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
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 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
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")
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()
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)
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
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)
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)
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
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()
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)
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
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
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()]
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)
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)
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)
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))
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()
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
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()
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")
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)
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]
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
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));
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)
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()
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')
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) )
#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()
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")
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' )
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)
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) )
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
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)
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)
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)
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()
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 )
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
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)
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()
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...")