def fit(model, hists, fitmethod, eps=1.0e-7):
"""Fit beam shapes to Beam Imaging data.
model: Beam shape model (derived from BeamShapeCore).
hists: List of four TH2F with BI data.
fitmethod: Function(pdf, data) that fits pdf to data.
eps: Value of convergence criteria.
"""
RooAbsReal.defaultIntegratorConfig().setEpsAbs(eps)
RooAbsReal.defaultIntegratorConfig().setEpsRel(eps)
modfuncs = model.model_functions()
datahist = [
RooDataHist('scan{0}Beam{1}RestDataHist'.format(c, i),
'scan{0}Beam{1}RestDataHist'.format(c, i),
RooArgList(model.xvar(), model.yvar()), hists[j])
for j, (i, c) in enumerate(ic)
]
sample = RooCategory('sample', 'sample')
for (i, c) in ic:
sample.defineType('{0}_ScanData_Beam{1}Rest'.format(c, i))
combdata = RooDataHist('combdata', 'combined data',
RooArgList(model.xvar(), model.yvar()),
RooFit.Index(sample),
RooFit.Import('X_ScanData_Beam1Rest', datahist[0]),
RooFit.Import('Y_ScanData_Beam1Rest', datahist[1]),
RooFit.Import('X_ScanData_Beam2Rest', datahist[2]),
RooFit.Import('Y_ScanData_Beam2Rest', datahist[3]))
simpdf = RooSimultaneous('simpdf', 'simultaneous pdf', sample)
for j, (i, c) in enumerate(ic):
simpdf.addPdf(modfuncs[j], '{0}_ScanData_Beam{1}Rest'.format(c, i))
result = fitmethod(simpdf, combdata)
return result, modfuncs, datahist
def performFitInLeptonAbsEta(data_histogram, signal_histogram, bkg1_histogram,
bkg2_histogram):
N_Data = data_histogram.Integral()
N_signal = signal_histogram.Integral()
N_bkg1 = bkg1_histogram.Integral()
N_bkg2 = bkg2_histogram.Integral()
leptonAbsEta = RooRealVar("leptonAbsEta", "leptonAbsEta", 0., 3.)
variables = RooArgList()
variables.add(leptonAbsEta)
variable_set = RooArgSet()
variable_set.add(leptonAbsEta)
lowerBound = 0
upperBound = N_Data * 2
data_RooDataHist = RooDataHist("data", "dataset with leptonAbsEta",
variables, data_histogram)
signal_RooDataHist = RooDataHist("rh_signal", "signal", variables,
signal_histogram)
bkg1_RooDataHist = RooDataHist("rh_bkg1", "bkg1", variables,
bkg1_histogram)
bkg2_RooDataHist = RooDataHist("rh_bkg2", "bkg2", variables,
bkg2_histogram)
signal_RooHistPdf = RooHistPdf("pdf_signal", "Signal PDF", variable_set,
signal_RooDataHist, 0)
signal_RooHistPdf = RooHistPdf("pdf_signal", "Signal PDF", variable_set,
signal_RooDataHist, 0)
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 add_data(self, data_histogram):
print "[MjFit.add_data] INFO : Adding data histogram"
# Add a data histogram
self.data_histogram_ = data_histogram.Clone()
self.data_histogram_.SetDirectory(0)
self.data_roohistogram_ = RooDataHist('data_roohistogram','data_roohistogram',RooArgList(self.mjj_),self.data_histogram_)
self.data_roohistogram_.Print()
def DoRooFit(histo, title):
can = makeCMSCanvas(str(random.random()),"Fit result ",900,700)
#Varible
if "ele" in title:
x1 = RooRealVar("x1","m_{e^{+}e^{-}}",80,100)
if "mu" in title:
x1 = RooRealVar("x1","m_{#mu^{+}#mu^{-}}",80,100)
#Define CB function
m = RooRealVar("mean_{CB}","mean of gaussian",60,120)
s = RooRealVar("#sigma_{CB}","width of gaussian",0,3)
a = RooRealVar("#alpha_{CB}","mean of gaussian",0,100)
n = RooRealVar("n_{CB}","width of gaussian",0,5)
CB = RooCBShape("CB","CB PDF",x1, m, s, a, n)
m.setConstant(kFALSE)
s.setConstant(kFALSE)
a.setConstant(kFALSE)
n.setConstant(kFALSE)
#Define Gaussian function
mean1 = RooRealVar("mean_{G}","mean of gaussian",-60,60)
sigma1 = RooRealVar("#sigma_{G}","width of gaussian",0,10)
gauss1 = RooGaussian("gauss1","gaussian PDF",x1,mean1,sigma1)
mean1.setConstant(kFALSE)
sigma1.setConstant(kFALSE)
#Starting values of the parameters
mean1.setVal(1.0)
sigma1.setVal(1.0)
m.setVal(90.0)
s.setVal(1.0)
a.setVal(10.0)
n.setVal(2.0)
# Construct CB (x) gauss
x1.setBins(10000, "cache")
CBxG = RooFFTConvPdf("CBxG", "CB (X) gauss", x1, CB, gauss1)
can.cd()
d = RooDataHist("d","d",RooArgList(x1),RooFit.Import(histo))
CBxG.fitTo(d, RooLinkedList())
# Plot PDF and toy data overlaid
xframe2 = x1.frame(RooFit.Name("xframe"),RooFit.Title("")) # RooPlot
d.plotOn(xframe2, RooLinkedList() )
CBxG.paramOn(xframe2, RooFit.Layout(0.65,0.99,0.9))
xframe2.getAttText().SetTextSize(0.03)
CBxG.plotOn(xframe2)
xframe2.Draw()
can.SaveAs("DataVsMC/FitResults/"+title+"_Roofit.pdf")
can.SaveAs("DataVsMC/FitResults/"+title+"_Roofit.png")
return;
def Gausintialization(self):
round_energy = round(float(self.energy),-1)
if round_energy ==240 : round_energy = 250
self.x = RooRealVar("signal_%s_%dGeV"%(self.crystal,round_energy),"signal_%s_%dGeV"%(self.crystal,round_energy),max(0.,self.peak_position*(1-self.xaxis_scale)),self.peak_position*(1+self.xaxis_scale))
self.roohist = RooDataHist("roohist_fit_%s_%s"%(self.crystal,self.energy),"roohist_fit_%s_%s"%(self.crystal,self.energy),RooArgList(self.x),self.hist)
self.m = RooRealVar("mean_%s_%s"%(self.crystal,self.energy),"mean_%s_%s"%(self.crystal,self.energy),self.peak_position,max(0.,self.peak_position*(1-self.xaxis_scale)),self.peak_position*(1+self.xaxis_scale))
self.s = RooRealVar("sigma_%s_%s"%(self.crystal,self.energy),"sigma_%s_%s"%(self.crystal,self.energy),self.s_initial,0.001,1.)
self.sig = RooGaussian("signal_%s_%s"%(self.crystal,self.energy),"signal_%s_%s"%(self.crystal,self.energy),self.x,self.m,self.s)
def CBintialization(self):
round_energy = round(float(self.energy),-1)
if round_energy ==240 : round_energy = 250
self.x = RooRealVar("signal_%s_%dGeV"%(self.crystal,round_energy),"signal_%s_%dGeV"%(self.crystal,round_energy),max(0.,self.peak_position*(1-self.xaxis_scale)),self.peak_position*(1+self.xaxis_scale))
self.roohist = RooDataHist("roohist_fit_%s_%s"%(self.crystal,self.energy),"roohist_fit_%s_%s"%(self.crystal,self.energy),RooArgList(self.x),self.hist)
self.m = RooRealVar("mean_%s_%s"%(self.crystal,self.energy),"mean_%s_%s"%(self.crystal,self.energy),self.peak_position,max(0.,self.peak_position*(1-self.xaxis_scale)),self.peak_position*(1+self.xaxis_scale))
self.s = RooRealVar("sigma_%s_%s"%(self.crystal,self.energy),"sigma_%s_%s"%(self.crystal,self.energy),self.s_initial,0.001,1.) #500.
self.a = RooRealVar("alpha_%s_%s"%(self.crystal,self.energy),"alpha_%s_%s"%(self.crystal,self.energy),self.a_initial,-10.,10)
self.n = RooRealVar("exp_%s_%s"%(self.crystal,self.energy),"exp_%s_%s"%(self.crystal,self.energy),self.n_initial,0.,30)
self.sig = RooCBShape("signal_%s_%s"%(self.crystal,self.energy),"signal_%s_%s"%(self.crystal,self.energy),self.x,self.m,self.s,self.a,self.n)
def CB2intialization(self):
round_energy = round(float(self.energy),-1)
self.x = RooRealVar("signal_%s_%dGeV"%(self.crystal,round_energy),"signal_%s_%dGeV"%(self.crystal,round_energy),max(0.,self.peak_position*(1-self.xaxis_scale)),self.peak_position*(1+self.xaxis_scale))
self.roohist = RooDataHist("roohist_fit_%s_%s"%(self.crystal,self.energy),"roohist_fit_%s_%s"%(self.crystal,self.energy),RooArgList(self.x),self.hist)
self.m = RooRealVar("mean_%s_%s"%(self.crystal,self.energy),"mean_%s_%s"%(self.crystal,self.energy),self.peak_position,max(0.,self.peak_position*(1-self.xaxis_scale)),self.peak_position*(1+self.xaxis_scale))
self.s = RooRealVar("sigma_%s_%s"%(self.crystal,self.energy),"sigma_%s_%s"%(self.crystal,self.energy),self.s_initial,10,500)
self.a = RooRealVar("alpha_%s_%s"%(self.crystal,self.energy),"alpha_%s_%s"%(self.crystal,self.energy),self.a_initial,-10.,10)
self.a2 = RooRealVar("alpha2_%s_%s"%(self.crystal,self.energy),"alpha2_%s_%s"%(self.crystal,self.energy),self.a2_initial,-10.,10)
self.n = RooRealVar("exp_%s_%s"%(self.crystal,self.energy),"exp_%s_%s"%(self.crystal,self.energy),self.n_initial,0.,30)
self.n2 = RooRealVar("exp2_%s_%s"%(self.crystal,self.energy),"exp2_%s_%s"%(self.crystal,self.energy),self.n2_initial,0.,30)
self.sig = ROOT.My_double_CB("signal_%s_%s"%(self.crystal,self.energy),"signal_%s_%s"%(self.crystal,self.energy),self.x,self.m,self.s,self.a,self.n,self.a2,self.n2)
def saveLandauHistoAmplitude(histo,
outfile,
canvas,
XaxisTitle="",
YaxisTitle="",
plotTitle="",
stats=0):
ex = "Null Fit"
amplitude = RooRealVar("amplitude", "Cluster Amplitude", 0, 5000)
landau_data = RooDataHist("landau_data", "landau_data",
RooArgList(amplitude), histo)
ml = RooRealVar("ml", "mean landau", 1500., 1000, 2000)
sl = RooRealVar("sl", "sigma landau", 250, 100, 1000)
landau = RooLandau("lx", "lx", amplitude, ml, sl)
mg = RooRealVar("mg", "mg", 0)
sg = RooRealVar("sg", "sg", 100, 20, 500)
gauss = RooGaussian("gauss", "gauss", amplitude, mg, sg)
lxg = RooNumConvPdf("lxg", "lxg", amplitude, landau, gauss)
result = lxg.fitTo(landau_data)
frame = amplitude.frame()
landau_data.plotOn(frame)
lxg.plotOn(frame)
frame.Draw("")
frame.SetTitle(plotTitle)
frame.GetXaxis().SetTitle(XaxisTitle)
frame.GetYaxis().SetTitle(YaxisTitle)
frame.SetStats(stats)
frame.Write(plotTitle)
canvas.Print(outfile + ".pdf")
peak = []
try:
mean = RooRealVar(result.floatParsFinal().find("landau_mean"))
err = RooRealVar(mean.errorVar())
peak.append(mean.GetVal())
peak.append(err.GetVal())
except Exception as ex:
print(ex)
peak.append(0)
peak.append(0)
return peak
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 addHistData(self,
ws,
ds_name,
item_title,
var_set_name,
var_set_type,
ds_object,
sets=None):
legend = '[exostConfig::addHistData]:'
arg_list = RooArgList()
if (var_set_type == 'set'):
#arg_list.add(ws.set(var_set_name))
arg_list.add(sets[var_set_name])
elif (var_set_type == 'var'):
arg_list.add(ws.var(var_set_name))
else:
print legend, 'error: unknown var_set_type, cannot create dataset', ds_name
return -1
#create the hist dataset
ds = RooDataHist(ds_name, item_title, arg_list, ds_object, 1)
# import the datahist. Note workaround 'import' being a reserved word
getattr(ws, 'import')(ds)
def doFit(hist,output,rap="BB",flavour="DATA",trackType="TunePNew",funct="doubleCB"):
sig = []
sige = []
meanList = []
meanListe = []
nChi2 = []
gSystem.Load("./RooCruijff_cxx.so")
gSystem.Load("./RooDCBShape_cxx.so")
for i,h in enumerate(hist):
ws = RooWorkspace("tempWS")
mass = RooRealVar('mass','mass',91, xLow, xHigh )
getattr(ws,'import')(mass,RooCmdArg())
dataHist = RooDataHist("hist","hist",RooArgList(ws.var("mass")),h)
getattr(ws,'import')(dataHist,RooCmdArg())
ws.writeToFile("tmpWorkspace.root")
subprocess.call(["python","fitCapsule.py",output,rap,flavour,trackType,funct,"%d"%xLow,"%d"%xHigh,"%d"%rebinFactor,"%d"%i])
returnFile = TFile("tmpWorkspaceReturn.root","OPEN")
wsReturn = returnFile.Get("tempWS")
sig.append(wsReturn.var("Sig").getVal())
sige.append(wsReturn.var("Sige").getVal())
meanList.append(wsReturn.var("Mean").getVal())
meanListe.append(wsReturn.var("Meane").getVal())
nChi2.append(wsReturn.var("chi2").getVal()/wsReturn.var("nDOF").getVal())
return meanList,meanListe,sig,sige, nChi2
def makeHistPdf(hist, ws, x):
theVars = RooArgList(x)
v = RooArgSet(x)
dataHist = RooDataHist(hist.GetName() + '_dh', 'dataHist', theVars, hist)
hpdf = RooHistPdf(hist.GetName() + '_pdf', 'hist pdf', v, dataHist)
getattr(ws, 'import')(hpdf)
return hpdf
def generate_hist(self, hist, varname, n):
_var = RooRealVar(varname, varname, hist.GetMean())
_dh = RooDataHist("dh", "dh", RooArgList(_var), hist)
_pdf = RooHistPdf("pdf", "pdf", RooArgSet(_var), _dh)
_ds = _pdf.generateBinned(RooArgSet(_var), n)
_h = _ds.createHistogram('hist', _var)
#_h.Draw()
#raw_input('press <enter> to continue...')
return copy.deepcopy(_h)
def saveLandauHistoSignalToNoise(histo,outfile,canvas,XaxisTitle="",YaxisTitle="",plotTitle="",stats=0):
ex = "Null Fit"
signal_to_noise = RooRealVar("signal_to_noise", "Signal to Noise", 0, 50)
landau_data = RooDataHist("landau_data", "landau_data", RooArgList(signal_to_noise), histo)
ml = RooRealVar("ml","mean landau",25, 20, 26)
sl = RooRealVar("sl","sigma landau", 5, 2, 10)
landau = RooLandau("lx","lx",signal_to_noise,ml,sl)
mg = RooRealVar("mg","mg",0) ;
sg = RooRealVar("sg","sg", 2, 1, 8)
gauss = RooGaussian("gauss","gauss",signal_to_noise,mg,sg)
lxg = RooNumConvPdf("lxg", "lxg", signal_to_noise, landau, gauss)
result = lxg.fitTo(landau_data)
frame = signal_to_noise.frame()
landau_data.plotOn(frame)
lxg.plotOn(frame)
frame.Draw("")
frame.SetTitle(plotTitle)
frame.GetXaxis().SetTitle(XaxisTitle)
frame.GetYaxis().SetTitle(YaxisTitle)
frame.SetStats(stats)
frame.Write(plotTitle)
canvas.Print(outfile+".pdf")
peak = []
try:
mean = RooRealVar(result.floatParsFinal().find("landau_mean"))
err = RooRealVar(mean.errorVar())
peak.append(mean.GetVal())
peak.append(err.GetVal())
except Exception as ex:
print(ex)
peak.append(0)
peak.append(0)
return peak
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 Fit(h, Lc_M):
h_LcM = RooDataHist('h_LcM', 'h_LcM', RooArgList(Lc_M), h)
#Create a workspace named 'w' with the model to fit the Lc_Mass peak
nsig = RooRealVar("nsig", "N signal evts", 100000, 0, 5000000)
nbkg = RooRealVar("nbkg", "N bkg evts", 400000, 0, 1E10)
w = CreateMassFitModel(Lc_M, nsig, nbkg)
model = w.pdf("model")
model.fitTo(h_LcM, RF.Extended(True))
return model, w, h_LcM
def makePlot(s):
if (type(s) == TH1D):
s.SetStats(0)
x = RooRealVar("x", "x", 0, 3)
l = RooArgList(x)
dh = RooDataHist("dh", "dh", l, s)
frame = x.frame()
dh.plotOn(frame)
#frame.SetTitle(name)
#frame.GetXaxis().SetTitle(s.GetXaxis().GetTitle())
frame.GetYaxis().SetTitle(s.GetYaxis().GetTitle())
# frame.GetYaxis().SetTopMargin(0.15)
#frame.Draw(draw)
return frame
elif (type(s) == TH2D):
s.SetStats(0)
x = RooRealVar("x", "x", 0, 3)
y = RooRealVar("y", "y", 0, 3)
l = RooArgList(x, y)
dh = RooDataHist("dh", "dh", l, s)
t = TGraph2D(s)
s.SetMarkerSize(1.0)
s.SetMarkerStyle(1)
#t.GetXaxis().SetTitle(s.GetXaxis().GetTitle())
return s
elif (type(s) == TGraph2D):
s.SetMarkerSize(1.0)
s.SetMarkerStyle(1)
return s
def compare_binning(filename):
scans = ('X1', 'Y1', 'X2', 'Y2')
with BareRootFile(filename) as f:
oldres = [f.get('residualHist{0}'.format(scan)) for scan in scans]
oldchi = [f.get_val('chisq{0}'.format(scan)) for scan in scans]
olddof = [f.get_val('dof{0}'.format(scan)) for scan in scans]
name = f.get_val('name')
bcidpos = name.find('bcid')
dataname = name[16:name.rfind('_', 0, bcidpos - 1)]
fitname = name[name.rfind('_', 0, bcidpos - 1) + 1:bcidpos - 1]
bcid = int(name[bcidpos + 4:])
model = {
'SG': SingleGauss,
'DG': DoubleGaussFit,
'TG': TripleGaussFit
}[fitname]()
model.load_root(filename)
modfuncs = model.model_functions()
with open('res/hist/{0}.json'.format(dataname)) as f:
json = load(f)
nbins = json['nbins']
scaling = json['scaling']
datafile = '{0}/{1}.root'.format(json['datapath'], json['name'])
hists = []
with BareRootFile(datafile) as f:
for histname in [
'hist_Beam2MoveX_bunch{0}Add', 'hist_Beam2MoveY_bunch{0}Add',
'hist_Beam1MoveX_bunch{0}Add', 'hist_Beam1MoveY_bunch{0}Add'
]:
hist = f.get(histname.format(bcid))
hists.append(hist)
datahist = [
RooDataHist('scan{0}Beam{1}RestDataHist'.format(c, i),
'scan{0}Beam{1}RestDataHist'.format(c, i),
RooArgList(model.xvar(), model.yvar()), hists[j])
for j, (i, c) in enumerate([('1', 'X'), ('1', 'Y'), ('2',
'X'), ('2', 'Y')])
]
chisq, dof, hdata, hmodel = compute_chisq(model, modfuncs, datahist, nbins)
scDat, scMod, scRes = residual_hist(hdata, hmodel, scaling)
for i, scan in enumerate(scans):
oldval = oldchi[i] / olddof[i]
newval = chisq[i] / dof[i]
print '{0}: {1:.4f} {2:.4f}'.format(scan, oldval, newval)
return scRes, oldres
def add_signal(self, signal_name, signal_histogram): print "[MjjFit.add_signal] INFO : Adding signal histogram " + signal_name # Add a signal histogram. # Scale to sigma=1 (in whatever units the data luminosity is given in), so the 'r' parameter corresponds to the limit on the cross section. if self.luminosity_ == 0: print "[MjjFit.add_signal] ERROR : Please set luminosity first (MjjFit.set_luminosity(###))." sys.exit(1) self.signal_names_.append(signal_name) self.signal_histograms_[signal_name] = signal_histogram.Clone() self.signal_histograms_[signal_name].SetDirectory(0) self.signal_histograms_[signal_name].Scale(1. * self.luminosity_ / self.signal_histograms_[signal_name].Integral()) self.signal_roohistograms_[signal_name] = RooDataHist(signal_histogram.GetName() + "_rdh", signal_histogram.GetName() + "_rdh", RooArgList(self.mjj_), signal_histograms[signal_name]) self.signal_roohistograms_[signal_name].Print()
def loadData(self, weight = False):
if self.ws.data('data_obs'):
return self.ws.data('data_obs')
unbinnedName = 'data_obs'
if self.pars.binData:
unbinnedName = 'data_unbinned'
data = self.utils.File2Dataset(self.pars.DataFile, unbinnedName,
self.ws, weighted = weight)
if self.pars.binData:
data = RooDataHist('data_obs', 'data_obs', self.ws.set('obsSet'),
data)
getattr(self.ws, 'import')(data)
data = self.ws.data('data_obs')
return data
def makePDFs(Files, pdf_list, evntCount):
for (inputMass, inputFilename) in Files:
histo = load_histos(inputFilename)
evntCount.append(histo.Integral())
# could construct name and title from inputmass
if 'nom' in inputFilename:
myString = inputFilename.partition("230_")[2].partition(
"_result")[0] + str(inputMass)
else:
myString = inputFilename.partition("jes_")[2].partition(
"_result")[0] + str(inputMass)
# Convert the TH1F Histogram (data_hist) into a RooDataHist object
data_hist = RooDataHist(myString, myString, RooArgList(x), histo)
# Turn the RooDataHist object into a RooHistPdf object
pdf = RooHistPdf(myString, myString, RooArgSet(x), data_hist)
# both the data_hist and pdf must survive
pdf_list.append([data_hist, pdf])
def histoToRooHistFunc(ws, cfg, section, histo, name):
nBins = len(cfg.get(section, 'obsBins').split(',')) - 1
makeLastBinOverflow(histo, nBins)
histo.SetName('%s_%s_input' % (section, name))
getattr(ws, 'import')(histo)
#make the histogram, using the binning defined by the histogram
rdh = RooDataHist(
'%s_%s_yields' % (section, name), '%s_%s_yields' % (section, name),
RooArgList(ws.var('%s_%s' % (cfg.get(section, 'obsVar'), section))),
RooFit.Import(ws.obj('%s_%s_input' % (section, name)), False))
getattr(ws, 'import')(rdh)
#make the hist func so we can get at the yield
ws.factory("""
RooHistFunc::%s_%s_model
({%s_%s},%s_%s_yields)
""" % (section, name, cfg.get(section, 'obsVar'), section, section, name))
def Hist2Pdf(self, hist, pdfName, ws, order=0):
if ws.pdf(pdfName):
return ws.pdf(pdfName)
try:
obs = [self.pars.varNames[x] for x in self.pars.var]
except AttributeError:
obs = self.pars.var
varList = RooArgList()
for v in obs:
varList.add(ws.var(v))
newHist = RooDataHist(pdfName + '_hist', pdfName + '_hist', varList,
hist)
thePdf = RooHistPdf(pdfName, pdfName, RooArgSet(varList), newHist,
order)
getattr(ws, 'import')(thePdf)
return ws.pdf(pdfName)
def loadDataFromWorkspace(self, other, cut = None):
#pull unbinned data from other workspace
unbinnedData = other.data('data_unbinned')
if not unbinnedData:
unbinnedData = other.data('data_obs')
if cut:
unbinnedData = unbinnedData.reduce(cut)
unbinnedData.Print()
if self.pars.binData:
#bin and import data
unbinnedData.SetName('data_unbinned')
getattr(self.ws, 'import')(unbinnedData)
data = RooDataHist('data_obs', 'data_obs', other.set('obsSet'),
unbinnedData)
getattr(self.ws, 'import')(data)
else:
#just import data
unbinnedData.SetName('data_obs')
getattr(self.ws, 'import')(unbinnedData)
def __init__( self, fit_data ):
MapStrRootPtr = stl.map( stl.string, "TH1*" )
StrHist = stl.pair( stl.string, "TH1*" )
self.fit_data = fit_data
self.models = {}
self.sample = RooCategory( 'sample', 'sample' )
self.roofit_variables = []
input_hists = MapStrRootPtr()
# first create observables
# Since we are looking for normalisation in equivalent regions
# the number of events in each sample has to be identical
# Hence, pick one fit_data to create the set of observables
fit_data_1 = fit_data.itervalues().next()
samples = fit_data_1.samples
self.observables = {}
N_min = 0
N_max = fit_data_1.n_data() * 2
for sample in samples:
self.observables[sample] = Observable( 'n_' + sample,
'number of ' + sample + " events",
fit_data_1.normalisation[sample],
N_min,
N_max,
"events" )
# next create the models
for variable, fit_input in fit_data.iteritems():
self.models[variable] = fit_input.get_roofit_model( variable, self.observables )
self.sample.defineType( variable )
self.sample.setLabel ( variable )
data = deepcopy( fit_input.real_data_histogram() )
input_hists.insert( StrHist( variable, data ) )
self.roofit_variables.append( fit_input.fit_variable )
self.comb_data = RooDataHist( "combData",
"combined data",
RooArgList( self.roofit_variables[0] ),
self.sample,
input_hists,
)
def generate(histList, varName, varLow, varHigh, histSig="", weightSig=0.):
"""
Generate toy data
@param histList List of histograms
@param varname Variable name to use
@param varLow Lower edge of left bin
@param varHigh Upper edge of right bin
@param histSig Optional signal histogram
@param weightSig Optional signal weight
"""
testHist_pred = TH1D()
for (iHist, hist) in enumerate(histList):
if not iHist:
testHist_pred = hist.Clone("hPRED")
else:
testHist_pred.Add(hist)
testHist_pred_ds = TH1D(
"hPSDATA", "hPSDATA", testHist_pred.GetNbinsX(),
testHist_pred.GetXaxis().GetBinLowEdge(1),
testHist_pred.GetXaxis().GetBinUpEdge(testHist_pred.GetNbinsX()))
nEvents_pred = int(testHist_pred.Integral())
print "nEvents_pred = %d" % nEvents_pred
var = RooRealVar(varName, varName, varLow, varHigh)
dh_pred = RooDataHist("dhPSDATA", "dhPSDATA", RooArgList(var),
RooFit.Import(testHist_pred))
ph_pred = RooHistPdf("phPSDATA", "phPSDATA", RooArgSet(var), dh_pred)
ds_pred = ph_pred.generate(RooArgSet(var), nEvents_pred)
testHist_pred_ds = ds_pred.fillHistogram(testHist_pred_ds, RooArgList(var))
return testHist_pred_ds
def MakeRooDataHist(phys_process, fullrange_=False):
MPruned_Merged.Scale(0.0)
iweight = 0
for irootfile in Utils.samples[phys_process]['files']:
file01 = TFile('AnalysisHistograms_V7/' + irootfile, 'READ')
#file01 = TFile(irootfile, 'READ')
MPruned_ttbar_sb = file01.Get('histfacFatJet_ZLight/h_Mjj0')
MPruned_ttbar_sig = file01.Get(
'MonoHFatJetSelection_JetAndLeptonVeto/h_Mjj0')
if debug_:
print "before ", MPruned_ttbar_sb.Integral()
print "before ", MPruned_ttbar_sig.Integral()
if phys_process != 'data_obs':
MPruned_ttbar_sb.Scale(
Utils.samples[phys_process]['weight'][iweight])
MPruned_ttbar_sig.Scale(
Utils.samples[phys_process]['weight'][iweight])
if debug_:
print "weight = ", Utils.samples[phys_process]['weight'][iweight]
print "after ", MPruned_ttbar_sb.Integral()
print "after ", MPruned_ttbar_sig.Integral()
MPruned_Merged.Add(MPruned_ttbar_sb)
if fullrange_ | options.fullrange:
MPruned_Merged.Add(MPruned_ttbar_sig)
## convert Histogram to RooDataHist
MPruned_ttbar_hist = RooDataHist('MPruned_ttbar_hist',
'MPruned_ttbar_hist',
RooArgList(J_Mass), MPruned_Merged)
iweight = iweight + 1
print "final intgral = ", MPruned_Merged.Integral()
return MPruned_ttbar_hist
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 broadenspec(ms, E_0):
spec = decayspec(ms, E_0)
smear = core(E_0_center)
newspec = TH1D("", "", spec.GetNbinsX(), -17.5, 17.5)
for i in range(1, spec.GetNbinsX() + 1):
newspec.SetBinContent(i, spec.GetBinContent(i))
#x = RooRealVar("x","x",-30+E_0_center, 5+E_0_center)
x = RooRealVar("x", "x", -17.5, 17.5)
data = RooDataHist("", "", RooArgList(x), newspec)
specpdf = RooHistPdf("", "", RooArgSet(x), data)
#y = RooRealVar("y","y",-30, 5)
x.setBins(10000)
smearpdf = RooFit.bindPdf(smear, x)
fft = RooFFTConvPdf("tt", "tt", x, specpdf, smearpdf)
#fft.setShift(0, -18574)
#c1 = TCanvas()
#frame = x.frame()
#fft.plotOn(frame)
#frame.Draw()
tf = fft.asTF(RooArgList(x))
tf.SetNpx(10000)
rtf = tf.Clone()
return rtf
def generate(histList,varName,varLow,varHigh,histSig="",weightSig=0.):
testHist_pred = TH1D()
for (iHist,hist) in enumerate(histList):
if not iHist:
testHist_pred = hist.Clone("hPRED")
else:
testHist_pred.Add(hist)
testHist_pred_ds = TH1D("hPSDATA","hPSDATA",testHist_pred.GetNbinsX(),testHist_pred.GetXaxis().GetBinLowEdge(1),testHist_pred.GetXaxis().GetBinUpEdge(testHist_pred.GetNbinsX()))
nEvents_pred = int(testHist_pred.Integral())
print "nEvents_pred = %d" % nEvents_pred
var = RooRealVar(varName,varName,varLow,varHigh)
dh_pred = RooDataHist("dhPSDATA","dhPSDATA",RooArgList(var),RooFit.Import(testHist_pred))
ph_pred = RooHistPdf("phPSDATA","phPSDATA",RooArgSet(var),dh_pred)
ds_pred = ph_pred.generate(RooArgSet(var),nEvents_pred)
testHist_pred_ds = ds_pred.fillHistogram(testHist_pred_ds,RooArgList(var))
return testHist_pred_ds
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")
latex.SetTextAlign(12)
latex.SetTextFont(42)
latex.SetTextSize(0.035)
ws = RooWorkspace("ws")
x = RooRealVar("x", "p_{T}^{reco}/p_{T}^{parton}", xlow,xup)
obs = RooArgList(x)
c1 = TCanvas("c1", "c1",600,600) ;
if logy: c1.SetLogy()
# Loop over input files (currently disabled)
infile = TFile.Open(infilename)
for ih in xrange(ih_,ih_+1):
h = getattr(infile, "hh1_%i" % ih)
data = RooDataHist("data_%i" %ih, "", obs, h)
getattr(ws, 'import')(data)
hl = getattr(infile, "hl_%i" % ih)
frame = x.frame()
if h.Integral() < 150:
h.Draw()
latex.DrawLatex(0.19, 0.90, "%.0f<p_{T}^{Z}<%.0f, %.0f<p_{T}^{j}<%.0f, %.1f<|#eta^{j}|<%.1f" % tuple([hl.GetBinContent(l) for l in xrange(1,7)]))
latex.DrawLatex(0.19, 0.85, "#color[1]{FullSim}")
latex.DrawLatex(0.42, 0.85, "#color[1]{#mu=%.3f, #sigma=%.3f}" % (h.GetMean(), h.GetRMS()))
gPad.Print("plots/zmmtozbb_fit%i_%s.png" % (ifunc,h.GetName() if logy else h.GetName()+"_lin"))
#gPad.Print("plots/zmmtozbb_fit%i_%s.pdf" % (ifunc,h.GetName() if logy else h.GetName()+"_lin"))
continue
def fitHistogram( histToFit, channel, variable='', bin='', treeSuffix ='' ) :
# Declare variables x,mean,sigma with associated name, title, initial value and allowed range
x = RooRealVar("M(jj)","M(jj)",40,500)
title = channel
if variable != '' and bin != '' :
title = '%s_%s_%s' % (channel, variable, bin)
histToFit = RooDataHist('histToFit', 'histToFit', RooArgList( x ), RooFit.Import( histToFit ) )
frame = x.frame(RooFit.Title( title ))
histToFit.plotOn( frame )
# Setup fit function
fitFunction = None
# Stuff for gauss
mg = RooRealVar("mean","mean of gaussian",86,50,120)
sg = RooRealVar("sigma","width of gaussian",10,2,50)
gauss = RooGaussian("gauss","gaussian PDF",x,mg,sg)
CBmean = RooRealVar("CBmean", "CBmean",110, 5, 500)
CBsigma = RooRealVar("CBsigma", "CBsigma",40, 20, 150)
CBalpha = RooRealVar("CBalpha", "CBalpha", -0.5, -20., 0.)
# CBalpha = RooRealVar("CBalpha", "CBalpha", 10, 0., 20.)
CBn = RooRealVar("CBn", "CBn", 1., 0., 20.)
crystalBall = RooCBShape("crystalBall","Crystal Ball resolution model", x, CBmean, CBsigma, CBalpha, CBn)
fracGauss = RooRealVar("fracGauss", "fracGauss", 0.4, 0, 1)
gaussPlusCrystalBall = RooAddPdf("gaussPlusCrystalBall", "Gauss plus Crystal Ball", RooArgList(gauss, crystalBall), RooArgList( fracGauss ) )
fitFunction = gaussPlusCrystalBall
# # Fit pdf to data
fitFunction.fitTo(histToFit, RooFit.PrintLevel(-1))
# Plot histogram being fitted
histToFit.plotOn(frame)
# Plot fit functions and components
fitFunction.plotOn(frame, RooFit.LineColor(kRed))
toPlot = RooArgSet(crystalBall)
fitFunction.plotOn(frame, RooFit.Components(toPlot), RooFit.LineStyle(kDashed), RooFit.LineColor(kRed))
toPlot = RooArgSet(gauss)
fitFunction.plotOn(frame, RooFit.Components(toPlot), RooFit.LineStyle(kDashed), RooFit.LineColor(kBlue))
fitFunction.paramOn(frame,RooFit.Layout(0.55, 0.9, 0.9)) ;
# # Draw frame on a canvas
c = TCanvas("WMass","WMass",800,400)
gPad.SetLeftMargin(0.15)
frame.GetYaxis().SetTitleOffset(1.6)
frame.Draw()
gPad.Update()
if variable == '' and bin == '':
c.Print( 'plots/WStudies/%s%s.pdf' % ( channel, treeSuffix ) )
elif variable != '' and bin != '':
outputDir = 'plots/WStudies/%s%s/%s' % (channel, treeSuffix, variable)
if not os.path.exists( outputDir ):
os.makedirs( outputDir )
c.Print( '%s/%s.pdf' % (outputDir, bin) )
# raw_input('Carry on')
# mg.Print()
return mg
def main(options,args):
from ROOT import gSystem, gROOT, gStyle
gROOT.SetBatch()
gSystem.Load("libRooFitCore")
if options.doWebPage:
from lip.Tools.rootutils import loadToolsLib, apply_modifs
loadToolsLib()
from ROOT import TFile, RooFit, RooArgSet, RooDataHist, RooKeysPdf, RooHistPdf, TCanvas, TLegend, TLatex, TArrow, TPaveText, RooAddPdf, RooArgList
from ROOT import kWhite, kBlue, kOpenSquare
if options.doWebPage:
from ROOT import HtmlHelper, HtmlTag, HtmlTable, HtmlPlot
rootglobestyle.setTDRStyle()
gStyle.SetMarkerSize(1.5)
gStyle.SetTitleYOffset(1.5)
gStyle.SetPadLeftMargin(0.16)
gStyle.SetPadRightMargin(0.05)
gStyle.SetPadTopMargin(0.05)
gStyle.SetPadBottomMargin(0.13)
gStyle.SetLabelFont(42,"XYZ")
gStyle.SetLabelOffset(0.007, "XYZ")
gStyle.SetLabelSize(0.05,"XYZ")
gStyle.SetTitleSize(0.06,"XYZ")
gStyle.SetTitleXOffset(0.9)
gStyle.SetTitleYOffset(1.24)
gStyle.SetTitleFont(42,"XYZ")
##
## Read files
##
options.outdir = "%s_m%1.0f" % ( options.outdir, options.mH )
if options.fp:
options.outdir += "_fp"
ncat=options.ncat
cats=options.cats
if cats is "":
categories =[ "_cat%d" % i for i in range(0,ncat) ]
else:
categories =[ "_cat%s" % i for i in cats.split(",") ]
if options.mva:
clables = { "_cat0" : ("MVA > 0.89",""),
"_cat1" : ("0.74 #leq MVA","MVA < 0.89"),
"_cat2" : ("0.545 #leq MVA","MVA < 0.74"),
"_cat3" : ("0.05 #leq MVA","MVA < 0.545"),
"_cat4" : ("Di-jet","Tagged"),
"_cat5" : ("Di-jet","Tagged"),
"_combcat" : ("All Classes","Combined")
}
else:
clables = { "_cat0" : ("max(|#eta|<1.5","min(R_{9})>0.94"),
"_cat1" : ("max(|#eta|<1.5","min(R_{9})<0.94"),
"_cat2" : ("max(|#eta|>1.5","min(R_{9})>0.94"),
"_cat3" : ("max(|#eta|>1.5","min(R_{9})<0.94"),
"_cat4" : ("Di-jet","Tagged"),
"_cat5" : ("Di-jet","Tagged"),
"_combcat" : ("All Classes","Combined")
}
helper = Helper()
fin = TFile.Open(options.infile)
helper.files.append(fin)
ws = fin.Get("cms_hgg_workspace")
mass = ws.var("CMS_hgg_mass")
mass.SetTitle("m_{#gamma#gamma}");
mass.setUnit("GeV");
mass.setRange(100.,150.)
mass.setBins(100,"plot")
mass.setBins(5000)
print ws
aset = RooArgSet(mass)
helper.objs.append( mass )
helper.objs.append( aset )
fitopt = ( RooFit.Minimizer("Minuit2", ""), RooFit.Minos(False), RooFit.SumW2Error(False), RooFit.NumCPU(8) )
if not options.binned and not options.refit:
finpdf = TFile.Open(options.infilepdf)
helper.files.append(finpdf)
wspdf = finpdf.Get("wsig")
else:
wspdf = ws
for c in categories:
processes = [ "ggh", "vbf", "wzh" ]
if options.fp:
processes = [ "vbf", "wzh" ]
### elif clables[c][0] == "Di-jet":
### processes = [ "vbf", "ggh" ]
dsname = "sig_mass_m%1.0f%s" % (options.mH,c)
print dsname
print ws
ds = ws.data( "sig_%s_mass_m%1.0f%s" % (processes[0],options.mH,c) ).Clone(dsname)
for proc in processes[1:]:
ds.append( ws.data( "sig_%s_mass_m%1.0f%s" % (proc,options.mH,c) ) )
helper.dsets.append( ds )
if options.binned:
binned_ds = RooDataHist( "binned_%s" % dsname,"binned_%s" % dsname,aset, ds)
pdf = RooKeysPdf( "pdf_%s_%s" % (dsname, f), "pdf_%s" % dsname, mass, ds )
plot_pdf = RooHistPdf( "pdf_%s" % dsname, "pdf_%s" % dsname, aset, plot_ds )
helper.add( binned_ds, binned_ds.GetName() )
else:
if options.refit:
if options.refitall and clables[c][0] != "Di-jet":
rpdfs = []
for proc in processes:
for ngaus in range(1,4):
pp = build_pdf(ws,"%s_%s" % (c,proc),ngaus,ngaus==3 )
pp.fitTo( ws.data( "sig_%s_mass_m%1.0f%s" % (proc,options.mH,c)), RooFit.Strategy(0), *fitopt )
rpdfs.append(pp)
pdf = RooAddPdf("hggpdfrel%s" % c, "hggpdfrel%s" % c, RooArgList(*tuple(rpdfs) ))
else:
if options.refitall and clables[c][0] == "Di-jet":
for ngaus in range(1,5):
pdf = build_pdf(ws,c,ngaus,ngaus==5)
pdf.fitTo(ds, RooFit.Strategy(0), *fitopt )
else:
for ngaus in range(1,4):
pdf = build_pdf(ws,c,ngaus,ngaus==3)
pdf.fitTo(ds, RooFit.Strategy(0), *fitopt )
else:
pdfs = (wspdf.pdf( "hggpdfrel%s_%s" % (c, p)) for p in processes )
pdf = RooAddPdf("hggpdfrel%s" % c, "hggpdfrel%s" % c, RooArgList(*pdfs ))
helper.add(pdf,pdf.GetName())
plot_pdf = pdf.Clone("pdf_%s" % dsname)
plot_ds = RooDataHist( "plot_%s" % dsname,"plot_%s" % dsname, aset, "plot")
plot_ds.add( ds )
cdf = pdf.createCdf(aset)
hmin, hmax, hm = get_FWHM( mass, pdf, cdf, options.mH-10., options.mH+10. )
wmin, wmax = get_eff_sigma( mass, pdf, cdf, options.mH-10., options.mH+10. )
### hmin, hmax, hm = get_FWHM( points )
helper.add( plot_ds, plot_ds.GetName() )
helper.add( plot_pdf, plot_pdf.GetName() )
helper.add( (wmin,wmax), "eff_sigma%s" % c )
helper.add( (hmin, hmax, hm), "FWHM%s" % c )
helper.add( ds.sumEntries(), "sumEntries%s" %c ) # signal model integral
# data integral for PAS tables
data = ws.data( "data_mass%s"%c)
helper.add( data.sumEntries("CMS_hgg_mass>=%1.4f && CMS_hgg_mass<=%1.4f"%(options.mH-10.,options.mH+10.)),"data_sumEntries%s"%c)
del cdf
del pdf
dsname = "sig_mass_m%1.0f_combcat" % options.mH
print dsname
combined_ds = helper.dsets[0].Clone(dsname)
for d in helper.dsets[1:]:
combined_ds.append(d)
if options.binned:
binned_ds = RooDataHist( "binned_%s" % dsname,"binned_%s" % dsname,aset, combined_ds)
pdf = RooKeysPdf( "pdf_%s" % (dsname), "pdf_%s" % dsname, mass, combined_ds )
plot_pdf = RooHistPdf( "pdf_%s" % dsname, "pdf_%s" % dsname, aset, plot_ds )
helper.add( binned_ds, binned_ds.GetName() )
else:
#### pdf = build_pdf(ws,"_combcat")
#### pdf.fitTo(combined_ds, RooFit.Strategy(0), *fitopt )
#### plot_pdf = pdf.Clone( "pdf_%s" % dsname )
pdf = RooAddPdf( "pdf_%s" % dsname, "pdf_%s" % dsname, RooArgList( *(helper.histos["hggpdfrel%s" % c] for c in categories) ) )
plot_pdf = pdf
cdf = pdf.createCdf(aset)
plot_ds = RooDataHist( "plot_%s" % dsname,"plot_%s" % dsname, aset, "plot")
plot_ds.add( combined_ds )
wmin, wmax = get_eff_sigma( mass, pdf, cdf, options.mH-10., options.mH+10. )
hmin, hmax, hm = get_FWHM( mass, pdf, cdf, options.mH-10., options.mH+10. )
helper.add( plot_ds, plot_ds.GetName() )
helper.add( plot_pdf, plot_pdf.GetName() )
helper.add( (wmin,wmax), "eff_sigma_combcat" )
helper.add( (hmin, hmax, hm), "FWHM_combcat" )
helper.add( plot_ds.sumEntries(), "sumEntries_combcat" )
mass.setRange("higgsrange",options.mH-25.,options.mH+15.);
del cdf
del pdf
del helper.dsets
### label = TLatex(0.1812081,0.8618881,"#scale[0.8]{#splitline{CMS preliminary}{Simulation}}")
label = TLatex(0.7,0.86,"#scale[0.65]{#splitline{CMS preliminary}{Simulation}}")
label.SetNDC(1)
##
## Make web page with plots
##
if options.doWebPage:
hth = HtmlHelper(options.outdir)
hth.navbar().cell( HtmlTag("a") ).firstChild().txt("..").set("href","../?C=M;O=D")
hth.navbar().cell( HtmlTag("a") ).firstChild().txt("home").set("href","./")
tab = hth.body().add( HtmlTable() )
ip = 0
for c in ["_combcat"]+categories:
### for c in categories:
if options.doWebPage and ip % 4 == 0:
row = tab.row()
ip = ip + 1
dsname = "sig_mass_m%1.0f%s" % (options.mH,c)
canv = TCanvas(dsname,dsname,600,600)
helper.objs.append(canv)
### leg = TLegend(0.4345638,0.6835664,0.9362416,0.9178322)
leg = TLegend(0.2,0.96,0.5,0.55)
#apply_modifs( leg, [("SetLineColor",kWhite),("SetFillColor",kWhite),("SetFillStyle",0),("SetLineStyle",0)] )
hplotcompint = mass.frame(RooFit.Bins(250),RooFit.Range("higgsrange"))
helper.objs.append(hplotcompint)
helper.objs.append(leg)
plot_ds =helper.histos["plot_%s" % dsname ]
plot_pdf =helper.histos["pdf_%s" % dsname ]
wmin,wmax = helper.histos["eff_sigma%s" % c ]
hmin, hmax, hm = helper.histos["FWHM%s" % c ]
print hmin, hmax, hm
style = ( RooFit.LineColor(kBlue), RooFit.LineWidth(2), RooFit.FillStyle(0) )
style_seff = ( RooFit.LineWidth(2), RooFit.FillStyle(1001), RooFit.VLines(), RooFit.LineColor(15), )
style_ds = ( RooFit.MarkerStyle(kOpenSquare), )
plot_ds.plotOn(hplotcompint,RooFit.Invisible())
plot_pdf.plotOn(hplotcompint,RooFit.NormRange("higgsrange"),RooFit.Range(wmin,wmax), RooFit.FillColor(19),
RooFit.DrawOption("F"), *style_seff)
seffleg = hplotcompint.getObject(int(hplotcompint.numItems()-1))
plot_pdf.plotOn(hplotcompint,RooFit.NormRange("higgsrange"),RooFit.Range(wmin,wmax), RooFit.LineColor(15), *style_seff)
plot_pdf.plotOn(hplotcompint,RooFit.NormRange("higgsrange"),RooFit.Range("higgsrange"),*style)
pdfleg = hplotcompint.getObject(int(hplotcompint.numItems()-1))
plot_ds.plotOn(hplotcompint,*style_ds)
pointsleg = hplotcompint.getObject(int(hplotcompint.numItems()-1))
iob = int( hplotcompint.numItems() - 1 )
leg.AddEntry( pointsleg, "Simulation", "pe" )
leg.AddEntry( pdfleg, "Parametric model", "l" )
leg.AddEntry( seffleg, "#sigma_{eff} = %1.2f GeV " % ( 0.5*(wmax-wmin) ), "fl" )
clabel = TLatex(0.74,0.65,"#scale[0.65]{#splitline{%s}{%s}}" % clables[c])
clabel.SetNDC(1)
helper.objs.append(clabel)
hm = hplotcompint.GetMaximum()*0.5*0.9
### hm = pdfleg.GetMaximum()*0.5
fwhmarrow = TArrow(hmin,hm,hmax,hm)
fwhmarrow.SetArrowSize(0.03)
helper.objs.append(fwhmarrow)
fwhmlabel = TPaveText(0.20,0.58,0.56,0.48,"brNDC")
fwhmlabel.SetFillStyle(0)
fwhmlabel.SetLineColor(kWhite)
reducedFWHM = (hmax-hmin)/2.3548200
fwhmlabel.AddText("FWHM/2.35 = %1.2f GeV" % reducedFWHM)
helper.objs.append(fwhmlabel)
hplotcompint.SetTitle("");
hplotcompint.GetXaxis().SetNoExponent(True);
hplotcompint.GetXaxis().SetTitle("m_{#gamma#gamma} (GeV)");
hplotcompint.GetXaxis().SetNdivisions(509);
## hplotcompint.GetYaxis().SetTitle("A.U.");
## hplotcompint.GetYaxis().SetRangeUser(0.,hplotcompint.GetMaximum()*1.4);
hplotcompint.Draw();
leg.Draw("same")
label.Draw("same")
clabel.Draw("same")
fwhmarrow.Draw("<>")
fwhmlabel.Draw("same")
plot_ds.sumEntries()
if options.doWebPage:
hpl = HtmlPlot(canv,False,"",True,True,True)
hpl.caption("<i>%s</i>" % canv.GetTitle())
row.cell( hpl )
else:
if os.path.isdir(options.outdir) is False:
os.mkdir(options.outdir)
for ext in "C","png","pdf":
canv.SaveAs( os.path.join(options.outdir,"%s.%s" % (canv.GetName(), ext)) )
if "comb" in c:
ip = 0
if options.doWebPage:
print "Creating pages..."
hth.dump()
for f in helper.files:
f.Close()
gROOT.Reset()
from pprint import pprint
pprint(helper)
print 'Summary statistics per event class'
print 'Cat\tSignal\t\tData/GeV (in %3.1f+/-10)\tsigEff\tFWHM/2.35'%options.mH
sigTotal=0.
dataTotal=0.
for c in categories:
sigVal = helper.histos["sumEntries%s"%c]
datVal = helper.histos["data_sumEntries%s"%c]
sigTotal+=sigVal
dataTotal+=datVal
for c in categories:
sigVal = helper.histos["sumEntries%s"%c]
datVal = helper.histos["data_sumEntries%s"%c]
effSig = 0.5*(helper.histos["eff_sigma%s"%c][1]-helper.histos["eff_sigma%s"%c][0])
fwhm = (helper.histos["FWHM%s"%c][1]-helper.histos["FWHM%s"%c][0]) / 2.3548200
print c, '\t%3.1f (%3.1f%%)\t%3.1f (%3.1f%%)\t\t\t%2.2f\t%2.2f'%(sigVal,100.*sigVal/sigTotal,datVal/(10.+10.),100.*datVal/dataTotal,effSig,fwhm)
print "Done."
def fit2GausPol1(Elo, Ehi, Emid, hist, name_prefix = None,
area1_lo = None, area1_hi = None,
area2_lo = None, area2_hi = None,
offset_lo = None, offset_hi = None,
slope_lo = None, slope_hi = None,
nbkg_lo = None, nbkg_hi = None,
verbose = None):
""" Fitting two gaussian peaks on top of a first order polynomial
background. Some parameters are hardcoded.
A name can be provided to make the fit parameters unique.
"""
if area1_lo is None:
area1_lo = 1.
if area1_hi is None:
area1_hi = 1.E4
if area2_lo is None:
area2_lo = 1.
if area2_hi is None:
area2_hi = 1.E4
if nbkg_lo is None:
nbkg_lo = 0.
if nbkg_hi is None:
nbkg_hi = 1.E4
if offset_lo is None:
offset_lo = -10.
if offset_hi is None:
offset_hi = 100.
if slope_lo is None:
slope_lo = -0.1
if slope_hi is None:
slope_hi = 0.1
if verbose is not None:
print "Fitting two Gaussian peaks on top of a 1st order polynomial\
background to the", hist.GetName(), "in range:", Elo, "--", Ehi, "keV"
ws = ROOT.RooWorkspace()
bookstr = "Polynomial::pol1_bkg(" +\
"edep[" + Elo.__str__() + ", " + Ehi.__str__() +"]" +\
", {bkg_offset[" + offset_lo.__str__() + "," +offset_hi.__str__()+ "]" +\
", bkg_slope[" + slope_lo.__str__() +"," + slope_hi.__str__() +"]"+\
"} )"
if verbose is not None:
print bookstr
ws.factory(bookstr)
bookstr = "Gaussian::peak1_pdf(" +\
"edep[" + Elo.__str__() + ", " + Ehi.__str__() +"]" +\
", centroid1[" + Elo.__str__() + ", " + (Emid+2).__str__() +"]" +\
", sigma1[0.3, 10.])"
if verbose is not None:
print bookstr
ws.factory(bookstr)
bookstr = "Gaussian::peak2_pdf(" +\
"edep[" + Elo.__str__() + ", " + Ehi.__str__() +"]" +\
", centroid2[" + (Emid-2).__str__() + ", " + Ehi.__str__() +"]" +\
", sigma2[0.3, 10.])"
if verbose is not None:
print bookstr
ws.factory(bookstr)
bookstr = "SUM:sum_pdf(area1[" + area1_lo.__str__() + "," + area1_hi.__str__() + "]*peak1_pdf"+\
", area2[" + area2_lo.__str__() + "," + area2_hi.__str__() + "]*peak2_pdf"+\
", nbkg[" + nbkg_lo.__str__()+ "," + nbkg_hi.__str__() + "]*pol1_bkg)"
if verbose is not None:
print bookstr
ws.factory(bookstr)
#ws.factory("SUM:sum_pdf(area1[0,10000]*peak1_pdf, area2[0,1E5]*peak2_pdf, nbkg[0,100000]*pol1_bkg)")
edep = ws.var("edep")
pdf = ws.pdf("sum_pdf")
centroid1 = ws.var("centroid1")
centroid2 = ws.var("centroid2")
datahist = RooDataHist("tmp_datahist","tmp_datahist", RooArgList(edep), hist)
model_config = RooStats.ModelConfig("model_config", ws)
model_config.SetPdf(pdf)
model_config.SetParametersOfInterest(RooArgSet(ws.var("area1"),
ws.var("area2")))
model_config.SetObservables(RooArgSet(ws.var("edep")))
model_config.SetNuisanceParameters(RooArgSet(ws.var("nbkg")))
pllCal = RooStats.ProfileLikelihoodCalculator(datahist, model_config)
fit_result = pllCal.fFitResult
pllCal.SetConfidenceLevel(0.683)
interval = pllCal.GetInterval()
POIs = model_config.GetParametersOfInterest()
#lowerlimit = interval.LowerLimit(firstPOI)
#upperlimit = interval.UpperLimit(firstPOI)
if verbose is not None:
print "Fit status:",fit_result.status()
frame = edep.frame()
datahist.plotOn(frame,
RooFit.MarkerSize(0.3),
RooFit.MarkerColor(ROOT.kAzure))
pdf.plotOn(frame,
RooFit.Components("peak1_pdf"),
RooFit.LineColor(ROOT.kOrange))
pdf.plotOn(frame,
RooFit.Components("peak2_pdf"),
RooFit.LineColor(ROOT.kOrange+2))
pdf.plotOn(frame,
RooFit.Components("pol1_bkg"),
RooFit.LineStyle(ROOT.kDotted),
RooFit.LineColor(ROOT.kMagenta))
pdf.plotOn(frame,
RooFit.LineColor(ROOT.kRed))
c = TCanvas()
c.SetWindowSize(400,250)
frame.Draw()
output = {}
output["fit_status"] = fit_result.status()
output["area1"] = POIs["area1"]
output["area2"] = POIs["area2"]
output["nbkg"] = ws.var("nbkg")
output["centroid1"] = ws.var("centroid1")
output["centroid2"] = ws.var("centroid2")
output["sigma1"] = ws.var("sigma1")
output["sigma2"] = ws.var("sigma2")
output["bkg_slope"] = ws.var("bkg_slope")
output["bkg_offset"] = ws.var("bkg_offset")
output["canvas"] = c
for param in output:
if hasattr(output[param], 'GetName'):
oldname = output[param].GetName()
if name_prefix is None:
newname = hist.GetName() + "_" + Elo.__str__()\
+ "_" + Ehi.__str__() + "_" + oldname
else:
newname = oldname
output[param].SetName(newname)
del ws
return output
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)
signal.fitTo(roohistSig)
frame = x.frame()
roohistSig.plotOn(frame)
signal.plotOn(frame)
signal.plotOn(frame,RooFit.Components('bkg'),RooFit.LineColor(ROOT.kRed),RooFit.LineWidth(2),RooFit.LineStyle(ROOT.kDashed))
frame.GetXaxis().SetRangeUser(900,4500)
frame.GetXaxis().SetTitle('m_{jj} (GeV)')
frame.Draw()
parsSig = signal.getParameters(roohistSig)
parsSig.setAttribAll('Constant', True)
if fitDat:
sbhist.Add(hvv,signalweight)
sbhist.Scale(hqcd.Integral()/sbhist.Integral())
mchist=sbhist.Clone("mchist")
if cuts.index(cut)==1:
summchist=sbhist.Clone("mchist")
else:
summchist.Add(sbhist)
sighist=hvv.Clone("sighist")
if cuts.index(cut)==1:
sumsighist=hvv.Clone("sighist")
else:
sumsighist.Add(hvv)
if fit=="data":
if cut>1:
data=RooDataHist("data"+str(cut),"data",RooArgList(mass),sumdatahist)
else:
data=RooDataHist("data"+str(cut),"data",RooArgList(mass),datahist)
elif fit=="b":
if cut>1:
data=RooDataHist("data"+str(cut),"QCD",RooArgList(mass),sumqcdhist)
else:
data=RooDataHist("data"+str(cut),"QCD",RooArgList(mass),qcdhist)
elif fit=="sb":
if cut>1:
data=RooDataHist("data"+str(cut),"QCD + V+jets",RooArgList(mass),summchist)
else:
data=RooDataHist("data"+str(cut),"QCD + V+jets",RooArgList(mass),mchist)
if cut>1:
signal=RooDataHist("signal"+str(cut),"signal",RooArgList(mass),sumsighist)
else:
def fitWPeak():
# Get histograms from file
mcHist = 0
with root_open( inputFile, 'read' ) as file:
gROOT.cd()
mcHist = file.Get(inputHistogram).Clone('histToFit')
# mcHist = file.Get(inputHistogram).GetStack().Last().Clone('histToFit')
# Data hist gone out of scope?
print mcHist
# Import the data to fit
# Declare variables x,mean,sigma with associated name, title, initial value and allowed range
x = RooRealVar("M(jj)","M(jj)",40,180)
histToFit = RooDataHist('histToFit', 'histToFit', RooArgList( x ), RooFit.Import( mcHist ) )
frame = x.frame(RooFit.Title("E Plus Jets"))
histToFit.plotOn( frame )
# Setup fit function
fitFunction = None
print fitFunction
if whatToFit == 'realLife':
# Stuff for gauss
mg = RooRealVar("mean","mean of gaussian",86,50,120)
sg = RooRealVar("sigma","width of gaussian",10,0,50)
gauss = RooGaussian("gauss","gaussian PDF",x,mg,sg)
CBmean = RooRealVar("CBmean", "CBmean",110, 60, 200)
CBsigma = RooRealVar("CBsigma", "CBsigma",40, 20, 100)
CBalpha = RooRealVar("CBalpha", "CBalpha", -0.5, -20., 0.)
# CBalpha = RooRealVar("CBalpha", "CBalpha", 10, 0., 20.)
CBn = RooRealVar("CBn", "CBn", 1., 0., 20.)
crystalBall = RooCBShape("crystalBall","Crystal Ball resolution model", x, CBmean, CBsigma, CBalpha, CBn)
fracGauss = RooRealVar("fracGauss", "fracGauss", 0.4, 0, 1)
gaussPlusCrystalBall = RooAddPdf("gaussPlusCrystalBall", "Gauss plus Crystal Ball", RooArgList(gauss, crystalBall), RooArgList( fracGauss ) )
fitFunction = gaussPlusCrystalBall
elif whatToFit == 'partons':
mg = RooRealVar("mean","mean of gaussian",86,50,120)
sg = RooRealVar("sigma","width of gaussian",10,0,50)
breitWigner = RooBreitWigner('bw', 'bw', x, mg, sg)
fitFunction = breitWigner
elif whatToFit == 'genJetsFromPartons':
# mg = RooRealVar("mean","mean of gaussian",86,50,120)
# sg = RooRealVar("sigma","width of gaussian",10,0,50)
# breitWigner = RooBreitWigner('bw', 'bw', x, mg, sg)
# fitFunction = breitWigner
# mg = RooRealVar("mean","mean of gaussian",86,50,120)
# sg = RooRealVar("sigma","width of gaussian",1,0,20)
# width = RooRealVar("width","width of gaussian",5,0,50)
# voigtian = RooVoigtian("voigt","voigt",x,mg,sg,width);
# fitFunction = voigtian
mg = RooRealVar("mean","mean of gaussian",86,50,120)
sg = RooRealVar("sigma","width of gaussian",10,0,50)
gauss = RooGaussian("gauss","gaussian PDF",x,mg,sg)
fitFunction = gauss
print fitFunction
# # Fit pdf to data
fitFunction.fitTo(histToFit)
# Plot histogram being fitted
histToFit.plotOn(frame)
# Plot fit functions and components
fitFunction.plotOn(frame, RooFit.LineColor(kRed))
print 'Chi square with 7 : ',frame.chiSquare(7)
print frame.chiSquare()
print fitFunction.getParameters(histToFit).selectByAttrib('Constant', kFALSE).getSize()
toPlot = RooArgSet(crystalBall)
fitFunction.plotOn(frame, RooFit.Components(toPlot), RooFit.LineStyle(kDashed), RooFit.LineColor(kRed))
toPlot = RooArgSet(gauss)
fitFunction.plotOn(frame, RooFit.Components(toPlot), RooFit.LineStyle(kDashed), RooFit.LineColor(kBlue))
fitFunction.paramOn(frame,RooFit.Layout(0.55, 0.9, 0.9)) ;
# Print values of mean and sigma (that now reflect fitted values and errors)
mg.Print()
sg.Print()
# CBmean.Print()
# CBsigma.Print()
# CBalpha.Print()
# CBn.Print()
# fracGauss.Print()
# # Draw frame on a canvas
c = TCanvas("WMass","WMass",800,400)
gPad.SetLeftMargin(0.15)
frame.GetYaxis().SetTitleOffset(1.6)
frame.Draw()
gPad.Update()
c.Print('EPlusJets_mjj_fit.pdf')
raw_input()
elif accfn == 'arctan':
acc_cond = '(@0<0.0002)'
expr = '(atan(@0*exp(@1*@0-@2)))'
PDF = RooGenericPdf('PDF', '%s ? 0 : %s' % (acc_cond, expr),
RooArgList(time, turnon, offset))
elif accfn == 'erf':
acc_cond = '(@1<0.0002)'
expr = '(0.5*(TMath::Erf((@1-@2)/@0)+1))'
PDF = RooGenericPdf('PDF', '%s ? 0 : %s' % (acc_cond, expr),
RooArgList(turnon, time, offset))
else:
print 'Unknown acceptance type. Aborting'
assert(False)
# Dataset to fit to
datahist = RooDataHist('datahist', 'Dataset from a histogram',
RooArgList(time), RooFit.Import(timehist2, False))
# Debug
datahist.Print('v')
# Fit
PDF.fitTo(datahist, RooFit.SumW2Error(True), RooFit.NumCPU(1),
RooFit.Range(epsilon, 0.005),
RooFit.Optimize(False), RooFit.Verbose(True), RooFit.Strategy(2))
# Plot
tframe1 = time.frame(RooFit.Name('ptime'),
RooFit.Title('Lifetime acceptance fitted to %s' % accfn))
datahist.plotOn(tframe1, RooFit.MarkerStyle(kFullTriangleUp))
PDF.plotOn(tframe1, RooFit.LineColor(kGreen))
# canvas2 = TCanvas('canvas2', 'Acceptance', 800, 600)
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("analysis", type=str, help="Analysis name")
parser.add_argument("model", type=str, help="Model (Hbb, RSG)")
#parser.add_argument("--inputData", dest="inputData", required=True,
# help="Input data spectrum",
# metavar="INPUT_DATA")
parser.add_argument("--dataHistname", dest="dataHistname", type=str, default="h_data",
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", default="qq",
help="Final state (e.g. qq, qg, gg)",
metavar="FINAL_STATE")
parser.add_argument("--fit_functions", dest="fit_functions", default="f1,f2,f3,f4,f5", help="List of fit functions")
#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",
help="Output path where datacards and workspaces will be stored. If not specified, this is derived from limit_configuration.",
metavar="OUTPUT_PATH")
parser.add_argument("--correctTrigger", dest="correctTrigger",
action='store_true',
help="Include trigger correction in PDF")
parser.add_argument("-l", "--lumi", dest="lumi",
default=19700., 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("--fitSignal", action="store_true", help="Use signal fitted shapes (CB+Voigtian) instead of histogram templates")
#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=8000., 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()
fit_functions = args.fit_functions.split(",")
# mass points for which resonance shapes will be produced
masses = []
if args.fitBonly:
masses.append(750)
else:
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, RooProdPdf, RooEffProd, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, RooExtendPdf
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0,kFALSE)
RooMsgService.instance().setStreamStatus(1,kFALSE)
# input data file
#inputData = TFile(limit_config.get_data_input(args.analysis))
# input data histogram
#hData = inputData.Get(args.dataHistname)
#hData.SetDirectory(0)
data_file = TFile(analysis_config.get_b_histogram_filename(args.analysis, "BJetPlusX_2012"))
hData = data_file.Get("BHistograms/h_pfjet_mjj")
hData.SetDirectory(0)
# input sig file
if not args.fitSignal:
print "[create_datacards] INFO : Opening resonance shapes file at " + limit_config.get_resonance_shapes(args.analysis, args.model)
inputSig = TFile(limit_config.get_resonance_shapes(args.analysis, args.model), "READ")
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
if args.correctTrigger:
trigger_efficiency_pdf = trigger_efficiency.get_pdf(args.analysis, mjj)
trigger_efficiency_formula = trigger_efficiency.get_formula(args.analysis, mjj)
else:
trigger_efficiency_pdf = trigger_efficiency.get_trivial_pdf(mjj)
trigger_efficiency_formula = trigger_efficiency.get_trivial_formula(mjj)
for mass in masses:
print ">> Creating datacard and workspace for %s resonance with m = %i GeV..."%(args.final_state, int(mass))
rooDataHist = RooDataHist('rooDatahist','rooDathist',RooArgList(mjj),hData)
if not args.fitSignal:
hSig = inputSig.Get( "h_" + args.final_state + "_" + str(int(mass)) )
if not hSig:
raise Exception("Couldn't find histogram " + "h_" + args.final_state + "_" + str(int(mass)) + " in file " + limit_config.get_resonance_shapes(args.analysis, args.model))
# 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())
# If using fitted signal shapes, load the signal PDF
if args.fitSignal:
print "[create_datacards] Loading fitted signal PDFs from " + analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses)), "READ")
w_signal = f_signal_pdfs.Get("w_signal")
input_parameters = signal_fits.get_parameters(w_signal.pdf("signal"))
# Make a new PDF with nuisance parameters
signal_pdf_notrig, signal_vars = signal_fits.make_signal_pdf_systematic("bukin", mjj, mass=mass)
signal_pdf_name = signal_pdf_notrig.GetName()
signal_pdf_notrig.SetName(signal_pdf_name + "_notrig")
#signal_pdf = RooProdPdf(signal_pdf_name, signal_pdf_name, signal_pdf_notrig, trigger_efficiency_pdf)
signal_pdf = RooEffProd(signal_pdf_name, signal_pdf_name, signal_pdf_notrig, trigger_efficiency_formula)
# Copy input parameter values
signal_vars["xp_0"].setVal(input_parameters["xp"][0])
signal_vars["xp_0"].setError(input_parameters["xp"][1])
signal_vars["xp_0"].setConstant()
signal_vars["sigp_0"].setVal(input_parameters["sigp"][0])
signal_vars["sigp_0"].setError(input_parameters["sigp"][1])
signal_vars["sigp_0"].setConstant()
signal_vars["xi_0"].setVal(input_parameters["xi"][0])
signal_vars["xi_0"].setError(input_parameters["xi"][1])
signal_vars["xi_0"].setConstant()
signal_vars["rho1_0"].setVal(input_parameters["rho1"][0])
signal_vars["rho1_0"].setError(input_parameters["rho1"][1])
signal_vars["rho1_0"].setConstant()
signal_vars["rho2_0"].setVal(input_parameters["rho2"][0])
signal_vars["rho2_0"].setError(input_parameters["rho2"][1])
signal_vars["rho2_0"].setConstant()
f_signal_pdfs.Close()
signal_parameters = {}
signal_pdfs_notrig = {}
signal_pdfs = {}
signal_norms = {}
background_pdfs = {}
background_pdfs_notrig = {}
background_parameters = {}
background_norms = {}
signal_epdfs = {}
background_epdfs = {}
models = {}
fit_results = {}
for fit_function in fit_functions:
print "[create_datacards] INFO : On fit function {}".format(fit_function)
if args.fitSignal:
# Make a copy of the signal PDF, so that each fitTo call uses its own copy.
# The copy should have all variables set constant.
#signal_pdfs[fit_function], signal_parameters[fit_function] = signal_fits.copy_signal_pdf("bukin", signal_pdf, mjj, tag=fit_function, include_systematics=True)
signal_pdfs_notrig[fit_function] = ROOT.RooBukinPdf(signal_pdf_notrig, signal_pdf_notrig.GetName() + "_" + fit_function)
signal_pdfs[fit_function] = RooEffProd(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigger_efficiency_formula)
#signal_pdfs[fit_function] = RooProdPdf(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigger_efficiency_pdf)
iterator = signal_pdfs_notrig[fit_function].getVariables().createIterator()
this_parameter = iterator.Next()
while this_parameter:
this_parameter.setConstant()
this_parameter = iterator.Next()
else:
signal_pdfs[fit_function] = RooHistPdf('signal_' + fit_function,'signal_' + fit_function, RooArgSet(mjj), rooSigHist)
signal_norms[fit_function] = RooRealVar('signal_norm_' + fit_function, 'signal_norm_' + fit_function, 0., 0., 1e+05)
if args.fitBonly:
signal_norms[fit_function].setConstant()
background_pdfs_notrig[fit_function], background_parameters[fit_function] = make_background_pdf(fit_function, mjj, collision_energy=8000.)
background_pdf_name = background_pdfs_notrig[fit_function].GetName()
background_pdfs_notrig[fit_function].SetName(background_pdf_name + "_notrig")
background_pdfs[fit_function] = RooEffProd(background_pdf_name, background_pdf_name, background_pdfs_notrig[fit_function], trigger_efficiency_formula)
#background_pdfs[fit_function] = RooProdPdf(background_pdf_name, background_pdf_name, background_pdfs_notrig[fit_function], trigger_efficiency_pdf)
#background_pdfs[fit_function] = background_pdfs_notrig[fit_function]
#background_pdfs[fit_function].SetName(background_pdf_name)
# Initial values
if "trigbbh" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(55.)
background_parameters[fit_function]["p1"].setMin(20.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(28.)
background_parameters[fit_function]["p2"].setVal(-22.)
background_parameters[fit_function]["p3"].setVal(10.)
elif "trigbbl" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(82.)
background_parameters[fit_function]["p1"].setMin(60.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(41.)
background_parameters[fit_function]["p2"].setVal(-45.)
background_parameters[fit_function]["p3"].setVal(10.)
data_integral = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
background_norms[fit_function] = RooRealVar('background_' + fit_function + '_norm', 'background_' + fit_function + '_norm', data_integral, 0., 1.e8)
signal_epdfs[fit_function] = RooExtendPdf('esignal_' + fit_function, 'esignal_' + fit_function, signal_pdfs[fit_function], signal_norms[fit_function])
background_epdfs[fit_function] = RooExtendPdf('ebackground_' + fit_function, 'ebackground_' + fit_function, background_pdfs[fit_function], background_norms[fit_function])
models[fit_function] = RooAddPdf('model_' + fit_function, 's+b', RooArgList(background_epdfs[fit_function], signal_epdfs[fit_function]))
if args.runFit:
print "[create_datacards] INFO : Starting fit with function {}".format(fit_function)
fit_results[fit_function] = models[fit_function].fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Extended(kTRUE), RooFit.Strategy(args.fitStrategy), RooFit.Verbose(0))
print "[create_datacards] INFO : Done with fit {}. Printing results.".format(fit_function)
fit_results[fit_function].Print()
print "[create_datacards] DEBUG : End args.runFit if block."
# needed if want to evaluate limits without background systematics
if args.fixBkg:
background_norms[fit_function].setConstant()
for par_name, par in background_parameters[fit_function].iteritems():
par.setConstant()
# -----------------------------------------
#signal_pdfs_syst = {}
# JES and JER uncertainties
if args.fitSignal:
print "[create_datacards] INFO : Getting signal PDFs from " + analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses)))
w_signal = f_signal_pdfs.Get("w_signal")
if "jes" in systematics:
xp_central = signal_vars["xp_0"].getVal()
#print w_signal.pdf("signal__JESUp")
#print signal_fits.get_parameters(w_signal.pdf("signal__JESUp"))
xp_up = signal_fits.get_parameters(w_signal.pdf("signal__JESUp"))["xpJESUp"][0]
xp_down = signal_fits.get_parameters(w_signal.pdf("signal__JESDown"))["xpJESDown"][0]
signal_vars["dxp"].setVal(max(abs(xp_up - xp_central), abs(xp_down - xp_central)))
if signal_vars["dxp"].getVal() > 2 * mass * 0.1:
print "[create_datacards] WARNING : Large dxp value. dxp = {}, xp_down = {}, xp_central = {}, xp_up = {}".format(signal_vars["dxp"].getVal(), xp_down, xp_central, xp_up)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant(False)
else:
signal_vars["dxp"].setVal(0.)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant()
signal_vars["dxp"].setError(0.)
signal_vars["dxp"].setConstant()
if "jer" in systematics:
sigp_central = signal_vars["sigp_0"].getVal()
sigp_up = signal_fits.get_parameters(w_signal.pdf("signal__JERUp"))["sigpJERUp"][0]
sigp_down = signal_fits.get_parameters(w_signal.pdf("signal__JERDown"))["sigpJERDown"][0]
signal_vars["dsigp"].setVal(max(abs(sigp_up - sigp_central), abs(sigp_down - sigp_central)))
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant(False)
else:
signal_vars["dsigp"].setVal(0.)
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant()
signal_vars["dsigp"].setError(0.)
signal_vars["dsigp"].setConstant()
#for variation in ["JERUp", "JERDown"]:
# signal_pdfs_syst[variation] = w_signal.pdf("signal__" + variation)
#for variation, pdf in signal_pdfs_syst.iteritems():
# signal_parameters = pdf.getVariables()
# iter = signal_parameters.createIterator()
# var = iter.Next()
# while var:
# var.setConstant()
# var = iter.Next()
f_signal_pdfs.Close()
else:
# dictionaries holding systematic variations of the signal shape
hSig_Syst = {}
hSig_Syst_DataHist = {}
sigCDF = TGraph(hSig.GetNbinsX()+1)
if "jes" in systematics or "jer" in systematics:
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 "jes" in systematics:
hSig_Syst['JESUp'] = copy.deepcopy(hSig)
hSig_Syst['JESDown'] = copy.deepcopy(hSig)
if "jer" in systematics:
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 "jes" in systematics:
for i in range(1, hSig.GetNbinsX()+1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i+1)
jes = 1. - systematics["jes"]
xLowPrime = jes*xLow
xUpPrime = jes*xUp
hSig_Syst['JESUp'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jes = 1. + systematics["jes"]
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'])
# produce JER signal shapes
if "jer" in systematics:
for i in range(1, hSig.GetNbinsX()+1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i+1)
jer = 1. - systematics["jer"]
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. + systematics["jer"]
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'])
# -----------------------------------------
# create a datacard and corresponding workspace
postfix = (('_' + args.postfix) if args.postfix != '' else '')
wsName = 'workspace_' + args.final_state + '_m' + str(mass) + postfix + '.root'
w = RooWorkspace('w','workspace')
if args.fitSignal:
signal_pdf.SetName("signal")
getattr(w,'import')(signal_pdf,RooFit.Rename("signal"))
# Create a norm variable "signal_norm" which normalizes the PDF to unity.
norm = args.lumi
#signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", 1. / norm, 0.1 / norm, 10. / norm)
#if args.analysis == "trigbbh_CSVTM" and mass >= 1100:
signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", norm/100., norm/100. / 10., norm * 10.)
#else:
# signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", norm, norm / 10., norm * 10.)
print "[create_datacards] INFO : Set signal norm to {}".format(signal_norm.getVal())
signal_norm.setConstant()
getattr(w,'import')(signal_norm,ROOT.RooCmdArg())
#if "jes" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JESUp'],RooFit.Rename("signal__JESUp"))
# getattr(w,'import')(signal_pdfs_syst['JESDown'],RooFit.Rename("signal__JESDown"))
#if "jer" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JERUp'],RooFit.Rename("signal__JERUp"))
# getattr(w,'import')(signal_pdfs_syst['JERDown'],RooFit.Rename("signal__JERDown"))
else:
getattr(w,'import')(rooSigHist,RooFit.Rename("signal"))
if "jes" in systematics:
getattr(w,'import')(hSig_Syst_DataHist['JESUp'],RooFit.Rename("signal__JESUp"))
getattr(w,'import')(hSig_Syst_DataHist['JESDown'],RooFit.Rename("signal__JESDown"))
if "jer" in systematics:
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:
for fit_function in fit_functions:
print "Importing background PDF"
print background_pdfs[fit_function]
background_pdfs[fit_function].Print()
getattr(w,'import')(background_pdfs[fit_function],ROOT.RooCmdArg(),RooFit.Rename("background_" + fit_function), RooFit.RecycleConflictNodes())
w.pdf("background_" + fit_function).Print()
getattr(w,'import')(background_norms[fit_function],ROOT.RooCmdArg(),RooFit.Rename("background_" + fit_function + "_norm"))
getattr(w,'import')(fit_results[fit_function])
getattr(w,'import')(signal_norms[fit_function],ROOT.RooCmdArg())
if args.fitBonly:
getattr(w,'import')(models[fit_function],ROOT.RooCmdArg(),RooFit.RecycleConflictNodes())
getattr(w,'import')(rooDataHist,RooFit.Rename("data_obs"))
w.Print()
print "Starting save"
if args.output_path:
if not os.path.isdir( os.path.join(os.getcwd(),args.output_path) ):
os.mkdir( os.path.join(os.getcwd(),args.output_path) )
print "[create_datacards] INFO : Writing workspace to file {}".format(os.path.join(args.output_path,wsName))
w.writeToFile(os.path.join(args.output_path,wsName))
else:
print "[create_datacards] INFO : Writing workspace to file {}".format(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitBonly=args.fitBonly, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))
w.writeToFile(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitBonly=args.fitBonly, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))
# Clean up
for name, obj in signal_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in fit_results.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, dict_l2 in background_parameters.iteritems():
for name2, obj in dict_l2.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in models.iteritems():
if obj:
obj.IsA().Destructor(obj)
rooDataHist.IsA().Destructor(rooDataHist)
w.IsA().Destructor(w)
# Make datacards only if S+B fitted
if not args.fitBonly:
beffUnc = 0.3
boffUnc = 0.06
for fit_function in fit_functions:
if args.output_path:
dcName = 'datacard_' + args.final_state + '_m' + str(mass) + postfix + '_' + fit_function + '.txt'
print "[create_datacards] INFO : Writing datacard to file {}".format(os.path.join(args.output_path,dcName))
datacard = open(os.path.join(args.output_path,dcName),'w')
else:
print "[create_datacards] INFO : Writing datacard to file {}".format(limit_config.get_datacard_filename(args.analysis, args.model, mass, fit_function, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))
datacard = open(limit_config.get_datacard_filename(args.analysis, args.model, mass, fit_function, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger), 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if ("jes" in systematics or "jer" in systematics) and not args.fitSignal:
if args.output_path:
datacard.write('shapes * * '+wsName+' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write('shapes * * '+os.path.basename(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))+' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
if args.output_path:
datacard.write('shapes * * '+wsName+' w:$PROCESS\n')
else:
datacard.write('shapes * * '+os.path.basename(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))+' w:$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_' + fit_function + '\n')
datacard.write('process 0 1\n')
if args.fitSignal:
datacard.write('rate 1 1\n')
else:
datacard.write('rate -1 1\n')
datacard.write('------------------------------\n')
datacard.write('lumi lnN %f -\n'%(1.+systematics["luminosity"]))
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.fitSignal:
if "jes" in systematics:
datacard.write("alpha_jes param 0.0 1.0\n")
if "jer" in systematics:
datacard.write("alpha_jer param 0.0 1.0\n")
else:
if "jes" in systematics:
datacard.write('JES shape 1 -\n')
if "jer" in systematics:
datacard.write('JER shape 1 -\n')
# flat parameters --- flat prior
datacard.write('background_' + fit_function + '_norm flatParam\n')
if args.decoBkg:
datacard.write('deco_eig1 flatParam\n')
datacard.write('deco_eig2 flatParam\n')
else:
for par_name, par in background_parameters[fit_function].iteritems():
datacard.write(fit_function + "_" + par_name + ' flatParam\n')
datacard.close()
print "[create_datacards] INFO : Done with this datacard"
#print '>> Datacards and workspaces created and stored in %s/'%( os.path.join(os.getcwd(),args.output_path) )
print "All done."
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 getMistagBinBounds(config, mistag, mistagdistrib):
"""
suggest a binning for turning per-event mistag into mistag categories
This routine takes a mistag observable and a PDF or data set, and suggests
a number of mistag category boundaries which have approximately equal
statistics in each category.
config -- config dictionary
mistag -- mistag observable (eta)
mistagdistrib -- a PDF or a RooDataSet
returns a (python) list of mistag category bin bounds
relevant configuration dictionary keys:
'NMistagCategories':
number of mistag categories for which to suggest a set of bin bounds
"""
mistag.setBins(1000, 'MistagBinBounds')
from ROOT import RooArgSet, RooHistPdf, RooDataHist
if (mistagdistrib.InheritsFrom('RooAbsData') and not
mistagdistrib.InheritsFrom('RooDataHist')):
# ok, unbinned data set, get only tagged events, and form a binned clone
argset = RooArgSet(mistag)
mistagdistrib = mistagdistrib.reduce(
RooFit.SelectVars(argset), RooFit.cut('0 != qt'))
ROOT.SetOwnership(mistagdistrib, True)
dhist = RooDataHist(
'%s_binned' % mistagdistrib.GetName(),
'%s_binned' % mistagdistrib.GetName(),
mistagdistrib.get(), 'MistagBinBounds')
dhist.add(mistagdistrib)
mistagdistrib = dhist
if mistagdistrib.InheritsFrom('RooAbsData'):
# convert a binned dataset to a RooHistPdf
dhist = mistagdistrib
mistagdistrib = RooHistPdf('%s_pdf' % dhist.GetName(),
'%s_pdf' % dhist.GetName(), RooArgSet(mistag), dhist)
if (mistagdistrib.InheritsFrom('RooAbsPdf')):
# use createCdf to obtain the CDF
cdfroofit = mistagdistrib.createCdf(
RooArgSet(mistag), RooArgSet(mistag))
ROOT.SetOwnership(cdfroofit, True)
def cdf(x):
oldval = mistag.getVal()
mistag.setVal(x)
retVal = cdfroofit.getVal()
mistag.setVal(oldval)
return retVal
if (mistagdistrib.InheritsFrom('RooHistPdf') and
(abs(cdf(mistag.getMin())) > 1e-9 or
abs(cdf(mistag.getMax()) - 1.) > 1e-9)):
# createCdf does not work properly for RooHistPdf in older ROOT
# versions because RooHistPdf does not support integrals over
# subranges, so we have to fake this functionality until it's
# supported by RooFit upstream
#
# capture histogram bin boundaries and contents
print 'WARNING: Your version of RooFit still has buggy analytical ' \
'integrals for RooHistPdf - activating workaround.'
binboundlist = mistagdistrib.binBoundaries(
mistag, mistag.getMin(), mistag.getMax())
ROOT.SetOwnership(binboundlist, True)
binbounds = [ v for v in binboundlist ]
del binboundlist
bincontents = [ ]
oldval = mistag.getVal()
for i in xrange(0, len(binbounds) - 1):
mistag.setVal(0.5 * (binbounds[i] + binbounds[i + 1]))
bincontents.append(mistagdistrib.getValV(RooArgSet(mistag)))
mistag.setVal(oldval)
# build CDF from histogram
def cdf(x):
s = 0.
for i in xrange(0, len(binbounds) - 1):
if x < binbounds[i]:
break
elif x >= binbounds[i + 1]:
s += bincontents[i]
else:
s += (bincontents[i] * (x - binbounds[i]) /
(binbounds[i + 1] - binbounds[i]))
break
return s
# find x for which f(x) = y by bisection
def mybisect(y, f, lo, hi):
initdx = abs(hi - lo)
flo, fhi = f(lo) - y, f(hi) - y
if 0. == flo: return lo
elif 0. == fhi: return hi
mid = .5 * (lo + hi)
while (abs(hi - lo) > 1e-15 and abs(hi - lo) / initdx > 1e-15):
fmid = f(mid) - y
if 0. == fmid: break
elif flo * fmid < 0.: hi, fhi = mid, fmid
elif fmid * fhi < 0.: lo, flo = mid, fmid
else: raise ValueError('no sign change in f(x) between %g and %g'
% (lo, hi))
mid = .5 * (lo + hi)
return mid
# find binning with roughly same stats by inverting the CDF by bisection
lo, hi, binsum = mistag.getMin(), mistag.getMax(), cdf(mistag.getMax())
retVal = [ lo ]
for i in xrange(1, config['NMistagCategories']):
retVal.append(mybisect(binsum *
float(i) / float(config['NMistagCategories']), cdf, lo, hi))
retVal.append(hi)
print 'INFO: suggested mistag category bounds: %s' % str(retVal)
return retVal
dspihist = dspi_data.fillHistogram(dspihist, RooArgList(time))
dskhist = dsk_data.fillHistogram(dskhist, RooArgList(time))
ratiohist.Divide(dskhist, dspihist)
# relative normalisation
fintegral = ratio.createIntegral(RooArgSet(time), 'fullrange').getVal()
hintegral = ratiohist.Integral('width') # has weights, use width
norm = fintegral / hintegral
print '=' * 5, ' Integrals ', '=' * 5
print 'Function integral / histogram integral = %g / %g = %g' % (
fintegral, hintegral, norm)
ratiohist.Scale(norm)
# make dataset from histogram
ratiodset = RooDataHist('ratiodset', '', RooArgList(time), ratiohist)
ratiodset.Print('v')
## Plot
tframe = time.frame(RooFit.Title('Time acceptance ratio'))
paramset = RooArgSet(rturnon, roffset, rbeta)
ratio.plotOn(tframe, RooFit.VisualizeError(fitresult, paramset, 1, False))
ratio.plotOn(tframe)
ratiodset.plotOn(tframe, RooFit.MarkerStyle(kFullDotMedium))
tframe.Draw()
# Print
if doPrint:
print 'Plotting to file: plots/DsK_ratio_%s.{png,pdf}' % timestamp
def makePlots():
histoFile = TFile('histos_'+sample+'.root')
histos = {}
for key in histoFile.GetListOfKeys():
histos[key.GetName()] = histoFile.Get(key.GetName())
outFile_name = 'plots_'+sample+'.pdf'
c1 = TCanvas('c1', 'c1')
c1.Print(outFile_name+'[')
# Plot comparing m_Phi various method
drawMultiPlot(outFile_name,';m_{KK [MeV]};', 'm_phi',
**{'m_{KK}' : histos['m_KK'],
'm_{#Phi}' : histos['m_Phi'],
'm_{#Phi} DTF #tau' : histos['m_DTFTau_Phi'],
'm_{#Phi} DTF #Phi' : histos['m_DTF_Phi']})
# Plot comparing m_Tau various method
drawMultiPlot(outFile_name, ';m_{KK#mu [MeV]};', 'm_tau',
**{'m_{KK#mu}' : histos['m_KKMu'],
'm_{#Phi#mu}' : histos['m_PhiMu'],
'm_{#tau} DTF #Phi' : histos['m_DTF_Tau']})
# m_KPi and m_PiK
drawMultiPlot(outFile_name, ';m_{K#pi} [MeV];', 'm_KPi',
**{'m_{K#pi}' : histos['m_KPi'],
'm_{#piK}' : histos['m_PiK']})
# m_KKpi and m_PhiPi
drawMultiPlot(outFile_name, ';m_{KK#pi} [MeV];', 'm_KKPi',
**{'m_{KK#pi}' : histos['m_KKPi'],
'm_{#Phi#pi}' : histos['m_PhiPi']})
# m_KPiPi_SS and KPiPi_OS
drawMultiPlot(outFile_name, ';m_{K#pi#pi} [MeV];', 'm_KPiPi',
**{'m_{K^{+}#pi^{-}#pi^{-}}' : histos['m_KPiPi_SS'],
'm_{K^{-}#pi^{+}#pi^{-}}' : histos['m_KPiPi_OS']})
drawMultiPlot(outFile_name, 'not in D peak;m_{K#pi#pi} [MeV];', 'm_KPiPi_noD',
**{'m_{K^{+}#pi^{-}#pi^{-}}' : histos['m_KPiPi_SS_noD'],
'm_{K^{-}#pi^{+}#pi^{-}}' : histos['m_KPiPi_OS_noD']})
#Plot various histos
for key in ('m_PiPi', 'm_PiPi_noD', 'm_KPi_fromMu','m_KPi_fromMu_noD' , 'm_KKPi_D' ,'Tau_DTF_PROB', 'Tau_DTFTau_PROB'):
histos[key].Draw()
c1.Update()
c1.Print(outFile_name)
c1.Print('plots/'+sample+'/'+key+'.pdf')
from ROOT import RooFit, RooRealVar, RooDataHist, RooArgList
# Fit m_DTF_Phi
gStyle.SetOptFit(1111)
histo = histos['m_DTF_Phi']
x = RooRealVar('x', 'm_{#Phi}', 1008,1032,'MeV')
ral = RooArgList(x)
dh = RooDataHist ("dh","dh",ral,RooFit.Import(histo))
frame = x.frame(RooFit.Title('Mass Decay Tree Fitter m_{#Phi} constrained'))
dh.plotOn(frame)
mean = RooRealVar("mean","mean",1020,1010,1025)
sigma = RooRealVar("sigma","sigma",3,0.1,10)
alpha = RooRealVar('alpha', 'alpha', 1, 0.1, 10)
param_n = RooRealVar('param_n','param_n', 2, 0.1, 10)
#pdf = ROOT.RooGaussian("gauss","gauss",x,mean,sigma)
pdf = ROOT.RooBreitWigner('BW','BW',x,mean, sigma)
#pdf = ROOT.RooVoigtian('voit','voit', x, mean, sigma)
#pdf = ROOT.RooCBShape('CB','CB', x, mean, sigma, alpha, param_n)
fit_region = x.setRange("fit_region",1015,1025)
pdf.fitTo(dh, RooFit.Range("fit_region"))
pdf.paramOn(frame, RooFit.Layout(0.1,0.44,0.9))
pdf.plotOn(frame)
chi2 = round(frame.chiSquare(),2)
leg = TLegend(0.3,0,.10,.10)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.AddEntry(0,'#chi^{2} ='+str(chi2),'')
frame.addObject(leg)
frame.Draw()
c1.Update()
c1.Print(outFile_name)
# Fit m_Kpipi
histo = histos['m_KPiPi_SS_noD']
x = RooRealVar('x', 'm_{K#pi#pi}', 1100,1900,'MeV')
ral = RooArgList(x)
dh = RooDataHist ("dh","dh",ral,RooFit.Import(histo))
frame = x.frame(RooFit.Title('Combinatorial mass not in D- region'))
dh.plotOn(frame)
mean = RooRealVar("mean","mean",1510,1100,1900)
sigma = RooRealVar("sigma","sigma",107,0.1,300)
alpha = RooRealVar('alpha', 'alpha', 1, 0.1, 10)
param_n = RooRealVar('param_n','param_n', 2, 0.1, 10)
#pdf = ROOT.RooGaussian("gauss","gauss",x,mean,sigma)
pdf = ROOT.RooCBShape('CB','CB', x, mean, sigma, alpha, param_n)
fit_region = x.setRange("fit_region",1100,1900)
pdf.fitTo(dh, RooFit.Range("fit_region"))
pdf.paramOn(frame, RooFit.Layout(0.1,0.44,0.9))
pdf.plotOn(frame)
chi2 = round(frame.chiSquare(),2)
leg = TLegend(0.3,0,.10,.10)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.AddEntry(0,'#chi^{2} ='+str(chi2),'')
frame.addObject(leg)
frame.Draw()
c1.Update()
c1.Print(outFile_name)
histo.Fit('gaus')
histo.Draw()
c1.Update()
c1.Print(outFile_name)
# # Traditional Root way to fit
# func = TF1('myBW', '[0]*TMath::BreitWigner(x,[1],[2])', 1010,1025)
# # func = TF1('myfunc', '[0]*TMath::Gaus(x,[1],[2])', 1010,1025)
# func = TF1('myfunc', '[0]*TMath::Voigt(x-[1],[2],[3])', 1010,1025)
# func.SetParameter(0,1048)
# func.SetParName(0,'Norm')
# func.SetParameter(1,1020)
# func.SetParName(1,'mean')
# func.SetParameter(2,4)
# func.SetParName(2,'gamma')
# func = TF1('myfunc', '[0]*TMath::Voigt(x-[1],[2],[3])', 1010,1025)
# func.SetParameter(0,1048)
# func.SetParName(0,'Norm')
# func.SetParameter(1,1020)
# func.SetParName(1,'mean')
# func.SetParameter(2,4)
# func.SetParName(2,'sigma')
# func.SetParameter(3,4)
# func.SetParName(2,'gamma')
# histo.Fit('myfunc')
# #histo.Fit('gaus')
# histo.Draw()
# c1.Update()
# c1.Print(outFile_name)
c1.Print(outFile_name+']')
# In[8]:
cheb = RooChebychev("cheb","Background",mass,aset)
gauss = RooGaussian("gauss","gaussian PDF ",mass,mean,sigma)
tot = RooAddPdf("tot","g+cheb",gauss,cheb,sFrac)
# In[9]:
masslist = RooArgList(mass)
dh = RooDataHist("dh","dh",masslist,hist)
numEvts = dh.sum(False)
print numEvts
# In[10]:
tot.fitTo(dh)
# In[11]:
massFrame = mass.frame()
massFrame.SetTitle("Phi signal")
k2=RooRealVar("k2","normalization factor", 1.0, 0.5, 1.5)
k1 =RooRealVar("k1","k1",rttbar,0.,0.5);
#k2 =RooRealVar("k2","k2",rQCD,0.0,0.2);
nttbar =RooRealVar("nttbar","number of ttbar events", n_ttbar , n_ttbar, n_ttbar)
k1nttbar=RooFormulaVar("k1nttbar","number of ttbar events after fitting","k*nttbar",RooArgList(k1,nttbar) )
n_mc = n_ttbar+n_background
nmc = RooRealVar("nmc","number of mc events", n_mc , n_mc, n_mc)
k2nmc = RooFormulaVar("k2nmc","number of mc events"," ",RooArgList(k2,nmc))
#RooRealVar nbackground("nbackground","number of background events", n_background , n_background, n_background);
#RooFormulaVar k2background("k2background","number of background events after fitting","k2*nbackground",RooArgList(k2,nbackground) );
nqcd = RooRealVar("nqcd","number of qcd events", n_qcd , n_qcd, n_qcd)
#k2nqcd = RooFormulaVar("k2nqcd","number of qcd events"," ",RooArgList(k2,nqcd))
data = RooDataHist("data", "data set with (x)", RooArgList(x), h_data_m3HistS)
ttbar = RooDataHist("ttbar", "data set with (x)", RooArgList(x), h_ttbar_m3HistS)
background = RooDataHist("background","data set with (x)", RooArgList(x), h_background_m3Hist)
qcd = RooDataHist("qcd", "data set with (x)", RooArgList(x), h_qcd_data_m3HistS)
# RooHistPdf::RooHistPdf(const char* name, const char* title, const RooArgSet& vars, const RooDataHist& dhist, int intOrder = 0) =>
#print "ttbar type: "+str(type(ttbar))
#print "rooArglist(x):"+str(type(RooArgList(x)))
ttbarpdf = RooHistPdf("ttbarpdf", "ttbarpdf", RooArgList(x), RooArgList(x), ttbar)
backgroundpdf = RooHistPdf("backgroundpdf","backgroundpdf", RooArgList(x), RooArgList(x), background)
qcdpdf = RooHistPdf("qcdpdf", "qcdpdf", RooArgList(x), RooArgList(x), qcd)
#ttbarpdf = RooHistPdf(ttbar,"ttbarpdf")
#backgroundpdf = RooHistPdf(background,"backgroundpdf")
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) )
genFitter.expectedFromPars()
genFitter.resetYields()
genstartpars = genPdf.getParameters(fitter.ws.set('obsSet'))
genFitter.ws.defineSet("params", genstartpars)
genFitter.ws.saveSnapshot("genInitPars", genstartpars)
print "genPars:"
genFitter.ws.set('params').Print('v')
if opts.extendedGen:
data = genPdf.generate(genFitter.ws.set('obsSet'), RooFit.Name('data_obs'),RooFit.Extended())
else:
data = genPdf.generate(genFitter.ws.set('obsSet'), RooFit.Name('data_obs'))
if fitter.pars.binData:
data = RooDataHist('data_obs', 'data_obs', genFitter.ws.set('obsSet'), data)
data.Print('v')
getattr(fitter.ws, 'import')(data)
print 'Generated Data Events: %.0f' % (data.sumEntries())
elif opts.runMCGenToySim:
print "performing Toy Generation from MC:"
print "Generating ", opts.extdiboson, " diboson events"
dibosonfiles = getattr(pars, 'dibosonFiles')
dibosontoymodels = [-1]
dibosonPdf = fitter.makeComponentPdf('dibosontoy',dibosonfiles,dibosontoymodels)
dibosontoymc = dibosonPdf.generate(fitter.ws.set('obsSet'),opts.extdiboson,RooFit.Name('data_obs'))
dibosontoymc.Print()
gentoymc=dibosontoymc
print "Generating ", opts.extWpJ, " WpJ events"
gROOT.ForceStyle()
gROOT.SetStyle('tdrStyle')
filename = 'dijetHisto_data_signal.root'
inf = TFile.Open(filename)
h = inf.Get('h_mjj')
x = RooRealVar('mjj','mjj',900,4500)
NBINS = 180
p1 = RooRealVar('p1','p1',5,0,20)
p2 = RooRealVar('p2','p2',5,0,20)
p3 = RooRealVar('p3','p3',0.1,0,1)
model = RooGenericPdf('model','pow(1-@0/8000,@1)/pow(@0/8000,@2+@3*log(@0/8000))',RooArgList(x,p1,p2,p3))
roohist = RooDataHist('roohist','roohist',RooArgList(x),h)
res = model.fitTo(roohist)
can = TCanvas('can_Mjj_Data','can_Mjj_Data',900,600)
gPad.SetLogy()
can.cd(1).SetBottomMargin(0.4);
frame1 = x.frame()
frame2 = x.frame();
roohist.plotOn(frame1,RooFit.Binning(NBINS))
model.plotOn(frame1)
hpull = frame1.pullHist();
frame2.addPlotable(hpull,'p');
frame1.SetMinimum(0.5)
h_target_residuals_nbp_vs_p_6hit.Fill(momentum, delta_y)
h_gbl_target_residuals_bp_vs_p_6hit.Fill(momentum, gbl_delta_x)
h_gbl_target_residuals_nbp_vs_p_6hit.Fill(momentum, gbl_delta_y)
canvas.Print("track_parameters.pdf[")
#--- Fit the D0 plots ---#
#------------------------#
track_d0 = RooRealVar("track_d0", "D0 (mm)", -1.5, 1.5)
track_d0_mean = RooRealVar("track_d0_mean", "Track D0 Mean", 0, -1.5, 1.5)
track_d0_sigma = RooRealVar("track_d0_sigma", "Track D0 Sigma", .5, 0, 2)
track_d0_gaussian = RooGaussModel("track_d0_gaussian", "Track D0 Gaussian", track_d0, track_d0_mean, track_d0_sigma)
track_d0_data = RooDataHist("track_d0_data", "Track D0 Data", RooArgList(track_d0), h_track_d0)
track_d0_plot = track_d0.frame()
track_d0_plot.SetTitle("")
track_d0_data.plotOn(track_d0_plot, RooFit.MarkerColor(kAzure+2), RooFit.LineColor(kAzure+2))
track_d0_gaussian.fitTo(track_d0_data)
track_d0_gaussian.plotOn(track_d0_plot, RooFit.LineColor(kAzure+2))
gbl_track_d0 = RooRealVar("gbl_track_d0", "D0 (mm)", -1.5, 1.5)
gbl_track_d0_mean = RooRealVar("gbl_track_d0_mean", "Track D0 Mean", 0, -1.5, 1.5)
gbl_track_d0_sigma = RooRealVar("gbl_track_d0_sigma", "Track D0 Sigma", .5, 0, 2)
gbl_track_d0_gaussian = RooGaussModel("gbl_track_d0_gaussian", "Track D0 Gaussian", gbl_track_d0, gbl_track_d0_mean, gbl_track_d0_sigma)
gbl_track_d0_data = RooDataHist("track_d0_data", "Track D0 Data", RooArgList(gbl_track_d0), h_gbl_track_d0)
gbl_track_d0_plot = gbl_track_d0.frame()
gbl_track_d0_plot.SetTitle("")
#tree_sig.Project("hist_mass_1GeV","mjj","deltaETAjj<1.3")
hSig.Print()
hSig.Draw()
#hSig = infSig.Get(inputHistName)
#hSig.Rebin(20)
# -----------------------------------------
# define observable
#test -- restrict mjj range
#x = RooRealVar('mjj','mjj',1500,6000)
#x = RooRealVar('mjj','mjj',1118,6099)
x = RooRealVar('mjj','mjj',1118,9067)
dataHist_data=RooDataHist("RooDataHist","RooDataHist",RooArgList(x),hDat)
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))
qcdhist.Scale(doubleBfake)
sigWhist.Scale(doubleBfake)
sigZhist.Scale(0.1512*doubleBeff+(1.-0.1512)*doubleBfake)
if category=="anti-btag":
qcdhist.Scale(1.-doubleBfake)
sigWhist.Scale(1.-doubleBfake)
sigZhist.Scale(0.1512*(1.-doubleBeff)+(1.-0.1512)*(1.-doubleBfake))
sighist=sigWhist.Clone("sighist")
sighist.Add(sigZhist)
sbhist=hqcd.Clone("sbhist")
sbhist.Add(sighist,signalweight)
if fit=="data":
data=pseudodata[name]
elif fit=="b":
data=RooDataHist("data"+name,"QCD",RooArgList(mass),qcdhist)
elif fit=="sb":
data=RooDataHist("data"+name,"QCD + V+jets",RooArgList(mass),sbhist)
signal=RooDataHist("signal"+name,"signal",RooArgList(mass),sighist)
signalW=RooDataHist("signalW"+name,"signalW",RooArgList(mass),sigWhist)
signalZ=RooDataHist("signalZ"+name,"signalZ",RooArgList(mass),sigZhist)
nsigbkg=RooRealVar("nsigbkg"+name,"number of signal background events",sighist.Integral(),0,10*sighist.Integral())
nbkg=RooRealVar("nbkg"+name,"number of background events",qcdhist.Integral(),0,10*qcdhist.Integral())
nbkg1=RooRealVar("nbkg1"+name,"number of background events",qcdhist.Integral(),0,10*qcdhist.Integral())
nbkg2=RooRealVar("nbkg2"+name,"number of background events",qcdhist.Integral(),0,10*qcdhist.Integral())
l0=RooRealVar("l0","l0",100.,0.,1000.)
l1=RooRealVar("l1","l1",1.,0.,1000.)
l2=RooRealVar("l2","l2",1.,0.,1000.)
#sigbkg=RooPolynomial("sigbkg"+name,"bkg",mass,RooArgList(l0,l1,l2))
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)
signal.plotOn(frame,RooFit.Components('bkg'),RooFit.LineColor(ROOT.kRed),RooFit.LineWidth(2),RooFit.LineStyle(ROOT.kDashed))
frame.GetXaxis().SetRangeUser(900,4500)
frame.GetXaxis().SetTitle('m_{jj} (GeV)')
frame.Draw()
parsSig = signal.getParameters(roohistSig)
parsSig.setAttribAll('Constant', True)
if fitDat:
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", 40, 200)
nsig = RooRealVar("nsig", "#signal events", 200, 0.0, 10000)
nbkg = RooRealVar("nbkg", "#background events", 800, 0.0, 200000)
# 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)
# 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)
# Construct composite pdf
model = RooAddPdf("model", "model", RooArgList(gx, px), 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", 40, 200)
mean_ctl = RooRealVar("mean_ctl", "mean_ctl", mu2, 40, 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, 40, 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")
# 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)
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("px"),
RooFit.ProjWData(RooArgSet(sample), combData),
RooFit.LineStyle(kDashed),
)
# 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),
)
simPdf.plotOn(
frame2,
RooFit.Slice(sample, "control"),
RooFit.Components("gx_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
raw_input()
def fitLandauGaus(hist, full = False):
## c1 = ROOT.TCanvas()
## c1.Divide(2)
## c1.cd(1)
## hist.Draw()
neg_landau = False
if hist.GetMean() < 0.:
neg_landau = True
if neg_landau:
hist = turnHisto(hist)
hist.Rebin(2)
hist.SetTitle('')
hist.SetName('hSignal')
## c1.cd(2)
## hist.Draw('hist')
## c1.SaveAs('foobar.pdf')
### #if neg_landau:
### # func = ROOT.TF1('my_landau','[0] * TMath::Landau(-x,[1],[2])', hist.GetXaxis().GetXmin(), hist.GetXaxis().GetXmax())
### # func.SetParameters(1, hist.GetMean(), hist.GetRMS() )
### #else:
### func = ROOT.TF1('my_landau','[0] * TMath::Landau(x,[1],[2])', hist.GetXaxis().GetXmin(), hist.GetXaxis().GetXmax())
### func.SetParameters(1, hist.GetMean(), hist.GetRMS() )
### hist.Fit('my_landau','q')
### fit_res = []
### fit_res.append(func.GetParameter(0) if not neg_landau else func.GetParameter(0))
### fit_res.append(func.GetParameter(1) if not neg_landau else -1.*func.GetParameter(1))
### fit_res.append(func.GetParameter(2) if not neg_landau else func.GetParameter(2))
### return hist, fit_res
## ROOFIT VERSION
xmin = hist.GetXaxis().GetXmin()
xmax = hist.GetXaxis().GetXmax()
mean = hist.GetMean()
mp = hist.GetXaxis().GetBinCenter(hist.GetMaximumBin())
rms = hist.GetRMS()
flandau = ROOT.TF1('flandau','landau',mp-20,mp+40)
flandau.SetLineWidth(1)
flandau.SetLineColor(ROOT.kBlue)
hist2 = hist.Clone(hist.GetName()+'_2')
hist2.Scale(1./hist2.GetBinContent(hist2.GetMaximumBin()))
hist2.Fit(flandau,'Q','',mp-20,mp+40)
flandau2 = flandau.Clone('flandau2')
flandau2.SetRange(0,500)
flandau2.SetLineStyle(2)
for i in range(flandau.GetNpar()):
flandau2.SetParLimits(i,flandau.GetParameter(i),flandau.GetParameter(i))
hist2.Fit(flandau2,'Q+')#,'same',mp-20,mp+40)
for i in range(flandau.GetNpar()):
hist2.GetFunction('flandau2').SetParameter(i,flandau.GetParameter(i))
for i in range(flandau.GetNpar()):
print flandau.GetParameter(i),flandau2.GetParameter(i)
x = RooRealVar('x', 'signal / adc', 0,500)
x.setRange("signal",mp - 40, mp+90)
x.setRange("draw",0,500)
ral = RooArgList(x)
dh = RooDataHist('dh', 'dh', ral, RooFit.Import(hist))
if full:
ml = RooRealVar('ml', 'mean landau' , mp, mp-20., mp+30)
sl = RooRealVar('sl', 'sigma landau', 10, 1., 25.)
else:
ml = RooRealVar('ml', 'mean landau' , mean, mean-40., mean)
sl = RooRealVar('sl', 'sigma landau', 10., 6., 14.)
landau = RooLandau ('lx', 'lx', x, ml, sl)
mean = 0
if full:
mg = RooRealVar ('mg', 'mean gaus' , 0,0,0)
sg = RooRealVar ('sg', 'sigma gaus', flandau.GetParameter(2), 0.1, 30.)
else:
mg = RooRealVar ('mg', 'mean gaus' , 0,0,0) #mean, mean-30., mean+30.)
sg = RooRealVar ('sg', 'sigma gaus', 2., 0.1, 20.)
gaus = RooGaussian('gx', 'gx', x, mg, sg)
x.setBins(1000,'cache')
## Construct landau (x) gauss
lxg = RooFFTConvPdf('lxg','landau (x) gaus', x, landau, gaus)
lxg.fitTo(dh,RooFit.Range("signal"))
#,RooFit.Normalization(ROOT.RooAbsReal.NumEvent,1))
a = lxg.getParameters(dh)
print 'fit par0 %+6.1f'%flandau.GetParameter(0)
print 'fit par1 %+6.1f'%flandau.GetParameter(1)
print 'fit par2 %+6.1f'%flandau.GetParameter(2)
print 'mp %+6.1f'%mp
print 'rms %+6.1f'%rms
print 'lxg.getParameters(dh).getRealValue(\'ml\'): %+6.1f'% a.getRealValue('ml')
print 'lxg.getParameters(dh).getRealValue(\'sl\'): %+6.1f'% a.getRealValue('sl')
print 'lxg.getParameters(dh).getRealValue(\'sg\'): %+6.1f'% a.getRealValue('sg')
frame = x.frame(RooFit.Title('landau (x) gauss convolution'),RooFit.Range("draw"))
#,RooFit.Normalization(ROOT.RooAbsReal.NumEvent,1))
dh.plotOn(frame,RooFit.Range("draw"))
#,RooFit.Normalization(1./dh.numEntries(),ROOT.RooAbsReal.Raw))
lxg.plotOn(frame,RooFit.LineColor(ROOT.kRed),RooFit.Range("draw"))
#,RooFit.Normalization(1,ROOT.RooAbsReal.Raw))
#lxg.plotOn(frame,RooFit.LineColor(ROOT.kBlue),RooFit.Range("signal"),RooFit.Components('lx,gx'))
# c = ROOT.TCanvas('lg_convolution','landau (x) gaus', 600, 600)
# c.Divide(2)
# c.cd(1)
# hist.Draw()
# c.cd(2)
# ROOT.gPad.SetLeftMargin(0.15)
# frame.GetYaxis().SetTitleOffset(1.4)
# frame.Draw()
# c.SaveAs('histograms/outputhisto'+hist.GetName().split('pz')[1]+'.pdf')
return dh, copy.deepcopy(a), copy.deepcopy(frame),copy.deepcopy(hist2)
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
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')
w = RooWorkspace('w','workspace')
getattr(w,'import')(model)
getattr(w,'import')(roohist)
w.Print()
w.writeToFile('RS'+str(mass)+'_workspace.root')