.Range("WholeRange") )
totalInt = pdf.createIntegral( RooArgSet(mass_var) ,RooFit.Range("WholeRange"))
#totalInt.Print() , RooFit.NormSet( RooArgSet(mass_var) ),
fracInt5 = pdf.createIntegral( RooArgSet(mass_var) ,RooFit.Range("HiggsMassWindow5"))
fracInt2 = pdf.createIntegral( RooArgSet(mass_var) ,RooFit.Range("HiggsMassWindow2"))
fracInt10 = pdf.createIntegral( RooArgSet(mass_var) ,RooFit.Range("HiggsMassWindow10"))
#fracInt.Print()
if "A" in option :
option = "L"
pdf_information[ pdf_index ] = { "name":pdf.GetName() , "pdf":pdf , "ratio5":fracInt5.getVal()/totalInt.getVal(), "ratio2":fracInt2.getVal()/totalInt.getVal(), "ratio10":fracInt10.getVal()/totalInt.getVal() }
doSig = False
if len(sys.argv) > 3 :
fsigName = sys.argv[2]
fsig = TFile.Open( fsigName )
sigPdf = fsig.Get( sys.argv[3] )
