예제 #1
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def generate_testfiles():
    h = TH1D("gaussian_hist", "Gaussian histgram", 100, -3, 3)
    h.FillRandom("gaus", 1000)

    file = TFile(
        os.path.dirname(__file__) + ('/../testfiles/root_testfiles.root'),
        'RECREATE')
    file.cd()
    h.Write()

    x = RooRealVar("D0_M", "m(K_{S}^{0}K^{+}K^{-})", 1860, 1800, 1930,
                   "\\mathrm{MeV}/c^{2}")
    x.setBins(130)
    m1 = RooRealVar("m1", "mean 1", 1864, 1860, 1870)
    s1 = RooRealVar("s1", "sigma 1", 2, 0, 5)
    g1 = RooGaussian("g1", "Gaussian 1", x, m1, s1)

    m2 = RooRealVar("m2", "mean 2", 1864, 1860, 1870)
    s2 = RooRealVar("s2", "sigma 2", 4, 0, 5)
    g2 = RooGaussian("g2", "Gaussian 2", x, m2, s2)

    f1 = RooRealVar("f", "f", 0.5, 0, 1)
    m = RooAddPdf("model", "model", RooArgList(g1, g2), f1)

    data = m.generate(x, 1e6)

    x.Write("x")
    m.Write("model")
    data.Write("data")

    file.Close()

    return
예제 #2
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    def _make_underlying_model(self):
        self.pdfs = {}
        self.yields = {}  # yields are plain floats
        self.ryields = {}  # keep track of roofit objects for memory management
        nbins, xmin, xmax = self.plot.histos[0].GetBinning()
        self.xvar = RooRealVar("x", "x", xmin, xmax)
        self.xvar.setBins(nbins)
        self.pdfs = {}
        self.hists = []
        pdfs = RooArgList()
        yields = RooArgList()
        for compname, comp in self.plot.histosDict.iteritems():
            if comp.weighted.Integral() == 0:
                continue
            assert (isinstance(comp, Histogram))
            hist = RooDataHist(compname, compname, RooArgList(self.xvar),
                               comp.weighted)
            SetOwnership(hist, False)
            # self.hists.append(hist)
            pdf = RooHistPdf(compname, compname, RooArgSet(self.xvar), hist)
            self.pdfs[compname] = pdf
            # self.pdfs[compname].Print()
            pdfs.add(pdf)
            nevts = comp.Integral(xmin=xmin, xmax=xmax)
            nmin = min(0, nevts * (1 - comp.uncertainty))
            nmax = nevts * (1 + comp.uncertainty)
            theyield = RooRealVar('n{}'.format(compname),
                                  'n{}'.format(compname), nevts, nmin, nmax)
            self.ryields[compname] = theyield
            self.yields[compname] = nevts
            yields.add(theyield)

        self.underlying_model = RooAddPdf('model', 'model', pdfs, yields)
예제 #3
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 def predict(self, x, theta_true):
     """
     Run the unbinned ML fit
     """
     
     # Data
     roodata = RooDataSet('data', 'data', RooArgSet(self.phistar))
     for xval in x:
         self.phistar.setVal(xval)
         roodata.add(RooArgSet(self.phistar))
     
     theta = RooRealVar('theta', 'theta', 0.5, self.theta_min, self.theta_max)
     
     # The combined pdf
     model = RooAddPdf('model', 'model',
                       RooArgList(self.pdfs['A'], self.pdfs['H']),
                       RooArgList(theta))
     
     with stdout_redirected_to('%s/minuit_output.log' % self.outdir):
         res = model.fitTo(roodata, Save(True))
         nll = res.minNll()
     
     fitted_theta = theta.getValV()
     
     # Get Lambda(theta_true | theta_best)
     with stdout_redirected_to():
         logl = model.createNLL(roodata)
     
     theta.setVal(theta_true)
     nll_theta_true = logl.getValV()
     nll_ratio = nll_theta_true - nll
     
     return fitted_theta, nll, nll_ratio
예제 #4
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def rooFit203():

    print ">>> setup model..."
    x = RooRealVar("x", "x", -10, 10)
    mean = RooRealVar("mean", "mean of gaussian", 0, -10, 10)
    gauss = RooGaussian("gauss", "gaussian PDF", x, mean, RooConst(1))

    # Construct px = 1 (flat in x)
    px = RooPolynomial("px", "px", x)

    # Construct model = f*gx + (1-f)px
    f = RooRealVar("f", "f", 0., 1.)
    model = RooAddPdf("model", "model", RooArgList(gauss, px), RooArgList(f))
    data = model.generate(RooArgSet(x), 10000)  # RooDataSet

    print ">>> fit to full data range..."
    result_full = model.fitTo(data, Save(kTRUE))  # RooFitResult

    print "\n>>> fit \"signal\" range..."
    # Define "signal" range in x as [-3,3]
    x.setRange("signal", -3, 3)
    result_sig = model.fitTo(data, Save(kTRUE),
                             Range("signal"))  # RooFitResult

    print "\n>>> plot and print results..."
    # Make plot frame in x and add data and fitted model
    frame1 = x.frame(Title("Fitting a sub range"))  # RooPlot
    data.plotOn(frame1, Name("data"))
    model.plotOn(frame1, Range("Full"), LineColor(kBlue),
                 Name("model"))  # Add shape in full ranged dashed
    model.plotOn(frame1, LineStyle(kDashed), LineColor(kRed),
                 Name("model2"))  # By default only fitted range is shown

    print "\n>>> result of fit on all data:"
    result_full.Print()

    print "\n>>> result of fit in in signal region (note increased error on signal fraction):"
    result_sig.Print()

    print ">>> draw on canvas..."
    canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600)
    legend = TLegend(0.2, 0.85, 0.4, 0.65)
    legend.SetTextSize(0.032)
    legend.SetBorderSize(0)
    legend.SetFillStyle(0)
    gPad.SetLeftMargin(0.14)
    gPad.SetRightMargin(0.02)
    frame1.GetYaxis().SetLabelOffset(0.008)
    frame1.GetYaxis().SetTitleOffset(1.4)
    frame1.GetYaxis().SetTitleSize(0.045)
    frame1.GetXaxis().SetTitleSize(0.045)
    frame1.Draw()
    legend.AddEntry("data", "data", 'LEP')
    legend.AddEntry("model", "fit (full range)", 'L')
    legend.AddEntry("model2", "fit (signal range)", 'L')
    legend.Draw()
    canvas.SaveAs("rooFit203.png")
예제 #5
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def test_correlated_values():

    try:
        import uncertainties
    except ImportError:
        raise SkipTest("uncertainties package is not installed")
    from rootpy.stats.correlated_values import correlated_values

    # construct pdf and toy data following example at
    # http://root.cern.ch/drupal/content/roofit

    # --- Observable ---
    mes = RooRealVar("mes", "m_{ES} (GeV)", 5.20, 5.30)

    # --- Parameters ---
    sigmean = RooRealVar("sigmean", "B^{#pm} mass", 5.28, 5.20, 5.30)
    sigwidth = RooRealVar("sigwidth", "B^{#pm} width", 0.0027, 0.001, 1.)

    # --- Build Gaussian PDF ---
    signal = RooGaussian("signal", "signal PDF", mes, sigmean, sigwidth)

    # --- Build Argus background PDF ---
    argpar = RooRealVar("argpar", "argus shape parameter", -20.0, -100., -1.)
    background = RooArgusBG("background", "Argus PDF",
                            mes, RooFit.RooConst(5.291), argpar)

    # --- Construct signal+background PDF ---
    nsig = RooRealVar("nsig", "#signal events", 200, 0., 10000)
    nbkg = RooRealVar("nbkg", "#background events", 800, 0., 10000)
    model = RooAddPdf("model", "g+a",
                      RooArgList(signal,background),
                      RooArgList(nsig,nbkg))

    # --- Generate a toyMC sample from composite PDF ---
    data = model.generate(RooArgSet(mes), 2000)

    # --- Perform extended ML fit of composite PDF to toy data ---
    fitresult = model.fitTo(data, RooFit.Save(), RooFit.PrintLevel(-1))

    nsig, nbkg = correlated_values(["nsig", "nbkg"], fitresult)

    # Arbitrary math expression according to what the `uncertainties`
    # package supports, automatically computes correct error propagation
    sum_value = nsig + nbkg
    value, error = sum_value.nominal_value, sum_value.std_dev

    workspace = Workspace(name='workspace')
    # import the data
    assert_false(workspace(data))
    with TemporaryFile():
        workspace.Write()
def test_correlated_values():

    try:
        import uncertainties
    except ImportError:
        raise SkipTest("uncertainties package is not installed")
    from rootpy.stats.correlated_values import correlated_values

    # construct pdf and toy data following example at
    # http://root.cern.ch/drupal/content/roofit

    # --- Observable ---
    mes = RooRealVar("mes", "m_{ES} (GeV)", 5.20, 5.30)

    # --- Parameters ---
    sigmean = RooRealVar("sigmean", "B^{#pm} mass", 5.28, 5.20, 5.30)
    sigwidth = RooRealVar("sigwidth", "B^{#pm} width", 0.0027, 0.001, 1.)

    # --- Build Gaussian PDF ---
    signal = RooGaussian("signal", "signal PDF", mes, sigmean, sigwidth)

    # --- Build Argus background PDF ---
    argpar = RooRealVar("argpar", "argus shape parameter", -20.0, -100., -1.)
    background = RooArgusBG("background", "Argus PDF",
                            mes, RooFit.RooConst(5.291), argpar)

    # --- Construct signal+background PDF ---
    nsig = RooRealVar("nsig", "#signal events", 200, 0., 10000)
    nbkg = RooRealVar("nbkg", "#background events", 800, 0., 10000)
    model = RooAddPdf("model", "g+a",
                      RooArgList(signal,background),
                      RooArgList(nsig,nbkg))

    # --- Generate a toyMC sample from composite PDF ---
    data = model.generate(RooArgSet(mes), 2000)

    # --- Perform extended ML fit of composite PDF to toy data ---
    fitresult = model.fitTo(data, RooFit.Save(), RooFit.PrintLevel(-1))

    nsig, nbkg = correlated_values(["nsig", "nbkg"], fitresult)

    # Arbitrary math expression according to what the `uncertainties`
    # package supports, automatically computes correct error propagation
    sum_value = nsig + nbkg
    value, error = sum_value.nominal_value, sum_value.std_dev

    workspace = Workspace(name='workspace')
    # import the data
    assert_false(workspace(data))
    with TemporaryFile():
        workspace.Write()
예제 #7
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def toy_run(nevents):
    lower = -1
    upper = 1
    # create observables
    obs = RooRealVar("obs", "obs1", lower, upper)
    # create parameters
    mean1 = RooRealVar("mean1", "mean of gaussian", 0, -1, 1)
    sigma1 = RooRealVar("sigma1", "sigma of gaussian", 0.1, -1, 1)
    gauss1 = RooGaussian("gauss1", "gaussian PDF", obs, mean1, sigma1)

    mean2 = RooRealVar("mean2", "mean of gaussian", 0.5, -1, 1)
    sigma2 = RooRealVar("sigma2", "sigma of gaussian", 0.2, -1, 1)
    gauss2 = RooGaussian("gauss2", "gaussian PDF", obs, mean2, sigma2)
    frac = RooRealVar("frac", "Fraction of a gauss", 0.5, 0, 1)
    arg_list = RooArgList(
        gauss1,
        gauss2,
        gauss2,
        gauss2,
        gauss2,
        # gauss2,
        gauss2,
        gauss2,
        gauss1)
    arg_list.addOwned(gauss2)
    pdf = RooAddPdf(
        "sum_pdf",
        "sum of pdfs",
        arg_list,
        RooArgList(
            frac,
            frac,
            frac,
            # frac,
            # frac,
            frac,
            frac,
            frac,
            frac,
            frac))

    # obs, pdf = build_pdf()

    timer = zfit_benchmark.timer.Timer(f"Toys {nevents}")
    with timer:
        data = pdf.generate(RooArgSet(obs), nevents)
        pdf.fitTo(data)
        # mgr.generateAndFit(n_toys, nevents)

    return float(timer.elapsed)
예제 #8
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def PlotSignalShapes(Selection):
    f__ = TFile.Open( "datacards/22June/2dPlots.root")
    signal_fname_1 = ("signals/22June/out_{sample:s}_syst.root", "cms_hgg_13TeV" )
    signal_fname_2 = ("signals/22June/out_ctcv_{sample:s}_syst.root" , "ctcv" )
    samples = {"thw":signal_fname_2, "thq":signal_fname_2, "tth":signal_fname_1 , "vh":signal_fname_1 }
    purity_h_name = "{sample:s}/"+Selection+"/h{sample:s}_"+Selection+"_purity_CtCv"
    purities = RooArgList()
    signalshapes = RooArgList()

    ctOverCvs = []

    mVar = None
    ctovercv_vals = None
    for sample in samples :
        purity = CtCvCpInfo("purity_" + sample)
        ctovercv_vals = sorted(purity.AllCtOverCVs.keys())
        purity.FillFrom2DHisto( f__.Get( purity_h_name.format( sample=sample ) ) )
        purity.GetCtOverCv()
        purities.add( purity.CtOverCvDataHistFunc )
        objsToKeep.append( purity )

        sFile = TFile.Open( samples[sample][0].format( sample=sample ) )
        ws = sFile.Get( samples[sample][1] )
        pdf = ws.pdf("RV{sample:s}_mh125".format( sample=sample) )
        objsToKeep.append(sFile)
        objsToKeep.append(ws)
        objsToKeep.append(pdf)
        signalshapes.add( pdf )

        ctOverCvs.append( ws.var( "CtOverCv" ) )
        mVar = ws.var("CMS_hgg_mass")
        
    ret = RooAddPdf("signal" , "signal" , signalshapes , purities )
    objsToKeep.append( ret )
    plot = mVar.frame()
    options = ""
    for ctovercv in ctovercv_vals :
        for var in ctOverCvs:
            var.setVal( ctovercv )
        name = "name%g" % ctovercv
        ret.plotOn( plot , RooFit.DrawOption(options) , RooFit.Name(name) )
        
        c = TCanvas()
        plot.Draw()
        c.SaveAs("a.gif+")

        if not "same" in options :
            options += " same"

    return c
예제 #9
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    def makeMorphingPdf(self, component, useAlternateModels, convModels):
        if self.ws.pdf(component):
            return self.ws.pdf(component)
        
        filesNom = getattr(self.pars, '%s_NomFiles' % component)
        modelsNom = getattr(self.pars, '%s_NomModels' % component)
        filesMU = getattr(self.pars, '%s_MUFiles' % component)
        modelsMU = getattr(self.pars, '%s_MUModels' % component)
        filesMD = getattr(self.pars, '%s_MDFiles' % component)
        modelsMD = getattr(self.pars, '%s_MDModels' % component)
        filesSU = getattr(self.pars, '%s_SUFiles' % component)
        modelsSU = getattr(self.pars, '%s_SUModels' % component)
        filesSD = getattr(self.pars, '%s_SDFiles' % component)
        modelsSD = getattr(self.pars, '%s_SDModels' % component)
        if useAlternateModels:
            modelsNom = getattr(self.pars, '%s_NomModelsAlt' % component)
            modelsMU = getattr(self.pars, '%s_MUModelsAlt' % component)
            modelsMD = getattr(self.pars, '%s_MDModelsAlt' % component)
            modelsSU = getattr(self.pars, '%s_SUModelsAlt' % component)
            modelsSD = getattr(self.pars, '%s_SDModelsAlt' % component)

        # Adds five (sub)components for the component with suffixes Nom, MU, MD, SU, SD
        NomPdf = self.makeComponentPdf('%s_Nom' % component, filesNom, modelsNom, False, convModels)
        if hasattr(self, '%s_NomExpected' % component):
            setattr(self, '%sExpected' % component,
                    getattr(self, '%s_NomExpected' % component))
        MUPdf = self.makeComponentPdf('%s_MU' % component, filesMU, modelsMU, False, convModels)
        MDPdf = self.makeComponentPdf('%s_MD' % component, filesMD, modelsMD, False, convModels)
        SUPdf = self.makeComponentPdf('%s_SU' % component, filesSU, modelsSU, False, convModels)
        SDPdf = self.makeComponentPdf('%s_SD' % component, filesSD, modelsSD, False, convModels)

        fMU_comp = self.ws.factory("fMU_%s[0., -1., 1.]" % component)
        fSU_comp = self.ws.factory("fSU_%s[0., -1., 1.]" % component)

        fMU = RooFormulaVar("f_fMU_%s" % component, "1.0*@0*(@0 >= 0.)", 
                            RooArgList( fMU_comp ) )
        fMD = RooFormulaVar("f_fMD_%s" % component, "-1.0*@0*(@0 < 0.)", 
                            RooArgList( fMU_comp ) )
        fSU = RooFormulaVar("f_fSU_%s" % component, "@0*(@0 >= 0.)", 
                            RooArgList( fSU_comp ) )
        fSD = RooFormulaVar("f_fSD_%s" % component, "@0*(-1)*(@0 < 0.)", 
                            RooArgList( fSU_comp ) )
        fNom = RooFormulaVar("f_fNom_%s" % component, "(1.-abs(@0)-abs(@1))", 
                             RooArgList(fMU_comp,fSU_comp) )
        morphPdf = RooAddPdf(component,component, 
                             RooArgList(MUPdf,MDPdf,SUPdf,SDPdf,NomPdf),
                             RooArgList(fMU, fMD, fSU, fSD, fNom))
        morphPdf.SetName(component)
        getattr(self.ws, 'import')(morphPdf)
        return self.ws.pdf(component)
예제 #10
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def get_num_sig_bkg(hist_DataTemplate,
                    hist_SignalTemplate,
                    hist_BackgdTemplate,
                    fit_range_min,
                    fit_range_max):
    '''Given 3 input histograms (TH1F), and a fit range, this function finds
    the amount of signal and background that sum up to the data histogram.
    It does histogram fits.'''
    # Find range of data template
    data_min = hist_DataTemplate.GetXaxis().GetXmin()
    data_max = hist_DataTemplate.GetXaxis().GetXmax()
    
    # Create basic variables
    x = RooRealVar("x","x",data_min,data_max)
    x.setBins(hist_DataTemplate.GetXaxis().GetNbins())  # Binned x values
    nsig = RooRealVar("nsig","number of signal events"    , 0, hist_DataTemplate.Integral())
    nbkg = RooRealVar("nbkg","number of background events", 0, hist_DataTemplate.Integral())
    
    # Create RooDataHists from input TH1Fs
    dh = RooDataHist("dh","dh",RooArgList(x),hist_DataTemplate)
    ds = RooDataHist("ds","ds",RooArgList(x),hist_SignalTemplate)
    db = RooDataHist("db","db",RooArgList(x),hist_BackgdTemplate)
    
    # Create Probability Distribution Functions from Monte Carlo
    sigPDF = RooHistPdf("sigPDF", "sigPDF", RooArgSet(x), ds)
    bkgPDF = RooHistPdf("bkgPDF", "bkgPDF", RooArgSet(x), db)
    
    model = RooAddPdf("model","(g1+g2)+a",RooArgList(bkgPDF,sigPDF),RooArgList(nbkg,nsig))
    
    # Find the edges of the bins that contain the fit range min/max
    data_min = hist_DataTemplate.GetXaxis().GetBinLowEdge(hist_DataTemplate.GetXaxis().FindFixBin(fit_range_min))
    data_max = hist_DataTemplate.GetXaxis().GetBinUpEdge(hist_DataTemplate.GetXaxis().FindFixBin(fit_range_max))
    
    r = model.fitTo(dh,RooFit.Save(),RooFit.Minos(0),RooFit.PrintEvalErrors(0),
                    RooFit.Extended(),RooFit.Range(data_min,data_max))
    r.Print("v")

    #print nsig.getVal(), nsig.getError(), nbkg.getVal(), nbkg.getError()
    #  Create pull distribution
    #mcstudy = RooMCStudy(model, RooArgSet(x), RooFit.Binned(1), RooFit.Silence(),
    #                     RooFit.Extended(),
    #                     RooFit.FitOptions(RooFit.Save(1),
    #                                       RooFit.PrintEvalErrors(0),
    #                                       RooFit.Minos(0))
    #                    )
    #mcstudy.generateAndFit(500)                          # Generate and fit toy MC
    #pull_dist = mcstudy.plotPull(nsig, -3.0, 3.0, 30, 1)  # make pull distribution
    pull_dist = None
    return [nsig.getVal(), nsig.getError(), nbkg.getVal(), nbkg.getError(), pull_dist]
def tripleG(doublegaus, mean, sigma3_, f2_, tagged_mass, w):

    sigma3       = RooRealVar ("#sigma_{TG3}"  , "sigmaTG3"        ,  sigma3_    ,      0,   0.2, "GeV")
    signalGauss3 = RooGaussian("thirdGauss"    , "thirdGauss"      ,  tagged_mass,   mean, sigma3)
    f2           = RooRealVar ("f2"            , "f2"              ,  f2_        ,     0.,    1. )
    triplegaus   = RooAddPdf  ("triplegaus"    , "doublegaus+gaus3",  RooArgList(doublegaus,signalGauss3), RooArgList(f2))
    _import(w,triplegaus)
예제 #12
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def get_roofit_model( histograms, fit_boundaries, name = 'model' ):
    data_label = 'data'
    samples = sorted( histograms.keys() )
    samples.remove( data_label )
    roofit_histograms = {}
    roofit_pdfs = {}
    roofit_variables = {}
    variables = RooArgList()
    variable_set = RooArgSet()

    fit_variable = RooRealVar( name , name, fit_boundaries[0], fit_boundaries[1] )
    variables.add( fit_variable )
    variable_set.add( fit_variable )
    
    roofit_histograms[data_label] = RooDataHist( data_label,
                                                     data_label,
                                                     variables,
                                                     histograms[data_label] )
    
    pdf_arglist = RooArgList()
    variable_arglist = RooArgList()
    N_total = histograms[data_label].Integral() * 2
    N_min = 0
    for sample in samples:
        roofit_histogram = RooDataHist( sample, sample, variables, histograms[sample] )
        roofit_histograms[sample] = roofit_histogram
        roofit_pdf = RooHistPdf ( 'pdf' + sample, 'pdf' + sample, variable_set, roofit_histogram, 0 )
        roofit_pdfs[sample] = roofit_pdf
        roofit_variable = RooRealVar( sample, "number of " + sample + " events", histograms[sample].Integral(), N_min, N_total, "event" )
        roofit_variables[sample] = roofit_variable
        pdf_arglist.add( roofit_pdf )
        variable_arglist.add( roofit_variable )
        
    model = RooAddPdf( name, name, pdf_arglist, variable_arglist )
    return model, roofit_histograms, fit_variable
예제 #13
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def test_plottable():

    # construct pdf and toy data following example at
    # http://root.cern.ch/drupal/content/roofit

    # Observable
    mes = RooRealVar("mes", "m_{ES} (GeV)", 5.20, 5.30)

    # Parameters
    sigmean = RooRealVar("sigmean", "B^{#pm} mass", 5.28, 5.20, 5.30)
    sigwidth = RooRealVar("sigwidth", "B^{#pm} width", 0.0027, 0.001, 1.)

    # Build Gaussian PDF
    signal = RooGaussian("signal", "signal PDF", mes, sigmean, sigwidth)

    # Build Argus background PDF
    argpar = RooRealVar("argpar", "argus shape parameter", -20.0, -100., -1.)
    background = RooArgusBG("background", "Argus PDF",
                            mes, RooFit.RooConst(5.291), argpar)

    # Construct signal+background PDF
    nsig = RooRealVar("nsig", "#signal events", 200, 0., 10000)
    nbkg = RooRealVar("nbkg", "#background events", 800, 0., 10000)
    model = RooAddPdf("model", "g+a",
                      RooArgList(signal, background),
                      RooArgList(nsig, nbkg))

    # Generate a toyMC sample from composite PDF
    data = model.generate(RooArgSet(mes), 2000)

    # Perform extended ML fit of composite PDF to toy data
    fitresult = model.fitTo(data, RooFit.Save(), RooFit.PrintLevel(-1))

    # Plot toy data and composite PDF overlaid
    mesframe = asrootpy(mes.frame())
    data.plotOn(mesframe)
    model.plotOn(mesframe)

    for obj in mesframe.objects:
        assert_true(obj)
    for curve in mesframe.curves:
        assert_true(curve)
    for hist in mesframe.data_hists:
        assert_true(hist)
    assert_true(mesframe.plotvar)
    with TemporaryFile():
        mesframe.Write()
예제 #14
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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)
예제 #15
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    def predict(self, x, theta_true):
        """
        Run an unbinned ML fit to make predictions
        """
        
        # Create RooDataSet
        xs = self.scaler.transform(x)
        preds = self.model.predict(xs)[:, 1]

        min_nn_output_local, max_nn_output_local = np.min(preds), np.max(preds)
        if min_nn_output_local < self.min_nn_output:
            self.min_nn_output = min_nn_output_local
        if max_nn_output_local > self.max_nn_output:
            self.max_nn_output = max_nn_output_local
        
        roodata = RooDataSet('data', 'data', RooArgSet(self.roopred))
        for pred in preds:
            self.roopred.setVal(pred)
            roodata.add(RooArgSet(self.roopred))

        
        # Fit
        theta = RooRealVar('theta', 'theta', 0.5, self.theta_min, self.theta_max)
        
        model = RooAddPdf('model', 'model',
                          RooArgList(self.pdfs['A'], self.pdfs['H']),
                          RooArgList(theta))
        
        
        with stdout_redirected_to('%s/minuit_output.log' % self.outdir):
            res = model.fitTo(roodata, Save(True))
            nll = res.minNll()

        fitstatus = res.status()
        fitstatus |= (not subprocess.call(['grep', 'p.d.f value is less than zero', 'output_MLE_unbinned/minuit_output.log']))

        fitted_theta = theta.getValV()
        
        # Get Lambda(theta_true | theta_best)
        logl = model.createNLL(roodata)
        
        theta.setVal(theta_true)
        nll_theta_true = logl.getValV()
        nll_ratio = nll_theta_true - nll
  
        return fitted_theta, nll, nll_ratio, fitstatus
예제 #16
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	def fit(self, save_to, signal_name=None, fix_p3=False, fit_range=[300., 1200.], fit_strategy=1):
		# Run a RooFit fit

		# Create background PDF
		p1 = RooRealVar('p1','p1',args.p1,0.,100.)
		p2 = RooRealVar('p2','p2',args.p2,0.,60.)
		p3 = RooRealVar('p3','p3',args.p3,-10.,10.)
		if args.fix_p3:
			p3.setConstant()
		background_pdf = RooGenericPdf('background_pdf','(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(self.collision_energy,self.collision_energy,self.collision_energy),RooArgList(self.mjj_,p1,p2,p3))
		background_pdf.Print()
		data_integral = data_histogram.Integral(data_histogram.GetXaxis().FindBin(float(fit_range[0])),data_histogram.GetXaxis().FindBin(float(fit_range[1])))
		background_norm = RooRealVar('background_norm','background_norm',data_integral,0.,1e+08)
		background_norm.Print()

		# Create signal PDF and fit model
		if signal_name:
			signal_pdf = RooHistPdf('signal_pdf', 'signal_pdf', RooArgSet(self.mjj_), self.signal_roohistograms_[signal_name])
			signal_pdf.Print()
			signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+05,1e+05)
			signal_norm.Print()
			model = RooAddPdf("model","s+b",RooArgList(background_pdf,signal_pdf),RooArgList(background_norm,signal_norm))
		else:
			model = RooAddPdf("model","b",RooArgList(background_pdf),RooArgList(background_norm))

		# Run fit
		res = model.fitTo(data_, RooFit.Save(kTRUE), RooFit.Strategy(fit_strategy))

		# Save to workspace
		self.workspace_ = RooWorkspace('w','workspace')
		#getattr(w,'import')(background,ROOT.RooCmdArg())
		getattr(self.workspace_,'import')(background_pdf,RooFit.Rename("background"))
		getattr(self.workspace_,'import')(background_norm,ROOT.RooCmdArg())
		getattr(self.workspace_,'import')(self.data_roohistogram_,RooFit.Rename("data_obs"))
		getattr(self.workspace_, 'import')(model, RooFit.Rename("model"))
		if signal_name:
			getattr(self.workspace_,'import')(signal_roohistogram,RooFit.Rename("signal"))
			getattr(self.workspace_,'import')(signal_pdf,RooFit.Rename("signal_pdf"))
			getattr(self.workspace_,'import')(signal_norm,ROOT.RooCmdArg())
	
		self.workspace_.Print()
		self.workspace_.writeToFile(save_to)
		if signal_name:
			roofit_results[signal_name] = save_to
		else:
			roofit_results["background"] = save_to
예제 #17
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def get_num_sig_bkg(hist_DataTemplate, hist_SignalTemplate,
                    hist_BackgdTemplate, fit_range_min, fit_range_max):
    '''Given 3 input histograms (TH1F), and a fit range, this function finds
    the amount of signal and background that sum up to the data histogram.
    It does histogram fits.'''
    # Find range of data template
    data_min = hist_DataTemplate.GetXaxis().GetXmin()
    data_max = hist_DataTemplate.GetXaxis().GetXmax()

    # Create basic variables
    x = RooRealVar("x", "x", data_min, data_max)
    x.setBins(hist_DataTemplate.GetXaxis().GetNbins())  # Binned x values
    nsig = RooRealVar("nsig", "number of signal events", 0,
                      hist_DataTemplate.Integral())
    nbkg = RooRealVar("nbkg", "number of background events", 0,
                      hist_DataTemplate.Integral())

    # Create RooDataHists from input TH1Fs
    dh = RooDataHist("dh", "dh", RooArgList(x), hist_DataTemplate)
    ds = RooDataHist("ds", "ds", RooArgList(x), hist_SignalTemplate)
    db = RooDataHist("db", "db", RooArgList(x), hist_BackgdTemplate)

    # Create Probability Distribution Functions from Monte Carlo
    sigPDF = RooHistPdf("sigPDF", "sigPDF", RooArgSet(x), ds)
    bkgPDF = RooHistPdf("bkgPDF", "bkgPDF", RooArgSet(x), db)

    model = RooAddPdf("model", "(g1+g2)+a", RooArgList(bkgPDF, sigPDF),
                      RooArgList(nbkg, nsig))

    # Find the edges of the bins that contain the fit range min/max
    data_min = hist_DataTemplate.GetXaxis().GetBinLowEdge(
        hist_DataTemplate.GetXaxis().FindFixBin(fit_range_min))
    data_max = hist_DataTemplate.GetXaxis().GetBinUpEdge(
        hist_DataTemplate.GetXaxis().FindFixBin(fit_range_max))

    r = model.fitTo(dh, RooFit.Save(), RooFit.Minos(0),
                    RooFit.PrintEvalErrors(0), RooFit.Extended(),
                    RooFit.Range(data_min, data_max))
    r.Print("v")

    #print nsig.getVal(), nsig.getError(), nbkg.getVal(), nbkg.getError()
    return [nsig.getVal(), nsig.getError(), nbkg.getVal(), nbkg.getError()]
예제 #18
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 def __init__(self, gauss_1n, cb_2xn, name):
     self.gauss_1n = gauss_1n
     self.cb_2xn = cb_2xn
     #number in the model
     n1n_nam = "num_1n_" + name
     self.num_1n = RooRealVar(n1n_nam, n1n_nam, 200, 0, 3000) # 1
     n2n_nam = "num_2n_" + name
     self.num_2n = RooRealVar(n2n_nam, n2n_nam, 100, 0, 3000) # 0.5
     #1D model Gauss + Crystal Ball
     model_nam = "model_" + name
     self.model = RooAddPdf(model_nam, model_nam, RooArgList(self.gauss_1n, self.cb_2xn), RooArgList(self.num_1n, self.num_2n))
def doubleG(mean_, sigma1_, sigma2_, f1_, tagged_mass, w, fn):

    mean         = RooRealVar ("mean^{%s}"%fn          , "massDG"         ,  mean_      ,      5,    6, "GeV")
    sigma1       = RooRealVar ("#sigma_{1}^{%s}"%fn    , "sigmaDG1"       ,  sigma1_    ,      0,    1, "GeV")
    signalGauss1 = RooGaussian("dg_firstGauss_%s"%fn   , "firstGauss"     ,  tagged_mass,   mean, sigma1)

    sigma2       = RooRealVar ("#sigma_{2}^{%s}"%fn    , "sigmaDG2"       ,  sigma2_    ,      0,   0.12, "GeV")
    signalGauss2 = RooGaussian("dg_secondGauss_%s"%fn  , "secondGauss"    ,  tagged_mass,   mean, sigma2)

    f1           = RooRealVar ("f^{%s}"%fn             , "f1"             ,  f1_        ,     0.,    1. )
    doublegaus   = RooAddPdf  ("doublegaus_%s"%fn      , "gaus1+gaus2"    ,  RooArgList(signalGauss1,signalGauss2), RooArgList(f1))
    _import(w,doublegaus)
예제 #20
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def rooFit502():

    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 ">>> generate data..."
    data = model.generate(RooArgSet(x), 1000)  # RooDataSet

    print ">>> create workspace, import data and model..."
    workspace = RooWorkspace("workspace", "workspace")  # empty RooWorkspace
    getattr(workspace, 'import')(model)  # import model and all its components
    getattr(workspace, 'import')(data)  # import data
    #workspace.import(model) # causes synthax error in python
    #workspace.import(data)  # causes synthax error in python

    print "\n>>> print workspace contents:"
    workspace.Print()

    print "\n>>> save workspace in file..."
    workspace.writeToFile("rooFit502_workspace.root")

    print ">>> save workspace in memory (gDirectory)..."
    gDirectory.Add(workspace)
예제 #21
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    def _make_underlying_model(self):
        self.pdfs = {}
        self.yields = {}  # yields are plain floats
        self.ryields = {}  # keep track of roofit objects for memory management
        h = self._histos[0]
        nbins = h.GetXaxis().GetNbins()
        xmin = h.GetXaxis().GetXmin()
        xmax = h.GetXaxis().GetXmax()
        self.xvar = RooRealVar("x", "x", xmin, xmax)
        self.xvar.setBins(nbins)
        self.pdfs = {}
        self.hists = []
        pdfs = RooArgList()
        yields = RooArgList()
        for histo in self._histos:
            if histo.Integral() == 0:
                continue
            compname = histo.GetName()
            hist = RooDataHist(compname, compname, RooArgList(self.xvar),
                               histo)
            SetOwnership(hist, False)
            # self.hists.append(hist)
            pdf = RooHistPdf(compname, compname, RooArgSet(self.xvar), hist)
            self.pdfs[compname] = pdf
            # self.pdfs[compname].Print()
            pdfs.add(pdf)
            nevts = histo.Integral()
            uncertainty = self._uncertainty
            nmin = min(0, nevts * (1 - uncertainty))
            nmax = nevts * (1 + uncertainty)
            theyield = RooRealVar('n{}'.format(compname),
                                  'n{}'.format(compname), nevts, nmin, nmax)
            self.ryields[compname] = theyield
            self.yields[compname] = nevts
            yields.add(theyield)

        self.underlying_model = RooAddPdf('model', 'model', pdfs, yields)
예제 #22
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 def __init__(self, adc_east, adc_west):
     #input ADC values
     self.adc_east = adc_east
     self.adc_west = adc_west
     #self.adc_east = RooRealVar("adc_east", "adc_east", 10, 1300)
     #self.adc_west = RooRealVar("adc_west", "adc_west", 10, 1300)
     #east Gaussian
     self.gauss_east = Gauss(self.adc_east, "east")
     self.gauss_east.mean_1n.setVal(72.9)
     self.gauss_east.sigma_1n.setVal(21.4)
     #west Gaussian
     self.gauss_west = Gauss(self.adc_west, "west")
     self.gauss_west.mean_1n.setVal(87.7)
     self.gauss_west.sigma_1n.setVal(26.9)
     #east Crystal Ball
     self.cb_east = CrystalBall(self.adc_east, "east")
     self.cb_east.mean_2n.setVal(166.)
     self.cb_east.sigma_2n.setVal(42.1)
     self.cb_east.alpha_2xn.setVal(-0.7)
     self.cb_east.n_2xn.setVal(0.5)
     #west Crystal Ball
     self.cb_west = CrystalBall(self.adc_west, "west")
     self.cb_west.mean_2n.setVal(174.1)
     self.cb_west.sigma_2n.setVal(29.3)
     self.cb_west.alpha_2xn.setVal(-0.3)
     self.cb_west.n_2xn.setVal(0.8)
     # (g_e + c_e)*(g_w + c_w) = g_e*g_w + c_e*c_w + g_e*c_w + c_e*g_w
     #self.num_max = 3000
     self.num_max = 300000
     #1n1n 2D Gaussian
     self.pdf_1n1n = RooProdPdf("pdf_1n1n", "pdf_1n1n", RooArgList(self.gauss_east.gauss_1n, self.gauss_west.gauss_1n))
     self.num_1n1n = RooRealVar("num_1n1n", "num_1n1n", 200, 0, self.num_max) # 1
     #1n2xn Gaussian * Crystal Ball
     self.pdf_1n2xn = RooProdPdf("pdf_1n2xn", "pdf_1n2xn", RooArgList(self.gauss_east.gauss_1n, self.cb_west.cb_2xn))
     self.num_1n2xn = RooRealVar("num_1n2xn", "num_1n2xn", 100, 0, self.num_max) # 1
     #2xn1n Crystal Ball * Gaussian
     self.pdf_2xn1n = RooProdPdf("pdf_2xn1n", "pdf_2xn1n", RooArgList(self.cb_east.cb_2xn, self.gauss_west.gauss_1n))
     self.num_2xn1n = RooRealVar("num_2xn1n", "num_2xn1n", 100, 0, self.num_max) # 1
     #2xn2xn 2D Crystal Ball
     self.pdf_2xn2xn = RooProdPdf("pdf_2xn2xn", "pdf_2xn2xn", RooArgList(self.cb_east.cb_2xn, self.cb_west.cb_2xn))
     self.num_2xn2xn = RooRealVar("num_2xn2xn", "num_2xn2xn", 50, 0, self.num_max) # 1
     #fit model
     self.model = RooAddPdf("model", "model", RooArgList(self.pdf_1n1n, self.pdf_1n2xn, self.pdf_2xn1n, self.pdf_2xn2xn),
     RooArgList(self.num_1n1n, self.num_1n2xn, self.num_2xn1n, self.num_2xn2xn))
예제 #23
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def test_plottable():

    # construct pdf and toy data following example at
    # http://root.cern.ch/drupal/content/roofit

    # Observable
    mes = RooRealVar("mes", "m_{ES} (GeV)", 5.20, 5.30)

    # Parameters
    sigmean = RooRealVar("sigmean", "B^{#pm} mass", 5.28, 5.20, 5.30)
    sigwidth = RooRealVar("sigwidth", "B^{#pm} width", 0.0027, 0.001, 1.)

    # Build Gaussian PDF
    signal = RooGaussian("signal", "signal PDF", mes, sigmean, sigwidth)

    # Build Argus background PDF
    argpar = RooRealVar("argpar", "argus shape parameter", -20.0, -100., -1.)
    background = RooArgusBG("background", "Argus PDF",
                            mes, RooFit.RooConst(5.291), argpar)

    # Construct signal+background PDF
    nsig = RooRealVar("nsig", "#signal events", 200, 0., 10000)
    nbkg = RooRealVar("nbkg", "#background events", 800, 0., 10000)
    model = RooAddPdf("model", "g+a",
                      RooArgList(signal, background),
                      RooArgList(nsig, nbkg))

    # Generate a toyMC sample from composite PDF
    data = model.generate(RooArgSet(mes), 2000)

    # Perform extended ML fit of composite PDF to toy data
    fitresult = model.fitTo(data, RooFit.Save(), RooFit.PrintLevel(-1))

    # Plot toy data and composite PDF overlaid
    mesframe = asrootpy(mes.frame())
    data.plotOn(mesframe)
    model.plotOn(mesframe)

    for obj in mesframe.objects:
        assert_true(obj)
    for curve in mesframe.curves:
        assert_true(curve)
    for hist in mesframe.data_hists:
        assert_true(hist)
    assert_true(mesframe.plotvar)
    with TemporaryFile():
        mesframe.Write()
예제 #24
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def build_pdf():
    lower = -1
    upper = 1
    # create observables
    obs = RooRealVar("obs", "obs1", lower, upper)
    # create parameters
    mean1 = RooRealVar("mean1", "mean of gaussian", 0, -1, 1)
    sigma1 = RooRealVar("sigma1", "sigma of gaussian", 0.1, -1, 1)
    gauss1 = RooGaussian("gauss1", "gaussian PDF", obs, mean1, sigma1)

    mean2 = RooRealVar("mean2", "mean of gaussian", 0.5, -1, 1)
    sigma2 = RooRealVar("sigma2", "sigma of gaussian", 0.2, -1, 1)
    gauss2 = RooGaussian("gauss2", "gaussian PDF", obs, mean2, sigma2)

    mean3 = RooRealVar("mean3", "mean of gaussian", 0.5, -1, 1)
    sigma3 = RooRealVar("sigma3", "sigma of gaussian", 0.3, -1, 1)
    gauss3 = RooGaussian("gauss3", "gaussian PDF", obs, mean3, sigma3)

    mean4 = RooRealVar("mean4", "mean of gaussian", 0.5, -1, 1)
    sigma4 = RooRealVar("sigma4", "sigma of gaussian", 0.4, -1, 1)
    gauss4 = RooGaussian("gauss4", "gaussian PDF", obs, mean4, sigma4)

    mean5 = RooRealVar("mean5", "mean of gaussian", 0.5, -1, 1)
    sigma5 = RooRealVar("sigma5", "sigma of gaussian", 0.5, -1, 1)
    gauss5 = RooGaussian("gauss5", "gaussian PDF", obs, mean5, sigma5)

    frac1 = RooRealVar("frac", "Fraction of a gauss", 0.5, 0, 1)
    frac2 = RooRealVar("frac", "Fraction of a gauss", 0.5, 0, 1)
    frac3 = RooRealVar("frac", "Fraction of a gauss", 0.5, 0, 1)
    frac4 = RooRealVar("frac", "Fraction of a gauss", 0.5, 0, 1)
    model = RooAddPdf(
        "sum_pdf", "sum of pdfs",
        RooArgList(RooArgList(gauss1, gauss2),
                   RooArgList(gauss3, gauss4, gauss5)),
        RooArgList(frac1, frac2, frac3, frac4))
    return obs, model
예제 #25
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def rooFit602():
    
    print ">>> setup model..."
    x      = RooRealVar("x","x",0,10)
    mean   = RooRealVar("mean","mean of gaussian",5)
    sigma1 = RooRealVar("sigma1","width of gaussian",0.5)
    sigma2 = RooRealVar("sigma2","width of gaussian",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,RooArgSet(a0,a1))
    sig1frac = RooRealVar("sig1frac","fraction of component 1 in signal",0.8,0.,1.)
    sig      = RooAddPdf("sig","Signal",RooArgList(sig1,sig2),sig1frac)
    bkgfrac = RooRealVar("bkgfrac","fraction of background",0.5,0.,1.)
    model   = RooAddPdf("model","g1+g2+a",RooArgList(bkg,sig),bkgfrac)
    
    print ">>> create binned dataset..."
    data = model.generate(RooArgSet(x),10000) # RooDataSet
    hist = data.binnedClone() # RooDataHist
    
    # Construct a chi^2 of the data and the model.
    # When a p.d.f. is used in a chi^2 fit, the probability density scaled
    # by the number of events in the dataset to obtain the fit function
    # If model is an extended p.d.f, the expected number events is used
    # instead of the observed number of events.
    model.chi2FitTo(hist)

    # NB: It is also possible to fit a RooAbsReal function to a RooDataHist
    # using chi2FitTo(). 

    # Note that entries with zero bins are _not_ allowed 
    # for a proper chi^2 calculation and will give error
    # messages
    data_small = date.reduce(EventRange(1,100)) # RooDataSet
    hist_small = data_small.binnedClone() # RooDataHist
    chi2_lowstat("chi2_lowstat","chi2",model,hist_small)
    print ">>> chi2_lowstat.getVal() = %s" % chi2_lowstat.getVal()
예제 #26
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        if args.fitBonly: signal_norm.setConstant()
        signal_norm.Print()

        p1 = RooRealVar('p1','p1',args.p1,0.,100.)
        p2 = RooRealVar('p2','p2',args.p2,0.,60.)
        p3 = RooRealVar('p3','p3',args.p3,-10.,10.)
        if args.fixP3: p3.setConstant()

        background = RooGenericPdf('background','(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(sqrtS,sqrtS,sqrtS),RooArgList(mjj,p1,p2,p3))
        background.Print()
        dataInt = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
        background_norm = RooRealVar('background_norm','background_norm',dataInt,0.,1e+08)
        background_norm.Print()

        # S+B model
        model = RooAddPdf("model","s+b",RooArgList(background,signal),RooArgList(background_norm,signal_norm))

        rooDataHist = RooDataHist('rooDatahist','rooDathist',RooArgList(mjj),hData)
        rooDataHist.Print()

        if args.runFit:
            res = model.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
            if not args.decoBkg: res.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")
예제 #27
0
    m = RooRealVar('mean', 'mean', float(mass),
                   float(mass) - 200,
                   float(mass) + 200)
    s = RooRealVar('sigma', 'sigma', 0.1 * float(mass), 0, 10000)
    a = RooRealVar('alpha', 'alpha', 1, -10, 10)
    n = RooRealVar('n', 'n', 1, 0, 100)
    sig = RooCBShape('sig', 'sig', x, m, s, a, n)

    p = RooRealVar('p', 'p', 1, 0, 5)
    x0 = RooRealVar('x0', 'x0', 1000, 100, 5000)

    bkg = RooGenericPdf('bkg', '1/(exp(pow(@0/@1,@2))+1)',
                        RooArgList(x, x0, p))

    fsig = RooRealVar('fsig', 'fsig', 0.5, 0., 1.)
    signal = RooAddPdf('signal', 'signal', sig, bkg, fsig)

    # -----------------------------------------
    # fit signal
    canSname = 'can_Mjj' + str(mass)
    canS = TCanvas(canSname, canSname, 900, 600)
    gPad.SetLogy()

    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)
예제 #28
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def main(infiles=None):

    infile = infiles[0]

    var = "leadmupt"
    bounds = [25, 300]

    c1 = ROOT.TCanvas("NLL", "NLL", 1000, 1000)

    x = ROOT.RooRealVar(var, var, bounds[0], bounds[1])
    aset = ROOT.RooArgSet(x, "aset")

    frame = x.frame()

    f = ROOT.TFile.Open(infile)
    tree = f.Get(f.GetListOfKeys().At(0).GetName())

    tree.Print()
    nentries = tree.GetEntries()

    y = []

    dh2 = ROOT.TH1F()

    data = ROOT.RooDataSet("Data", "Data", aset)
    for n in range(nentries):
        tree.GetEntry(n)
        y.append(getattr(tree, var))
        if y[n] <= bounds[1] and y[n] >= bounds[0]:
            x.setVal(y[n])
            data.add(aset)
            dh2.Fill(y[n])

    data.plotOn(frame)

    dh = RooDataHist("dh", "dh", RooArgSet(x), data)

    nbins = dh2.GetNbinsX()
    nbinsy = dh2.GetNbinsX()
    print("nbins: ", nbins)
    print("nbinsy: ", nbinsy)
    for i in range(nbins):
        if dh2.GetBinContent(dh2.GetBin(i)) == 0:
            print("bin: ", i)
            #dh2.SetBinError(bin,0.01)

    ## CREATE GAUSSIAN MODEL
    mx = RooRealVar("mx", "mx", 10, 0, 350)
    sx = RooRealVar("sx", "sx", 3, 0, 10)
    gx = RooGaussian("gx", "gx", x, mx, sx)

    ## CREATE EXPONENTIAL MODEL
    lambda1 = RooRealVar("lambda1", "slope1", -100, 100)
    expo1 = RooExponential("expo1", "exponential PDF 1", x, lambda1)
    lambda2 = RooRealVar("lambda2", "slope2", -.03, -1000, 1000)
    expo2 = RooExponential("expo2", "exponential PDF 2", x, lambda2)

    l1 = RooRealVar("l1", "yield1", 100, 0, 10000)
    l2 = RooRealVar("l2", "yield2", 100, 0, 10000)

    #sum = RooAddPdf("sum","exp and gauss",RooArgList(expo1,gx),RooArgList(l1,l2))
    sum = RooAddPdf("sum", "2 exps", RooArgList(expo1, expo2),
                    RooArgList(l1, l2))

    ## Construct binned likelihood
    nll = RooNLLVar("nll", "nll", expo1, data, ROOT.RooFit.Extended(True))

    ## Start Minuit session on NLL
    m = RooMinuit(nll)
    m.migrad()
    m.hesse()
    r1 = m.save()

    #sum.plotOn(frame,ROOT.RooFit.LineColor(1))
    #sum.plotOn(frame,ROOT.RooFit.Components("expo1"),ROOT.RooFit.LineColor(2))
    #sum.plotOn(frame,ROOT.RooFit.Components("expo2"),ROOT.RooFit.LineColor(3))

    expo1.plotOn(frame)

    ## Construct Chi2
    chi2 = RooChi2Var("chi2", "chi2", expo2, dh)

    ## Start Minuit session on Chi2
    m2 = RooMinuit(chi2)
    m2.migrad()
    m2.hesse()
    r2 = m2.save()

    frame.Draw()

    c2 = ROOT.TCanvas("Chi2", "Chi2", 1000, 1000)
    frame2 = x.frame()

    data.plotOn(frame2)

    expo2.plotOn(frame2)
    #sum.plotOn(frame2,ROOT.RooFit.LineColor(4))
    #sum.plotOn(frame2,ROOT.RooFit.Components("expo1"),ROOT.RooFit.LineColor(5))
    #sum.plotOn(frame2,ROOT.RooFit.Components("expo2"),ROOT.RooFit.LineColor(6))

    ## Print results
    print("result of likelihood fit")
    r1.Print("v")
    print("result of chi2 fit")
    r2.Print("v")

    frame2.Draw()

    c1.Draw()
    c2.Draw()

    rep = ''
    while not rep in ['q', 'Q']:
        rep = input('enter "q" to quit: ')
        if 1 < len(rep):
            rep = rep[0]
예제 #29
0
파일: fitnul.py 프로젝트: zhangcepku/ihep
def fitMass(rangem=[0.4, 2.0], iflag=1, iopt_bkgshape=0, CBpar=[0., 0., 0.]):
    global myc, fitres
    m0 = sum(rangem) / 2
    #w0=(rangem[1]-rangem[0])/10
    w0 = 0.004
    mass = RooRealVar("ee_ivm", "ee_ivm", rangem[0], rangem[1])

    if iflag == 1:
        ###Construct signal pdf with gaus
        mean = RooRealVar("mean", "mean", m0)
        sigma = RooRealVar("sigma", "sigma", w0)
        signal = RooGaussian("signal", "signal", mass, mean, sigma)
    elif iflag == 2 or iflag == 3:
        ## Construct signal pdf with CB function
        ##print "outinfo",x,CBpar[0],CBpar[1],CBpar[2],CBpar[3]
        cbmean = RooRealVar("cbmean", "cbmean", m0)
        cbsigma = RooRealVar("cbsigma", "cbsigma", CBpar[0])
        n1 = RooRealVar("n1", "", CBpar[1])
        alpha = RooRealVar("alpha", "", CBpar[2])
        cbsigma.setConstant(ROOT.kTRUE)
        n1.setConstant(ROOT.kTRUE)
        alpha.setConstant(ROOT.kTRUE)
        signal = RooCBShape("cball", "crystal ball1", mass, cbmean, cbsigma,
                            alpha, n1)


#    elif iflag ==3:
#        pass
    else:
        print "ERROR, please specify signal shape for fitting!!"
        sys.exit()

    # Construct background pdf
    a0 = RooRealVar("a0", "a0", 0.1, -1, 1)
    a1 = RooRealVar("a1", "a1", 0.004, -1, 1)
    a2 = RooRealVar("a2", "a2", 0.001, -1, 1)
    if iopt_bkgshape == 0:
        background = RooChebychev("background", "background", mass,
                                  RooArgList(a0, a1))
    else:
        background = RooChebychev("background", "background", mass,
                                  RooArgList(a0, a1, a2))
    # Construct composite pdf
    if iflag == 1:
        up_nsig = 40
    else:
        up_nsig = 60
    nsig = RooRealVar("nsig", "nsig", 5, 0.0, up_nsig)
    nbkg = RooRealVar("nbkg", "nbkg", 800, 0, 3000)

    #frac = RooRealVar("frac", "frac", 0.001, 0.0001, 0.1)
    model = RooAddPdf("model", "model", RooArgList(signal, background),
                      RooArgList(nsig, nbkg))
    #model = RooAddPdf("model", "model", RooArgList(signal, background), RooArgList(frac))
    mcdata = RooDataSet(
        "ds", "ds", RooArgSet(mass), RooFit.Import(data),
        RooFit.Cut("ee_ivm<" + str(rangem[1]) + "&&ee_ivm>" + str(rangem[0])))
    if optp == 1:
        ipr = 1
        verbose = 0
    elif optp == 2:
        ipr = 1
        verbose = 1
    else:
        ipr = -1
        verbose = 0

    fitres=model.fitTo(mcdata,RooFit.Save(),RooFit.Minos(1), RooFit.Strategy(2),\
                       RooFit.PrintLevel(ipr), RooFit.Verbose(verbose))
    nll = RooNLLVar("nll", "nll", model, mcdata,
                    RooFit.Range(rangem[0], rangem[1]))
    pll = nll.createProfile(RooArgSet(nsig))
    Profile = RooProfileLL("Profile", "Profile", nll, RooArgSet(nsig))
    llhoodP = RooFormulaVar("llhoodP", "exp(-0.5*Profile)",
                            RooArgList(Profile))
    xframe2 = nsig.frame(RooFit.Title("number of signal"))
    nllplot = nll.plotOn(xframe2, RooFit.ShiftToZero())
    themin = RooConstVar("themin", "themin", nllplot.GetMinimum())

    llhood = RooFormulaVar("llhood", "exp(-0.5*(nll-themin*0.95))",
                           RooArgList(nll, themin))

    if optp:
        xframe = mass.frame(RooFit.Title("mass of ee pair"))

        xframe3 = nsig.frame(RooFit.Title("number of signal"))
        xframe3.SetYTitle("Likelihood")

        mcdata.plotOn(xframe)
        model.plotOn(xframe)
        model.plotOn(xframe, RooFit.Components("background"),
                     RooFit.LineStyle(ROOT.kDashed),
                     RooFit.LineColor(ROOT.kRed))
        model.plotOn(xframe, RooFit.Components("cball"),
                     RooFit.LineStyle(ROOT.kDashed),
                     RooFit.LineColor(ROOT.kGreen))

        myc.cd(1)
        xframe.Draw()

        #pll.plotOn(xframe2,RooFit.LineColor(ROOT.kRed))
        if optp: print "***** archmin ", themin.Print()
        #llhoodP.plotOn(xframe3, RooFit.LineColor(ROOT.kRed))

        llhood.plotOn(xframe3)
        myc.cd(2)

        xframe2.SetMinimum(0)
        xframe2.Draw()
        myc.cd(3)
        xframe3.Draw()
        myc.Update()
        raw_input()

    nsig.setRange("IntRange1", 0, 1000.)
    Int1 = llhood.createIntegral(RooArgSet(nsig),
                                 ROOT.RooFit.Range('IntRange1'))
    Int1Val = Int1.getVal()
    i = 0
    hit = False
    while not (hit):
        i = i + 1
        nsig.setRange("IntRange2", 0, float(i))
        Int2 = llhood.createIntegral(RooArgSet(nsig),
                                     ROOT.RooFit.Range('IntRange2'))
        if Int2.getVal() >= Int1Val * 0.9:
            if optp: print "&&& ", i
            hit = True
            return i
예제 #30
0
def fit_gau2_che(var, dataset, title='', print_pars=False, test=False,
                 mean_=None, sigma_=None, sigma1_=None, sigmaFraction_=None):
    # define background

    c0 = RooRealVar('c0', 'constant', 0.1, -1, 1)
    c1 = RooRealVar('c1', 'linear', 0.6, -1, 1)
    c2 = RooRealVar('c2', 'quadratic', 0.1, -1, 1)
    c3 = RooRealVar('c3', 'c3', 0.1, -1, 1)

    bkg = RooChebychev('bkg', 'background pdf', var,
                       RooArgList(c0, c1, c2, c3))
    
    # define signal
    val = 5.28
    dmean = 0.05 
    valL = val - dmean
    valR = val + dmean

    if mean_ is None:
        mean = RooRealVar("mean", "mean", val, valL, valR)
    else:
        mean = RooRealVar("mean", "mean", mean_)

    val = 0.05
    dmean = 0.02
    valL = val - dmean
    valR = val + dmean

    if sigma_ is None:
        sigma = RooRealVar('sigma', 'sigma', val, valL, valR)
    else:
        sigma = RooRealVar('sigma', 'sigma', sigma_)

    if sigma1_ is None:
        sigma1 = RooRealVar('sigma1', 'sigma1', val, valL, valR)
    else:
        sigma1 = RooRealVar('sigma1', 'sigma1', sigma1_)

    peakGaus = RooGaussian("peakGaus", "peakGaus", var, mean, sigma)
    peakGaus1 = RooGaussian("peakGaus1", "peakGaus1", var, mean, sigma1)    
    
    if sigmaFraction_ is None:
        sigmaFraction = RooRealVar("sigmaFraction", "Sigma Fraction", 0.5, 0., 1.)
    else:
        sigmaFraction = RooRealVar("sigmaFraction", "Sigma Fraction", sigmaFraction_)

    glist = RooArgList(peakGaus, peakGaus1)
    peakG = RooAddPdf("peakG","peakG", glist, RooArgList(sigmaFraction))
    
    listPeak = RooArgList("listPeak")
    
    listPeak.add(peakG)
    listPeak.add(bkg)
    
    fbkg = 0.45
    nEntries = dataset.numEntries()

    val=(1-fbkg)* nEntries
    listArea = RooArgList("listArea")
    
    areaPeak = RooRealVar("areaPeak", "areaPeak", val, 0.,nEntries)
    listArea.add(areaPeak)

    nBkg = fbkg*nEntries
    areaBkg = RooRealVar("areaBkg","areaBkg", nBkg, 0.,nEntries)
    
    listArea.add(areaBkg)
    model = RooAddPdf("model", "fit model", listPeak, listArea)

    if not test:
        fitres = model.fitTo(dataset, RooFit.Extended(kTRUE),
                             RooFit.Minos(kTRUE),RooFit.Save(kTRUE))

    nbins = 35
    frame = var.frame(nbins)

    frame.GetXaxis().SetTitle("B^{0} mass (GeV/c^{2})")
    frame.GetXaxis().CenterTitle()
    frame.GetYaxis().CenterTitle()
    frame.SetTitle(title)

    mk_size = RooFit.MarkerSize(0.3)
    mk_style = RooFit.MarkerStyle(kFullCircle)
    dataset.plotOn(frame, mk_size, mk_style)

    model.plotOn(frame)

    as_bkg = RooArgSet(bkg)
    cp_bkg = RooFit.Components(as_bkg)
    line_style = RooFit.LineStyle(kDashed)
    model.plotOn(frame, cp_bkg, line_style)

    if print_pars:
        fmt = RooFit.Format('NEU')
        lyt = RooFit.Layout(0.65, 0.95, 0.92)
        param = model.paramOn(frame, fmt, lyt)
        param.getAttText().SetTextSize(0.02)
        param.getAttText().SetTextFont(60)
    
    frame.Draw()

    pars = [areaBkg, areaPeak, c0, c1, c2, c3, mean, sigma, sigma1, sigmaFraction]
    return pars
        rrv_number_signal = old_workspace.var("rate_BulkWW_xww_for_unbin");
        
        rrv_number_Total_background_MC = RooRealVar("rrv_number_Total_background_MC_xww","rrv_number_Total_background_MC_xww",
                rrv_number_WJets.getVal()+
                rrv_number_VV.getVal()+
                rrv_number_TTbar.getVal()+
                rrv_number_STop.getVal());

        rrv_number_Total_background_MC.setError(TMath.Sqrt(
                rrv_number_WJets.getError()* rrv_number_WJets.getError()+
                rrv_number_VV.getError()* rrv_number_VV.getError()+
                rrv_number_TTbar.getError()* rrv_number_TTbar.getError()+
                rrv_number_STop.getError() *rrv_number_STop.getError()
                ));

        model_Total_background_MC = RooAddPdf("model_Total_background_MC_xww","model_Total_background_MC_xww",RooArgList(old_workspace.pdf("WJets_xww_%s_%s"%(options.channel,options.category)), old_workspace.pdf("VV_xww_%s_%s"%(options.channel,options.category)),old_workspace.pdf("TTbar_xww_%s_%s"%(options.channel,options.category)),old_workspace.pdf("STop_xww_%s_%s"%(options.channel,options.category))),RooArgList(rrv_number_WJets,rrv_number_VV,rrv_number_TTbar,rrv_number_STop));

        rrv_number_signal.setVal(rrv_number_signal.getVal()*6.25);

        #### scale factor in order to scale MC to data in the final plot -> in order to avoid the normalization to data which is done by default in rooFit
        scale_number_Total_background_MC = rrv_number_Total_background_MC.getVal()/old_workspace.data(datasetname+"_xww_"+options.channel+"_"+options.category).sumEntries();
        scale_number_signal = rrv_number_signal.getVal()/old_workspace.data(datasetname+"_xww_"+options.channel+"_"+options.category).sumEntries();

        model_Total_background_MC.plotOn(mplot,RooFit.Normalization(scale_number_Total_background_MC),RooFit.Name("total_MC"),RooFit.Components("WJets_xww_%s_%s,VV_xww_%s_%s,TTbar_xww_%s_%s,STop_xww_%s_%s"%(options.channel,options.category,options.channel,options.category,options.channel,options.category,options.channel,options.category)),RooFit.DrawOption("L"), RooFit.LineColor(kRed), RooFit.VLines(),RooFit.LineWidth(2));

            
        model_signal_background_MC = RooAddPdf("model_signal_background_MC_xww","model_signal_background_MC_xww",RooArgList(model_pdf,model_Total_background_MC),RooArgList(rrv_number_signal,rrv_number_Total_background_MC));

        model_signal_background_MC.plotOn(mplot,RooFit.Normalization(scale_number_Total_background_MC+scale_number_signal),RooFit.Name("total_SpB_MC"),RooFit.Components("BulkWW_xww_%s_%s,model_Total_background_MC_xww"%(options.channel,options.category)),RooFit.DrawOption("L"), RooFit.LineColor(kBlue), RooFit.VLines(),RooFit.LineWidth(2),RooFit.LineStyle(7));

        model_pdf.plotOn(mplot,RooFit.Name("total_S_MC"),RooFit.Normalization(scale_number_signal),RooFit.DrawOption("L"), RooFit.LineColor(kGreen+2), RooFit.VLines(),RooFit.LineWidth(2),RooFit.LineStyle(kDashed));
예제 #32
0
if fitSig: 

    # define parameters for signal fit
    m = RooRealVar('mean','mean',float(mass),float(mass)-200,float(mass)+200)
    s = RooRealVar('sigma','sigma',0.1*float(mass),0,10000)
    a = RooRealVar('alpha','alpha',1,-10,10)
    n = RooRealVar('n','n',1,0,100)
    sig = RooCBShape('sig','sig',x,m,s,a,n)        

    p  = RooRealVar('p','p',1,0,5)
    x0 = RooRealVar('x0','x0',1000,100,5000)

    bkg = RooGenericPdf('bkg','1/(exp(pow(@0/@1,@2))+1)',RooArgList(x,x0,p))

    fsig= RooRealVar('fsig','fsig',0.5,0.,1.)
    signal = RooAddPdf('signal','signal',sig,bkg,fsig)

    # -----------------------------------------
    # fit signal
    canSname = 'can_Mjj'+str(mass)
    if useSub:
      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)
예제 #33
0
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
def rf501_simultaneouspdf():
    signal_1, bkg_1, signal_2, bkg_2 = get_templates()
    # C r e a t e   m o d e l   f o r   p h y s i c s   s a m p l e
    # -------------------------------------------------------------

    # Create observables
    x = RooRealVar( "x", "x", 0, 200 ) 
    x.setBins(n_bins)
    nsig = RooRealVar( "nsig", "#signal events", N_signal_obs, 0., 2*N_data )
    nbkg = RooRealVar( "nbkg", "#background events", N_bkg1_obs, 0., 2*N_data )

    # Construct signal pdf
#     mean = RooRealVar( "mean", "mean", mu4, 40, 200 ) 
#     sigma = RooRealVar( "sigma", "sigma", sigma4, 0.1, 20 )
#     gx = RooGaussian( "gx", "gx", x, mean, sigma ) 
    roofit_signal_1 = RooDataHist( 'signal_1', 'signal_1', RooArgList(x), signal_1 )
    signal_1_pdf = RooHistPdf ( 'signal_1_pdf' , 'signal_1_pdf', RooArgSet(x), roofit_signal_1) 

    # Construct background pdf
#     mean_bkg = RooRealVar( "mean_bkg", "mean_bkg", mu3, 40, 200 ) 
#     sigma_bkg = RooRealVar( "sigma_bkg", "sigma_bkg", sigma3, 0.1, 20 ) 
#     px = RooGaussian( "px", "px", x, mean_bkg, sigma_bkg ) 
    roofit_bkg_1 = RooDataHist( 'bkg_1', 'bkg_1', RooArgList(x), bkg_1 )
    bkg_1_pdf = RooHistPdf ( 'bkg_1_pdf' , 'bkg_1_pdf', RooArgSet(x), roofit_bkg_1) 

    # Construct composite pdf
    model = RooAddPdf( "model", "model", RooArgList( signal_1_pdf, bkg_1_pdf ), RooArgList( nsig, nbkg ) ) 



    # C r e a t e   m o d e l   f o r   c o n t r o l   s a m p l e
    # --------------------------------------------------------------

    # Construct signal pdf. 
    # NOTE that sigma is shared with the signal sample model
    y = RooRealVar( "y", "y", 0, 200 )
    y.setBins(n_bins)
    mean_ctl = RooRealVar( "mean_ctl", "mean_ctl", mu2, 0, 200 ) 
    sigma_ctl = RooRealVar( "sigma", "sigma", sigma2, 0.1, 10 ) 
    gx_ctl = RooGaussian( "gx_ctl", "gx_ctl", y, mean_ctl, sigma_ctl ) 

    # Construct the background pdf
    mean_bkg_ctl = RooRealVar( "mean_bkg_ctl", "mean_bkg_ctl", mu1, 0, 200 ) 
    sigma_bkg_ctl = RooRealVar( "sigma_bkg_ctl", "sigma_bkg_ctl", sigma1, 0.1, 20 ) 
    px_ctl = RooGaussian( "px_ctl", "px_ctl", y, mean_bkg_ctl, sigma_bkg_ctl ) 

    # Construct the composite model
#     f_ctl = RooRealVar( "f_ctl", "f_ctl", 0.5, 0., 20. ) 
    model_ctl = RooAddPdf( "model_ctl", "model_ctl", RooArgList( gx_ctl, px_ctl ),
                           RooArgList( nsig, nbkg ) ) 
    


    # G e t   e v e n t s   f o r   b o t h   s a m p l e s 
    # ---------------------------------------------------------------
    real_data, real_data_ctl = get_data()
    real_data_hist = RooDataHist( 'real_data_hist',
                                 'real_data_hist',
                                 RooArgList( x ),
                                 real_data )
    real_data_ctl_hist = RooDataHist( 'real_data_ctl_hist',
                                     'real_data_ctl_hist',
                                     RooArgList( y ),
                                     real_data_ctl )
    input_hists = MapStrRootPtr()
    input_hists.insert( StrHist( "physics", real_data ) )
    input_hists.insert( StrHist( "control", real_data_ctl ) )

    # C r e a t e   i n d e x   c a t e g o r y   a n d   j o i n   s a m p l e s 
    # ---------------------------------------------------------------------------
    # Define category to distinguish physics and control samples events
    sample = RooCategory( "sample", "sample" ) 
    sample.defineType( "physics" ) 
    sample.defineType( "control" ) 

    # Construct combined dataset in (x,sample)
    combData = RooDataHist( "combData", "combined data", RooArgList( x), sample ,
                           input_hists )


    # C o n s t r u c t   a   s i m u l t a n e o u s   p d f   i n   ( x , s a m p l e )
    # -----------------------------------------------------------------------------------

    # Construct a simultaneous pdf using category sample as index
    simPdf = RooSimultaneous( "simPdf", "simultaneous pdf", sample ) 

    # Associate model with the physics state and model_ctl with the control state
    simPdf.addPdf( model, "physics" ) 
    simPdf.addPdf( model_ctl, "control" ) 

#60093.048127    173.205689173    44.7112503776

    # P e r f o r m   a   s i m u l t a n e o u s   f i t
    # ---------------------------------------------------
    model.fitTo( real_data_hist,
                RooFit.Minimizer( "Minuit2", "Migrad" ),
                        RooFit.NumCPU( 1 ),
#                         RooFit.Extended(),
#                         RooFit.Save(), 
                        )
    summary = 'fit in signal region\n'
    summary += 'nsig: ' + str( nsig.getValV() ) + ' +- ' + str( nsig.getError() ) + '\n' 
    summary += 'nbkg: ' + str( nbkg.getValV() ) + ' +- ' + str( nbkg.getError() ) + '\n'
#     
#     model_ctl.fitTo( real_data_ctl_hist )
#     summary += 'fit in control region\n'
#     summary += 'nsig: ' + str( nsig.getValV() ) + ' +- ' + str( nsig.getError() ) + '\n' 
#     summary += 'nbkg: ' + str( nbkg.getValV() ) + ' +- ' + str( nbkg.getError() ) + '\n' 
# 
#     # Perform simultaneous fit of model to data and model_ctl to data_ctl
#     simPdf.fitTo( combData ) 
#     summary += 'Combined fit\n'
#     summary += 'nsig: ' + str( nsig.getValV() ) + ' +- ' + str( nsig.getError() ) + '\n' 
#     summary += 'nbkg: ' + str( nbkg.getValV() ) + ' +- ' + str( nbkg.getError() ) + '\n' 


    # P l o t   m o d e l   s l i c e s   o n   d a t a    s l i c e s 
    # ----------------------------------------------------------------

    # Make a frame for the physics sample
    frame1 = x.frame( RooFit.Bins( 30 ), RooFit.Title( "Physics sample" ) ) 

    # Plot all data tagged as physics sample
    combData.plotOn( frame1, RooFit.Cut( "sample==sample::physics" ) ) 

    # Plot "physics" slice of simultaneous pdf. 
    # NBL You _must_ project the sample index category with data using ProjWData 
    # as a RooSimultaneous makes no prediction on the shape in the index category 
    # and can thus not be integrated
    simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ),
                   RooFit.ProjWData( RooArgSet( sample ), combData ), ) 
    simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ),
                   RooFit.Components( "signal_1_pdf" ),
                   RooFit.ProjWData( RooArgSet( sample ), combData ),
                   RooFit.LineStyle( kDashed ),
                   ) 
    simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ),
                   RooFit.Components( "bkg_1_pdf" ),
                   RooFit.ProjWData( RooArgSet( sample ), combData ),
                   RooFit.LineStyle( kDashed ),
                   RooFit.LineColor( kRed ) ) 

    # The same plot for the control sample slice
    frame2 = y.frame( RooFit.Bins( 30 ), RooFit.Title( "Control sample" ) ) 
    combData.plotOn( frame2, RooFit.Cut( "sample==sample::control" ) ) 
    simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ),
                  RooFit.ProjWData( RooArgSet( sample ), combData ) ) 
    simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ),
                  RooFit.Components( "px_ctl" ),
                  RooFit.ProjWData( RooArgSet( sample ), combData ),
                  RooFit.LineStyle( kDashed ) ) 



    c = TCanvas( "rf501_simultaneouspdf", "rf403_simultaneouspdf", 800, 400 ) 
    c.Divide( 2 ) 
    c.cd( 1 )
    gPad.SetLeftMargin( 0.15 )
    frame1.GetYaxis().SetTitleOffset( 1.4 )
    frame1.Draw() 
    c.cd( 2 )
    gPad.SetLeftMargin( 0.15 )
    frame2.GetYaxis().SetTitleOffset( 1.4 )
    frame2.Draw() 
    
    print summary
    print real_data.Integral()
    raw_input()
예제 #35
0
def fitChicSpectrum(dataset, binname):
    """ Fit chic spectrum"""

    x = RooRealVar('Qvalue', 'Q', 9.7, 10.1)
    x.setBins(80)

    mean_1 = RooRealVar("mean_1", "mean ChiB1", 9.892, 9, 10, "GeV")
    sigma_1 = RooRealVar("sigma_1", "sigma ChiB1", 0.0058, 'GeV')
    a1_1 = RooRealVar('#alpha1_1', '#alpha1_1', 0.748)
    n1_1 = RooRealVar('n1_1', 'n1_1', 2.8)
    a2_1 = RooRealVar('#alpha2_1', '#alpha2_1', 1.739)
    n2_1 = RooRealVar('n2_1', 'n2_1', 3.0)

    deltam = RooRealVar('deltam', 'deltam', 0.01943)

    mean_2 = RooFormulaVar("mean_2", "@0+@1", RooArgList(mean_1, deltam))
    sigma_2 = RooRealVar("sigma_2", "sigma ChiB2", 0.0059, 'GeV')
    a1_2 = RooRealVar('#alpha1_2', '#alpha1_2', 0.738)
    n1_2 = RooRealVar('n1_2', 'n1_2', 2.8)
    a2_2 = RooRealVar('#alpha2_2', '#alpha2_2', 1.699)
    n2_2 = RooRealVar('n2_2', 'n2_2', 3.0)

    parameters = RooArgSet()

    parameters.add(RooArgSet(sigma_1, sigma_2))
    parameters = RooArgSet(a1_1, a2_1, n1_1, n2_1)
    parameters.add(RooArgSet(a1_2, a2_2, n1_2, n2_2))

    chib1_pdf = My_double_CB('chib1', 'chib1', x, mean_1, sigma_1, a1_1, n1_1,
                             a2_1, n2_1)
    chib2_pdf = My_double_CB('chib2', 'chib2', x, mean_2, sigma_2, a1_2, n1_2,
                             a2_2, n2_2)

    #background
    q01S_Start = 9.5
    alpha = RooRealVar("#alpha", "#alpha", 1.5, -1, 3.5)  #0.2 anziche' 1
    beta = RooRealVar("#beta", "#beta", -2.5, -7., 0.)
    q0 = RooRealVar("q0", "q0", q01S_Start)  #,9.5,9.7)
    delta = RooFormulaVar("delta", "TMath::Abs(@0-@1)", RooArgList(x, q0))
    b1 = RooFormulaVar("b1", "@0*(@1-@2)", RooArgList(beta, x, q0))
    signum1 = RooFormulaVar("signum1", "( TMath::Sign( -1.,@0-@1 )+1 )/2.",
                            RooArgList(x, q0))

    background = RooGenericPdf("background", "Background",
                               "signum1*pow(delta,#alpha)*exp(b1)",
                               RooArgList(signum1, delta, alpha, b1))

    parameters.add(RooArgSet(alpha, beta, q0))

    #together
    chibs = RooArgList(chib1_pdf, chib2_pdf, background)

    n_chib = RooRealVar("n_chib", "n_chib", 2075, 0, 100000)
    ratio_21 = RooRealVar("ratio_21", "ratio_21", 0.5, 0, 1)
    n_chib1 = RooFormulaVar("n_chib1", "@0/(1+@1)",
                            RooArgList(n_chib, ratio_21))
    n_chib2 = RooFormulaVar("n_chib2", "@0/(1+1/@1)",
                            RooArgList(n_chib, ratio_21))
    n_background = RooRealVar('n_background', 'n_background', 4550, 0, 50000)
    ratio_list = RooArgList(n_chib1, n_chib2, n_background)

    modelPdf = RooAddPdf('ModelPdf', 'ModelPdf', chibs, ratio_list)

    frame = x.frame(RooFit.Title('m'))
    range = x.setRange('range', 9.7, 10.1)
    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')
예제 #36
0
def dofit(roodataset, hname):
  
    mass_chib =  10.5103 # from PES uncorrected mass measurement

    deltaM    =  0.0105   #  MeV theoretical expectations
    ratio21   =  0.45     # same as chic2/chic1 and chib2/chib1
    
    # the following numbers are from an old 3P gun simulation
    # that needs to be re-done
    
    sigma1    =  0.003#0.0031 
    sigma2    =  0.003#0.0035
    
    alpha1    =  0.95
    alpha2    =  1.12

    n         =  2.5  


    mass1_v   = RooRealVar('mchi1','m_{#chi1}',mass_chib)
    deltaM_v  = RooRealVar('deltaM','#Delta_{m}',deltaM,0.005,0.015)
    mass2_v   = RooFormulaVar('mchi2','@0+@1',RooArgList(mass1_v,deltaM_v))
    sigma1_v  = RooRealVar('sigma1','#sigma_1',sigma1)
    sigma2_v  = RooRealVar('sigma2','#sigma_2',sigma2)

    alpha1_v  = RooRealVar('alpha1','#alpha_1',alpha1)
    alpha2_v  = RooRealVar('alpha2','#alpha_2',alpha2)

    n_v       = RooRealVar('n','n',n)

    ratio21_v = RooRealVar('ratio21','r_{21}',ratio21)

    
    x = RooRealVar("invm3S","#chi_{b} Data",10.4,10.7)

    # choose here binning of mass plot
    x.setBins(150)


    #signal pdf
    chib1 = RooCBShape('chib1','chib1',x,mass1_v,sigma1_v,alpha1_v,n_v)
    chib2 = RooCBShape('chib2','chib2',x,mass2_v,sigma2_v,alpha2_v,n_v)
 
    
    # define background
    q01S_Start = 10.4
    alpha =    RooRealVar("#alpha","#alpha",1.5,0.2,3.5)
    beta =     RooRealVar("#beta","#beta",-2.5,-7.,0.)
    #q0   =      RooRealVar("q0","q0",q01S_Start,q01S_Start-0.05,q01S_Start+0.05)
    q0   =      RooRealVar("q0","q0",q01S_Start)
    delta =     RooFormulaVar("delta","TMath::Abs(@0-@1)",RooArgList(x,q0))
    b1 =        RooFormulaVar("b1","@0*(@1-@2)",RooArgList(beta,x,q0))
    signum1 =   RooFormulaVar( "signum1","( TMath::Sign( -1.,@0-@1 )+1 )/2.", RooArgList(x,q0) )

    background = RooGenericPdf("background","Background", "signum1*pow(delta,#alpha)*exp(b1)", RooArgList(signum1,delta,alpha,b1) )


 
 
    n_evts_1 = RooRealVar('N_{3P_{1}}','N_{3P_{1}}',50,30,1000)
    n_evts_2 = RooFormulaVar('N_{3P_{2}}','@0*@1',RooArgList(n_evts_1,ratio21_v))
    n_bck    = RooRealVar('nbkg','n_{bkg}',500,0,100000)


    #build final pdf
    modelPdf = RooAddPdf('ModelPdf', 'ModelPdf', RooArgList(chib1,chib2,background),RooArgList(n_evts_1,n_evts_2,n_bck))
    
    # fit
    low_cut = x.setRange("low_cut",10.4,10.7)
    result = modelPdf.fitTo(roodataset, RooFit.Save(), RooFit.Range("low_cut") )
   
    frame = x.frame(RooFit.Title("m(#chi_{b}(3P))"))
    roodataset.plotOn(frame, RooFit.MarkerSize(0.7))
    modelPdf.plotOn(frame, RooFit.LineWidth(1))


    modelPdf.plotOn(frame, RooFit.LineWidth(2) )

    frame.GetXaxis().SetTitle('m_{#gamma #mu^{+} #mu^{-}} - m_{#mu^{+} #mu^{-}} + m^{PDG}_{#Upsilon(3S)}  [GeV/c^{2}]' )
    #frame.GetYaxis().SetTitle( "Events/15.0 MeV " )
    frame.GetXaxis().SetTitleSize(0.04)
    frame.GetYaxis().SetTitleSize(0.04)
    frame.GetXaxis().SetTitleOffset(1.1)
    frame.GetXaxis().SetLabelSize(0.04)
    frame.GetYaxis().SetLabelSize(0.04)

    frame.SetLineWidth(1)
    frame.SetName("fit_resonance")

    chi2 = frame.chiSquare()
    chi2 = round(chi2,2)
    leg=TLegend(0.50,0.7,0.60,0.8)
    leg.AddEntry(0,'#chi^{2} ='+str(chi2),'')
    leg.SetBorderSize(0)
    leg.SetFillColor(0)
    leg.SetTextSize(0.06)

    gROOT.SetStyle("Plain")

    frame.SaveAs(str(hname) + '.root')

#   param_set = RooArgSet(n_evts_Roo4, m_chib[1][3],alpha, beta, q0)

    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), RooFit.Parameters(param_set))
    modelPdf.paramOn(frame, RooFit.Layout(0.725,0.9875,0.9))
    frame.Draw()
    leg.Draw("same")
    canvas.SaveAs( str(hname) + '.png' )
def main():
    # usage description
    usage = "Example: ./scripts/createDatacards.py --inputData inputs/rawhistV7_Run2015D_scoutingPFHT_UNBLINDED_649_838_JEC_HLTplusV7_Mjj_cor_smooth.root --dataHistname mjj_mjjcor_gev --inputSig inputs/ResonanceShapes_gg_13TeV_Scouting_Spring15.root -f gg -o datacards -l 1866 --lumiUnc 0.027 --massrange 1000 1500 50 --runFit --p1 5 --p2 7 --p3 0.4 --massMin 838 --massMax 2037 --fitStrategy 2"

    # input parameters
    parser = ArgumentParser(description='Script that creates combine datacards and corresponding RooFit workspaces',epilog=usage)

    parser.add_argument("--inputData", dest="inputData", required=True,
                        help="Input data spectrum",
                        metavar="INPUT_DATA")

    parser.add_argument("--dataHistname", dest="dataHistname", required=True,
                        help="Data histogram name",
                        metavar="DATA_HISTNAME")

    parser.add_argument("--inputSig", dest="inputSig", required=True,
                        help="Input signal shapes",
                        metavar="INPUT_SIGNAL")

    parser.add_argument("-f", "--final_state", dest="final_state", required=True,
                        help="Final state (e.g. qq, qg, gg)",
                        metavar="FINAL_STATE")

    parser.add_argument("-f2", "--type", dest="atype", required=True, help="Type (e.g. hG, lG, hR, lR)")

    parser.add_argument("-o", "--output_path", dest="output_path", required=True,
                        help="Output path where datacards and workspaces will be stored",
                        metavar="OUTPUT_PATH")

    parser.add_argument("-l", "--lumi", dest="lumi", required=True,
                        default=1000., type=float,
                        help="Integrated luminosity in pb-1 (default: %(default).1f)",
                        metavar="LUMI")

    parser.add_argument("--massMin", dest="massMin",
                        default=500, type=int,
                        help="Lower bound of the mass range used for fitting (default: %(default)s)",
                        metavar="MASS_MIN")

    parser.add_argument("--massMax", dest="massMax",
                        default=1200, type=int,
                        help="Upper bound of the mass range used for fitting (default: %(default)s)",
                        metavar="MASS_MAX")

    parser.add_argument("--p1", dest="p1",
                        default=5.0000e-03, type=float,
                        help="Fit function p1 parameter (default: %(default)e)",
                        metavar="P1")

    parser.add_argument("--p2", dest="p2",
                        default=9.1000e+00, type=float,
                        help="Fit function p2 parameter (default: %(default)e)",
                        metavar="P2")

    parser.add_argument("--p3", dest="p3",
                        default=5.0000e-01, type=float,
                        help="Fit function p3 parameter (default: %(default)e)",
                        metavar="P3")

    parser.add_argument("--lumiUnc", dest="lumiUnc",
                        required=True, type=float,
                        help="Relative uncertainty in the integrated luminosity",
                        metavar="LUMI_UNC")

    parser.add_argument("--jesUnc", dest="jesUnc",
                        type=float,
                        help="Relative uncertainty in the jet energy scale",
                        metavar="JES_UNC")

    parser.add_argument("--jerUnc", dest="jerUnc",
                        type=float,
                        help="Relative uncertainty in the jet energy resolution",
                        metavar="JER_UNC")

    parser.add_argument("--sqrtS", dest="sqrtS",
                        default=13000., type=float,
                        help="Collision center-of-mass energy (default: %(default).1f)",
                        metavar="SQRTS")

    parser.add_argument("--fixP3", dest="fixP3", default=False, action="store_true", help="Fix the fit function p3 parameter")

    parser.add_argument("--runFit", dest="runFit", default=False, action="store_true", help="Run the fit")

    parser.add_argument("--fitBonly", dest="fitBonly", default=False, action="store_true", help="Run B-only fit")

    parser.add_argument("--fixBkg", dest="fixBkg", default=False, action="store_true", help="Fix all background parameters")

    parser.add_argument("--decoBkg", dest="decoBkg", default=False, action="store_true", help="Decorrelate background parameters")

    parser.add_argument("--fitStrategy", dest="fitStrategy", type=int, default=1, help="Fit strategy (default: %(default).1f)")

    parser.add_argument("--debug", dest="debug", default=False, action="store_true", help="Debug printout")

    parser.add_argument("--postfix", dest="postfix", default='', help="Postfix for the output file names (default: %(default)s)")

    parser.add_argument("--pyes", dest="pyes", default=False, action="store_true", help="Make files for plots")

    parser.add_argument("--jyes", dest="jyes", default=False, action="store_true", help="Make files for JES/JER plots")

    parser.add_argument("--pdir", dest="pdir", default='testarea', help="Name a directory for the plots (default: %(default)s)")

    parser.add_argument("--chi2", dest="chi2", default=False, action="store_true", help="Compute chi squared")

    parser.add_argument("--widefit", dest="widefit", default=False, action="store_true", help="Fit with wide bin hist")

    mass_group = parser.add_mutually_exclusive_group(required=True)
    mass_group.add_argument("--mass",
                            type=int,
                            nargs = '*',
                            default = 1000,
                            help="Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
                            )
    mass_group.add_argument("--massrange",
                            type=int,
                            nargs = 3,
                            help="Define a range of masses to be produced. Format: min max step",
                            metavar = ('MIN', 'MAX', 'STEP')
                            )
    mass_group.add_argument("--masslist",
                            help = "List containing mass information"
                            )

    args = parser.parse_args()

    if args.atype == 'hG':
	fstr = "bbhGGBB"
	in2 = 'bcorrbin/binmodh.root'
    elif args.atype == 'hR':
	fstr = "bbhRS"
	in2 = 'bcorrbin/binmodh.root'
    elif args.atype == 'lG':
	fstr = "bblGGBB"
	in2 = 'bcorrbin/binmodl.root'
    else:
	fstr = "bblRS"
	in2 = 'bcorrbin/binmodl.root'

    # check if the output directory exists
    if not os.path.isdir( os.path.join(os.getcwd(),args.output_path) ):
        os.mkdir( os.path.join(os.getcwd(),args.output_path) )

    # mass points for which resonance shapes will be produced
    masses = []

    if args.massrange != None:
        MIN, MAX, STEP = args.massrange
        masses = range(MIN, MAX+STEP, STEP)
    elif args.masslist != None:
        # A mass list was provided
        print  "Will create mass list according to", args.masslist
        masslist = __import__(args.masslist.replace(".py",""))
        masses = masslist.masses
    else:
        masses = args.mass

    # sort masses
    masses.sort()

    # import ROOT stuff
    from ROOT import gStyle, TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE, TCanvas, TLegend, TPad, TLine
    from ROOT import RooHist, RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf

    if not args.debug:
        RooMsgService.instance().setSilentMode(kTRUE)
        RooMsgService.instance().setStreamStatus(0,kFALSE)
        RooMsgService.instance().setStreamStatus(1,kFALSE)

    # input data file
    inputData = TFile(args.inputData)
    # input data histogram
    hData = inputData.Get(args.dataHistname)

    inData2 = TFile(in2)
    hData2 = inData2.Get('h_data')

    # input sig file
    inputSig = TFile(args.inputSig)

    sqrtS = args.sqrtS

    # mass variable
    mjj = RooRealVar('mjj','mjj',float(args.massMin),float(args.massMax))

    # integrated luminosity and signal cross section
    lumi = args.lumi
    signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section

    for mass in masses:

        print ">> Creating datacard and workspace for %s resonance with m = %i GeV..."%(args.final_state, int(mass))

        # get signal shape
        hSig = inputSig.Get( "h_" + args.final_state + "_" + str(int(mass)) )
        # normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
        hSig.Scale(signalCrossSection*lumi/hSig.Integral()) # divide by a number that provides roughly an r value of 1-10
        rooSigHist = RooDataHist('rooSigHist','rooSigHist',RooArgList(mjj),hSig)
        print 'Signal acceptance:', (rooSigHist.sumEntries()/hSig.Integral())
        signal = RooHistPdf('signal','signal',RooArgSet(mjj),rooSigHist)
        signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+05,1e+05)
        if args.fitBonly: signal_norm.setConstant()

        p1 = RooRealVar('p1','p1',args.p1,0.,100.)
        p2 = RooRealVar('p2','p2',args.p2,0.,60.)
        p3 = RooRealVar('p3','p3',args.p3,-10.,10.)
	p4 = RooRealVar('p4','p4',5.6,-50.,50.)
	p5 = RooRealVar('p5','p5',10.,-50.,50.)
	p6 = RooRealVar('p6','p6',.016,-50.,50.)
	p7 = RooRealVar('p7','p7',8.,-50.,50.)
	p8 = RooRealVar('p8','p8',.22,-50.,50.)
	p9 = RooRealVar('p9','p9',14.1,-50.,50.)
	p10 = RooRealVar('p10','p10',8.,-50.,50.)
	p11 = RooRealVar('p11','p11',4.8,-50.,50.)
	p12 = RooRealVar('p12','p12',7.,-50.,50.)
	p13 = RooRealVar('p13','p13',7.,-50.,50.)
	p14 = RooRealVar('p14','p14',7.,-50.,50.)
	p15 = RooRealVar('p15','p15',1.,-50.,50.)
	p16 = RooRealVar('p16','p16',9.,-50.,50.)
	p17 = RooRealVar('p17','p17',0.6,-50.,50.)

        if args.fixP3: p3.setConstant()

        background = RooGenericPdf('background','(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(sqrtS,sqrtS,sqrtS),RooArgList(mjj,p1,p2,p3))
        dataInt = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
        background_norm = RooRealVar('background_norm','background_norm',dataInt,0.,1e+08)

	background2 = RooGenericPdf('background2','(pow(@0/%.1f,-@1)*pow(1-@0/%.1f,@2))'%(sqrtS,sqrtS),RooArgList(mjj,p4,p5))
        dataInt2 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
        background_norm2 = RooRealVar('background_norm2','background_norm2',dataInt2,0.,1e+08)

	background3 = RooGenericPdf('background3','(1/pow(@1+@0/%.1f,@2))'%(sqrtS),RooArgList(mjj,p6,p7))
        dataInt3 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
        background_norm3 = RooRealVar('background_norm3','background_norm3',dataInt3,0.,1e+08)

	background4 = RooGenericPdf('background4','(1/pow(@1+@2*@0/%.1f+pow(@0/%.1f,2),@3))'%(sqrtS,sqrtS),RooArgList(mjj,p8,p9,p10))
        dataInt4 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
        background_norm4 = RooRealVar('background_norm4','background_norm4',dataInt4,0.,1e+08)

	background5 = RooGenericPdf('background5','(pow(@0/%.1f,-@1)*pow(1-pow(@0/%.1f,1/3),@2))'%(sqrtS,sqrtS),RooArgList(mjj,p11,p12))
        dataInt5 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
        background_norm5 = RooRealVar('background_norm5','background_norm5',dataInt5,0.,1e+08)

	background6 = RooGenericPdf('background6','(pow(@0/%.1f,2)+@1*@0/%.1f+@2)'%(sqrtS,sqrtS),RooArgList(mjj,p13,p14))
        dataInt6 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
        background_norm6 = RooRealVar('background_norm6','background_norm6',dataInt6,0.,1e+08)

	background7 = RooGenericPdf('background7','((-1+@1*@0/%.1f)*pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(sqrtS,sqrtS,sqrtS),RooArgList(mjj,p15,p16,p17))
        dataInt7 = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
        background_norm7 = RooRealVar('background_norm7','background_norm7',dataInt7,0.,1e+08)

        # S+B model
        model = RooAddPdf("model","s+b",RooArgList(background,signal),RooArgList(background_norm,signal_norm))
	model2 = RooAddPdf("model2","s+b2",RooArgList(background2,signal),RooArgList(background_norm2,signal_norm))
	model3 = RooAddPdf("model3","s+b3",RooArgList(background3,signal),RooArgList(background_norm3,signal_norm))
	model4 = RooAddPdf("model4","s+b4",RooArgList(background4,signal),RooArgList(background_norm4,signal_norm))
	model5 = RooAddPdf("model5","s+b5",RooArgList(background5,signal),RooArgList(background_norm5,signal_norm))
	model6 = RooAddPdf("model6","s+b6",RooArgList(background6,signal),RooArgList(background_norm6,signal_norm))
	model7 = RooAddPdf("model7","s+b7",RooArgList(background7,signal),RooArgList(background_norm7,signal_norm))

        rooDataHist = RooDataHist('rooDatahist','rooDathist',RooArgList(mjj),hData)


        if args.runFit:
	    mframe = mjj.frame()
	    rooDataHist.plotOn(mframe, ROOT.RooFit.Name("setonedata"), ROOT.RooFit.Invisible())
	    res = model.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
	    model.plotOn(mframe, ROOT.RooFit.Name("model1"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kRed)) 
	    res2 = model2.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
            model2.plotOn(mframe, ROOT.RooFit.Name("model2"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kOrange))
	    res3 = model3.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
            model3.plotOn(mframe, ROOT.RooFit.Name("model3"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
	    res4 = model4.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
            model4.plotOn(mframe, ROOT.RooFit.Name("model4"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
	    res5 = model5.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
            model5.plotOn(mframe, ROOT.RooFit.Name("model5"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kViolet))
	    res6 = model6.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
#           model6.plotOn(mframe, ROOT.RooFit.Name("model6"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kPink))
	    res7 = model7.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
#           model7.plotOn(mframe, ROOT.RooFit.Name("model7"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kAzure))

	    rooDataHist2 = RooDataHist('rooDatahist2','rooDathist2',RooArgList(mjj),hData2)
	    rooDataHist2.plotOn(mframe, ROOT.RooFit.Name("data"))

	    canvas = TCanvas("cdouble", "cdouble", 800, 1000)

	    gStyle.SetOptStat(0);
            gStyle.SetOptTitle(0);
	    top = TPad("top", "top", 0., 0.5, 1., 1.)
	    top.SetBottomMargin(0.03)
	    top.Draw()
	    top.SetLogy()
            bottom = TPad("bottom", "bottom", 0., 0., 1., 0.5)
	    bottom.SetTopMargin(0.02)
	    bottom.SetBottomMargin(0.2)
	    bottom.Draw()

	    top.cd()
	    frame_top = TH1D("frame_top", "frame_top", 100, 526, 1500)
	    frame_top.GetXaxis().SetTitleSize(0)
	    frame_top.GetXaxis().SetLabelSize(0)
	    frame_top.GetYaxis().SetLabelSize(0.04)
            frame_top.GetYaxis().SetTitleSize(0.04)
            frame_top.GetYaxis().SetTitle("Events")
	    frame_top.SetMaximum(1000.)
	    frame_top.SetMinimum(0.1)
	    frame_top.Draw("axis")
            mframe.Draw("p e1 same")

            bottom.cd()
	    frame_bottom = TH1D("frame_bottom", "frame_bottom", 100, 526, 1500)
            frame_bottom.GetXaxis().SetTitle("m_{jj} [GeV]")
	    frame_bottom.GetYaxis().SetTitle("Pull")

  	    frame_bottom.GetXaxis().SetLabelSize(0.04)
	    frame_bottom.GetXaxis().SetTitleSize(0.06)
	    frame_bottom.GetXaxis().SetLabelOffset(0.01)
	    frame_bottom.GetXaxis().SetTitleOffset(1.1)

	    frame_bottom.GetYaxis().SetLabelSize(0.04)
	    frame_bottom.GetYaxis().SetTitleSize(0.04)
	    frame_bottom.GetYaxis().SetTitleOffset(0.85)

	    frame_bottom.SetMaximum(4.)
            frame_bottom.SetMinimum(-3.)

	    frame_bottom.Draw("axis")

	    zero = TLine(526., 0., 1500., 0.)
	    zero.SetLineColor(ROOT.EColor.kBlack)
	    zero.SetLineStyle(1)
	    zero.SetLineWidth(2)
	    zero.Draw("same")

	    # Ratio histogram with no errors (not so well defined, since this isn't a well-defined efficiency)
	    newHist = mframe.getHist("data")
	    curve = mframe.getObject(1)
	    hresid = newHist.makePullHist(curve,kTRUE)
	    resframe = mjj.frame()
	    mframe.SetAxisRange(526.,1500.)
	    resframe.addPlotable(hresid,"B X")
	    resframe.Draw("same")
	    canvas.cd()
	    canvas.SaveAs("testdouble.pdf")
		

	    if args.pyes:
	    	c = TCanvas("c","c",800,800)
		mframe.SetAxisRange(300.,1300.)
	    	c.SetLogy()
#	    	mframe.SetMaximum(10)
#	    	mframe.SetMinimum(1)
	    	mframe.Draw()
	    	fitname = args.pdir+'/5funcfit_m'+str(mass)+fstr+'.pdf'
	    	c.SaveAs(fitname)

	        cpull = TCanvas("cpull","cpull",800,800)
	    	pulls = mframe.pullHist("data","model3")
	    	pulls.Draw("ABX")
	   	pullname = args.pdir+'/pull_m'+str(mass)+fstr+'.pdf'
	    	cpull.SaveAs(pullname)

		cpull2 = TCanvas("cpull2","cpull2",800,800)
                pulls2 = mframe.pullHist("setonedata","model1")
                pulls2.Draw("ABX")
                pull2name = args.pdir+'/pull2_m'+str(mass)+fstr+'.pdf'
                cpull2.SaveAs(pull2name)

	    if args.widefit:	
		mframew = mjj.frame()
    	        rooDataHist2.plotOn(mframew, ROOT.RooFit.Name("data"))
                res6 = model.fitTo(rooDataHist2, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
            	model.plotOn(mframew, ROOT.RooFit.Name("model1"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kRed))
            	res7 = model2.fitTo(rooDataHist2, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
            	model2.plotOn(mframew, ROOT.RooFit.Name("model2"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kOrange))
            	res8 = model3.fitTo(rooDataHist2, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
            	model3.plotOn(mframew, ROOT.RooFit.Name("model3"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
            	res9 = model4.fitTo(rooDataHist2, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
            	model4.plotOn(mframew, ROOT.RooFit.Name("model4"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
            	res10 = model5.fitTo(rooDataHist2, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
            	model5.plotOn(mframew, ROOT.RooFit.Name("model5"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kViolet))

                if args.pyes:
                    c = TCanvas("c","c",800,800)
                    mframew.SetAxisRange(300.,1300.)
                    c.SetLogy()
#                   mframew.SetMaximum(10)
#                   mframew.SetMinimum(1)
                    mframew.Draw()
                    fitname = args.pdir+'/5funcfittowide_m'+str(mass)+fstr+'.pdf'
		    c.SaveAs(fitname)

                    cpull = TCanvas("cpull","cpull",800,800)
                    pulls = mframew.pullHist("data","model1")
                    pulls.Draw("ABX")
                    pullname = args.pdir+'/pullwidefit_m'+str(mass)+fstr+'.pdf'
                    cpull.SaveAs(pullname)


	    if args.chi2:
		    fullInt = model.createIntegral(RooArgSet(mjj))
		    norm = dataInt/fullInt.getVal()
		    chi1 = 0.
		    fullInt2 = model2.createIntegral(RooArgSet(mjj))
        	    norm2 = dataInt2/fullInt2.getVal()
	      	    chi2 = 0.
		    fullInt3 = model3.createIntegral(RooArgSet(mjj))
       		    norm3 = dataInt3/fullInt3.getVal()
	            chi3 = 0.
		    fullInt4 = model4.createIntegral(RooArgSet(mjj))
       		    norm4 = dataInt4/fullInt4.getVal()
         	    chi4 = 0.
		    fullInt5 = model5.createIntegral(RooArgSet(mjj))
	            norm5 = dataInt5/fullInt5.getVal()
     	            chi5 = 0.
		    for i in range(args.massMin, args.massMax):
        	        new = 0
			new2 = 0
			new3 = 0
			new4 = 0
			new5 = 0
			height = hData.GetBinContent(i)
	        	xLow = hData.GetXaxis().GetBinLowEdge(i)
			xUp = hData.GetXaxis().GetBinLowEdge(i+1)
			obs = height*(xUp-xLow)
			mjj.setRange("intrange",xLow,xUp)
			integ = model.createIntegral(RooArgSet(mjj),ROOT.RooFit.NormSet(RooArgSet(mjj)),ROOT.RooFit.Range("intrange"))
			exp = integ.getVal()*norm
			new = pow(exp-obs,2)/exp
                	chi1 = chi1 + new
			integ2 = model2.createIntegral(RooArgSet(mjj),ROOT.RooFit.NormSet(RooArgSet(mjj)),ROOT.RooFit.Range("intrange"))
                	exp2 = integ2.getVal()*norm2
                	new2 = pow(exp2-obs,2)/exp2
                	chi2 = chi2 + new2
			integ3 = model3.createIntegral(RooArgSet(mjj),ROOT.RooFit.NormSet(RooArgSet(mjj)),ROOT.RooFit.Range("intrange"))
                	exp3 = integ3.getVal()*norm3
                	new3 = pow(exp3-obs,2)/exp3
                	chi3 = chi3 + new3
			integ4 = model4.createIntegral(RooArgSet(mjj),ROOT.RooFit.NormSet(RooArgSet(mjj)),ROOT.RooFit.Range("intrange"))
                	exp4 = integ4.getVal()*norm4
			if exp4 != 0:
                	    new4 = pow(exp4-obs,2)/exp4
                	else:
			    new4 = 0
			chi4 = chi4 + new4
			integ5 = model5.createIntegral(RooArgSet(mjj),ROOT.RooFit.NormSet(RooArgSet(mjj)),ROOT.RooFit.Range("intrange"))
                	exp5 = integ5.getVal()*norm5
                	new5 = pow(exp5-obs,2)/exp5
                	chi5 = chi5 + new5
	    	    print "chi1 %d "%(chi1)
	    	    print "chi2 %d "%(chi2)
	    	    print "chi3 %d "%(chi3)
	    	    print "chi4 %d "%(chi4)
	    	    print "chi5 %d "%(chi5)

	    if not args.decoBkg: 
		print " "
		res.Print()
#	        res2.Print()
#		res3.Print()
#		res4.Print()
#		res5.Print()
#		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)

        if args.jerUnc != None:
            hSig_Syst['JERUp'] = copy.deepcopy(hSig)
            hSig_Syst['JERDown'] = copy.deepcopy(hSig)

        # reset signal histograms
        for key in hSig_Syst.keys():
            hSig_Syst[key].Reset()
            hSig_Syst[key].SetName(hSig_Syst[key].GetName() + '_' + key)

        # produce JES signal shapes
        if args.jesUnc != None:
            for i in range(1, hSig.GetNbinsX()+1):
                xLow = hSig.GetXaxis().GetBinLowEdge(i)
                xUp = hSig.GetXaxis().GetBinLowEdge(i+1)
                jes = 1. - args.jesUnc
                xLowPrime = jes*xLow
                xUpPrime = jes*xUp
                hSig_Syst['JESUp'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
                jes = 1. + args.jesUnc
                xLowPrime = jes*xLow
                xUpPrime = jes*xUp
                hSig_Syst['JESDown'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
            hSig_Syst_DataHist['JESUp'] = RooDataHist('hSig_JESUp','hSig_JESUp',RooArgList(mjj),hSig_Syst['JESUp'])
            hSig_Syst_DataHist['JESDown'] = RooDataHist('hSig_JESDown','hSig_JESDown',RooArgList(mjj),hSig_Syst['JESDown'])
	    
	    if args.jyes:
		c2 = TCanvas("c2","c2",800,800)
	    	mframe2 = mjj.frame(ROOT.RooFit.Title("JES One Sigma Shifts"))
	    	mframe2.SetAxisRange(525.,1200.)
		hSig_Syst_DataHist['JESUp'].plotOn(mframe2, ROOT.RooFit.Name("JESUP"),ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kRed), ROOT.RooFit.LineColor(ROOT.EColor.kRed))
	    	hSig_Syst_DataHist['JESDown'].plotOn(mframe2,ROOT.RooFit.Name("JESDOWN"),ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kBlue), ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
	    	rooSigHist.plotOn(mframe2, ROOT.RooFit.DataError(2),ROOT.RooFit.Name("SIG"),ROOT.RooFit.DrawOption("L"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kGreen), ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
	    	mframe2.Draw()
		mframe2.GetXaxis().SetTitle("Dijet Mass (GeV)")
		leg = TLegend(0.7,0.8,0.9,0.9)
		leg.AddEntry(mframe2.findObject("SIG"),"Signal Model","l")
		leg.AddEntry(mframe2.findObject("JESUP"),"+1 Sigma","l")
		leg.AddEntry(mframe2.findObject("JESDOWN"),"-1 Sigma","l")
		leg.Draw()
	    	jesname = args.pdir+'/jes_m'+str(mass)+fstr+'.pdf'
	    	c2.SaveAs(jesname)

        # produce JER signal shapes
        if args.jesUnc != None:
            for i in range(1, hSig.GetNbinsX()+1):
                xLow = hSig.GetXaxis().GetBinLowEdge(i)
                xUp = hSig.GetXaxis().GetBinLowEdge(i+1)
                jer = 1. - args.jerUnc
                xLowPrime = jer*(xLow-float(mass))+float(mass)
                xUpPrime = jer*(xUp-float(mass))+float(mass)
                hSig_Syst['JERUp'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
                jer = 1. + args.jerUnc
                xLowPrime = jer*(xLow-float(mass))+float(mass)
                xUpPrime = jer*(xUp-float(mass))+float(mass)
                hSig_Syst['JERDown'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
            hSig_Syst_DataHist['JERUp'] = RooDataHist('hSig_JERUp','hSig_JERUp',RooArgList(mjj),hSig_Syst['JERUp'])
            hSig_Syst_DataHist['JERDown'] = RooDataHist('hSig_JERDown','hSig_JERDown',RooArgList(mjj),hSig_Syst['JERDown'])

	    if args.jyes:
	    	c3 = TCanvas("c3","c3",800,800)
            	mframe3 = mjj.frame(ROOT.RooFit.Title("JER One Sigma Shifts"))
	    	mframe3.SetAxisRange(525.,1200.)
		hSig_Syst_DataHist['JERUp'].plotOn(mframe3,ROOT.RooFit.Name("JERUP"),ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kRed), ROOT.RooFit.LineColor(ROOT.EColor.kRed))
            	hSig_Syst_DataHist['JERDown'].plotOn(mframe3,ROOT.RooFit.Name("JERDOWN"),ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kBlue), ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
            	rooSigHist.plotOn(mframe3,ROOT.RooFit.DrawOption("L"),ROOT.RooFit.Name("SIG"), ROOT.RooFit.DataError(2), ROOT.RooFit.LineStyle(1), ROOT.RooFit.MarkerColor(ROOT.EColor.kGreen), ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
            	mframe3.Draw()
	    	mframe3.GetXaxis().SetTitle("Dijet Mass (GeV)")
		leg = TLegend(0.7,0.8,0.9,0.9)
                leg.AddEntry(mframe3.findObject("SIG"),"Signal Model","l")
                leg.AddEntry(mframe3.findObject("JERUP"),"+1 Sigma","l")
                leg.AddEntry(mframe3.findObject("JERDOWN"),"-1 Sigma","l")
                leg.Draw()	
		jername = args.pdir+'/jer_m'+str(mass)+fstr+'.pdf'
           	c3.SaveAs(jername)


        # -----------------------------------------
        # create a datacard and corresponding workspace
        postfix = (('_' + args.postfix) if args.postfix != '' else '')
        dcName = 'datacard_' + args.final_state + '_m' + str(mass) + postfix + '.txt'
        wsName = 'workspace_' + args.final_state + '_m' + str(mass) + postfix + '.root'

        w = RooWorkspace('w','workspace')
        getattr(w,'import')(rooSigHist,RooFit.Rename("signal"))
        if args.jesUnc != None:
            getattr(w,'import')(hSig_Syst_DataHist['JESUp'],RooFit.Rename("signal__JESUp"))
            getattr(w,'import')(hSig_Syst_DataHist['JESDown'],RooFit.Rename("signal__JESDown"))
        if args.jerUnc != None:
            getattr(w,'import')(hSig_Syst_DataHist['JERUp'],RooFit.Rename("signal__JERUp"))
            getattr(w,'import')(hSig_Syst_DataHist['JERDown'],RooFit.Rename("signal__JERDown"))
        if args.decoBkg:
            getattr(w,'import')(background_deco,ROOT.RooCmdArg())
        else:
            getattr(w,'import')(background,ROOT.RooCmdArg(),RooFit.Rename("background"))

	#if use different fits for shape uncertainties
	#getattr(w,'import')(,ROOT.RooCmdArg(),RooFit.Rename("background__bkgUp"))
	#getattr(w,'import')(,ROOT.RooCmdArg(),RooFit.Rename("background__bkgDown"))
	
	getattr(w,'import')(background_norm,ROOT.RooCmdArg())
        getattr(w,'import')(rooDataHist,RooFit.Rename("data_obs"))
        w.Print()
        w.writeToFile(os.path.join(args.output_path,wsName))

	beffUnc = 0.3
	boffUnc = 0.06

        datacard = open(os.path.join(args.output_path,dcName),'w')
        datacard.write('imax 1\n')
        datacard.write('jmax 1\n')
        datacard.write('kmax *\n')
        datacard.write('---------------\n')
        if args.jesUnc != None or args.jerUnc != None:
            datacard.write('shapes * * '+wsName+' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
        else:
            datacard.write('shapes * * '+wsName+' w:$PROCESS\n')
        datacard.write('---------------\n')
        datacard.write('bin 1\n')
        datacard.write('observation -1\n')
        datacard.write('------------------------------\n')
        datacard.write('bin          1          1\n')
        datacard.write('process      signal     background\n')
        datacard.write('process      0          1\n')
        datacard.write('rate         -1         1\n')
        datacard.write('------------------------------\n')
        datacard.write('lumi  lnN    %f         -\n'%(1.+args.lumiUnc))
	datacard.write('beff  lnN    %f         -\n'%(1.+beffUnc))
	datacard.write('boff  lnN    %f         -\n'%(1.+boffUnc))
	datacard.write('bkg   lnN     -         1.03\n')
        if args.jesUnc != None:
            datacard.write('JES  shape   1          -\n')
        if args.jerUnc != None:
            datacard.write('JER  shape   1          -\n')
        # flat parameters --- flat prior
        datacard.write('background_norm  flatParam\n')
        if args.decoBkg:
            datacard.write('deco_eig1  flatParam\n')
            datacard.write('deco_eig2  flatParam\n')
            if not args.fixP3: datacard.write('deco_eig3  flatParam\n')
        else:
            datacard.write('p1  flatParam\n')
            datacard.write('p2  flatParam\n')
            if not args.fixP3: datacard.write('p3  flatParam\n')
        datacard.close()


    print '>> Datacards and workspaces created and stored in %s/'%( os.path.join(os.getcwd(),args.output_path) )
예제 #38
0
    #sig=RooHistPdf("sig","sig",RooArgSet(mass),signal)

    nsig=RooRealVar("nsig"+str(cut),"number of signal events",100,0,1e10)
    nbkg=RooRealVar("nbkg"+str(cut),"number of background events",100,0,1e10)
    nbkg1=RooRealVar("nbkg1"+str(cut),"number of background events",100,0,1e10)
    nbkg2=RooRealVar("nbkg2"+str(cut),"number of background events",100,0,1e10)

    l0=RooRealVar("l0","l0",100.,0.,1000.)
    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()
예제 #39
0
def main():

    parser = OptionParser()
    parser.add_option("-d", "--dir", type="string", dest="outDir", metavar="DIR", default="./",
                      help="output directory for .png")
        

    (options, args) = parser.parse_args()

    if os.path.exists(options.outDir) and options.outDir!="./":
        print "Sorry, file ",options.outDir," already exist, choose another name\n"
        exit(1)
    else:
        os.system("mkdir -p "+options.outDir)


    """
    Set the style ...
    """

    myNewStyle = TStyle("myNewStyle","A better style for my plots")
    setStyle(myNewStyle)

    TH1F.SetDefaultSumw2(True)

    # Histogram range
    xlow = 70.
    xup = 110.
    
    # Mass range for fit
    minM_fit = 75.
    maxM_fit = 105.

    # Ratio plot range
    minR = 0.8
    maxR = 1.2

    # TLines for ratio plot
    line = TLine(minM_fit,1,maxM_fit,1)
    line.SetLineWidth(2)
    line.SetLineColor(2)

    # Canvas
    spectrum_height = 0.75
    ratio_correction = 1.4
    ratio_height = (1-spectrum_height)*ratio_correction
    xOffset = 0.08
    yOffset = 0.04

    cTest = TCanvas("cTest","cTest")
    
    c2 = TCanvas("c2","c2")
    c2.SetFillColor(0)
    c2.Divide(1,2)

    
    c3 = TCanvas("c3","c3")
    c3.SetFillColor(0)
    c3.Divide(1,2)

    # Files MuScleFit
    fDataBefore = TFile("h3_Z_data_beforeCorrection.root")
    fDataAfter  = TFile("h3_Z_data_afterCorrection.root")
    fMcBefore = TFile("h3_Z_mc_beforeCorrection.root")
    fMcAfter  = TFile("h3_Z_mc_afterCorrection.root")
    
                
    # Retrieve histograms and fit
    hDataBefore = fDataBefore.Get("h1_Z_data")
    hDataAfter  = fDataAfter.Get("h1_Z_data")
    hMcBefore   = fMcBefore.Get("h1_Z_mc")
    hMcAfter    = fMcAfter.Get("h1_Z_mc")

    # Identifiers
    ids = ["data_before","data_after","mc_before","mc_after"]

    # Create histograms dictionary
    histos = {}
    histos["data_before"] = hDataBefore
    histos["data_after"]  = hDataAfter
    histos["mc_before"]   = hMcBefore
    histos["mc_after"]    = hMcAfter

    histosSubtr = {}

    # Create parameters dictionary
    expPars = {}
    signalPars = {}

    for i in ids:
        # RooFit definitions
        ## RooRealVars
        x = RooRealVar("x","M_{#mu#mu} (GeV)",minM_fit,maxM_fit)
        mean = RooRealVar("mean","mean",91.19,87.,94.)
        meanCB = RooRealVar("meanCB","meanCB",0.,-10.,10.)
        meanCB.setConstant(True)
        width = RooRealVar("width","width",2.4952,2.3,2.6)
        width.setConstant(True)
        sigma = RooRealVar("sigma","sigma",1.3,0.001,3.)
        #    sigma.setConstant(True)
        slope = RooRealVar("slope","slope",-0.1,-1.0,0.)
        #    slope.setConstant(True)
        alpha = RooRealVar("alpha","alpha",1.,0.,30.)
        #    alpha.setConstant(True)
        N = RooRealVar("N","N",2.,0.,100.)
        #    N.setConstant(True)
        fsig = RooRealVar("fsig","fsig",0.9,0.,1.0)
        
        ## PDFs
        relBW = RooGenericPdf("relBW","relBW","@0/(pow(@0*@0-@1*@1,2) + @2*@2*@0*@0*@0*@0/(@1*@1))",RooArgList(x,mean,width))
        CB = RooCBShape("CB","CB",x,meanCB,sigma,alpha,N)
        expo = RooExponential("expo","expo",x,slope)
        relBWTimesCB = RooFFTConvPdf("relBWTimesCB","relBWTimesCB",x,relBW,CB)
        relBWTimesCBPlusExp = RooAddPdf("relBWTimesCBPlusExp","relBWTimesCBPlusExp",relBWTimesCB,expo,fsig)

        # Fit
        frame = x.frame()
        h = histos[i]
        # h.Rebin(10)
        h.Sumw2()
        nbin = h.GetNbinsX()
        dh = RooDataHist("dh","dh",RooArgList(x),h)
        dh.plotOn(frame)
        relBWTimesCBPlusExp.fitTo(dh)
        relBWTimesCBPlusExp.plotOn(frame)
        relBWTimesCBPlusExp.paramOn(frame)

        # Plot
        c = TCanvas("c_"+i,"c_"+i) 
        c.SetFillColor(0)
        c.cd()
        frame.Draw()
        c.SaveAs(options.outDir+"/DimuonWithFit_"+i+".png")
        
        # Extract the result of the fit
        expParams = []
        expCoef = slope.getValV()
        fSig    = fsig.getValV()
        binLow = h.GetXaxis().FindBin(minM_fit)
        binHigh = h.GetXaxis().FindBin(maxM_fit)
        nEntries = h.Integral(binLow,binHigh)
        expParams = [expCoef,fSig,nEntries,binLow,binHigh]
        expPars[i] = expParams

        signalParams = [mean.getVal(),width.getVal(),meanCB.getVal(),sigma.getVal(),alpha.getVal(),N.getVal()]
        signalPars[i] = signalParams

        # Subtract the bkg from the histograms
        h_woBkg = h.Clone()
        h_bkg = TH1F("h_bkg_"+i,"Histogram of bkg events",nbin,xlow,xup)
        
        h_bkg.Sumw2()
        expNorm = (math.fabs(expCoef)*(1-fSig)*nEntries)/(math.exp(expCoef*minM_fit)-math.exp(expCoef*maxM_fit))
        for ibin in range(binLow,binHigh):
            w = integrateExp(expNorm,expCoef,h_bkg.GetBinLowEdge(ibin),h_bkg.GetBinLowEdge(ibin+1))
            h_bkg.SetBinContent(ibin,w)

        h_woBkg.Add(h_bkg,-1)
        nEvts_woBkg = h_woBkg.Integral(binLow,binHigh)
        h_woBkg.Scale(1/nEvts_woBkg)
        histosSubtr[i] = h_woBkg


        del expParams, c
        del relBWTimesCBPlusExp, relBW, CB, relBWTimesCB, expo
        del x, mean, width, sigma, fsig, N, alpha, slope, meanCB
        del frame, dh, h, h_woBkg, h_bkg



    # BEFORE CORRECTIONS
    
#     # RooFit definitions (again, sorry)
#     ## RooRealVars
#     x = RooRealVar("x","M_{#mu#mu} (GeV)",minM_fit,maxM_fit)
#     mean = RooRealVar("mean","mean",91.19)
#     meanCB = RooRealVar("meanCB","meanCB",0.)
#     width = RooRealVar("width","width",2.4952)
#     sigma = RooRealVar("sigma","sigma",1.3)
#     alpha = RooRealVar("alpha","alpha",1.)
#     N = RooRealVar("N","N",2.)

#     ## PDFs (again, sorry)
#     relBW = RooGenericPdf("relBW","relBW","@0/(pow(@0*@0-@1*@1,2) + @2*@2*@0*@0*@0*@0/(@1*@1))",RooArgList(x,mean,width))
#     CB = RooCBShape("CB","CB",x,meanCB,sigma,alpha,N)
#     relBWTimesCB = RooFFTConvPdf("relBWTimesCB","relBWTimesCB",x,relBW,CB)

    
    # Ratio plot after background subtraction
    histosSubtr["data_before"].GetXaxis().SetRangeUser(minM_fit,maxM_fit)
    histosSubtr["data_before"].GetYaxis().SetRangeUser(0.,histosSubtr["data_before"].GetMaximum()+0.1*histosSubtr["data_before"].GetMaximum())
    histosSubtr["data_before"].SetLineColor(1)

    histosSubtr["mc_before"].GetXaxis().SetRangeUser(minM_fit,maxM_fit)
    if histosSubtr["mc_before"].GetMaximum()>histosSubtr["data_before"].GetMaximum():
        histosSubtr["data_before"].GetYaxis().SetRangeUser(0.,histosSubtr["mc_before"].GetMaximum()+0.1*histosSubtr["mc_before"].GetMaximum())
    histosSubtr["mc_before"].SetLineColor(2)

    ## This is the simple overlay of the normalized spectra
    # c2.cd(1)
    r.SetOwnership(c2, False)
    c2_spectrum = c2.GetListOfPrimitives().FindObject("c2_1")
    c2_ratio    = c2.GetListOfPrimitives().FindObject("c2_2")
    c2_spectrum.SetPad(0.+xOffset, (1 - spectrum_height)+yOffset, 1.+xOffset, 1.+yOffset)
    c2_ratio.SetPad(0.+xOffset, 0.+yOffset, 1.+xOffset, ratio_height+yOffset)
    c2_ratio.SetTopMargin(0)   
    c2_ratio.SetBottomMargin(0.2)
    c2_spectrum.SetLeftMargin(0.12)
    c2_spectrum.SetRightMargin(0.15)
    c2_ratio.SetLeftMargin(0.12)
    c2_ratio.SetRightMargin(0.15)

    leg=0
    leg = TLegend(0.10,0.75,0.40,0.90)
    leg.SetHeader("Before corrections")
    leg.SetFillColor(0)
    leg.SetTextSize(0.06)
    leg.AddEntry(histosSubtr["data_before"],"DATA")
    leg.AddEntry(histosSubtr["mc_before"],"MC")
    setHistoStyle(histosSubtr["data_before"])
    setHistoStyle(histosSubtr["mc_before"])
    histosSubtr["data_before"].SetTitle("Dimuon mass spectrum (after background subtraction) data vs. MC")
    histosSubtr["data_before"].GetXaxis().SetTitle("M_{#mu#mu} (GeV)")
    histosSubtr["data_before"].GetYaxis().SetTitle("Arbitrary units")
    histosSubtr["mc_before"].GetXaxis().SetTitle("M_{#mu#mu} (GeV)")
    histosSubtr["mc_before"].GetYaxis().SetTitle("Arbitrary units")
    
    c2_spectrum.cd()
#     c2_spectrum.SetLogy()
    histosSubtr["data_before"].Draw("HIST")
    histosSubtr["mc_before"].Draw("HISTsame")
    tpt_pars_data = TPaveText(0.13, 0.45, 0.4, 0.59, "NDC")
    tpt_pars_data.SetTextSize(0.045)
    tpt_pars_data.SetTextFont(42)
    tpt_pars_data.SetFillColor(0)
    tpt_pars_data.SetBorderSize(0)
    tpt_pars_data.SetMargin(0.01)
    tpt_pars_data.SetTextAlign(12)
    tpt_pars_data.AddText(0.0,0.9,"M^{fit}_{Z,data} = "+str(round(signalPars["data_before"][0],2))+" GeV")
    tpt_pars_data.AddText(0.0,0.4,"#sigma_{CB,data} = "+str(round(signalPars["data_before"][3],2))+" GeV")
    tpt_pars_data.Draw("same")
    
    tpt_pars_mc = TPaveText(0.13, 0.30, 0.4, 0.44, "NDC")
    tpt_pars_mc.SetTextSize(0.045)
    tpt_pars_mc.SetTextFont(42)
    tpt_pars_mc.SetFillColor(0)
    tpt_pars_mc.SetBorderSize(0)
    tpt_pars_mc.SetMargin(0.01)
    tpt_pars_mc.SetTextAlign(12)
    tpt_pars_mc.SetTextColor(2)
    tpt_pars_mc.AddText(0.0,0.9,"M^{fit}_{Z,MC} = "+str(round(signalPars["mc_before"][0],2))+" GeV")
    tpt_pars_mc.AddText(0.0,0.4,"#sigma_{CB,MC} = "+str(round(signalPars["mc_before"][3],2))+" GeV")
    tpt_pars_mc.Draw("same")
    leg.Draw("same")
    
#     mean.setVal(signalPars["data_before"][0])
#     sigma.setVal(signalPars["data_before"][3])
#     alpha.setVal(signalPars["data_before"][4])
#     N.setVal(signalPars["data_before"][5])
#     dh_data = RooDataHist("dh_data","dh_data",RooArgList(x),histosSubtr["data_before"])
#     frame_data = x.frame()
#     dh_data.plotOn(frame_data,RooFit.LineColor(1),RooFit.DrawOption("B"))
#     # relBWTimesCB.plotOn(frame_data,RooFit.LineColor(1))
#     frame_data.Draw()

#     mean.setVal(signalPars["mc_before"][0])
#     # mean.setVal(97)
#     sigma.setVal(signalPars["mc_before"][3])
#     alpha.setVal(signalPars["mc_before"][4])
#     N.setVal(signalPars["mc_before"][5])
#     dh_mc = RooDataHist("dh_mc","dh_mc",RooArgList(x),histosSubtr["mc_before"])
#     frame_mc = x.frame()
#     # dh_mc.plotOn(frame_mc,RooFit.LineColor(2),RooFit.MarkerColor(2),RooFit.MarkerSize(0.4),RooFit.DrawOption("HIST"))
#     # relBWTimesCB.plotOn(frame_mc,RooFit.LineColor(2))
#     # frame_mc.Draw("same")


    ## Ratio histogram
    h_Num_woBkg = histosSubtr["data_before"].Clone()
    h_Den_woBkg = histosSubtr["mc_before"].Clone()
    # h_Num_woBkg.Rebin(10)
    # h_Den_woBkg.Rebin(10)
    h_Num_woBkg.Divide(h_Den_woBkg)
    h_Num_woBkg.GetXaxis().SetRangeUser(minM_fit,maxM_fit)
    h_Num_woBkg.GetYaxis().SetRangeUser(minR,maxR)
    h_Num_woBkg.GetXaxis().SetTitleSize(0.09)
    h_Num_woBkg.GetYaxis().SetTitleSize(0.09)
    h_Num_woBkg.GetXaxis().SetLabelSize(0.08)
    h_Num_woBkg.GetYaxis().SetLabelSize(0.08)
    h_Num_woBkg.GetYaxis().SetTitleOffset(0.45)

    ## This is the DATA/MC ratio
    c2_ratio.cd()
    h_Num_woBkg.GetYaxis().SetTitle("DATA/MC Ratio")
    h_Num_woBkg.SetTitle("")
    # setHistoStyle(h_Num_woBkg)
    h_Num_woBkg.SetMarkerStyle(20)
    h_Num_woBkg.SetMarkerSize(0.85)
    h_Num_woBkg.Draw("E")
    line.Draw("same")
    c2.SaveAs(options.outDir+"/DimuonAfterBkgSub_beforeCorrections.png")
    c2.SaveAs(options.outDir+"/DimuonAfterBkgSub_beforeCorrections.pdf")
    c2.SaveAs(options.outDir+"/DimuonAfterBkgSub_beforeCorrections.eps")

    del tpt_pars_data, tpt_pars_mc, h_Num_woBkg


    # AFTER CORRECTIONS
    
#     # RooFit definitions (again, sorry)
#     ## RooRealVars
#     x = RooRealVar("x","M_{#mu#mu} (GeV)",minM_fit,maxM_fit)
#     mean = RooRealVar("mean","mean",91.19)
#     meanCB = RooRealVar("meanCB","meanCB",0.)
#     width = RooRealVar("width","width",2.4952)
#     sigma = RooRealVar("sigma","sigma",1.3)
#     alpha = RooRealVar("alpha","alpha",1.)
#     N = RooRealVar("N","N",2.)

#     ## PDFs (again, sorry)
#     relBW = RooGenericPdf("relBW","relBW","@0/(pow(@0*@0-@1*@1,2) + @2*@2*@0*@0*@0*@0/(@1*@1))",RooArgList(x,mean,width))
#     CB = RooCBShape("CB","CB",x,meanCB,sigma,alpha,N)
#     relBWTimesCB = RooFFTConvPdf("relBWTimesCB","relBWTimesCB",x,relBW,CB)

    
    # Ratio plot after background subtraction
    histosSubtr["data_after"].GetXaxis().SetRangeUser(minM_fit,maxM_fit)
    histosSubtr["data_after"].GetYaxis().SetRangeUser(0.,histosSubtr["data_after"].GetMaximum()+0.1*histosSubtr["data_after"].GetMaximum())
    histosSubtr["data_after"].SetLineColor(1)

    histosSubtr["mc_after"].GetXaxis().SetRangeUser(minM_fit,maxM_fit)
    if histosSubtr["mc_after"].GetMaximum()>histosSubtr["data_after"].GetMaximum():
        histosSubtr["data_after"].GetYaxis().SetRangeUser(0.,histosSubtr["mc_after"].GetMaximum()+0.1*histosSubtr["mc_after"].GetMaximum())
    histosSubtr["mc_after"].SetLineColor(2)

    ## This is the simple overlay of the normalized spectra
    # c3.cd(1)
    r.SetOwnership(c3, False)
    c3_spectrum = c3.GetListOfPrimitives().FindObject("c3_1")
    c3_ratio    = c3.GetListOfPrimitives().FindObject("c3_2")
    c3_spectrum.SetPad(0.+xOffset, (1 - spectrum_height)+yOffset, 1.+xOffset, 1.+yOffset)
    c3_ratio.SetPad(0.+xOffset, 0.+yOffset, 1.+xOffset, ratio_height+yOffset)
    c3_ratio.SetTopMargin(0)   
    c3_ratio.SetBottomMargin(0.2)
    c3_spectrum.SetLeftMargin(0.12)
    c3_spectrum.SetRightMargin(0.15)
    c3_ratio.SetLeftMargin(0.12)
    c3_ratio.SetRightMargin(0.15)

    leg=0
    leg = TLegend(0.10,0.75,0.40,0.90)
    leg.SetHeader("After corrections")
    leg.SetFillColor(0)
    leg.SetTextSize(0.06)
    leg.AddEntry(histosSubtr["data_after"],"DATA")
    leg.AddEntry(histosSubtr["mc_after"],"MC")
    setHistoStyle(histosSubtr["data_after"])
    setHistoStyle(histosSubtr["mc_after"])
    histosSubtr["data_after"].SetTitle("Dimuon mass spectrum (after background subtraction) data vs. MC")
    histosSubtr["data_after"].GetXaxis().SetTitle("M_{#mu#mu} (GeV)")
    histosSubtr["data_after"].GetYaxis().SetTitle("Arbitrary units")
    histosSubtr["mc_after"].GetXaxis().SetTitle("M_{#mu#mu} (GeV)")
    histosSubtr["mc_after"].GetYaxis().SetTitle("Arbitrary units")
    
    c3_spectrum.cd()
#     c3_spectrum.SetLogy()
    histosSubtr["data_after"].Draw("HIST")
    histosSubtr["mc_after"].Draw("HISTsame")
    tpt_pars_data = 0
    tpt_pars_data = TPaveText(0.13, 0.45, 0.4, 0.59, "NDC")
    tpt_pars_data.SetTextSize(0.045)
    tpt_pars_data.SetTextFont(42)
    tpt_pars_data.SetFillColor(0)
    tpt_pars_data.SetBorderSize(0)
    tpt_pars_data.SetMargin(0.01)
    tpt_pars_data.SetTextAlign(12)
    tpt_pars_data.AddText(0.0,0.9,"M^{fit}_{Z,data} = "+str(round(signalPars["data_after"][0],2))+" GeV")
    tpt_pars_data.AddText(0.0,0.4,"#sigma_{CB,data} = "+str(round(signalPars["data_after"][3],2))+" GeV")
    tpt_pars_data.Draw("same")

    tpt_pars_mc = 0
    tpt_pars_mc = TPaveText(0.13, 0.30, 0.4, 0.44, "NDC")
    tpt_pars_mc.SetTextSize(0.045)
    tpt_pars_mc.SetTextFont(42)
    tpt_pars_mc.SetFillColor(0)
    tpt_pars_mc.SetBorderSize(0)
    tpt_pars_mc.SetMargin(0.01)
    tpt_pars_mc.SetTextAlign(12)
    tpt_pars_mc.SetTextColor(2)
    tpt_pars_mc.AddText(0.0,0.9,"M^{fit}_{Z,MC} = "+str(round(signalPars["mc_after"][0],2))+" GeV")
    tpt_pars_mc.AddText(0.0,0.4,"#sigma_{CB,MC} = "+str(round(signalPars["mc_after"][3],2))+" GeV")
    tpt_pars_mc.Draw("same")
    leg.Draw("same")
    
#     mean.setVal(signalPars["data_after"][0])
#     sigma.setVal(signalPars["data_after"][3])
#     alpha.setVal(signalPars["data_after"][4])
#     N.setVal(signalPars["data_after"][5])
#     dh_data = RooDataHist("dh_data","dh_data",RooArgList(x),histosSubtr["data_after"])
#     frame_data = x.frame()
#     dh_data.plotOn(frame_data,RooFit.LineColor(1),RooFit.DrawOption("B"))
#     # relBWTimesCB.plotOn(frame_data,RooFit.LineColor(1))
#     frame_data.Draw()

#     mean.setVal(signalPars["mc_after"][0])
#     # mean.setVal(97)
#     sigma.setVal(signalPars["mc_after"][3])
#     alpha.setVal(signalPars["mc_after"][4])
#     N.setVal(signalPars["mc_after"][5])
#     dh_mc = RooDataHist("dh_mc","dh_mc",RooArgList(x),histosSubtr["mc_after"])
#     frame_mc = x.frame()
#     # dh_mc.plotOn(frame_mc,RooFit.LineColor(2),RooFit.MarkerColor(2),RooFit.MarkerSize(0.4),RooFit.DrawOption("HIST"))
#     # relBWTimesCB.plotOn(frame_mc,RooFit.LineColor(2))
#     # frame_mc.Draw("same")


    ## Ratio histogram
    h_Num_woBkg = histosSubtr["data_after"].Clone()
    h_Den_woBkg = histosSubtr["mc_after"].Clone()
    # h_Num_woBkg.Rebin(10)
    # h_Den_woBkg.Rebin(10)
    h_Num_woBkg.Divide(h_Den_woBkg)
    h_Num_woBkg.GetXaxis().SetRangeUser(minM_fit,maxM_fit)
    h_Num_woBkg.GetYaxis().SetRangeUser(minR,maxR)
    h_Num_woBkg.GetXaxis().SetTitleSize(0.09)
    h_Num_woBkg.GetYaxis().SetTitleSize(0.09)
    h_Num_woBkg.GetXaxis().SetLabelSize(0.08)
    h_Num_woBkg.GetYaxis().SetLabelSize(0.08)
    h_Num_woBkg.GetYaxis().SetTitleOffset(0.45)

    ## This is the DATA/MC ratio
    c3_ratio.cd()
    h_Num_woBkg.GetYaxis().SetTitle("DATA/MC Ratio")
    h_Num_woBkg.SetTitle("")
    # setHistoStyle(h_Num_woBkg)
    h_Num_woBkg.SetMarkerStyle(20)
    h_Num_woBkg.SetMarkerSize(0.85)
    h_Num_woBkg.Draw("E")
    line.Draw("same")
    c3.SaveAs(options.outDir+"/DimuonAfterBkgSub_afterCorrections.png")
    c3.SaveAs(options.outDir+"/DimuonAfterBkgSub_afterCorrections.pdf")
    c3.SaveAs(options.outDir+"/DimuonAfterBkgSub_afterCorrections.eps")
예제 #40
0
def alpha(channel):

    nElec = channel.count("e")
    nMuon = channel.count("m")
    nLept = nElec + nMuon
    nBtag = channel.count("b")

    # Channel-dependent settings
    # Background function. Semi-working options are: EXP, EXP2, EXPN, EXPTAIL
    if nLept == 0:
        treeName = "SR"
        signName = "XZh"
        colorVjet = sample["DYJetsToNuNu"]["linecolor"]
        triName = "HLT_PFMET"
        leptCut = "0==0"
        topVeto = selection["TopVetocut"]
        massVar = "X_cmass"
        binFact = 1
        fitFunc = "EXPN" if nBtag < 2 else "EXPN"
        fitAltFunc = "EXPTAIL" if nBtag < 2 else "EXPTAIL"
        fitFuncVjet = "ERFEXP" if nBtag < 2 else "EXP"
        fitAltFuncVjet = "POL" if nBtag < 2 else "POL"
        fitFuncVV = "EXPGAUS" if nBtag < 2 else "EXPGAUS"
        fitFuncTop = "GAUS2"
    elif nLept == 1:
        treeName = "WCR"
        signName = "XWh"
        colorVjet = sample["WJetsToLNu"]["linecolor"]
        triName = "HLT_Ele" if nElec > 0 else "HLT_Mu"
        leptCut = "isWtoEN" if nElec > 0 else "isWtoMN"
        topVeto = selection["TopVetocut"]
        massVar = "X_mass"
        binFact = 2
        if nElec > 0:
            fitFunc = "EXPTAIL" if nBtag < 2 else "EXPN"
            fitAltFunc = "EXPN" if nBtag < 2 else "POW"
        else:
            fitFunc = "EXPN" if nBtag < 2 else "EXPN"
            fitAltFunc = "EXPTAIL" if nBtag < 2 else "POW"
        fitFuncVjet = "ERFEXP" if nBtag < 2 else "EXP"
        fitAltFuncVjet = "POL" if nBtag < 2 else "POL"
        fitFuncVV = "EXPGAUS" if nBtag < 2 else "EXPGAUS"
        fitFuncTop = "GAUS3" if nBtag < 2 else "GAUS2"
    else:
        treeName = "XZh"
        signName = "XZh"
        colorVjet = sample["DYJetsToLL"]["linecolor"]
        triName = "HLT_Ele" if nElec > 0 else "HLT_Mu"
        leptCut = "isZtoEE" if nElec > 0 else "isZtoMM"
        topVeto = "X_dPhi>2.5"
        massVar = "X_mass"
        binFact = 2
        if nElec > 0:
            fitFunc = "EXPTAIL" if nBtag < 2 else "EXPTAIL"
            fitAltFunc = "POW" if nBtag < 2 else "POW"
        else:
            fitFunc = "EXPTAIL" if nBtag < 2 else "EXPTAIL"
            fitAltFunc = "POW" if nBtag < 2 else "POW"
        fitFuncVjet = "ERFEXP" if nBtag < 2 and nElec < 1 else "EXP"
        fitAltFuncVjet = "POL" if nBtag < 2 else "POL"
        fitFuncVV = "EXPGAUS2" if nBtag < 2 else "EXPGAUS2"
        fitFuncTop = "GAUS"

    btagCut = selection["2Btag"] if nBtag == 2 else selection["1Btag"]

    print "--- Channel", channel, "---"
    print "  number of electrons:", nElec, " muons:", nMuon, " b-tags:", nBtag
    print "  read tree:", treeName, "and trigger:", triName
    if ALTERNATIVE:
        print "  using ALTERNATIVE fit functions"
    print "-" * 11 * 2

    # Silent RooFit
    RooMsgService.instance().setGlobalKillBelow(RooFit.FATAL)

    # *******************************************************#
    #                                                       #
    #              Variables and selections                 #
    #                                                       #
    # *******************************************************#

    # Define all the variables from the trees that will be used in the cuts and fits
    # this steps actually perform a "projection" of the entire tree on the variables in thei ranges, so be careful once setting the limits
    X_mass = RooRealVar(massVar, "m_{X}" if nLept > 0 else "m_{T}^{X}", XBINMIN, XBINMAX, "GeV")
    J_mass = RooRealVar("fatjet1_prunedMassCorr", "jet corrected pruned mass", HBINMIN, HBINMAX, "GeV")
    CSV1 = RooRealVar("fatjet1_CSVR1", "", -1.0e99, 1.0e4)
    CSV2 = RooRealVar("fatjet1_CSVR2", "", -1.0e99, 1.0e4)
    nB = RooRealVar("fatjet1_nBtag", "", 0.0, 4)
    CSVTop = RooRealVar("bjet1_CSVR", "", -1.0e99, 1.0e4)
    X_dPhi = RooRealVar("X_dPhi", "", 0.0, 3.15)
    isZtoEE = RooRealVar("isZtoEE", "", 0.0, 2)
    isZtoMM = RooRealVar("isZtoMM", "", 0.0, 2)
    isWtoEN = RooRealVar("isWtoEN", "", 0.0, 2)
    isWtoMN = RooRealVar("isWtoMN", "", 0.0, 2)
    weight = RooRealVar("eventWeightLumi", "", -1.0e9, 1.0)

    # Define the RooArgSet which will include all the variables defined before
    # there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
    variables = RooArgSet(X_mass, J_mass, CSV1, CSV2, nB, CSVTop, X_dPhi)
    variables.add(RooArgSet(isZtoEE, isZtoMM, isWtoEN, isWtoMN, weight))

    # set reasonable ranges for J_mass and X_mass
    # these are used in the fit in order to avoid ROOFIT to look in regions very far away from where we are fitting
    # (honestly, it is not clear to me why it is necessary, but without them the fit often explodes)
    J_mass.setRange("h_reasonable_range", LOWMIN, HIGMAX)
    X_mass.setRange("X_reasonable_range", XBINMIN, XBINMAX)

    # Set RooArgSets once for all, see https://root.cern.ch/phpBB3/viewtopic.php?t=11758
    jetMassArg = RooArgSet(J_mass)
    # Define the ranges in fatJetMass - these will be used to define SB and SR
    J_mass.setRange("LSBrange", LOWMIN, LOWMAX)
    J_mass.setRange("HSBrange", HIGMIN, HIGMAX)
    J_mass.setRange("VRrange", LOWMAX, SIGMIN)
    J_mass.setRange("SRrange", SIGMIN, SIGMAX)

    # Set binning for plots
    J_mass.setBins(HBINS)
    X_mass.setBins(binFact * XBINS)

    # Define the selection for the various categories (base + SR / LSBcut / HSBcut )
    baseCut = leptCut + " && " + btagCut + "&&" + topVeto
    massCut = massVar + ">%d" % XBINMIN
    baseCut += " && " + massCut

    # Cuts
    SRcut = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), SIGMIN, J_mass.GetName(), SIGMAX)
    LSBcut = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), LOWMIN, J_mass.GetName(), LOWMAX)
    HSBcut = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), HIGMIN, J_mass.GetName(), HIGMAX)
    SBcut = baseCut + " && ((%s>%d && %s<%d) || (%s>%d && %s<%d))" % (
        J_mass.GetName(),
        LOWMIN,
        J_mass.GetName(),
        LOWMAX,
        J_mass.GetName(),
        HIGMIN,
        J_mass.GetName(),
        HIGMAX,
    )
    VRcut = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), LOWMAX, J_mass.GetName(), SIGMIN)

    # Binning
    binsJmass = RooBinning(HBINS, HBINMIN, HBINMAX)
    # binsJmass.addUniform(HBINS, HBINMIN, HBINMAX)
    binsXmass = RooBinning(binFact * XBINS, XBINMIN, XBINMAX)
    # binsXmass.addUniform(binFact*XBINS, XBINMIN, XBINMAX)

    # *******************************************************#
    #                                                       #
    #                      Input files                      #
    #                                                       #
    # *******************************************************#

    # Import the files using TChains (separately for the bkg "classes" that we want to describe: here DY and VV+ST+TT)
    treeData = TChain(treeName)
    treeMC = TChain(treeName)
    treeVjet = TChain(treeName)
    treeVV = TChain(treeName)
    treeTop = TChain(treeName)
    treeSign = {}
    nevtSign = {}
    for i, m in enumerate(massPoints):
        treeSign[m] = TChain(treeName)

    # Read data
    pd = getPrimaryDataset(triName)
    if len(pd) == 0:
        raw_input("Warning: Primary Dataset not recognized, continue?")
    for i, s in enumerate(pd):
        treeData.Add(NTUPLEDIR + s + ".root")

    # Read V+jets backgrounds
    for i, s in enumerate(["WJetsToLNu_HT", "DYJetsToNuNu_HT", "DYJetsToLL_HT"]):
        for j, ss in enumerate(sample[s]["files"]):
            treeVjet.Add(NTUPLEDIR + ss + ".root")

    # Read VV backgrounds
    for i, s in enumerate(["VV"]):
        for j, ss in enumerate(sample[s]["files"]):
            treeVV.Add(NTUPLEDIR + ss + ".root")

    # Read Top backgrounds
    for i, s in enumerate(["ST", "TTbar"]):
        for j, ss in enumerate(sample[s]["files"]):
            treeTop.Add(NTUPLEDIR + ss + ".root")

    # Read signals
    for i, m in enumerate(massPoints):
        for j, ss in enumerate(sample["%s_M%d" % (signName, m)]["files"]):
            treeSign[m].Add(NTUPLEDIR + ss + ".root")
            sfile = TFile(NTUPLEDIR + ss + ".root", "READ")
            shist = sfile.Get("Counters/Counter")
            nevtSign[m] = shist.GetBinContent(1)
            sfile.Close()

    # Sum all background MC
    treeMC.Add(treeVjet)
    treeMC.Add(treeVV)
    treeMC.Add(treeTop)

    # create a dataset to host data in sideband (using this dataset we are automatically blind in the SR!)
    setDataSB = RooDataSet(
        "setDataSB", "setDataSB", variables, RooFit.Cut(SBcut), RooFit.WeightVar(weight), RooFit.Import(treeData)
    )
    setDataLSB = RooDataSet(
        "setDataLSB", "setDataLSB", variables, RooFit.Import(setDataSB), RooFit.Cut(LSBcut), RooFit.WeightVar(weight)
    )
    setDataHSB = RooDataSet(
        "setDataHSB", "setDataHSB", variables, RooFit.Import(setDataSB), RooFit.Cut(HSBcut), RooFit.WeightVar(weight)
    )

    # Observed data (WARNING, BLIND!)
    setDataSR = RooDataSet(
        "setDataSR", "setDataSR", variables, RooFit.Cut(SRcut), RooFit.WeightVar(weight), RooFit.Import(treeData)
    )
    setDataVR = RooDataSet(
        "setDataVR", "setDataVR", variables, RooFit.Cut(VRcut), RooFit.WeightVar(weight), RooFit.Import(treeData)
    )  # Observed in the VV mass, just for plotting purposes

    setDataSRSB = RooDataSet(
        "setDataSRSB",
        "setDataSRSB",
        variables,
        RooFit.Cut("(" + SRcut + ") || (" + SBcut + ")"),
        RooFit.WeightVar(weight),
        RooFit.Import(treeData),
    )

    # same for the bkg datasets from MC, where we just apply the base selections (not blind)
    setVjet = RooDataSet(
        "setVjet", "setVjet", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeVjet)
    )
    setVjetSB = RooDataSet(
        "setVjetSB", "setVjetSB", variables, RooFit.Import(setVjet), RooFit.Cut(SBcut), RooFit.WeightVar(weight)
    )
    setVjetSR = RooDataSet(
        "setVjetSR", "setVjetSR", variables, RooFit.Import(setVjet), RooFit.Cut(SRcut), RooFit.WeightVar(weight)
    )
    setVV = RooDataSet(
        "setVV", "setVV", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeVV)
    )
    setVVSB = RooDataSet(
        "setVVSB", "setVVSB", variables, RooFit.Import(setVV), RooFit.Cut(SBcut), RooFit.WeightVar(weight)
    )
    setVVSR = RooDataSet(
        "setVVSR", "setVVSR", variables, RooFit.Import(setVV), RooFit.Cut(SRcut), RooFit.WeightVar(weight)
    )
    setTop = RooDataSet(
        "setTop", "setTop", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeTop)
    )
    setTopSB = RooDataSet(
        "setTopSB", "setTopSB", variables, RooFit.Import(setTop), RooFit.Cut(SBcut), RooFit.WeightVar(weight)
    )
    setTopSR = RooDataSet(
        "setTopSR", "setTopSR", variables, RooFit.Import(setTop), RooFit.Cut(SRcut), RooFit.WeightVar(weight)
    )

    print "  Data events SB: %.2f" % setDataSB.sumEntries()
    print "  V+jets entries: %.2f" % setVjet.sumEntries()
    print "  VV, VH entries: %.2f" % setVV.sumEntries()
    print "  Top,ST entries: %.2f" % setTop.sumEntries()

    nVV = RooRealVar("nVV", "VV normalization", setVV.sumEntries(SBcut), 0.0, 2 * setVV.sumEntries(SBcut))
    nTop = RooRealVar("nTop", "Top normalization", setTop.sumEntries(SBcut), 0.0, 2 * setTop.sumEntries(SBcut))
    nVjet = RooRealVar("nVjet", "Vjet normalization", setDataSB.sumEntries(), 0.0, 2 * setDataSB.sumEntries(SBcut))
    nVjet2 = RooRealVar("nVjet2", "Vjet2 normalization", setDataSB.sumEntries(), 0.0, 2 * setDataSB.sumEntries(SBcut))

    # Apply Top SF
    nTop.setVal(nTop.getVal() * topSF[nLept][nBtag])
    nTop.setError(nTop.getVal() * topSFErr[nLept][nBtag])

    # Define entries
    entryVjet = RooRealVar("entryVjets", "V+jets normalization", setVjet.sumEntries(), 0.0, 1.0e6)
    entryVV = RooRealVar("entryVV", "VV normalization", setVV.sumEntries(), 0.0, 1.0e6)
    entryTop = RooRealVar("entryTop", "Top normalization", setTop.sumEntries(), 0.0, 1.0e6)

    entrySB = RooRealVar("entrySB", "Data SB normalization", setDataSB.sumEntries(SBcut), 0.0, 1.0e6)
    entrySB.setError(math.sqrt(entrySB.getVal()))

    entryLSB = RooRealVar("entryLSB", "Data LSB normalization", setDataSB.sumEntries(LSBcut), 0.0, 1.0e6)
    entryLSB.setError(math.sqrt(entryLSB.getVal()))

    entryHSB = RooRealVar("entryHSB", "Data HSB normalization", setDataSB.sumEntries(HSBcut), 0.0, 1.0e6)
    entryHSB.setError(math.sqrt(entryHSB.getVal()))

    ###################################################################################
    #        _   _                                                                    #
    #       | \ | |                          | (_)         | | (_)                    #
    #       |  \| | ___  _ __ _ __ ___   __ _| |_ ___  __ _| |_ _  ___  _ __          #
    #       | . ` |/ _ \| '__| '_ ` _ \ / _` | | / __|/ _` | __| |/ _ \| '_ \         #
    #       | |\  | (_) | |  | | | | | | (_| | | \__ \ (_| | |_| | (_) | | | |        #
    #       |_| \_|\___/|_|  |_| |_| |_|\__,_|_|_|___/\__,_|\__|_|\___/|_| |_|        #
    #                                                                                 #
    ###################################################################################
    # fancy ASCII art thanks to, I guess, Jose

    # start by creating the fit models to get the normalization:
    # * MAIN and SECONDARY bkg are taken from MC by fitting the whole J_mass range
    # * 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"),
        RooFit.Save(1),
        RooFit.PrintLevel(1 if VERBOSE else -1),
    )
    frVjet2 = VjetMass2.fitTo(
        setVjet,
        RooFit.SumW2Error(True),
        RooFit.Range("h_reasonable_range"),
        RooFit.Strategy(2),
        RooFit.Minimizer("Minuit2"),
        RooFit.Save(1),
        RooFit.PrintLevel(1 if VERBOSE else -1),
    )

    if VERBOSE:
        print "********** Fit result [JET MASS Vjets] *" + "*" * 40, "\n", frVjet.Print(), "\n", "*" * 80

    # likelihoodScan(VjetMass, setVjet, [constVjet, offsetVjet, widthVjet])

    # *******************************************************#
    #                                                       #
    #                 VV, VH normalization                  #
    #                                                       #
    # *******************************************************#

    # Variables for VV
    # Error function and exponential to model the bulk
    constVV = RooRealVar("constVV", "slope of the exp", -0.030, -0.1, 0.0)
    offsetVV = RooRealVar("offsetVV", "offset of the erf", 90.0, 1.0, 300.0)
    widthVV = RooRealVar("widthVV", "width of the erf", 50.0, 1.0, 100.0)
    erfrVV = RooErfExpPdf("baseVV", "error function for VV jet mass", J_mass, constVV, offsetVV, widthVV)
    expoVV = RooExponential("baseVV", "error function for VV jet mass", J_mass, constVV)
    # gaussian for the V mass peak
    meanVV = RooRealVar("meanVV", "mean of the gaussian", 90.0, 60.0, 100.0)
    sigmaVV = RooRealVar("sigmaVV", "sigma of the gaussian", 10.0, 6.0, 30.0)
    fracVV = RooRealVar("fracVV", "fraction of gaussian wrt erfexp", 3.2e-1, 0.0, 1.0)
    gausVV = RooGaussian("gausVV", "gaus for VV jet mass", J_mass, meanVV, sigmaVV)
    # gaussian for the H mass peak
    meanVH = RooRealVar("meanVH", "mean of the gaussian", 125.0, 100.0, 150.0)
    sigmaVH = RooRealVar("sigmaVH", "sigma of the gaussian", 10.0, 5.0, 50.0)
    fracVH = RooRealVar("fracVH", "fraction of gaussian wrt erfexp", 1.5e-2, 0.0, 1.0)
    gausVH = RooGaussian("gausVH", "gaus for VH jet mass", J_mass, meanVH, sigmaVH)

    # Define VV model
    if fitFuncVV == "ERFEXPGAUS":
        VVMass = RooAddPdf("VVMass", fitFuncVV, RooArgList(gausVV, erfrVV), RooArgList(fracVV))
    elif fitFuncVV == "ERFEXPGAUS2":
        VVMass = RooAddPdf("VVMass", fitFuncVV, RooArgList(gausVH, gausVV, erfrVV), RooArgList(fracVH, fracVV))
    elif fitFuncVV == "EXPGAUS":
        VVMass = RooAddPdf("VVMass", fitFuncVV, RooArgList(gausVV, expoVV), RooArgList(fracVV))
    elif fitFuncVV == "EXPGAUS2":
        VVMass = RooAddPdf("VVMass", fitFuncVV, RooArgList(gausVH, gausVV, expoVV), RooArgList(fracVH, fracVV))
    else:
        print "  ERROR! Pdf", fitFuncVV, "is not implemented for VV"
        exit()

    # fit to secondary bkg in MC (whole range)
    frVV = VVMass.fitTo(
        setVV,
        RooFit.SumW2Error(True),
        RooFit.Range("h_reasonable_range"),
        RooFit.Strategy(2),
        RooFit.Minimizer("Minuit2"),
        RooFit.Save(1),
        RooFit.PrintLevel(1 if VERBOSE else -1),
    )

    if VERBOSE:
        print "********** Fit result [JET MASS VV] ****" + "*" * 40, "\n", frVV.Print(), "\n", "*" * 80

    # *******************************************************#
    #                                                       #
    #                 Top, ST normalization                 #
    #                                                       #
    # *******************************************************#

    # Variables for Top
    # Error Function * Exponential to model the bulk
    constTop = RooRealVar("constTop", "slope of the exp", -0.030, -1.0, 0.0)
    offsetTop = RooRealVar("offsetTop", "offset of the erf", 175.0, 50.0, 250.0)
    widthTop = RooRealVar("widthTop", "width of the erf", 100.0, 1.0, 300.0)
    gausTop = RooGaussian("baseTop", "gaus for Top jet mass", J_mass, offsetTop, widthTop)
    erfrTop = RooErfExpPdf("baseTop", "error function for Top jet mass", J_mass, constTop, offsetTop, widthTop)
    # gaussian for the W mass peak
    meanW = RooRealVar("meanW", "mean of the gaussian", 80.0, 70.0, 90.0)
    sigmaW = RooRealVar("sigmaW", "sigma of the gaussian", 10.0, 2.0, 20.0)
    fracW = RooRealVar("fracW", "fraction of gaussian wrt erfexp", 0.1, 0.0, 1.0)
    gausW = RooGaussian("gausW", "gaus for W jet mass", J_mass, meanW, sigmaW)
    # gaussian for the Top mass peak
    meanT = RooRealVar("meanT", "mean of the gaussian", 175.0, 150.0, 200.0)
    sigmaT = RooRealVar("sigmaT", "sigma of the gaussian", 12.0, 5.0, 30.0)
    fracT = RooRealVar("fracT", "fraction of gaussian wrt erfexp", 0.1, 0.0, 1.0)
    gausT = RooGaussian("gausT", "gaus for T jet mass", J_mass, meanT, sigmaT)

    if channel == "XZheeb" or channel == "XZheebb" or channel == "XZhmmb" or channel == "XZhmmbb":
        offsetTop = RooRealVar("offsetTop", "offset of the erf", 200.0, -50.0, 450.0)
        widthTop = RooRealVar("widthTop", "width of the erf", 100.0, 1.0, 1000.0)

    # Define Top model
    if fitFuncTop == "ERFEXPGAUS2":
        TopMass = RooAddPdf("TopMass", fitFuncTop, RooArgList(gausW, gausT, erfrTop), RooArgList(fracW, fracT))
    elif fitFuncTop == "ERFEXPGAUS":
        TopMass = RooAddPdf("TopMass", fitFuncTop, RooArgList(gausT, erfrTop), RooArgList(fracT))
    elif fitFuncTop == "GAUS3":
        TopMass = RooAddPdf("TopMass", fitFuncTop, RooArgList(gausW, gausT, gausTop), RooArgList(fracW, fracT))
    elif fitFuncTop == "GAUS2":
        TopMass = RooAddPdf("TopMass", fitFuncTop, RooArgList(gausT, gausTop), RooArgList(fracT))
    elif fitFuncTop == "GAUS":
        TopMass = RooGaussian("TopMass", fitFuncTop, J_mass, offsetTop, widthTop)
    else:
        print "  ERROR! Pdf", fitFuncTop, "is not implemented for Top"
        exit()

    # fit to secondary bkg in MC (whole range)
    frTop = TopMass.fitTo(
        setTop,
        RooFit.SumW2Error(True),
        RooFit.Range("h_reasonable_range"),
        RooFit.Strategy(2),
        RooFit.Minimizer("Minuit2"),
        RooFit.Save(1),
        RooFit.PrintLevel(1 if VERBOSE else -1),
    )

    if VERBOSE:
        print "********** Fit result [JET MASS TOP] ***" + "*" * 40, "\n", frTop.Print(), "\n", "*" * 80

    # likelihoodScan(TopMass, setTop, [offsetTop, widthTop])

    # *******************************************************#
    #                                                       #
    #                 All bkg normalization                 #
    #                                                       #
    # *******************************************************#

    #    nVjet.setConstant(False)
    #    nVjet2.setConstant(False)
    #
    #    constVjet.setConstant(False)
    #    offsetVjet.setConstant(False)
    #    widthVjet.setConstant(False)
    #    a0Vjet.setConstant(False)
    #    a1Vjet.setConstant(False)
    #    a2Vjet.setConstant(False)

    constVV.setConstant(True)
    offsetVV.setConstant(True)
    widthVV.setConstant(True)
    meanVV.setConstant(True)
    sigmaVV.setConstant(True)
    fracVV.setConstant(True)
    meanVH.setConstant(True)
    sigmaVH.setConstant(True)
    fracVH.setConstant(True)

    constTop.setConstant(True)
    offsetTop.setConstant(True)
    widthTop.setConstant(True)
    meanW.setConstant(True)
    sigmaW.setConstant(True)
    fracW.setConstant(True)
    meanT.setConstant(True)
    sigmaT.setConstant(True)
    fracT.setConstant(True)

    nVV.setConstant(True)
    nTop.setConstant(True)
    nVjet.setConstant(False)
    nVjet2.setConstant(False)

    # Final background model by adding the main+secondary pdfs (using 'coef': ratio of the secondary/main, from MC)
    TopMass_ext = RooExtendPdf("TopMass_ext", "extended p.d.f", TopMass, nTop)
    VVMass_ext = RooExtendPdf("VVMass_ext", "extended p.d.f", VVMass, nVV)
    VjetMass_ext = RooExtendPdf("VjetMass_ext", "extended p.d.f", VjetMass, nVjet)
    VjetMass2_ext = RooExtendPdf("VjetMass_ext", "extended p.d.f", VjetMass, nVjet2)
    BkgMass = RooAddPdf(
        "BkgMass", "BkgMass", RooArgList(TopMass_ext, VVMass_ext, VjetMass_ext), RooArgList(nTop, nVV, nVjet)
    )
    BkgMass2 = RooAddPdf(
        "BkgMass2", "BkgMass2", RooArgList(TopMass_ext, VVMass_ext, VjetMass2_ext), RooArgList(nTop, nVV, nVjet2)
    )
    BkgMass.fixAddCoefRange("h_reasonable_range")
    BkgMass2.fixAddCoefRange("h_reasonable_range")

    # Extended fit model to data in SB
    frMass = BkgMass.fitTo(
        setDataSB,
        RooFit.SumW2Error(True),
        RooFit.Extended(True),
        RooFit.Range("LSBrange,HSBrange"),
        RooFit.Strategy(2),
        RooFit.Minimizer("Minuit"),
        RooFit.Save(1),
        RooFit.PrintLevel(1 if VERBOSE else -1),
    )  # , RooFit.NumCPU(10)
    if VERBOSE:
        print "********** Fit result [JET MASS DATA] **" + "*" * 40, "\n", frMass.Print(), "\n", "*" * 80
    frMass2 = BkgMass2.fitTo(
        setDataSB,
        RooFit.SumW2Error(True),
        RooFit.Extended(True),
        RooFit.Range("LSBrange,HSBrange"),
        RooFit.Strategy(2),
        RooFit.Minimizer("Minuit"),
        RooFit.Save(1),
        RooFit.PrintLevel(1 if VERBOSE else -1),
    )
    if VERBOSE:
        print "********** Fit result [JET MASS DATA] **" + "*" * 40, "\n", frMass2.Print(), "\n", "*" * 80

    # if SCAN:
    #    likelihoodScan(VjetMass, setVjet, [constVjet, offsetVjet, widthVjet])

    # Fix normalization and parameters of V+jets after the fit to data
    nVjet.setConstant(True)
    nVjet2.setConstant(True)

    constVjet.setConstant(True)
    offsetVjet.setConstant(True)
    widthVjet.setConstant(True)
    a0Vjet.setConstant(True)
    a1Vjet.setConstant(True)
    a2Vjet.setConstant(True)

    # integrals for global normalization
    # do not integrate the composte model: results have no sense

    # integral for normalization in the SB
    iSBVjet = VjetMass.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("LSBrange,HSBrange"))
    iSBVV = VVMass.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("LSBrange,HSBrange"))
    iSBTop = TopMass.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("LSBrange,HSBrange"))

    # integral for normalization in the SR
    iSRVjet = VjetMass.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("SRrange"))
    iSRVV = VVMass.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("SRrange"))
    iSRTop = TopMass.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("SRrange"))

    # integral for normalization in the VR
    iVRVjet = VjetMass.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("VRrange"))
    iVRVV = VVMass.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("VRrange"))
    iVRTop = TopMass.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("VRrange"))

    # formual vars
    SByield = RooFormulaVar(
        "SByield", "extrapolation to SR", "@0*@1 + @2*@3 + @4*@5", RooArgList(iSBVjet, nVjet, iSBVV, nVV, iSBTop, nTop)
    )
    VRyield = RooFormulaVar(
        "VRyield", "extrapolation to VR", "@0*@1 + @2*@3 + @4*@5", RooArgList(iVRVjet, nVjet, iVRVV, nVV, iVRTop, nTop)
    )
    SRyield = RooFormulaVar(
        "SRyield", "extrapolation to SR", "@0*@1 + @2*@3 + @4*@5", RooArgList(iSRVjet, nVjet, iSRVV, nVV, iSRTop, nTop)
    )

    # fractions
    fSBVjet = RooRealVar(
        "fVjet", "Fraction of Vjet events in SB", iSBVjet.getVal() * nVjet.getVal() / SByield.getVal(), 0.0, 1.0
    )
    fSBVV = RooRealVar(
        "fSBVV", "Fraction of VV events in SB", iSBVV.getVal() * nVV.getVal() / SByield.getVal(), 0.0, 1.0
    )
    fSBTop = RooRealVar(
        "fSBTop", "Fraction of Top events in SB", iSBTop.getVal() * nTop.getVal() / SByield.getVal(), 0.0, 1.0
    )

    fSRVjet = RooRealVar(
        "fSRVjet", "Fraction of Vjet events in SR", iSRVjet.getVal() * nVjet.getVal() / SRyield.getVal(), 0.0, 1.0
    )
    fSRVV = RooRealVar(
        "fSRVV", "Fraction of VV events in SR", iSRVV.getVal() * nVV.getVal() / SRyield.getVal(), 0.0, 1.0
    )
    fSRTop = RooRealVar(
        "fSRTop", "Fraction of Top events in SR", iSRTop.getVal() * nTop.getVal() / SRyield.getVal(), 0.0, 1.0
    )

    # final normalization values
    bkgYield = SRyield.getVal()
    bkgYield2 = (
        (VjetMass2.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("SRrange"))).getVal()
        * nVjet2.getVal()
        + iSRVV.getVal() * nVV.getVal()
        + iSRTop.getVal() * nTop.getVal()
    )
    bkgYield_syst = math.sqrt(SRyield.getPropagatedError(frVV) ** 2 + SRyield.getPropagatedError(frTop) ** 2)
    bkgYield_stat = math.sqrt(SRyield.getPropagatedError(frMass) ** 2)
    bkgYield_alte = abs(bkgYield - bkgYield2)  # /bkgYield
    bkgYield_eig_norm = RooRealVar("predSR_eig_norm", "expected yield in SR", bkgYield, 0.0, 1.0e6)

    print "Events in channel", channel, ": V+jets %.3f (%.1f%%),   VV %.3f (%.1f%%),   Top %.3f (%.1f%%)" % (
        iSRVjet.getVal() * nVjet.getVal(),
        fSRVjet.getVal() * 100,
        iSRVV.getVal() * nVV.getVal(),
        fSRVV.getVal() * 100,
        iSRTop.getVal() * nTop.getVal(),
        fSRTop.getVal() * 100,
    )
    print "Events in channel", channel, ": Integral = $%.3f$ & $\pm %.3f$ & $\pm %.3f$ & $\pm %.3f$, observed = %.0f" % (
        bkgYield,
        bkgYield_stat,
        bkgYield_syst,
        bkgYield_alte,
        setDataSR.sumEntries() if not False else -1,
    )
mes = RooRealVar("mes", "m_{ES} (GeV)", 5.20, 5.30)
 
# --- Parameters ---
sigmean = RooRealVar("sigmean", "B^{#pm} mass", 5.28, 5.20, 5.30)
sigwidth = RooRealVar("sigwidth", "B^{#pm} width", 0.0027, 0.001, 1.)
 
# --- Build Gaussian PDF ---
signal = RooGaussian("signal", "signal PDF", mes, sigmean, sigwidth)

argpar = RooRealVar("argpar", "argus shape parameter", -20.0, -100., -1.)
background = RooArgusBG("background", "Argus PDF", mes, RooFit.RooConst(5.291), argpar)
 
# --- Construct signal+background PDF ---
nsig = RooRealVar("nsig", "#signal events", 200, 0., 10000)
nbkg = RooRealVar("nbkg", "#background events", 800, 0., 10000)
model = RooAddPdf("model", "g+a", RooArgList(signal, background), RooArgList(nsig, nbkg))

# --- Generate a toyMC sample from composite PDF ---
data = model.generate(RooArgSet(mes), 2000)
 
# --- Perform extended ML fit of composite PDF to toy data ---
model.fitTo(data)
 
# --- Plot toy data and composite PDF overlaid ---
mesframe = mes.frame()
data.plotOn(mesframe)
model.plotOn(mesframe)
model.plotOn(mesframe, RooFit.Components('background'), RooFit.LineStyle(kDashed))

mesframe.Draw()
pdf_tt = RooHistPdf("pdf_tt", "Signal pdf", vars_set, rh_tt, 0);
pdf_wj = RooHistPdf ("pdf_wj", "W+jets pdf", vars_set, rh_wj, 0);
pdf_zj = RooHistPdf ("pdf_zj", "Z+jets pdf", vars_set, rh_zj, 0);
pdf_qcd = RooHistPdf("pdf_qcd", "QCD pdf ", vars_set, rh_qcd, 0);
pdf_stop = RooHistPdf("pdf_stop", "single top pdf", vars_set, rh_stop, 0);
pdf_signal = RooHistPdf("pdf_signal", "single top pdf", vars_set, rh_signal, 0);

ntt = RooRealVar ("ntt", "number of tt signal events", n_ttbar, lowerBound, upperBound, "event");
nwj = RooRealVar  ("nwj", "number of W+jets bgnd events", n_wj, lowerBound, upperBound, "event");
nzj = RooRealVar  ("nzj", "number of Z+jets bgnd events", n_zj, lowerBound, upperBound, "event");
nqcd = RooRealVar("nqcd", "number of QCD bgnd events", n_qcd, lowerBound, upperBound, "event");
nstop = RooRealVar("nstop", "number of single top bgnd events", n_stop, lowerBound, upperBound, "event");
nSignal = RooRealVar("nSignal", "number of single top + ttbar events", n_stop + n_ttbar, lowerBound, upperBound, "event");
model = RooAddPdf("model", "sig+wj+zj+qcd+stop",
#            RooArgList(pdf_tt, pdf_wj, pdf_zj, pdf_qcd, pdf_stop),
#            RooArgList(ntt, nwj, nzj, nqcd, nstop)) ; 
            RooArgList(pdf_signal, pdf_wj, pdf_zj, pdf_qcd),
            RooArgList(nSignal, nwj, nzj, nqcd)) ; 
model.fitTo(data, RooFit.Minimizer("Minuit2", "Migrad"), RooFit.NumCPU(8))
#leptonAbsEtaframe1 = leptonAbsEta.frame();
#leptonAbsEtaframe2 = leptonAbsEta.frame();
#leptonAbsEtaframe3 = leptonAbsEta.frame();
#leptonAbsEtaframe4 = leptonAbsEta.frame();
#leptonAbsEtaframe5 = leptonAbsEta.frame();
#leptonAbsEtaframe6 = leptonAbsEta.frame();
#leptonAbsEtaframe7 = leptonAbsEta.frame();
#leptonAbsEtaframe8 = leptonAbsEta.frame();
#leptonAbsEtaframe9 = leptonAbsEta.frame();
#leptonAbsEtaframe10 = leptonAbsEta.frame();
#leptonAbsEtaframe11 = leptonAbsEta.frame();
#
def rf501_simultaneouspdf():
    # C r e a t e   m o d e l   f o r   p h y s i c s   s a m p l e
    # -------------------------------------------------------------

    # Create observables
    x = RooRealVar( "x", "x", -8, 8 ) 

    # Construct signal pdf
    mean = RooRealVar( "mean", "mean", 0, -8, 8 ) 
    sigma = RooRealVar( "sigma", "sigma", 0.3, 0.1, 10 ) 
    gx = RooGaussian( "gx", "gx", x, mean, sigma ) 

    # Construct background pdf
    a0 = RooRealVar( "a0", "a0", -0.1, -1, 1 ) 
    a1 = RooRealVar( "a1", "a1", 0.004, -1, 1 ) 
    px = RooChebychev( "px", "px", x, RooArgList( a0, a1 ) ) 

    # Construct composite pdf
    f = RooRealVar( "f", "f", 0.2, 0., 1. ) 
    model = RooAddPdf( "model", "model", RooArgList( gx, px ), RooArgList( f ) ) 



    # C r e a t e   m o d e l   f o r   c o n t r o l   s a m p l e
    # --------------------------------------------------------------

    # Construct signal pdf. 
    # NOTE that sigma is shared with the signal sample model
    mean_ctl = RooRealVar( "mean_ctl", "mean_ctl", -3, -8, 8 ) 
    gx_ctl = RooGaussian( "gx_ctl", "gx_ctl", x, mean_ctl, sigma ) 

    # Construct the background pdf
    a0_ctl = RooRealVar( "a0_ctl", "a0_ctl", -0.1, -1, 1 ) 
    a1_ctl = RooRealVar( "a1_ctl", "a1_ctl", 0.5, -0.1, 1 ) 
    px_ctl = RooChebychev( "px_ctl", "px_ctl", x, RooArgList( a0_ctl, a1_ctl ) ) 

    # Construct the composite model
    f_ctl = RooRealVar( "f_ctl", "f_ctl", 0.5, 0., 1. ) 
    model_ctl = RooAddPdf( "model_ctl", "model_ctl", RooArgList( gx_ctl, px_ctl ),
                           RooArgList( f_ctl ) ) 
    


    # G e n e r a t e   e v e n t s   f o r   b o t h   s a m p l e s 
    # ---------------------------------------------------------------

    # Generate 1000 events in x and y from model
    data = model.generate( RooArgSet( x ), 100 ) 
    data_ctl = model_ctl.generate( RooArgSet( x ), 2000 ) 



    # C r e a t e   i n d e x   c a t e g o r y   a n d   j o i n   s a m p l e s 
    # ---------------------------------------------------------------------------
    # Define category to distinguish physics and control samples events
    sample = RooCategory( "sample", "sample" ) 
    sample.defineType( "physics" ) 
    sample.defineType( "control" ) 

    # Construct combined dataset in (x,sample)
    combData = RooDataSet( "combData", "combined data", RooArgSet(x), RooFit.Index( sample ),
                          RooFit.Import( "physics", data ),
                          RooFit.Import( "control", data_ctl ) ) 



    # C o n s t r u c t   a   s i m u l t a n e o u s   p d f   i n   ( x , s a m p l e )
    # -----------------------------------------------------------------------------------

    # Construct a simultaneous pdf using category sample as index
    simPdf = RooSimultaneous( "simPdf", "simultaneous pdf", sample ) 

    # Associate model with the physics state and model_ctl with the control state
    simPdf.addPdf( model, "physics" ) 
    simPdf.addPdf( model_ctl, "control" ) 



    # P e r f o r m   a   s i m u l t a n e o u s   f i t
    # ---------------------------------------------------

    # Perform simultaneous fit of model to data and model_ctl to data_ctl
    simPdf.fitTo( combData ) 



    # P l o t   m o d e l   s l i c e s   o n   d a t a    s l i c e s 
    # ----------------------------------------------------------------

    # Make a frame for the physics sample
    frame1 = x.frame( RooFit.Bins( 30 ), RooFit.Title( "Physics sample" ) ) 

    # Plot all data tagged as physics sample
    combData.plotOn( frame1, RooFit.Cut( "sample==sample::physics" ) ) 

    # Plot "physics" slice of simultaneous pdf. 
    # NBL You _must_ project the sample index category with data using ProjWData 
    # as a RooSimultaneous makes no prediction on the shape in the index category 
    # and can thus not be integrated
    simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ),
                   RooFit.ProjWData( RooArgSet(sample), combData ) ) 
    simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ),
                   RooFit.Components( "px" ),
                   RooFit.ProjWData( RooArgSet(sample), combData ),
                   RooFit.LineStyle( kDashed ) ) 

    # The same plot for the control sample slice
    frame2 = x.frame( RooFit.Bins( 30 ), RooFit.Title( "Control sample" ) ) 
    combData.plotOn( frame2, RooFit.Cut( "sample==sample::control" ) ) 
    simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ),
                  RooFit.ProjWData( RooArgSet(sample), combData ) ) 
    simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ),
                  RooFit.Components( "px_ctl" ),
                  RooFit.ProjWData( RooArgSet(sample), combData ),
                  RooFit.LineStyle( kDashed ) ) 



    c = TCanvas( "rf501_simultaneouspdf", "rf403_simultaneouspdf", 800, 400 ) 
    c.Divide( 2 ) 
    c.cd( 1 )
    gPad.SetLeftMargin( 0.15 )
    frame1.GetYaxis().SetTitleOffset( 1.4 )
    frame1.Draw() 
    c.cd( 2 )
    gPad.SetLeftMargin( 0.15 )
    frame2.GetYaxis().SetTitleOffset( 1.4 )
    frame2.Draw() 
    raw_input()
def fitChicSpectrum(dataset,binname):
    """ Fit chic spectrum"""


    x = RooRealVar('Qvalue','Q',9.7,10.1)
    x.setBins(80)




    mean_1 = RooRealVar("mean_1","mean ChiB1",9.892,9,10,"GeV")
    sigma_1 = RooRealVar("sigma_1","sigma ChiB1",0.0058,'GeV')
    a1_1 = RooRealVar('#alpha1_1', '#alpha1_1', 0.748)
    n1_1 = RooRealVar('n1_1', 'n1_1',2.8 )
    a2_1 = RooRealVar('#alpha2_1', '#alpha2_1',1.739)
    n2_1 = RooRealVar('n2_1', 'n2_1', 3.0)


    deltam = RooRealVar('deltam','deltam',0.01943)
    
    mean_2 = RooFormulaVar("mean_2","@0+@1", RooArgList(mean_1,deltam))
    sigma_2 = RooRealVar("sigma_2","sigma ChiB2",0.0059,'GeV')
    a1_2 = RooRealVar('#alpha1_2', '#alpha1_2', 0.738)
    n1_2 = RooRealVar('n1_2', 'n1_2', 2.8)
    a2_2 = RooRealVar('#alpha2_2', '#alpha2_2', 1.699)
    n2_2 = RooRealVar('n2_2', 'n2_2', 3.0)

    
    parameters=RooArgSet()
    
    parameters.add(RooArgSet(sigma_1, sigma_2))
    parameters = RooArgSet(a1_1, a2_1, n1_1, n2_1)
    parameters.add(RooArgSet( a1_2, a2_2, n1_2, n2_2))
 
    chib1_pdf = My_double_CB('chib1', 'chib1', x, mean_1, sigma_1, a1_1, n1_1, a2_1, n2_1)
    chib2_pdf = My_double_CB('chib2', 'chib2', x, mean_2, sigma_2, a1_2, n1_2, a2_2, n2_2)

    
    #background
    q01S_Start = 9.5
    alpha   =   RooRealVar("#alpha","#alpha",1.5,-1,3.5)#0.2 anziche' 1
    beta    =   RooRealVar("#beta","#beta",-2.5,-7.,0.)
    q0      =   RooRealVar("q0","q0",q01S_Start)#,9.5,9.7)
    delta   =   RooFormulaVar("delta","TMath::Abs(@0-@1)",RooArgList(x,q0))
    b1      =   RooFormulaVar("b1","@0*(@1-@2)",RooArgList(beta,x,q0))
    signum1 =   RooFormulaVar( "signum1","( TMath::Sign( -1.,@0-@1 )+1 )/2.", RooArgList(x,q0) )
    
    
    background = RooGenericPdf("background","Background", "signum1*pow(delta,#alpha)*exp(b1)", RooArgList(signum1,delta,alpha,b1) )

    parameters.add(RooArgSet(alpha, beta, q0))

    #together
    chibs = RooArgList(chib1_pdf,chib2_pdf,background)    

    

    n_chib = RooRealVar("n_chib","n_chib",2075, 0, 100000)
    ratio_21 = RooRealVar("ratio_21","ratio_21",0.5,0,1)
    n_chib1 = RooFormulaVar("n_chib1","@0/(1+@1)",RooArgList(n_chib, ratio_21))
    n_chib2 = RooFormulaVar("n_chib2","@0/(1+1/@1)",RooArgList(n_chib, ratio_21))
    n_background = RooRealVar('n_background','n_background',4550, 0, 50000)
    ratio_list = RooArgList(n_chib1, n_chib2, n_background)


    modelPdf = RooAddPdf('ModelPdf', 'ModelPdf', chibs, ratio_list)


    frame = x.frame(RooFit.Title('m'))
    range = x.setRange('range',9.7,10.1)
    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' )
예제 #45
0
h.Rebin(20)
x = RooRealVar('mjj','mjj',900,4500)

m = RooRealVar('mean','mean',float(mass),float(mass)-200,float(mass)+200)
s = RooRealVar('sigma','sigma',0.1*float(mass),0,10000)
a = RooRealVar('alpha','alpha',1,-10,10)
n = RooRealVar('n','n',1,0,100)
sig = RooCBShape('sig','sig',x,m,s,a,n)        

p  = RooRealVar('p','p',1,0,5)
x0 = RooRealVar('x0','x0',1000,100,5000)

bkg = RooGenericPdf('bkg','1/(exp(pow(@0/@1,@2))+1)',RooArgList(x,x0,p))

fsig= RooRealVar('fsig','fsig',0.5,0.,1.)
model = RooAddPdf('model','model',sig,bkg,fsig)

can = TCanvas('can_Mjj'+str(mass),'can_Mjj'+str(mass),900,600)
h.Draw()
gPad.SetLogy() 

roohist = RooDataHist('roohist','roohist',RooArgList(x),h)


model.fitTo(roohist)
frame = x.frame()
roohist.plotOn(frame)
model.plotOn(frame)
model.plotOn(frame,RooFit.Components('bkg'),RooFit.LineColor(ROOT.kRed),RooFit.LineWidth(2),RooFit.LineStyle(ROOT.kDashed))
frame.Draw('same')
def doubleGausCB(cbshape, doublegaus, f3_, tagged_mass, w):
    f4           = RooRealVar ("f4"            , "f4"            ,  f4_  ,     0.,   1.)
    doublegauscb = RooAddPdf  ("doublegauscb"  , "doublegauscb"  ,  RooArgList(doublegaus,cbshape), RooArgList(f4))
    _import(w,doublegauscb)
예제 #47
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def main():
    # usage description
    usage = "Example: ./scripts/createDatacards.py --inputData inputs/dijetFitResults_FuncType0_nParFit4_MC_1invfb.root --dataHistname hist_mass_1GeV --inputSig inputs/ResonanceShapes_qg_13TeV_PU30_Spring15.root -f qg -o datacards -l 1000 --massrange 1200 7000 100"

    # input parameters
    parser = ArgumentParser(description='Script that creates combine datacards and corresponding RooFit workspaces',epilog=usage)

    parser.add_argument("--inputData", dest="inputData", required=True,
                        help="Input data spectrum",
                        metavar="INPUT_DATA")

    parser.add_argument("--dataHistname", dest="dataHistname", required=True,
                        help="Data histogram name",
                        metavar="DATA_HISTNAME")

    parser.add_argument("--inputSig", dest="inputSig", required=True,
                        help="Input signal shapes",
                        metavar="INPUT_SIGNAL")

    parser.add_argument("-f", "--final_state", dest="final_state", required=True,
                        help="Final state (e.g. qq, qg, gg)",
                        metavar="FINAL_STATE")

    parser.add_argument("-o", "--output_path", dest="output_path", required=True,
                        help="Output path where datacards and workspaces will be stored",
                        metavar="OUTPUT_PATH")

    parser.add_argument("-l", "--lumi", dest="lumi", required=True,
                        default=1000., type=float,
                        help="Integrated luminosity in pb-1 (default: %(default).1f)",
                        metavar="LUMI")

    parser.add_argument("--massMin", dest="massMin",
                        default=1118,
                        help="Lower bound of the mass range used for fitting (default: %(default)s)",
                        metavar="MASS_MIN")

    parser.add_argument("--massMax", dest="massMax",
                        default=9067,
                        help="Upper bound of the mass range used for fitting (default: %(default)s)",
                        metavar="MASS_MAX")

    parser.add_argument("--p1", dest="p1",
                        default=7.7666e+00, type=float,
                        help="Fit function p1 parameter (default: %(default)e)",
                        metavar="P1")

    parser.add_argument("--p2", dest="p2",
                        default=5.3748e+00, type=float,
                        help="Fit function p2 parameter (default: %(default)e)",
                        metavar="P2")

    parser.add_argument("--p3", dest="p3",
                        default=5.6385e-03, type=float,
                        help="Fit function p3 parameter (default: %(default)e)",
                        metavar="P3")

    parser.add_argument("--runFit", dest="runFit", default=False, action="store_true", help="Run the fit")

    parser.add_argument("--fitBonly", dest="fitBonly", default=False, action="store_true", help="Run B-only fit")

    parser.add_argument("--fitStrategy", dest="fitStrategy", type=int, default=0, help="Fit strategy (default: %(default).1f)")

    parser.add_argument("--sqrtS", dest="sqrtS", type=float, default=13000., help="Collision center-of-mass energy (default: %(default).1f)")

    parser.add_argument("--debug", dest="debug", default=False, action="store_true", help="Debug printout")

    mass_group = parser.add_mutually_exclusive_group(required=True)
    mass_group.add_argument("--mass",
                            type=int,
                            nargs = '*',
                            default = 1000,
                            help="Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
                            )
    mass_group.add_argument("--massrange",
                            type=int,
                            nargs = 3,
                            help="Define a range of masses to be produced. Format: min max step",
                            metavar = ('MIN', 'MAX', 'STEP')
                            )
    mass_group.add_argument("--masslist",
                            help = "List containing mass information"
                            )

    args = parser.parse_args()

    # check if the output directory exists
    if not os.path.isdir( os.path.join(os.getcwd(),args.output_path) ):
        os.mkdir( os.path.join(os.getcwd(),args.output_path) )

    # mass points for which resonance shapes will be produced
    masses = []

    if args.massrange != None:
        MIN, MAX, STEP = args.massrange
        masses = range(MIN, MAX+STEP, STEP)
    elif args.masslist != None:
        # A mass list was provided
        print  "Will create mass list according to", args.masslist
        masslist = __import__(args.masslist.replace(".py",""))
        masses = masslist.masses
    else:
        masses = args.mass

    # sort masses
    masses.sort()

    # import ROOT stuff
    from ROOT import TFile, TH1F, TH1D, kTRUE, kFALSE
    from ROOT import RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf

    if not args.debug:
        RooMsgService.instance().setSilentMode(kTRUE)
        RooMsgService.instance().setStreamStatus(0,kFALSE)
        RooMsgService.instance().setStreamStatus(1,kFALSE)

    # input data file
    inputData = TFile(args.inputData)
    # input data histogram
    hData = inputData.Get(args.dataHistname)

    # input sig file
    inputSig = TFile(args.inputSig)

    sqrtS = args.sqrtS

    for mass in masses:

        print ">> Creating datacard and workspace for %s resonance with m = %i GeV..."%(args.final_state, int(mass))

        hSig = inputSig.Get( "h_" + args.final_state + "_" + str(int(mass)) )

        # calculate acceptance of the dijet mass cut
        sigAcc = hSig.Integral(hSig.GetXaxis().FindBin(args.massMin),hSig.GetXaxis().FindBin(args.massMax))/hSig.Integral(1,hSig.GetXaxis().FindBin(args.massMax))

        mjj = RooRealVar('mjj','mjj',args.massMin,args.massMax)

        rooSigHist = RooDataHist('rooSigHist','rooSigHist',RooArgList(mjj),hSig)
        rooSigHist.Print()
        signal = RooHistPdf('signal','signal',RooArgSet(mjj),rooSigHist)
        signal.Print()
        signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+04,1e+04)
        if args.fitBonly: signal_norm.setConstant()
        signal_norm.Print()

        p1 = RooRealVar('p1','p1',args.p1,0.,100.)
        p2 = RooRealVar('p2','p2',args.p2,0.,60.)
        p3 = RooRealVar('p3','p3',args.p3,-10.,10.)

        background = RooGenericPdf('background','(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(sqrtS,sqrtS,sqrtS),RooArgList(mjj,p1,p2,p3))
        background.Print()
        dataInt = hData.Integral(hData.GetXaxis().FindBin(args.massMin),hData.GetXaxis().FindBin(args.massMax))
        background_norm = RooRealVar('background_norm','background_norm',dataInt,0.,1e+07)
        background_norm.Print()

        # S+B model
        model = RooAddPdf("model","s+b",RooArgList(background,signal),RooArgList(background_norm,signal_norm))

        rooDataHist = RooDataHist('rooDatahist','rooDathist',RooArgList(mjj),hData)
        rooDataHist.Print()

        if args.runFit:
            res = model.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
            res.Print()

        dcName = 'datacard_' + args.final_state + '_m' + str(mass) + '.txt'
        wsName = 'workspace_' + args.final_state + '_m' + str(mass) + '.root'

        w = RooWorkspace('w','workspace')
        getattr(w,'import')(signal)
        getattr(w,'import')(background)
        getattr(w,'import')(background_norm)
        getattr(w,'import')(rooDataHist,RooFit.Rename("data_obs"))
        w.Print()
        w.writeToFile(os.path.join(args.output_path,wsName))

        # -----------------------------------------
        # write a datacard
        lumi = args.lumi
        signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
        expectedSignalRate = signalCrossSection*lumi*sigAcc

        datacard = open(os.path.join(args.output_path,dcName),'w')
        datacard.write('imax 1\n')
        datacard.write('jmax 1\n')
        datacard.write('kmax *\n')
        datacard.write('---------------\n')
        datacard.write('shapes * * '+wsName+' w:$PROCESS\n')
        datacard.write('---------------\n')
        datacard.write('bin 1\n')
        datacard.write('observation -1\n')
        datacard.write('------------------------------\n')
        datacard.write('bin          1          1\n')
        datacard.write('process      signal     background\n')
        datacard.write('process      0          1\n')
        datacard.write('rate         '+str(expectedSignalRate)+'      1\n')
        datacard.write('------------------------------\n')
        #flat parameters --- flat prior
        datacard.write('background_norm  flatParam\n')
        datacard.write('p1  flatParam\n')
        datacard.write('p2  flatParam\n')
        datacard.write('p3  flatParam\n')
        datacard.close()

    print '>> Datacards and workspaces created and stored in %s/.'%( os.path.join(os.getcwd(),args.output_path) )
예제 #48
0
def doDataFit(Chib1_parameters,Chib2_parameters, cuts, inputfile_name = None, RooDataSet = None, ptBin_label='', plotTitle = "#chi_{b}",fittedVariable='qValue', printSigReso = False, noPlots = False, useOtherSignalParametrization = False, useOtherBackgroundParametrization = False, massFreeToChange = False, sigmaFreeToChange = False, legendOnPlot=True, drawPulls=False, titleOnPlot=False, cmsOnPlot=True, printLegend=True):

    if RooDataSet != None:
        dataSet = RooDataSet 
    elif inputfile_name != None:
        print "Creating DataSet from file "+str(inputfile_name)
        dataSet = makeRooDataset(inputfile_name)
    else:
        raise ValueError('No dataset and no inputfile passed to function doDataFit')
    
    if(fittedVariable == 'refittedMass'):
        x_var = 'rf1S_chib_mass'
        output_suffix = '_refit'
        x_axis_label= 'm_{#mu^{+} #mu^{-} #gamma} [GeV]'
    else:
        x_var = 'invm1S'
        output_suffix = '_qValue'
        x_axis_label = 'm_{#gamma #mu^{+} #mu^{-}} - m_{#mu^{+} #mu^{-}} + m^{PDG}_{#Upsilon}  [GeV]'
    
    cuts_str = str(cuts)
    #cuts_str = quality_cut + "photon_pt > 0.5 && abs(photon_eta) < 1.0 && ctpv < 0.01  && abs(dimuon_rapidity) < 1.3 && pi0_abs_mass > 0.025 &&  abs(dz) < 0.5"
    data = dataSet.reduce( RooFit.Cut(cuts_str) )
    
    print 'Creating pdf'
    x=RooRealVar(x_var, 'm(#mu #mu #gamma) - m(#mu #mu) + m_{#Upsilon}',9.7,10.1,'GeV')
    numBins = 80 # define here so that if I change it also the ndof change accordingly
    x.setBins(numBins)
    
    # cristal balls
    mean_1 = RooRealVar("mean_1","mean ChiB1",Chib1_parameters.mean,"GeV")
    sigma_1 = RooRealVar("sigma_1","sigma ChiB1",Chib1_parameters.sigma,'GeV')
    a1_1 = RooRealVar('#alpha1_1', '#alpha1_1', Chib1_parameters.a1)
    n1_1 = RooRealVar('n1_1', 'n1_1', Chib1_parameters.n1)
    a2_1 = RooRealVar('#alpha2_1', '#alpha2_1',Chib1_parameters.a2)
    n2_1 = RooRealVar('n2_1', 'n2_1', Chib1_parameters.n2)
    parameters = RooArgSet(a1_1, a2_1, n1_1, n2_1)
    
    mean_2 = RooRealVar("mean_2","mean ChiB2",Chib2_parameters.mean,"GeV")
    sigma_2 = RooRealVar("sigma_2","sigma ChiB2",Chib2_parameters.sigma,'GeV')
    a1_2 = RooRealVar('#alpha1_2', '#alpha1_2', Chib2_parameters.a1)
    n1_2 = RooRealVar('n1_2', 'n1_2', Chib2_parameters.n1)
    a2_2 = RooRealVar('#alpha2_2', '#alpha2_2', Chib2_parameters.a2)
    n2_2 = RooRealVar('n2_2', 'n2_2', Chib2_parameters.n2)
    parameters.add(RooArgSet( a1_2, a2_2, n1_2, n2_2))

    if massFreeToChange:
        # scale_mean = RooRealVar('scale_mean', 'Scale that multiplies masses found with MC', 0.8,1.2)
        # mean_1_fixed = RooRealVar("mean_1_fixed","mean ChiB1",Chib1_parameters.mean,"GeV")
        # mean_2_fixed = RooRealVar("mean_2_fixed","mean ChiB2",Chib2_parameters.mean,"GeV")
        # mean_1 = RooFormulaVar("mean_1",'@0*@1', RooArgList(scale_mean, mean_1_fixed))
        # mean_2 = RooFormulaVar("mean_2",'@0*@1', RooArgList(scale_mean, mean_2_fixed))
        variazione_m = 0.05 # 50 MeV
        diff_m_12 = RooRealVar('diff_m_12', 'Difference between masses chib1 and chib2',0.0194,'GeV') # 19.4 MeV from PDG
        mean_1=RooRealVar("mean_1","mean ChiB1",Chib1_parameters.mean,Chib1_parameters.mean-variazione_m,Chib1_parameters.mean+variazione_m ,"GeV")
        mean_2=RooFormulaVar('mean_2', '@0+@1',RooArgList(mean_1, diff_m_12))
        # mean_2=RooRealVar("mean_2","mean ChiB2",Chib2_parameters.mean,Chib2_parameters.mean-variazione_m,Chib2_parameters.mean+variazione_m ,"GeV")
        parameters.add(mean_1)
    else:
        parameters.add(RooArgSet(mean_1, mean_2))
        
    
    chib1_pdf = My_double_CB('chib1', 'chib1', x, mean_1, sigma_1, a1_1, n1_1, a2_1, n2_1)
    chib2_pdf = My_double_CB('chib2', 'chib2', x, mean_2, sigma_2, a1_2, n1_2, a2_2, n2_2)
    
    if sigmaFreeToChange:
        scale_sigma = RooRealVar('scale_sigma', 'Scale that multiplies sigmases found with MC', 1, 1.1)#1.01
        sigma_1_fixed = RooRealVar("sigma_1","sigma ChiB1",Chib1_parameters.sigma,'GeV')
        sigma_2_fixed = RooRealVar("sigma_2","sigma ChiB2",Chib2_parameters.sigma,'GeV')
        sigma_1 = RooFormulaVar("sigma_1",'@0*@1', RooArgList(scale_sigma, sigma_1_fixed))
        sigma_2 = RooFormulaVar("sigma_2",'@0*@1', RooArgList(scale_sigma, sigma_2_fixed))
        parameters.add(scale_sigma)
    else:
        parameters.add(RooArgSet(sigma_1, sigma_2))

    chib1_pdf = My_double_CB('chib1', 'chib1', x, mean_1, sigma_1, a1_1, n1_1, a2_1, n2_1)
    chib2_pdf = My_double_CB('chib2', 'chib2', x, mean_2, sigma_2, a1_2, n1_2, a2_2, n2_2)

    if useOtherSignalParametrization: # In this case I redefine cb_pdf
        cb1 = RooCBShape('cb1', 'cb1', x, mean_1, sigma_1, a1_1, n1_1)
        cb2 = RooCBShape('cb2', 'cb2', x, mean_2, sigma_2, a1_2, n1_2)
        # I use a2 as the sigma of my gaussian 
        gauss1 = RooCBShape('gauss1', 'gauss1',x, mean_1, a2_1, a1_1, n1_1)
        gauss2 = RooCBShape('gauss2', 'gauss2',x, mean_2, a2_2, a1_2, n1_2)
        # I use n2 as the ratio of cb with respect to gauss 
        chib1_pdf = RooAddPdf('chib1','chib1',RooArgList(cb1, gauss1),RooArgList(n2_1))
        chib2_pdf = RooAddPdf('chib2','chib2',RooArgList(cb2, gauss2),RooArgList(n2_2))
        
    
    #background
    q01S_Start = 9.5
    alpha   =   RooRealVar("#alpha","#alpha",1.5,-1,3.5)#0.2 anziche' 1
    beta    =   RooRealVar("#beta","#beta",-2.5,-7.,0.)
    q0      =   RooRealVar("q0","q0",q01S_Start)#,9.5,9.7)
    delta   =   RooFormulaVar("delta","TMath::Abs(@0-@1)",RooArgList(x,q0))
    b1      =   RooFormulaVar("b1","@0*(@1-@2)",RooArgList(beta,x,q0))
    signum1 =   RooFormulaVar( "signum1","( TMath::Sign( -1.,@0-@1 )+1 )/2.", RooArgList(x,q0) )
    
    
    background = RooGenericPdf("background","Background", "signum1*pow(delta,#alpha)*exp(b1)", RooArgList(signum1,delta,alpha,b1) )

    if useOtherBackgroundParametrization: # in thies case I redefine background
        a0 = RooRealVar('a0','a0',1.,-1.,1.) #,0.5,0.,1.)
        a1 = RooRealVar('a1','a1',0.1,-1.,1.) #-0.2,0.,1.)
        #a2 = RooRealVar('a2','a2',-0.1,1.,-1.)
        background = RooChebychev('background','Background',x,RooArgList(a0,a1))
        parameters.add(RooArgSet(a0, a1))
    else:
        parameters.add(RooArgSet(alpha, beta, q0))

    #together
    chibs = RooArgList(chib1_pdf,chib2_pdf,background)    
    
    # ndof
    floatPars = parameters.selectByAttrib("Constant",ROOT.kFALSE)
    ndof = numBins - floatPars.getSize() - 1

    # # Here I have as parameters N1, N2, and N_background
    # n_chib1 = RooRealVar("n_chib1","n_chib1",1250, 0, 50000)
    # n_chib2 =  RooRealVar("n_chib2","n_chib2",825, 0, 50000)
    # n_background = RooRealVar('n_background','n_background',4550, 0, 50000)
    # ratio_list = RooArgList(n_chib1, n_chib2, n_background)
    # modelPdf = RooAddPdf('ModelPdf', 'ModelPdf', chibs, ratio_list)

    # Here I have as parameters N_12, ratio_12, N_background
    n_chib = RooRealVar("n_chib","n_chib",2075, 0, 100000)
    ratio_21 = RooRealVar("ratio_21","ratio_21",0.6, 0, 1)
    n_chib1 = RooFormulaVar("n_chib1","@0/(1+@1)",RooArgList(n_chib, ratio_21))
    n_chib2 = RooFormulaVar("n_chib2","@0/(1+1/@1)",RooArgList(n_chib, ratio_21))
    n_background = RooRealVar('n_background','n_background',4550, 0, 50000)
    ratio_list = RooArgList(n_chib1, n_chib2, n_background)
    parameters.add(RooArgSet(n_chib1, n_chib2, n_background))
    modelPdf = RooAddPdf('ModelPdf', 'ModelPdf', chibs, ratio_list)
    
    print 'Fitting to data'
    fit_region = x.setRange("fit_region",9.7,10.1)
    result=modelPdf.fitTo(data,RooFit.Save(), RooFit.Range("fit_region"))
    
        
    # define frame
    frame = x.frame()
    frame.SetNameTitle("fit_resonance","Fit Resonanace")
    frame.GetXaxis().SetTitle(x_axis_label )
    frame.GetYaxis().SetTitle( "Events/5 MeV " )
    frame.GetXaxis().SetTitleSize(0.04)
    frame.GetYaxis().SetTitleSize(0.04)
    frame.GetXaxis().SetTitleOffset(1.1)
    frame.GetXaxis().SetLabelSize(0.04)
    frame.GetYaxis().SetLabelSize(0.04)
    frame.SetLineWidth(1)
    frame.SetTitle(plotTitle) 
    
    # plot things on frame
    data.plotOn(frame, RooFit.MarkerSize(0.7))
    chib1P_set = RooArgSet(chib1_pdf)
    modelPdf.plotOn(frame,RooFit.Components(chib1P_set), RooFit.LineColor(ROOT.kGreen+2), RooFit.LineStyle(2), RooFit.LineWidth(1))
    chib2P_set = RooArgSet(chib2_pdf)
    modelPdf.plotOn(frame, RooFit.Components(chib2P_set),RooFit.LineColor(ROOT.kRed), RooFit.LineStyle(2), RooFit.LineWidth(1))
    background_set =  RooArgSet(background)
    modelPdf.plotOn(frame,RooFit.Components(background_set), RooFit.LineColor(ROOT.kBlack), RooFit.LineStyle(2), RooFit.LineWidth(1))
    modelPdf.plotOn(frame, RooFit.LineWidth(2))
    frame.SetName("fit_resonance")  

    # Make numChib object
    numChib = NumChib(numChib=n_chib.getVal(), s_numChib=n_chib.getError(), ratio_21=ratio_21.getVal(), s_ratio_21=ratio_21.getError(), numBkg=n_background.getVal(), s_numBkg=n_background.getError(), corr_NB=result.correlation(n_chib, n_background),corr_NR=result.correlation(n_chib, ratio_21) , name='numChib'+output_suffix+ptBin_label,q0=q0.getVal(),s_q0=q0.getError(),alpha=alpha.getVal(),s_alpha=alpha.getError(), beta=beta.getVal(), s_beta=beta.getError(), chiSquare=frame.chiSquare())
    #numChib.saveToFile('numChib'+output_suffix+'.txt')

    if noPlots:
        chi2 = frame.chiSquare()
        del frame
        return numChib, chi2
    
    # Legend
    parameters_on_legend = RooArgSet()#n_chib, ratio_21, n_background)
    if massFreeToChange:
        #parameters_on_legend.add(scale_mean)
        parameters_on_legend.add(mean_1)
        #parameters_on_legend.add(mean_2)
    if sigmaFreeToChange:
        parameters_on_legend.add(scale_sigma)
    if massFreeToChange or sigmaFreeToChange:
        modelPdf.paramOn(frame, RooFit.Layout(0.1,0.6,0.2),RooFit.Parameters(parameters_on_legend))
    
    if printLegend: #chiquadro, prob, numchib...
        leg = TLegend(0.48,0.75,0.97,0.95)
        leg.SetBorderSize(0)
        #leg.SetTextSize(0.04)
        leg.SetFillStyle(0)
        chi2 = frame.chiSquare()
        probChi2 = TMath.Prob(chi2*ndof, ndof)
        chi2 = round(chi2,2)
        probChi2 = round(probChi2,2)
        leg.AddEntry(0,'#chi^{2} = '+str(chi2),'')
        leg.AddEntry(0,'Prob #chi^{2} = '+str(probChi2),'')
        N_bkg, s_N_bkg = roundPair(numChib.numBkg, numChib.s_numBkg)
        leg.AddEntry(0,'N_{bkg} = '+str(N_bkg)+' #pm '+str(s_N_bkg),'')
        N_chib12, s_N_chib12 = roundPair(numChib.numChib, numChib.s_numChib)
        leg.AddEntry(0,'N_{#chi_{b}} = '+str(N_chib12)+' #pm '+str(s_N_chib12),'')
        Ratio = numChib.calcRatio()
        s_Ratio = numChib.calcRatioError()
        Ratio, s_Ratio = roundPair(Ratio, s_Ratio)
        leg.AddEntry(0,'N_{2}/N_{1} = '+str(Ratio)+' #pm '+str(s_Ratio),'')

        if printSigReso: # Add Significance
            Sig =  numChib.calcSignificance()
            s_Sig = numChib.calcSignificanceError()
            Sig, s_Sig = roundPair(Sig, s_Sig)
            leg.AddEntry(0,'Sig = '+str(Sig)+' #pm '+str(s_Sig),'')
            if(Chib1_parameters.sigma>Chib2_parameters.sigma):
                Reso = Chib1_parameters.sigma * 1000 # So it's in MeV and not in GeV
                s_Reso = Chib1_parameters.s_sigma * 1000 # So it's in MeV and not in GeV
            else:
                Reso = Chib2_parameters.sigma * 1000 # So it's in MeV and not in GeV
                s_Reso = Chib2_parameters.s_sigma * 1000 # So it's in MeV and not in GeV
            Reso, s_Reso =roundPair(Reso, s_Reso)
            leg.AddEntry(0,'Reso = '+str(Reso)+' #pm '+str(s_Reso)+' MeV','')
            #N1 = numChib.numChib1
            #s_N1 = numChib.s_numChib1
            #N1, s_N1 = roundPair(N1, s_N1)
            #leg.AddEntry(0,'N_{1} = '+str(N1)+' #pm '+str(s_N1),'')
            #N2 = numChib.numChib2
            #s_N2 = numChib.s_numChib2
            #N2, s_N2 = roundPair(N2, s_N2)
            #leg.AddEntry(0,'N_{2} = '+str(N2)+' #pm '+str(s_N2),'')

        frame.addObject(leg)

    if legendOnPlot:  #  < pT <
        legend = TLegend(.06,.75,.53,.93)
        legend.SetFillStyle(0)
        legend.SetBorderSize(0)
        #legend.AddEntry(0,'CMS','')
        legend.AddEntry(0,str(cuts.upsilon_pt_lcut)+' GeV < p_{T}(#Upsilon) < '+str(cuts.upsilon_pt_hcut)+' GeV','')
        #legend.AddEntry(0,'p_{T}(#Upsilon)<'+str(cuts.upsilon_pt_hcut),'')
        frame.addObject(legend)

    if titleOnPlot:
        titleLegend = TLegend(.06,.4,.55,.73)
       
        #titleLegend.SetTextSize(0.03)
        titleLegend.SetFillStyle(0)
        titleLegend.SetBorderSize(0)
        titleLegend.AddEntry(0,plotTitle,'')
        frame.addObject(titleLegend)

    if cmsOnPlot:
        if printLegend:
            pvtxt = TPaveText(.1,.55,.55,.73,"NDC")
        else:
            pvtxt = TPaveText(0.5,0.75,0.97,0.9,"NDC") #(.06,.4,.55,.73)
        pvtxt.AddText('CMS Preliminary')
        pvtxt.AddText('pp, #sqrt{s} = 8 TeV')
        pvtxt.AddText('L = 20.7 fb^{-1}')
        pvtxt.SetFillStyle(0)
        pvtxt.SetBorderSize(0)
        pvtxt.SetTextSize(0.04)
        frame.addObject(pvtxt)
    
    # Canvas
    c1=TCanvas('Chib12_1P'+output_suffix+ptBin_label,'Chib12_1P'+output_suffix+ptBin_label)
    frame.Draw()
    if drawPulls:
        #c1=TCanvas(output_name+output_suffix,output_name+output_suffix,700, 625)
        hpull = frame.pullHist()
        framePulls = x.frame()
        framePulls.SetTitle(';;Pulls')
        framePulls.GetYaxis().SetLabelSize(0.18)
        framePulls.GetYaxis().SetTitle('Pulls')
        framePulls.GetYaxis().SetTitleSize(0.18)
        framePulls.GetYaxis().SetTitleOffset(0.15)
        framePulls.GetYaxis().SetNdivisions(005)
        framePulls.GetXaxis().SetLabelSize(0.16)
        framePulls.GetXaxis().SetTitle('')
        line0 = TLine(9.7, 0, 10.1, 0)
        line0.SetLineColor(ROOT.kBlue)
        line0.SetLineWidth(2)
        framePulls.addObject(line0)
        framePulls.addPlotable(hpull,"P") 
        framePulls.SetMaximum(5)
        framePulls.SetMinimum(-5)
        pad1 = TPad("pad1", "The pad 80% of the height",0.0,0.2,1.0,1.0)
        pad1.cd()
        frame.Draw()
        pad2 = TPad("pad2", "The pad 20% of the height",0.0,0.01,1.0,0.2)
        pad2.cd()
        framePulls.Draw()
        c1.cd()
        pad1.Draw()
        pad2.Draw()
    #c1.SaveAs('Chib12_1P'+output_suffix+'.png')
    print 'Chi2 = '+str(frame.chiSquare())
    

    # print ratio background/all in the signal refion
    signal_region = x.setRange("signal_region",9.87,9.92)
    pdf_integral = modelPdf.createIntegral(RooArgSet(x), RooFit.Range('signal_region')).getVal() * (n_chib.getVal() + n_background.getVal())
    bkg_integral = background.createIntegral(RooArgSet(x), RooFit.Range('signal_region')).getVal() * n_background.getVal()

    print 'Ratio bkg/all in signal region = '+str(bkg_integral/pdf_integral)

    return numChib, c1
예제 #49
0
gauss4 = RooGaussian("gauss4","gaussian PDF",x,mean4,sigma4)


# gauss 5
mean5 = RooRealVar("mean5","mean of gaussian",5,-10,10)
sigma5 = RooRealVar("sigma5","width of gaussian",0.5,0.1,10)

gauss5 = RooGaussian("gauss5","gaussian PDF",x,mean5,sigma5)


# add PDF gauss_combine1 = gauss4 + gauss5

frac_combine1   = RooRealVar("frac_combine1",   "fraction of gauss4 wrt gauss5", 0.7, 0.,   1.)

pdf_combine1 = RooAddPdf("pdf_combine1"," gauss4 + gauss5 ", RooArgList(gauss4 , gauss5 ), RooArgList( frac_combine1 ))


#pdf_combine1.plotOn(xframe3,RooFit.LineColor(RooFit.kBlue))

gauss4.plotOn(xframe3, RooFit.Normalization( frac_combine1.getVal()   ,RooAbsReal.Relative),RooFit.LineColor(RooFit.kOrange))
gauss5.plotOn(xframe3, RooFit.Normalization( 1-frac_combine1.getVal() ,RooAbsReal.Relative),RooFit.LineColor(RooFit.kCyan))
pdf_combine1.plotOn(xframe3, RooFit.Normalization(1.0,RooAbsReal.Relative) ,RooFit.LineColor(RooFit.kBlue))

# -------------------------------------------
# 4. use combine PDF to generate MC

xframe4 = x.frame(RooFit.Title("4. use combine PDF to generate MC"))

data2 = pdf_combine1.generate(RooArgSet(x),500)
예제 #50
0
a4 = RooRealVar("a4", "a4", 0.01, -5.0, 5.0)
a5 = RooRealVar("a5", "a5", 0.01, -5.0, 5.0)
a6 = RooRealVar("a6", "a6", 0.01, -5.0, 5.0)
a7 = RooRealVar("a7", "a7", 0.001, -5.0, 5.0)
a8 = RooRealVar("a8", "a8", 0.001, -5.0, 5.0)
aset = RooArgList(a0, a1, a2, a3, a4, a5, a6, a7, a8)
bkg = RooBernstein("cheb", "Background", mass, aset)
#bkg = RooExponential("bkg","bkg",mass,alpha)

#gauss = RooGaussian("gauss","gaussian PDF ",mass,mean,sigma)
sig_1 = RooGaussian("sig_1", "sig_1", mass, mean, sigma)
sig_2 = RooGaussian("sig_1", "sig_1", mass, mean, sigma_2)

sig_3 = RooGaussian("bump", "bump", mass, mean_3, sigma_3)

sig = RooAddPdf("sig", "g+g", sig_1, sig_2, gFrac)

#sig_1 = RooGaussian("sig_1","sig_1",mass,mean,sigma)

nSig = RooRealVar("nSig", "nSig", 100, 100, len(data["mass"].values))
nBkg = RooRealVar("nBkg", "nBkg", 1000, 100, len(data["mass"].values))

#tot = RooAddPdf("tot","g+cheb",RooArgList(sig,sig_3,bkg),RooArgList(sFrac,bumpFrac))
tot = RooAddPdf("tot", "g+cheb", RooArgList(sig_1, bkg),
                RooArgList(nSig, nBkg))
h1 = TH1F("hist", "hist", 200, 4.05, 5.75)
map(h1.Fill, data["mass"].values)

masslist = RooArgList(mass)
dh = RooDataHist("dh", "dh", masslist, h1)
pdf_zj = RooHistPdf ("pdf_zj", "Z+jets pdf", vars_set, rh_zj, 0);
pdf_VJ = RooHistPdf ("pdf_VJ", "Z+jets pdf", vars_set, rh_VJ, 0);
pdf_qcd = RooHistPdf("pdf_qcd", "QCD pdf ", vars_set, rh_qcd, 0);
pdf_qcd2 = RooHistPdf("pdf_qcd", "QCD pdf ", vars_set, rh_qcd2, 0);
pdf_stop = RooHistPdf("pdf_stop", "single top pdf", vars_set, rh_stop, 0);
pdf_signal = RooHistPdf("pdf_signal", "single top pdf", vars_set, rh_signal, 0);

ntt = RooRealVar ("ntt", "number of tt signal events", n_ttbar, lowerBound, upperBound, "event");
nwj = RooRealVar  ("nwj", "number of W+jets bgnd events", n_wj, lowerBound, upperBound, "event");
nzj = RooRealVar  ("nzj", "number of Z+jets bgnd events", n_zj, lowerBound, upperBound, "event");
nVJ = RooRealVar  ("nVJ", "number of Z+jets bgnd events", n_VJ, lowerBound, upperBound, "event");
nqcd = RooRealVar("nqcd", "number of QCD bgnd events", n_qcd, lowerBound, upperBound, "event");
nstop = RooRealVar("nstop", "number of single top bgnd events", n_stop, lowerBound, upperBound, "event");
nSignal = RooRealVar("nSignal", "number of single top + ttbar events", n_stop + n_ttbar, lowerBound, upperBound, "event");
model = RooAddPdf("model", "sig+wj+zj+qcd+stop",
            RooArgList(pdf_signal, pdf_VJ, pdf_qcd),
            RooArgList(nSignal, nVJ, nqcd)) ; 
fitResult = model.fitTo(data, RooFit.Minimizer("Minuit2", "Migrad"), 
                        RooFit.NumCPU(8), RooFit.Extended(True),
                        RooFit.SumW2Error(False), RooFit.Strategy(2), 
                        #verbosity
                        RooFit.PrintLevel(-1), RooFit.Warnings(False), RooFit.Verbose(False))

#ntt_fit = ntt.getVal();
nSignal_fit = nSignal.getVal();
nwj_fit = nwj.getVal();
#nzj_fit = nzj.getVal();
nVJ_fit = nVJ.getVal();
nqcd_fit = nqcd.getVal();
#nstop_fit = nstop.getVal();
    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)
        a0.setConstant(kTRUE)
        a1.setConstant(kTRUE)
        a2.setConstant(kTRUE)
        a3.setConstant(kTRUE)
        a4.setConstant(kTRUE)
예제 #53
0
def findOnePe(hist, ws, name='x', Npe = 1):
    fitPed(hist, ws, name)
    x = ws.var(name)

    ped = ws.pdf('ped')
    pedWidth = ws.var('pedWidth')

    pdfs = RooArgList(ped)
    pdfList = []

    fped = RooRealVar('fped', 'f_{ped}', 0.8, 0., 1.)
    fractions = RooArgList(fped)
    fList = []
    peList = []

    peMean = RooRealVar('peMean', 'mean_{pe}', 6., 0., 20.)
    peWidth = RooRealVar('peWidth', 'width_{pe}', pedWidth.getVal(), 0., 10.)

    for i in range(0, Npe):
        pem = RooFormulaVar('pem{0}'.format(i+1), '@0+{0}*@1'.format(i+1),
                            RooArgList(ws.var('pedMean'), peMean))
        peList.append(pem)
        npepdf = RooGaussian('pe{0}pdf'.format(i+1), 'pe{0}pdf'.format(i+1),
                             x, pem, pedWidth)
        pdfs.add(npepdf)
        pdfList.append(npepdf)

        fnpe = RooRealVar('fpe{0}'.format(i+1), 'fpe{0}'.format(i+1),
                          0.5, -0.1, 1.0)
        fractions.add(fnpe)
        fList.append(fnpe)

    #bgMean = RooRealVar("bgMean", "bgMean", 6.0, x.getMin(), x.getMax())
    bgScale = RooRealVar("bgScale", "bgScale", 0.5, -1.0, Npe + 1.0)
    bgMean = RooFormulaVar("bgMean", "@1+@0*@2",
                           RooArgList(peMean, ws.var('pedMean'), bgScale))
    bgWidthL = RooRealVar("bgWidthL", "bgWidthL", pedWidth.getVal()*2,
                          0., 25.)
    bgWidthR = RooRealVar("bgWidthR", "bgWidthR", pedWidth.getVal()*7,
                          0., 25.)

    bgGauss = RooBifurGauss("bgGauss", "bgGauss", x, bgMean,
                            bgWidthR, bgWidthR)

    if (Npe > 1):
        pdfs.add(bgGauss)
    else:
        fractions.remove(fractions.at(fractions.getSize()-1))

##     pem = RooFormulaVar('pem', '@0+@1', RooArgList(peMean, ws.var('pedMean')))
##     firstPe = RooGaussian('firstPe', 'firstPe', x, pem, peWidth)

##     pdfs.Print("v")
##     fractions.Print("v")
    pedPlusOne = RooAddPdf('pedPlusOne', 'pedPlusOne', pdfs, fractions, True)

    ## pedWidth = ped.GetParameter(2)
    ## pedMean = ped.GetParameter(1)
    ## pedA = ped.GetParameter(0)
    
    secondMax = hist.GetMaximumBin() + 1
    goingDown = True
    maxVal = hist.GetBinContent(secondMax)
    foundMax = False
    while (not foundMax) and (secondMax < hist.GetNbinsX()):
        tmpVal = hist.GetBinContent(secondMax+1)
        if (tmpVal < maxVal):
            if not goingDown:
                foundMax = True
            else:
                goingDown = True
                maxVal = tmpVal
                secondMax += 1
        elif (tmpVal > maxVal):
            goingDown = False
            maxVal = tmpVal
            secondMax += 1
        else:
            maxVal = tmpVal
            secondMax += 1

    secondMaxx = hist.GetBinCenter(secondMax)
    print 'found 2nd maximum in bin',secondMax,'value',secondMaxx

##     peMean.setVal(secondMaxx)
##     bgMean.setVal(secondMaxx*0.6)
    x.setRange('pedPlus_fit', x.getMin(), ws.var('pedMean').getVal()+pedWidth.getVal()*6.*(Npe+0))

    pedPlusOne.fitTo(ws.data('ds'), RooFit.Minos(False),
                     RooFit.Range('pedPlus_fit'),
                     RooFit.PrintLevel(1))

    getattr(ws, 'import')(pedPlusOne)
예제 #54
0
class BaseFitter(object):
    def __init__(self, plot):
        assert (isinstance(plot, DataMCPlot))
        self.plot = plot
        self._make_underlying_model()
        self._make_dataset()
        self._make_fit_model()
        self._fit()

    def _make_underlying_model(self):
        self.pdfs = {}
        self.yields = {}  # yields are plain floats
        self.ryields = {}  # keep track of roofit objects for memory management
        nbins, xmin, xmax = self.plot.histos[0].GetBinning()
        self.xvar = RooRealVar("x", "x", xmin, xmax)
        self.xvar.setBins(nbins)
        self.pdfs = {}
        self.hists = []
        pdfs = RooArgList()
        yields = RooArgList()
        for compname, comp in self.plot.histosDict.iteritems():
            if comp.weighted.Integral() == 0:
                continue
            assert (isinstance(comp, Histogram))
            hist = RooDataHist(compname, compname, RooArgList(self.xvar),
                               comp.weighted)
            SetOwnership(hist, False)
            # self.hists.append(hist)
            pdf = RooHistPdf(compname, compname, RooArgSet(self.xvar), hist)
            self.pdfs[compname] = pdf
            # self.pdfs[compname].Print()
            pdfs.add(pdf)
            nevts = comp.Integral(xmin=xmin, xmax=xmax)
            nmin = min(0, nevts * (1 - comp.uncertainty))
            nmax = nevts * (1 + comp.uncertainty)
            theyield = RooRealVar('n{}'.format(compname),
                                  'n{}'.format(compname), nevts, nmin, nmax)
            self.ryields[compname] = theyield
            self.yields[compname] = nevts
            yields.add(theyield)

        self.underlying_model = RooAddPdf('model', 'model', pdfs, yields)

    def _make_fit_model(self):
        pass

    def _make_dataset(self):
        nevents = sum(self.yields.values())
        self.data = self.underlying_model.generate(RooArgSet(self.xvar),
                                                   nevents)

    def _fit(self):
        self.tresult = self.underlying_model.fitTo(self.data,
                                                   RooFit.Extended(),
                                                   RooFit.Save(),
                                                   RooFit.PrintEvalErrors(-1))

    def print_result(self):
        self.tresult.Print()
        yzh = self.ryields['ZH']
        zh_val = yzh.getVal()
        zh_err = yzh.getError()
        percent_unc = zh_err / zh_val * 100.
        print 'ZH yield  = {:8.2f}'.format(zh_val)
        print 'ZH uncert = {:8.2f}%'.format(percent_unc)
        return percent_unc

    def draw_pdfs(self):
        self.pframe = self.xvar.frame()
        for pdf in self.pdfs.values():
            pdf.plotOn(self.pframe)
        self.pframe.Draw()

    def draw_data(self):
        self.mframe = self.xvar.frame()
        self.data.plotOn(self.mframe)
        self.underlying_model.plotOn(self.mframe)
        for icomp, compname in enumerate(self.pdfs):
            self.underlying_model.plotOn(self.mframe,
                                         RooFit.Components(compname),
                                         RooFit.LineColor(icomp + 1))
        self.mframe.Draw()
예제 #55
0
mean12 = RooRealVar("mean12","mean of gaussian",-1,-10,10)
sigma12 = RooRealVar("sigma12","width of gaussian",2,0.1,10)

gauss12 = RooGaussian("gauss12","gaussian PDF",x,mean12,sigma12)

# gauss 13
mean13 = RooRealVar("mean13","mean of gaussian",5,-10,10)
sigma13 = RooRealVar("sigma13","width of gaussian",1,0.1,10)

gauss13 = RooGaussian("gauss13","gaussian PDF",x,mean13,sigma13)

# add PDF gauss_combine2 = gauss12 + gauss13

frac_combine4   = RooRealVar("frac_combine4",   "fraction of gauss4 wrt gauss5", 0.7, 0.,   1.)

pdf_combine4 = RooAddPdf("pdf_combine4"," gauss12 + gauss13 ", RooArgList(gauss12 , gauss13 ), RooArgList( frac_combine4 ))


#pdf_combine1.plotOn(xframe3,RooFit.LineColor(RooFit.kBlue))

#gauss12.plotOn(xframe8, RooFit.Normalization( frac_combine4.getVal()   ,RooAbsReal.Relative),RooFit.LineColor(RooFit.kOrange))
#gauss13.plotOn(xframe8, RooFit.Normalization( 1-frac_combine4.getVal() ,RooAbsReal.Relative),RooFit.LineColor(RooFit.kCyan))
#pdf_combine2.plotOn(xframe8, RooFit.Normalization(1.0,RooAbsReal.Relative) ,RooFit.LineColor(RooFit.kBlue))

# generate toy MC

n_generate = 5000

data4 = pdf_combine4.generate(RooArgSet(x), n_generate )

예제 #56
0
def studyVqqResolution(rootFile):

    #get all from file
    histos={}
    inF=TFile.Open(rootFile)
    keys=inF.GetListOfKeys()
    for k in keys:
        obj=inF.Get(k.GetName())
        obj.SetDirectory(0)
        histos[k.GetName()]=obj
    inF.Close()

    #plot
    gROOT.SetBatch()
    gROOT.SetStyle('Plain')
    gStyle.SetOptStat(0)
    gStyle.SetOptFit(1111)
    gStyle.SetOptTitle(0)
    gStyle.SetStatFont(42)

    kin=['','30to40','40to50','50to75','75to100','100toInf']
    for k in kin:        
        c=TCanvas('c','c',600,600)
        c.cd()
        c.SetCanvasSize(1000,500)
        c.SetWindowSize(1000,500)
        c.Divide(2,1)
        c.cd(1)
        histos['deta'+k+'barrel'].SetLineWidth(2)
        histos['deta'+k+'barrel'].SetTitle('barrel')
        histos['deta'+k+'barrel'].Draw('hist')
        histos['deta'+k+'endcap'].SetLineWidth(2)
        histos['deta'+k+'endcap'].SetLineStyle(7)
        histos['deta'+k+'endcap'].SetTitle('endcap')
        histos['deta'+k+'endcap'].Draw('histsame')
        leg=TLegend(0.6,0.92,0.9,0.98)
        leg.SetFillStyle(0)
        leg.SetBorderSize(0)
        leg.SetTextFont(42)
        leg.AddEntry(histos['deta'+k+'barrel'],'barrel','f')
        leg.AddEntry(histos['deta'+k+'endcap'],'endcap','f')
        leg.SetNColumns(2)
        leg.Draw()
        drawHeader()
        c.cd(2)
        histos['dphi'+k+'barrel'].SetLineWidth(2)
        histos['dphi'+k+'barrel'].SetTitle('barrel')
        histos['dphi'+k+'barrel'].Draw('hist')
        histos['dphi'+k+'endcap'].SetLineWidth(2)
        histos['dphi'+k+'endcap'].SetLineStyle(7)
        histos['dphi'+k+'endcap'].SetTitle('endcap')
        histos['dphi'+k+'endcap'].Draw('histsame')
        c.Modified()
        c.Update()
        c.SaveAs('dr_%s.png'%k)


    labels=[]
    responseVars=['dpt','den','dphi','deta','dr']
    for r in responseVars:
        barrelResponse=TGraphErrors()
        barrelResponse.SetName(r+'barrelresponse')
        barrelResponse.SetLineWidth(2)
        barrelResponse.SetFillStyle(0)
        barrelResponse.SetMarkerStyle(20)
        barrelCoreResponse=barrelResponse.Clone(r+'barrelcoreresponse')
        endcapResponse=TGraphErrors()
        endcapResponse.SetName(r+'endcapresponse')
        endcapResponse.SetLineWidth(2)
        endcapResponse.SetFillStyle(0)
        endcapResponse.SetMarkerStyle(24)
        endcapCoreResponse=endcapResponse.Clone(r+'endcapresponse')
        for k in kin: 
            c.cd()
            c.Clear()
            c.SetWindowSize(1000,500)
            c.Divide(2,1)
            for i in [1,2] :
                c.cd(i)
                reg='barrel'
                if i==2: reg='endcap' 

                h=histos[r+k+reg]
                x=RooRealVar("x", h.GetXaxis().GetTitle(), h.GetXaxis().GetXmin(), h.GetXaxis().GetXmax())
                data=RooDataHist("data", "dataset with x", RooArgList(x), h)
                frame=x.frame()
                RooAbsData.plotOn( data, frame, RooFit.DataError(RooAbsData.SumW2) )

                mean1=RooRealVar("mean1","mean1",0,-0.5,0.5);
                sigma1=RooRealVar("sigma1","sigma1",0.1,0.01,1.0);
                gauss1=RooGaussian("g1","g",x,mean1,sigma1)
                
                if r=='dpt' or r=='den' :
                    mean2=RooRealVar("mean2","mean2",0,-0.5,0.5);
                    sigma2=RooRealVar("sigma2","sigma2",0.1,0.01,1.0);
                    alphacb=RooRealVar("alphacb","alphacb",1,0.1,3);
                    ncb=RooRealVar("ncb","ncb",4,1,100)
                    gauss2 = RooCBShape("cb2","cb",x,mean2,sigma2,alphacb,ncb);
                else:
                    mean1.setRange(0,0.5)
                    mean2=RooRealVar("mean2","mean",0,0,1);
                    sigma2=RooRealVar("sigma2","sigma",0.1,0.01,1.0);
                    gauss2=RooGaussian("g2","g",x,mean2,sigma2) ;

                frac = RooRealVar("frac","fraction",0.9,0.0,1.0)
                if data.sumEntries()<100 :
                    frac.setVal(1.0)
                    frac.setConstant(True)
                model = RooAddPdf("sum","g1+g2",RooArgList(gauss1,gauss2), RooArgList(frac))

                status=model.fitTo(data,RooFit.Save()).status()
                if status!=0 : continue

                model_cdf=model.createCdf(RooArgSet(x)) ;
                cl=0.90
                ul=0.5*(1.0+cl)
                closestToCL=1.0
                closestToUL=-1
                closestToMedianCL=1.0
                closestToMedian=-1
                for ibin in xrange(1,h.GetXaxis().GetNbins()*10):
                    xval=h.GetXaxis().GetXmin()+(ibin-1)*h.GetXaxis().GetBinWidth(ibin)/10.
                    x.setVal(xval)
                    cdfValToCL=math.fabs(model_cdf.getVal()-ul)
                    if cdfValToCL<closestToCL:
                        closestToCL=cdfValToCL
                        closestToUL=xval
                    cdfValToCL=math.fabs(model_cdf.getVal()-0.5)
                    if cdfValToCL<closestToMedianCL:
                        closestToMedianCL=cdfValToCL
                        closestToMedian=xval

                RooAbsPdf.plotOn(model,frame)
                frame.Draw()

                if i==1: drawHeader()
                labels.append( TPaveText(0.6,0.92,0.9,0.98,'brNDC') )
                ilab=len(labels)-1
                labels[ilab].SetName(r+k+'txt')
                labels[ilab].SetBorderSize(0)
                labels[ilab].SetFillStyle(0)
                labels[ilab].SetTextFont(42)
                labels[ilab].SetTextAlign(12)
                kinReg=k.replace('to','-')
                kinReg=kinReg.replace('Inf','#infty')
                labels[ilab].AddText('['+reg+'] '+kinReg)
                labels[ilab].Draw()
                
                resolutionVal=math.fabs(closestToUL-closestToMedian)
                responseGr=barrelResponse
                responseCoreGr=barrelCoreResponse
                coreResolutionVal=sigma1.getVal()
                coreResolutionErr=sigma1.getError()
                if frac.getVal()<0.7 and (sigma2.getVal()<sigma1.getVal()) :
                    coreResolutionVal=sigma2.getVal()
                    coreResolutionErr=sigma2.getError()


                if i==2 : 
                    responseGr=endcapResponse
                    responseCoreGr=endcapCoreResponse
                if k!='' :
                    nrespPts=responseGr.GetN()
                    kinAvg=150
                    kinWidth=50
                    if k=='30to40' : 
                        kinAvg=35
                        kinWidth=5
                    if k=='40to50' : 
                        kinAvg=45
                        kinWidth=5
                    if k=='50to75' : 
                        kinAvg=62.5
                        kinWidth=12.5
                    elif k=='75to100' :
                        kinAvg=87.5
                        kinWidth=12.5
                    responseGr.SetPoint(nrespPts,kinAvg,resolutionVal)
                    responseCoreGr.SetPoint(nrespPts,kinAvg,coreResolutionVal)
                    responseCoreGr.SetPointError(nrespPts,kinWidth,coreResolutionErr)

                labels.append( TPaveText(0.15,0.7,0.4,0.9,'brNDC') )
                ilab=len(labels)-1
                labels[ilab].SetName(r+k+'fitrestxt')
                labels[ilab].SetBorderSize(0)
                labels[ilab].SetFillStyle(0)
                labels[ilab].SetTextFont(42)
                labels[ilab].SetTextAlign(12)
                labels[ilab].AddText('Gaussian #1 (f=%3.3f)'%frac.getVal())
                labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean1.getVal(),mean1.getError()))
                labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma1.getVal(),sigma1.getError()))
                labels[ilab].AddText('Gaussian #2 (f=%3.3f)'%(1-frac.getVal()))
                labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean2.getVal(),mean2.getError()))
                labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma2.getVal(),sigma2.getError()))
                
                labels[ilab].Draw()

            c.Modified()
            c.Update()
            c.SaveAs(r+'res_'+k+'.png')
        
        frame=TGraphErrors()
        frame.SetPoint(0,0,0)
        frame.SetPoint(1,200,0.3)
        frame.SetMarkerStyle(1)
        frame.SetFillStyle(0)
        frame.SetName('frame')
        cresp=TCanvas('cresp','cresp',500,500)
        cresp.cd()
        frame.Draw('ap')
        barrelResponse.Draw('pl')
        endcapResponse.Draw('pl')
        frame.GetXaxis().SetTitle("Quark transverse momentum [GeV]") 
        frame.GetYaxis().SetTitle("Resolution %3.2f C.L."%cl )
        frame.GetYaxis().SetTitleOffset(1.4)
        frame.GetYaxis().SetNdivisions(10)
        drawHeader()
        leg=TLegend(0.6,0.92,0.9,0.98)
        leg.SetFillStyle(0)
        leg.SetBorderSize(0)
        leg.SetTextFont(42)
        leg.AddEntry(barrelResponse,'barrel','fp')
        leg.AddEntry(endcapResponse,'endcap','fp')
        leg.SetNColumns(2)
        leg.Draw()
        cresp.Modified()
        cresp.Update()
        cresp.SaveAs(r+'res_evol.png')

        frameCore=frame.Clone('framecore')
        cresp.Clear()
        frameCore.Draw('ap')
        barrelCoreResponse.Draw('pl')
        endcapCoreResponse.Draw('pl')
        frameCore.GetXaxis().SetTitle("Quark transverse momentum [GeV]") 
        frameCore.GetYaxis().SetTitle("Core resolution")
        frameCore.GetYaxis().SetTitleOffset(1.4)
        frameCore.GetYaxis().SetNdivisions(10)
        frameCore.GetYaxis().SetRangeUser(0,0.2)
        drawHeader()
        leg=TLegend(0.6,0.92,0.9,0.98)
        leg.SetFillStyle(0)
        leg.SetBorderSize(0)
        leg.SetTextFont(42)
        leg.AddEntry(barrelCoreResponse,'barrel','fp')
        leg.AddEntry(endcapCoreResponse,'endcap','fp')
        leg.SetNColumns(2)
        leg.Draw()
        cresp.Modified()
        cresp.Update()
        cresp.SaveAs(r+'rescore_evol.png')

    bosons=['h','z','w']
    kin=['','50','100']
    region=['','bb','eb','ee']
    for k in kin:        
        for r in region:

            c=TCanvas('c','c',600,600)
            c.cd()
            histos['mjj'+k+r].Rebin()
            histos['mjj'+k+r].Draw()
            ic=1
            leg=TLegend(0.6,0.92,0.9,0.98)
            leg.SetFillStyle(0)
            leg.SetBorderSize(0)
            leg.SetTextFont(42)
            leg.AddEntry(histos['mjj'+k+r],'inclusive','f')
            for b in bosons:
                if histos[b+'mjj'+k+r].Integral()<=0 : continue 
                ic=ic+1
                histos[b+'mjj'+k+r].Rebin()
                histos[b+'mjj'+k+r].SetLineColor(ic)
                histos[b+'mjj'+k+r].SetLineWidth(2)
                histos[b+'mjj'+k+r].SetMarkerColor(ic)
                histos[b+'mjj'+k+r].SetMarkerStyle(1)
                histos[b+'mjj'+k+r].SetFillStyle(3000+ic)
                histos[b+'mjj'+k+r].SetFillColor(ic)
                histos[b+'mjj'+k+r].Draw('histsame')
                leg.AddEntry(histos[b+'mjj'+k+r],b,"f")
            leg.SetNColumns(ic)
            leg.Draw()
            drawHeader()
            labels.append( TPaveText(0.65,0.8,0.9,0.9,'brNDC') )
            ilab=len(labels)-1
            labels[ilab].SetName(k+r+'mjj')
            labels[ilab].SetBorderSize(0)
            labels[ilab].SetFillStyle(0)
            labels[ilab].SetTextFont(42)
            labels[ilab].SetTextAlign(12)
            regionTitle="inclusive"
            if r == 'bb' : regionTitle='barrel-barrel'
            if r == 'eb' : regionTitle='endcap-barrel'
            if r == 'ee' : regionTitle='endcap-endcap'
            labels[ilab].AddText(regionTitle)
            ptthreshold=30
            if k!='' : ptthreshold=float(k)
            labels[ilab].AddText('p_{T}>%3.0f GeV'%ptthreshold)
            labels[ilab].Draw()
            
            c.Modified()
            c.Update()
            c.SaveAs('mjj'+k+r+'.png')


    massResolutionGrs=[]
    for r in region:
        massResolution=TGraphErrors()
        massResolution.SetName(r+'dm')
        massResolution.SetLineWidth(2)
        massResolution.SetFillStyle(0)
        massResolution.SetMarkerStyle(20+len(massResolutionGrs))
        massResolution.SetMarkerColor(1+len(massResolutionGrs))
        massResolution.SetLineColor(1+len(massResolutionGrs))
        massResolution.SetFillColor(1+len(massResolutionGrs))
        massResolutionGrs.append(massResolution)
        
        for k in kin:        

            c=TCanvas('c','c',600,600)
            c.cd()
            h=histos['dmjj'+k+r]
            x=RooRealVar("x", h.GetXaxis().GetTitle(), h.GetXaxis().GetXmin(), h.GetXaxis().GetXmax())
            data=RooDataHist("data", "dataset with x", RooArgList(x), h)
            frame=x.frame()
            RooAbsData.plotOn( data, frame, RooFit.DataError(RooAbsData.SumW2) )
            
            mean1=RooRealVar("mean1","mean1",0,-0.5,0.5);
            sigma1=RooRealVar("sigma1","sigma1",0.1,0.01,1.0);
            gauss1=RooGaussian("g1","g",x,mean1,sigma1)
            mean2=RooRealVar("mean2","mean2",0,-0.5,0.5);
            sigma2=RooRealVar("sigma2","sigma2",0.1,0.01,1.0);
            alphacb=RooRealVar("alphacb","alphacb",1,0.1,3);
            ncb=RooRealVar("ncb","ncb",4,1,100)
            gauss2 = RooCBShape("cb2","cb",x,mean2,sigma2,alphacb,ncb);
            frac = RooRealVar("frac","fraction",0.9,0.0,1.0)
            model = RooAddPdf("sum","g1+g2",RooArgList(gauss1,gauss2), RooArgList(frac))
            status=model.fitTo(data,RooFit.Save()).status()
            if status!=0 : continue
            RooAbsPdf.plotOn(model,frame)
            frame.Draw()

            labels.append( TPaveText(0.6,0.65,0.85,0.9,'brNDC') )
            ilab=len(labels)-1
            labels[ilab].SetName(r+k+'dmfitrestxt')
            labels[ilab].SetBorderSize(0)
            labels[ilab].SetFillStyle(0)
            labels[ilab].SetTextFont(42)
            labels[ilab].SetTextAlign(12)
            labels[ilab].AddText('Gaussian #1 (f=%3.3f)'%frac.getVal())
            labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean1.getVal(),mean1.getError()))
            labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma1.getVal(),sigma1.getError()))
            labels[ilab].AddText('Gaussian #2 (f=%3.3f)'%(1-frac.getVal()))
            labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean2.getVal(),mean2.getError()))
            labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma2.getVal(),sigma2.getError()))
            labels[ilab].Draw()

            drawHeader()
            labels.append( TPaveText(0.15,0.8,0.4,0.9,'brNDC') )
            ilab=len(labels)-1
            labels[ilab].SetName(k+r+'dmjj')
            labels[ilab].SetBorderSize(0)
            labels[ilab].SetFillStyle(0)
            labels[ilab].SetTextFont(42)
            labels[ilab].SetTextAlign(12)
            regionTitle="inclusive"
            if r == 'bb' : regionTitle='barrel-barrel'
            if r == 'eb' : regionTitle='endcap-barrel'
            if r == 'ee' : regionTitle='endcap-endcap'
            labels[ilab].AddText(regionTitle)
            ptthreshold=30
            if k!='' : ptthreshold=float(k)
            labels[ilab].AddText('p_{T}>%3.0f GeV'%ptthreshold)
            labels[ilab].Draw()

            c.Modified()
            c.Update()
            c.SaveAs('dmjj'+k+r+'.png')

            massResolution.SetTitle(regionTitle)
            ip=massResolution.GetN()
            x=40
            xerr=10
            if k=='50' :
                x=75
                xerr=25
            elif k=='100':
                x=150
                xerr=50
            y=sigma1.getVal()
            yerr=sigma1.getError()
            if frac.getVal()<0.8:
                if sigma2.getVal()<sigma1.getVal():
                    y=sigma2.getVal()
                    ey=sigma2.getError()
            massResolution.SetPoint(ip,x,y)
            massResolution.SetPointError(ip,xerr,yerr)
            

    frame=TGraphErrors()
    frame.SetPoint(0,0,0)
    frame.SetPoint(1,200,0.2)
    frame.SetMarkerStyle(1)
    frame.SetFillStyle(0)
    frame.SetName('dmframe')
    cdmevol=TCanvas('cdmevol','cdmevol',500,500)
    cdmevol.cd()
    frame.Draw('ap')
    leg=TLegend(0.6,0.92,0.9,0.98)
    leg.SetFillStyle(0)
    leg.SetBorderSize(0)
    leg.SetTextFont(42)
    for dmGr in massResolutionGrs :
        dmGr.Draw('pl')
        leg.AddEntry(dmGr,dmGr.GetTitle(),'fp')
    frame.GetXaxis().SetTitle("Leading quark transverse momentum [GeV]") 
    frame.GetYaxis().SetTitle("Core resolution")
    frame.GetYaxis().SetTitleOffset(1.4)
    frame.GetYaxis().SetNdivisions(10)
    drawHeader()
    leg.SetNColumns(2)
    leg.Draw()
    cdmevol.Modified()
    cdmevol.Update()
    cdmevol.SaveAs('dm_evol.png')


    c=TCanvas('c','c',600,600)
    c.cd()
    histos['sel'].Draw('histtext')
    drawHeader()
    c.Modified()
    c.Update()
    c.SaveAs('selection.png')


    return
예제 #57
0
if fitSig: 

    # define parameters for signal fit
    m = RooRealVar('mean','mean',float(mass),float(mass)-200,float(mass)+200)
    s = RooRealVar('sigma','sigma',0.1*float(mass),0,10000)
    a = RooRealVar('alpha','alpha',1,-10,10)
    n = RooRealVar('n','n',1,0,100)
    sig = RooCBShape('sig','sig',x,m,s,a,n)        

    p  = RooRealVar('p','p',1,0,5)
    x0 = RooRealVar('x0','x0',1000,100,5000)

    bkg = RooGenericPdf('bkg','1/(exp(pow(@0/@1,@2))+1)',RooArgList(x,x0,p))

    fsig= RooRealVar('fsig','fsig',0.5,0.,1.)
    signal = RooAddPdf('signal','signal',sig,bkg,fsig)

    # -----------------------------------------
    # fit signal
    canSname = 'can_Mjj'+str(mass)
    canS = TCanvas(canSname,canSname,900,600)
    gPad.SetLogy() 

    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)
예제 #58
0
m1 = RooRealVar('ttbar_mean1','ttbar_mean1',172,150,180)
s1 = RooRealVar('ttbar_sigma1','ttbar_sigma1',20,0,50)

m1Shift = RooFormulaVar('ttbar_shifted_mean1',"@0*@1",RooArgList(m1,kJES))
s1Shift = RooFormulaVar('ttbar_shifted_sigma1',"@0*@1",RooArgList(s1,kJER)); 

sig1 = RooGaussian('ttbar_gaus1','ttbar_gaus1',x,m1Shift,s1Shift)         

m2 = RooRealVar('ttbar_mean2' ,'ttbar_mean2',140,130,300)
s2 = RooRealVar('ttbar_sigma2','ttbar_sigma2',50,10,200)
sig2 = RooGaussian('ttbar_gaus2','ttbar_gaus2',x,m2,s2) 

fsig = RooRealVar('ttbar_f1','ttbar_f1',0.5,0,1)

signal = RooAddPdf('ttbar_pdf','ttbar_pdf',RooArgList(sig1,sig2),RooArgList(fsig))

# -----------------------------------------
# fit signal
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()
예제 #59
0
파일: alpha.py 프로젝트: wvieri/new_git
def alpha(channel):

    nElec = channel.count('e')
    nMuon = channel.count('m')
    nLept = nElec + nMuon
    nBtag = channel.count('b')
    
    # Channel-dependent settings
    # Background function. Semi-working options are: EXP, EXP2, EXPN, EXPTAIL
    if nLept == 0:
        treeName = 'SR'
        signName = 'XZh'
        colorVjet = sample['DYJetsToNuNu']['linecolor']
        triName = "HLT_PFMET"
        leptCut = "0==0"
        topVeto = selection["TopVetocut"]
        massVar = "X_cmass"
        binFact = 1
        #fitFunc = "EXP"
        #fitFunc = "EXP2"
        #fitFunc = "EXPN"
        #fitFunc = "EXPTAIL"
        fitFunc = "EXPN" if nBtag < 2 else "EXP"
        fitAltFunc = "EXPTAIL" if nBtag < 2 else "EXPTAIL"
        fitFuncVjet = "ERFEXP" if nBtag < 2 else "ERFEXP"
        fitFuncVV   = "EXPGAUS"
        fitFuncTop  = "GAUS2"
    elif nLept == 1:
        treeName = 'WCR'
        signName = 'XWh'
        colorVjet = sample['WJetsToLNu']['linecolor']
        triName = "HLT_Ele" if nElec > 0 else "HLT_Mu"
        leptCut = "isWtoEN" if nElec > 0 else "isWtoMN"
        topVeto = selection["TopVetocut"]
        massVar = "X_mass"
        binFact = 2
        if nElec > 0:
            fitFunc = "EXP" if nBtag < 2 else "EXP"
            fitAltFunc  = "EXPTAIL" if nBtag < 2 else "EXPTAIL"
        else:
            fitFunc = "EXPTAIL" if nBtag < 2 else "EXP"
            fitAltFunc  = "EXPN" if nBtag < 2 else "EXPTAIL"
        fitFuncVjet = "ERFEXP" if nBtag < 2 else "ERFEXP"
        fitFuncVV   = "EXPGAUS"
        fitFuncTop  = "GAUS3" if nBtag < 2 else "GAUS2"
    else:
        treeName = 'XZh'
        signName = 'XZh'
        colorVjet = sample['DYJetsToLL']['linecolor']
        triName = "HLT_Ele" if nElec > 0 else "HLT_Mu"
        leptCut = "isZtoEE" if nElec > 0 else "isZtoMM"
        topVeto = "0==0"
        massVar = "X_mass"
        binFact = 5
        if nElec > 0:
            fitFunc = "EXP" if nBtag < 2 else "EXP"
            fitAltFunc = "POW" if nBtag < 2 else "POW"
        else:
            fitFunc = "EXP" if nBtag < 2 else "EXP"
            fitAltFunc = "POW" if nBtag < 2 else "POW"
        fitFuncVjet = "ERFEXP" if nBtag < 2 else "EXP"
        fitFuncVV   = "EXPGAUS2"
        fitFuncTop  = "GAUS"
    
    btagCut = selection["2Btag"] if nBtag == 2 else selection["1Btag"]
    
    print "--- Channel", channel, "---"
    print "  number of electrons:", nElec, " muons:", nMuon, " b-tags:", nBtag
    print "  read tree:", treeName, "and trigger:", triName
    if ALTERNATIVE: print "  using ALTERNATIVE fit functions"
    print "-"*11*2
    
    # Silent RooFit
    RooMsgService.instance().setGlobalKillBelow(RooFit.FATAL)
    
    #*******************************************************#
    #                                                       #
    #              Variables and selections                 #
    #                                                       #
    #*******************************************************#
    
    # Define all the variables from the trees that will be used in the cuts and fits
    # this steps actually perform a "projection" of the entire tree on the variables in thei ranges, so be careful once setting the limits
    X_mass = RooRealVar(  massVar, "m_{X}" if nLept > 0 else "m_{T}^{X}", XBINMIN, XBINMAX, "GeV")
    J_mass = RooRealVar( "fatjet1_prunedMassCorr",       "corrected pruned mass", HBINMIN, HBINMAX, "GeV")
    CSV1 = RooRealVar(   "fatjet1_CSVR1",                           "",        -1.e99,   1.e4     )
    CSV2 = RooRealVar(   "fatjet1_CSVR2",                           "",        -1.e99,   1.e4     )
    nBtag = RooRealVar(  "fatjet1_nBtag",                           "",            0.,   4        )
    CSVTop = RooRealVar( "bjet1_CSVR",                              "",        -1.e99,   1.e4     )
    isZtoEE = RooRealVar("isZtoEE",                                 "",            0.,   2        )
    isZtoMM = RooRealVar("isZtoMM",                                 "",            0.,   2        )
    isWtoEN = RooRealVar("isWtoEN",                                 "",            0.,   2        )
    isWtoMN = RooRealVar("isWtoMN",                                 "",            0.,   2        )
    weight = RooRealVar( "eventWeightLumi",                         "",         -1.e9,   1.       )
    
    # Define the RooArgSet which will include all the variables defined before
    # there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
    variables = RooArgSet(X_mass, J_mass, CSV1, CSV2, nBtag, CSVTop)
    variables.add(RooArgSet(isZtoEE, isZtoMM, isWtoEN, isWtoMN, weight))
    
    # Define the ranges in fatJetMass - these will be used to define SB and SR
    J_mass.setRange("LSBrange", LOWMIN, LOWMAX)
    J_mass.setRange("HSBrange", HIGMIN, HIGMAX)
    J_mass.setRange("VRrange",  LOWMAX, SIGMIN)
    J_mass.setRange("SRrange",  SIGMIN, SIGMAX)
    J_mass.setBins(54)
    
    # Define the selection for the various categories (base + SR / LSBcut / HSBcut )
    baseCut = leptCut + " && " + btagCut + "&&" + topVeto
    massCut = massVar + ">%d" % XBINMIN
    baseCut += " && " + massCut
    
    # Cuts
    SRcut  = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), SIGMIN, J_mass.GetName(), SIGMAX)
    LSBcut = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), LOWMIN, J_mass.GetName(), LOWMAX)
    HSBcut = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), HIGMIN, J_mass.GetName(), HIGMAX)
    SBcut  = baseCut + " && ((%s>%d && %s<%d) || (%s>%d && %s<%d))" % (J_mass.GetName(), LOWMIN, J_mass.GetName(), LOWMAX, J_mass.GetName(), HIGMIN, J_mass.GetName(), HIGMAX)
    VRcut  = baseCut + " && %s>%d && %s<%d" % (J_mass.GetName(), LOWMAX, J_mass.GetName(), SIGMIN)
    
    # Binning
    binsJmass = RooBinning(HBINMIN, HBINMAX)
    binsJmass.addUniform(HBINS, HBINMIN, HBINMAX)
    binsXmass = RooBinning(XBINMIN, XBINMAX)
    binsXmass.addUniform(binFact*XBINS, XBINMIN, XBINMAX)
    
    #*******************************************************#
    #                                                       #
    #                      Input files                      #
    #                                                       #
    #*******************************************************#
    
    # Import the files using TChains (separately for the bkg "classes" that we want to describe: here DY and VV+ST+TT)
    treeData = TChain(treeName)
    treeMC   = TChain(treeName)
    treeVjet = TChain(treeName)
    treeVV   = TChain(treeName)
    treeTop  = TChain(treeName)
#    treeSign = {}
#    nevtSign = {}
    
    # Read data
    pd = getPrimaryDataset(triName)
    if len(pd)==0: raw_input("Warning: Primary Dataset not recognized, continue?")
    for i, s in enumerate(pd): treeData.Add(NTUPLEDIR + s + ".root")
    
    # Read V+jets backgrounds
    for i, s in enumerate(["WJetsToLNu_HT", "DYJetsToNuNu_HT", "DYJetsToLL_HT"]):
        for j, ss in enumerate(sample[s]['files']): treeVjet.Add(NTUPLEDIR + ss + ".root")
    
    # Read VV backgrounds
    for i, s in enumerate(["VV"]):
        for j, ss in enumerate(sample[s]['files']): treeVV.Add(NTUPLEDIR + ss + ".root")
    
    # Read Top backgrounds
    for i, s in enumerate(["ST", "TTbar"]):
        for j, ss in enumerate(sample[s]['files']): treeTop.Add(NTUPLEDIR + ss + ".root")
        
    # Sum all background MC
    treeMC.Add(treeVjet)
    treeMC.Add(treeVV)
    treeMC.Add(treeTop)
    
    # create a dataset to host data in sideband (using this dataset we are automatically blind in the SR!)
    setDataSB = RooDataSet("setDataSB", "setDataSB", variables, RooFit.Cut(SBcut), RooFit.WeightVar(weight), RooFit.Import(treeData))
    setDataLSB = RooDataSet("setDataLSB", "setDataLSB", variables, RooFit.Import(setDataSB), RooFit.Cut(LSBcut), RooFit.WeightVar(weight))
    setDataHSB = RooDataSet("setDataHSB", "setDataHSB", variables, RooFit.Import(setDataSB), RooFit.Cut(HSBcut), RooFit.WeightVar(weight))
    
    # Observed data (WARNING, BLIND!)
    setDataSR = RooDataSet("setDataSR", "setDataSR", variables, RooFit.Cut(SRcut), RooFit.WeightVar(weight), RooFit.Import(treeData))
    setDataVR = RooDataSet("setDataVR", "setDataVR", variables, RooFit.Cut(VRcut), RooFit.WeightVar(weight), RooFit.Import(treeData)) # Observed in the VV mass, just for plotting purposes
    
    # same for the bkg datasets from MC, where we just apply the base selections (not blind)
    setVjet = RooDataSet("setVjet", "setVjet", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeVjet))
    setVjetSB = RooDataSet("setVjetSB", "setVjetSB", variables, RooFit.Import(setVjet), RooFit.Cut(SBcut), RooFit.WeightVar(weight))
    setVjetSR = RooDataSet("setVjetSR", "setVjetSR", variables, RooFit.Import(setVjet), RooFit.Cut(SRcut), RooFit.WeightVar(weight))
    setVV = RooDataSet("setVV", "setVV", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeVV))
    setVVSB = RooDataSet("setVVSB", "setVVSB", variables, RooFit.Import(setVV), RooFit.Cut(SBcut), RooFit.WeightVar(weight))
    setVVSR = RooDataSet("setVVSR", "setVVSR", variables, RooFit.Import(setVV), RooFit.Cut(SRcut), RooFit.WeightVar(weight))
    setTop = RooDataSet("setTop", "setTop", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeTop))
    setTopSB = RooDataSet("setTopSB", "setTopSB", variables, RooFit.Import(setTop), RooFit.Cut(SBcut), RooFit.WeightVar(weight))
    setTopSR = RooDataSet("setTopSR", "setTopSR", variables, RooFit.Import(setTop), RooFit.Cut(SRcut), RooFit.WeightVar(weight))
    
    print "  Data events SB: %.2f" % setDataSB.sumEntries()
    print "  V+jets entries: %.2f" % setVjet.sumEntries()
    print "  VV, VH entries: %.2f" % setVV.sumEntries()
    print "  Top,ST entries: %.2f" % setTop.sumEntries()
    
    
    # the relative normalization of the varius bkg is taken from MC by counting all the events in the full fatJetMass range
    #coef = RooRealVar("coef", "coef", setVV.sumEntries()/setVjet.sumEntries(),0.,1.)
    coef_VV_Vjet = RooRealVar("coef2_1", "coef2_1", setVV.sumEntries()/setVjet.sumEntries(), 0., 1.)
    coef_Top_VVVjet = RooRealVar("coef3_21", "coef3_21", setTop.sumEntries()/(setVjet.sumEntries()+setVV.sumEntries()),0.,1.);
    coef_VV_Vjet.setConstant(True)
    coef_Top_VVVjet.setConstant(True)
    
    # Define entries
    entryVjet = RooRealVar("entryVjets",  "V+jets normalization", setVjet.sumEntries(), 0., 1.e6)
    entryVV = RooRealVar("entryVV",  "VV normalization", setVV.sumEntries(), 0., 1.e6)
    entryTop = RooRealVar("entryTop",  "Top normalization", setTop.sumEntries(), 0., 1.e6)
    
    entrySB = RooRealVar("entrySB",  "Data SB normalization", setDataSB.sumEntries(SBcut), 0., 1.e6)
    entrySB.setError(math.sqrt(entrySB.getVal()))
    
    entryLSB = RooRealVar("entryLSB",  "Data LSB normalization", setDataSB.sumEntries(LSBcut), 0., 1.e6)
    entryLSB.setError(math.sqrt(entryLSB.getVal()))

    entryHSB = RooRealVar("entryHSB",  "Data HSB normalization", setDataSB.sumEntries(HSBcut), 0., 1.e6)
    entryHSB.setError(math.sqrt(entryHSB.getVal()))
    
    #*******************************************************#
    #                                                       #
    #                    NORMALIZATION                      #
    #                                                       #
    #*******************************************************#
    
    # set reasonable ranges for J_mass and X_mass
    # these are used in the fit in order to avoid ROOFIT to look in regions very far away from where we are fitting 
    J_mass.setRange("h_reasonable_range", LOWMIN, HIGMAX)
    X_mass.setRange("X_reasonable_range", XBINMIN, XBINMAX)
    
    # Set RooArgSets once for all, see https://root.cern.ch/phpBB3/viewtopic.php?t=11758
    jetMassArg = RooArgSet(J_mass)
    
    #*******************************************************#
    #                                                       #
    #                 V+jets normalization                  #
    #                                                       #
    #*******************************************************#
    
    # Variables for V+jets
    constVjet   = RooRealVar("constVjet",   "slope of the exp",      -0.020, -1.,   0.)
    offsetVjet  = RooRealVar("offsetVjet",  "offset of the erf",     30.,   -50., 200.)
    widthVjet   = RooRealVar("widthVjet",   "width of the erf",     100.,     1., 200.)
    offsetVjet.setConstant(True)
    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)
    
    # Define V+jets model
    if fitFuncVjet == "ERFEXP": modelVjet = RooErfExpPdf("modelVjet", "error function for V+jets mass", J_mass, constVjet, offsetVjet, widthVjet)
    elif fitFuncVjet == "EXP": modelVjet = RooExponential("modelVjet", "exp for V+jets mass", J_mass, constVjet)
    elif fitFuncVjet == "POL": modelVjet = RooChebychev("modelVjet", "polynomial for V+jets mass", J_mass, RooArgList(a0Vjet, a1Vjet, a2Vjet))
    elif fitFuncVjet == "POW": modelVjet = RooGenericPdf("modelVjet", "powerlaw for X mass", "@0^@1", RooArgList(J_mass, a0Vjet))
    else:
        print "  ERROR! Pdf", fitFuncVjet, "is not implemented for Vjets"
        exit()
    
    # fit to main bkg in MC (whole range)
    frVjet = modelVjet.fitTo(setVjet, RooFit.SumW2Error(True), RooFit.Range("h_reasonable_range"), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.Save(1), RooFit.PrintLevel(1 if VERBOSE else -1))
    
    # integrals and number of events
    iSBVjet = modelVjet.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("LSBrange,HSBrange"))
    iLSBVjet = modelVjet.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("LSBrange"))
    iHSBVjet = modelVjet.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("HSBrange"))
    iSRVjet = modelVjet.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("SRrange"))
    iVRVjet = modelVjet.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("VRrange"))
    # Do not remove the following lines, integrals are computed here
    iALVjet = modelVjet.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg))
    nSBVjet = iSBVjet.getVal()/iALVjet.getVal()*setVjet.sumEntries(SBcut)
    nLSBVjet = iLSBVjet.getVal()/iALVjet.getVal()*setVjet.sumEntries(LSBcut)
    nHSBVjet = iHSBVjet.getVal()/iALVjet.getVal()*setVjet.sumEntries(HSBcut)
    nSRVjet = iSRVjet.getVal()/iALVjet.getVal()*setVjet.sumEntries(SRcut)
    
    drawPlot("JetMass_Vjet", channel, J_mass, modelVjet, setVjet, binsJmass, frVjet)

    if VERBOSE: print "********** Fit result [JET MASS Vjets] *"+"*"*40, "\n", frVjet.Print(), "\n", "*"*80
    
    #*******************************************************#
    #                                                       #
    #                 VV, VH normalization                  #
    #                                                       #
    #*******************************************************#
    
    # Variables for VV
    # Error function and exponential to model the bulk
    constVV  = RooRealVar("constVV",  "slope of the exp",  -0.030, -0.1,   0.)
    offsetVV = RooRealVar("offsetVV", "offset of the erf", 90.,     1., 300.)
    widthVV  = RooRealVar("widthVV",  "width of the erf",  50.,     1., 100.)
    erfrVV   = RooErfExpPdf("baseVV", "error function for VV jet mass", J_mass, constVV, offsetVV, widthVV)
    expoVV   = RooExponential("baseVV", "error function for VV jet mass", J_mass, constVV)
    # gaussian for the V mass peak
    meanVV   = RooRealVar("meanVV",   "mean of the gaussian",           90.,    60., 100.)
    sigmaVV  = RooRealVar("sigmaVV",  "sigma of the gaussian",          10.,     6.,  30.)
    fracVV   = RooRealVar("fracVV",   "fraction of gaussian wrt erfexp", 3.2e-1, 0.,   1.)
    gausVV   = RooGaussian("gausVV",  "gaus for VV jet mass", J_mass, meanVV, sigmaVV)
    # gaussian for the H mass peak
    meanVH   = RooRealVar("meanVH",   "mean of the gaussian",           125.,   100., 150.)
    sigmaVH  = RooRealVar("sigmaVH",  "sigma of the gaussian",           30.,     5.,  40.)
    fracVH   = RooRealVar("fracVH",   "fraction of gaussian wrt erfexp",  1.5e-2, 0.,   1.)
    gausVH   = RooGaussian("gausVH",  "gaus for VH jet mass", J_mass, meanVH, sigmaVH)
    
    # Define VV model
    if fitFuncVV == "ERFEXPGAUS": modelVV  = RooAddPdf("modelVV",   "error function + gaus for VV jet mass", RooArgList(gausVV, erfrVV), RooArgList(fracVV))
    elif fitFuncVV == "ERFEXPGAUS2": modelVV  = RooAddPdf("modelVV",   "error function + gaus + gaus for VV jet mass", RooArgList(gausVH, gausVV, erfrVV), RooArgList(fracVH, fracVV))
    elif fitFuncVV == "EXPGAUS": modelVV  = RooAddPdf("modelVV",   "error function + gaus for VV jet mass", RooArgList(gausVV, expoVV), RooArgList(fracVV))
    elif fitFuncVV == "EXPGAUS2": modelVV  = RooAddPdf("modelVV",   "error function + gaus + gaus for VV jet mass", RooArgList(gausVH, gausVV, expoVV), RooArgList(fracVH, fracVV))
    else:
        print "  ERROR! Pdf", fitFuncVV, "is not implemented for VV"
        exit()
    
    # fit to secondary bkg in MC (whole range)
    frVV = modelVV.fitTo(setVV, RooFit.SumW2Error(True), RooFit.Range("h_reasonable_range"), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.Save(1), RooFit.PrintLevel(1 if VERBOSE else -1))
    
    # integrals and number of events
    iSBVV = modelVV.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("LSBrange,HSBrange"))
    iLSBVV = modelVV.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("LSBrange"))
    iHSBVV = modelVV.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("HSBrange"))
    iSRVV = modelVV.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("SRrange"))
    iVRVV = modelVV.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("VRrange"))
    # Do not remove the following lines, integrals are computed here
    iALVV = modelVV.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg))
    nSBVV = iSBVV.getVal()/iALVV.getVal()*setVV.sumEntries(SBcut)
    nLSBVV = iLSBVV.getVal()/iALVV.getVal()*setVV.sumEntries(LSBcut)
    nHSBVV = iHSBVV.getVal()/iALVV.getVal()*setVV.sumEntries(HSBcut)
    nSRVV = iSRVV.getVal()/iALVV.getVal()*setVV.sumEntries(SRcut)
    rSBSRVV = nSRVV/nSBVV
    
    drawPlot("JetMass_VV", channel, J_mass, modelVV, setVV, binsJmass, frVV)
    
    if VERBOSE: print "********** Fit result [JET MASS VV] ****"+"*"*40, "\n", frVV.Print(), "\n", "*"*80
    
    #*******************************************************#
    #                                                       #
    #                 Top, ST normalization                 #
    #                                                       #
    #*******************************************************#
    
    # Variables for Top
    # Error Function * Exponential to model the bulk
    constTop  = RooRealVar("constTop",  "slope of the exp", -0.030,   -1.,   0.)
    offsetTop = RooRealVar("offsetTop", "offset of the erf", 175.0,   50., 250.)
    widthTop  = RooRealVar("widthTop",  "width of the erf",  100.0,    1., 300.)
    gausTop   = RooGaussian("baseTop",  "gaus for Top jet mass", J_mass, offsetTop, widthTop)
    erfrTop   = RooErfExpPdf("baseTop", "error function for Top jet mass", J_mass, constTop, offsetTop, widthTop)
    # gaussian for the W mass peak
    meanW     = RooRealVar("meanW",     "mean of the gaussian",           80., 70., 90.)
    sigmaW    = RooRealVar("sigmaW",    "sigma of the gaussian",          10.,  2., 20.)
    fracW     = RooRealVar("fracW",     "fraction of gaussian wrt erfexp", 0.1, 0.,  1.)
    gausW     = RooGaussian("gausW",    "gaus for W jet mass", J_mass, meanW, sigmaW)
    # gaussian for the Top mass peak
    meanT     = RooRealVar("meanT",     "mean of the gaussian",           175., 150., 200.)
    sigmaT    = RooRealVar("sigmaT",    "sigma of the gaussian",           12.,   5.,  50.)
    fracT     = RooRealVar("fracT",     "fraction of gaussian wrt erfexp",  0.1,  0.,   1.)
    gausT     = RooGaussian("gausT",    "gaus for T jet mass", J_mass, meanT, sigmaT)
    
    # Define Top model
    if fitFuncTop == "ERFEXPGAUS2": modelTop = RooAddPdf("modelTop",   "error function + gaus + gaus for Top jet mass", RooArgList(gausW, gausT, erfrTop), RooArgList(fracW, fracT))
    elif fitFuncTop == "ERFEXPGAUS": modelTop = RooAddPdf("modelTop",   "error function + gaus for Top jet mass", RooArgList(gausT, erfrTop), RooArgList(fracT))
    elif fitFuncTop == "GAUS3": modelTop  = RooAddPdf("modelTop",   "gaus + gaus + gaus for Top jet mass", RooArgList(gausW, gausT, gausTop), RooArgList(fracW, fracT))
    elif fitFuncTop == "GAUS2": modelTop  = RooAddPdf("modelTop",   "gaus + gaus for Top jet mass", RooArgList(gausT, gausTop), RooArgList(fracT))
    elif fitFuncTop == "GAUS": modelTop  = RooGaussian("modelTop", "gaus for Top jet mass", J_mass, offsetTop, widthTop)
    else:
        print "  ERROR! Pdf", fitFuncTop, "is not implemented for Top"
        exit()
    
    # fit to secondary bkg in MC (whole range)
    frTop = modelTop.fitTo(setTop, RooFit.SumW2Error(True), RooFit.Range("h_reasonable_range"), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.Save(1), RooFit.PrintLevel(1 if VERBOSE else -1))
    
    # integrals and number of events
    iSBTop = modelTop.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("LSBrange,HSBrange"))
    iLSBTop = modelTop.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("LSBrange"))
    iHSBTop = modelTop.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("HSBrange"))
    iSRTop = modelTop.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("SRrange"))
    iVRTop = modelTop.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("VRrange"))
    # Do not remove the following lines, integrals are computed here
    iALTop = modelTop.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg))
    nSBTop = iSBTop.getVal()/iALTop.getVal()*setTop.sumEntries(SBcut)
    nLSBTop = iLSBTop.getVal()/iALTop.getVal()*setTop.sumEntries(LSBcut)
    nHSBTop = iHSBTop.getVal()/iALTop.getVal()*setTop.sumEntries(HSBcut)
    nSRTop = iSRTop.getVal()/iALTop.getVal()*setTop.sumEntries(SRcut)
    
    drawPlot("JetMass_Top", channel, J_mass, modelTop, setTop, binsJmass, frTop)
    
    if VERBOSE: print "********** Fit result [JET MASS TOP] ***"+"*"*40, "\n", frTop.Print(), "\n", "*"*80
    
    #*******************************************************#
    #                                                       #
    #                 All bkg normalization                 #
    #                                                       #
    #*******************************************************#
    
    constVjet.setConstant(True)
    offsetVjet.setConstant(True)
    widthVjet.setConstant(True)
    a0Vjet.setConstant(True)
    a1Vjet.setConstant(True)
    a2Vjet.setConstant(True)
    
    constVV.setConstant(True)
    offsetVV.setConstant(True)
    widthVV.setConstant(True)
    meanVV.setConstant(True)
    sigmaVV.setConstant(True)
    fracVV.setConstant(True)
    meanVH.setConstant(True)
    sigmaVH.setConstant(True)
    fracVH.setConstant(True)
    
    constTop.setConstant(True)
    offsetTop.setConstant(True)
    widthTop.setConstant(True)
    meanW.setConstant(True)
    sigmaW.setConstant(True)
    fracW.setConstant(True)
    meanT.setConstant(True)
    sigmaT.setConstant(True)
    fracT.setConstant(True)
    
    
    # Final background model by adding the main+secondary pdfs (using 'coef': ratio of the secondary/main, from MC)
    model = RooAddPdf("model", "model", RooArgList(modelTop, modelVV, modelVjet), RooArgList(coef_Top_VVVjet, coef_VV_Vjet))#FIXME
    model.fixAddCoefRange("h_reasonable_range")
    
    # Extended fit model to data in SB
    # all the 3 sidebands (Low / High / the 2 combined) could be used
    # currently using the LOW+HIGH (the others are commented out)
    yieldLSB = RooRealVar("yieldLSB", "Lower SB normalization",  10, 0., 1.e6)
    yieldHSB = RooRealVar("yieldHSB", "Higher SB normalization", 10, 0., 1.e6)
    yieldSB  = RooRealVar("yieldSB",  "All SB normalization",    10, 0., 1.e6)
    #model_ext = RooExtendPdf("model_ext", "extended p.d.f",   model,  yieldLSB)
    #model_ext = RooExtendPdf("model_ext", "extended p.d.f",   model,  yieldHSB)
    model_ext = RooExtendPdf("model_ext", "extended p.d.f",   model,  yieldSB)
    #frMass = model_ext.fitTo(setDataSB, RooFit.ConditionalObservables(RooArgSet(J_mass)),RooFit.SumW2Error(True),RooFit.Extended(True),RooFit.Range("LSBrange"),RooFit.PrintLevel(-1))
    #frMass = model_ext.fitTo(setDataSB, RooFit.ConditionalObservables(RooArgSet(J_mass)),RooFit.SumW2Error(True),RooFit.Extended(True),RooFit.Range("HSBrange"),RooFit.PrintLevel(-1))
    #frMass = model_ext.fitTo(setDataSB, RooFit.ConditionalObservables(RooArgSet(J_mass)), RooFit.SumW2Error(True), RooFit.Extended(True), RooFit.Range("LSBrange,HSBrange"), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.PrintLevel(1 if VERBOSE else -1))
    
    #print "********** Fit result [JET MASS DATA] **"+"*"*40
    #print frMass.Print()
    #print "*"*80
    
    # Calculate integral of the model obtained from the fit to data (fraction of PDF that is within a given region)
    #nSB = model_ext.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("LSBrange,HSBrange"))
    #nSB = model_ext.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("LSBrange"))
    #nSB = model_ext.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("HSBrange"))
    #nSR = model_ext.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("SRrange"))
    #nVR = model_ext.createIntegral(jetMassArg, RooFit.NormSet(jetMassArg), RooFit.Range("VRrange"))
    
    # scale the yieldSB from SB to SR using the ratio of the PDFs defined by the two integrals
    SRyield = RooFormulaVar("SRyield", "extrapolation to SR","(@0-@1*@3-@2*@4) * @5/@6 +@1*@7+@2*@8", RooArgList(entrySB, entryVV, entryTop, iSBVV, iSBTop, iSRVjet, iSBVjet, iSRVV, iSRTop))
    VRyield = RooFormulaVar("VRyield", "extrapolation to VR","(@0-@1*@3-@2*@4) * @5/@6 +@1*@7+@2*@8", RooArgList(entrySB, entryVV, entryTop, iSBVV, iSBTop, iVRVjet, iSBVjet, iVRVV, iVRTop))
    HSByield = RooFormulaVar("SRyield", "extrapolation to SR","(@0-@1*@3-@2*@4) * @5/@6 +@1*@7+@2*@8", RooArgList(entryLSB, entryVV, entryTop, iLSBVV, iLSBTop, iHSBVjet, iLSBVjet, iHSBVV, iHSBTop))
    #   RooFormulaVar SRyield("SRyield","extrapolation to SR","(@0/@1)*@2",RooArgList(*nSR,*nSB,yieldLowerSB))
    #   RooFormulaVar SRyield("SRyield","extrapolation to SR","(@0/@1)*@2",RooArgList(*nSR,*nSB,yieldHigherSB))
    #SRyield = RooFormulaVar("SRyield", "extrapolation to SR","(@0/@1)*@2", RooArgList(nSR, nSB, entrySB))
    
    bkgYield            = SRyield.getVal()
    bkgYield_error      = math.sqrt(SRyield.getPropagatedError(frVjet)**2 + SRyield.getPropagatedError(frVV)**2 + SRyield.getPropagatedError(frTop)**2 + (entrySB.getError()*rSBSRVV)**2)
    bkgNorm             = entrySB.getVal() + SRyield.getVal() + VRyield.getVal()
    bkgYield_eig_norm   = RooRealVar("predSR_eig_norm", "expected yield in SR", bkgYield, 0., 1.e6)
    bkgYieldExt         = HSByield.getVal()
    
    drawPlot("JetMass", channel, J_mass, model, setDataSB, binsJmass, None, None, "", bkgNorm, True)

    
    print channel, "normalization = %.3f +/- %.3f, observed = %.0f" % (bkgYield, bkgYield_error, setDataSR.sumEntries() if not BLIND else -1)
    if VERBOSE: raw_input("Press Enter to continue...")