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)
