Python RooRealVar.setMin方法代码示例

# 需要导入模块: from ROOT import RooRealVar [as 别名]
# 或者: from ROOT.RooRealVar import setMin [as 别名]

#.........这里部分代码省略.........
    # * The two PDFs are added together using the relative normalizations of the two bkg from MC
    # * DATA is then fit in the sidebands only using the combined bkg PDF
    # * The results of the fit are then estrapolated in the SR and the integral is evaluated.
    # * This defines the bkg normalization in the SR

    # *******************************************************#
    #                                                       #
    #                 V+jets normalization                  #
    #                                                       #
    # *******************************************************#

    # Variables for V+jets
    constVjet = RooRealVar("constVjet", "slope of the exp", -0.020, -1.0, 0.0)
    offsetVjet = RooRealVar("offsetVjet", "offset of the erf", 30.0, -50.0, 400.0)
    widthVjet = RooRealVar("widthVjet", "width of the erf", 100.0, 1.0, 200.0)  # 0, 400
    a0Vjet = RooRealVar("a0Vjet", "width of the erf", -0.1, -5, 0)
    a1Vjet = RooRealVar("a1Vjet", "width of the erf", 0.6, 0, 5)
    a2Vjet = RooRealVar("a2Vjet", "width of the erf", -0.1, -1, 1)

    if channel == "XZhnnb":
        offsetVjet = RooRealVar("offsetVjet", "offset of the erf", 500.0, 200.0, 1000.0)
    if channel == "XZhnnbb":
        offsetVjet = RooRealVar("offsetVjet", "offset of the erf", 350.0, 200.0, 500.0)
    #    if channel == "XWhenb" or channel == "XZheeb":
    #        offsetVjet.setVal(120.)
    #        offsetVjet.setConstant(True)
    if channel == "XWhenb":
        offsetVjet = RooRealVar("offsetVjet", "offset of the erf", 120.0, 80.0, 155.0)
    if channel == "XWhenbb" or channel == "XZhmmb":
        offsetVjet = RooRealVar("offsetVjet", "offset of the erf", 67.0, 50.0, 100.0)
    if channel == "XWhmnb":
        offsetVjet = RooRealVar("offsetVjet", "offset of the erf", 30.0, -50.0, 600.0)
    if channel == "XZheeb":
        offsetVjet.setMin(-400)
        offsetVjet.setVal(0.0)
        offsetVjet.setMax(1000)
        widthVjet.setVal(1.0)

    # Define V+jets model
    if fitFuncVjet == "ERFEXP":
        VjetMass = RooErfExpPdf("VjetMass", fitFuncVjet, J_mass, constVjet, offsetVjet, widthVjet)
    elif fitFuncVjet == "EXP":
        VjetMass = RooExponential("VjetMass", fitFuncVjet, J_mass, constVjet)
    elif fitFuncVjet == "GAUS":
        VjetMass = RooGaussian("VjetMass", fitFuncVjet, J_mass, offsetVjet, widthVjet)
    elif fitFuncVjet == "POL":
        VjetMass = RooChebychev("VjetMass", fitFuncVjet, J_mass, RooArgList(a0Vjet, a1Vjet, a2Vjet))
    elif fitFuncVjet == "POW":
        VjetMass = RooGenericPdf("VjetMass", fitFuncVjet, "@0^@1", RooArgList(J_mass, a0Vjet))
    else:
        print "  ERROR! Pdf", fitFuncVjet, "is not implemented for Vjets"
        exit()

    if fitAltFuncVjet == "POL":
        VjetMass2 = RooChebychev("VjetMass2", "polynomial for V+jets mass", J_mass, RooArgList(a0Vjet, a1Vjet, a2Vjet))
    else:
        print "  ERROR! Pdf", fitAltFuncVjet, "is not implemented for Vjets"
        exit()

    # fit to main bkg in MC (whole range)
    frVjet = VjetMass.fitTo(
        setVjet,
        RooFit.SumW2Error(True),
        RooFit.Range("h_reasonable_range"),
        RooFit.Strategy(2),
        RooFit.Minimizer("Minuit2"),

# 需要导入模块: from ROOT import RooRealVar [as 别名]
# 或者: from ROOT.RooRealVar import setMin [as 别名]

#.........这里部分代码省略.........
#		res6.Print()
#		res7.Print()

            # decorrelated background parameters for Bayesian limits
            if args.decoBkg:
                signal_norm.setConstant()
                res = model.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
                res.Print()
                ## temp workspace for the PDF diagonalizer
                w_tmp = RooWorkspace("w_tmp")
                deco = PdfDiagonalizer("deco",w_tmp,res)
                # here diagonalizing only the shape parameters since the overall normalization is already decorrelated
                background_deco = deco.diagonalize(background)
                print "##################### workspace for decorrelation"
                w_tmp.Print("v")
                print "##################### original parameters"
                background.getParameters(rooDataHist).Print("v")
                print "##################### decorrelated parameters"
                # needed if want to evaluate limits without background systematics
                if args.fixBkg:
                    w_tmp.var("deco_eig1").setConstant()
                    w_tmp.var("deco_eig2").setConstant()
                    if not args.fixP3: w_tmp.var("deco_eig3").setConstant()
                background_deco.getParameters(rooDataHist).Print("v")
                print "##################### original pdf"
                background.Print()
                print "##################### decorrelated pdf"
                background_deco.Print()
                # release signal normalization
                signal_norm.setConstant(kFALSE)
                # set the background normalization range to +/- 5 sigma
                bkg_val = background_norm.getVal()
                bkg_error = background_norm.getError()
                background_norm.setMin(bkg_val-5*bkg_error)
                background_norm.setMax(bkg_val+5*bkg_error)
                background_norm.Print()
                # change background PDF names
                background.SetName("background_old")
                background_deco.SetName("background")

        # needed if want to evaluate limits without background systematics
        if args.fixBkg:
            background_norm.setConstant()
            p1.setConstant()
            p2.setConstant()
            p3.setConstant()

        # -----------------------------------------
        # dictionaries holding systematic variations of the signal shape
        hSig_Syst = {}
        hSig_Syst_DataHist = {}
        sigCDF = TGraph(hSig.GetNbinsX()+1)

        # JES and JER uncertainties
        if args.jesUnc != None or args.jerUnc != None:

            sigCDF.SetPoint(0,0.,0.)
            integral = 0.
            for i in range(1, hSig.GetNbinsX()+1):
                x = hSig.GetXaxis().GetBinLowEdge(i+1)
                integral = integral + hSig.GetBinContent(i)
                sigCDF.SetPoint(i,x,integral)

        if args.jesUnc != None:
            hSig_Syst['JESUp'] = copy.deepcopy(hSig)
            hSig_Syst['JESDown'] = copy.deepcopy(hSig)

# 需要导入模块: from ROOT import RooRealVar [as 别名]
# 或者: from ROOT.RooRealVar import setMin [as 别名]
                # needed if want to evaluate limits without background systematics
                if args.fixBkg:
                    w_tmp.var("deco_eig1").setConstant()
                    w_tmp.var("deco_eig2").setConstant()
                    if not args.fixP3: w_tmp.var("deco_eig3").setConstant()
                background_deco.getParameters(rooDataHist).Print("v")
                print "##################### original pdf"
                background.Print()
                print "##################### decorrelated pdf"
                background_deco.Print()
                # release signal normalization
                signal_norm.setConstant(kFALSE)
                # set the background normalization range to +/- 5 sigma
                bkg_val = background_norm.getVal()
                bkg_error = background_norm.getError()
                background_norm.setMin(bkg_val-5*bkg_error)
                background_norm.setMax(bkg_val+5*bkg_error)
                background_norm.Print()
                # change background PDF names
                background.SetName("background_old")
                background_deco.SetName("background")

        # needed if want to evaluate limits without background systematics
        if args.fixBkg:
            background_norm.setConstant()
            p1.setConstant()
            p2.setConstant()
            p3.setConstant()

        # -----------------------------------------
        # dictionaries holding systematic variations of the signal shape