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 generate_poly(self, b, low, high, nbins, nev):
ws = RooWorkspace('ws', 'ws')
ws.factory('Polynomial::bgPol(mass[200, 500], b[-0.001])')
ws.var('mass').setRange(low, high)
ws.var('b').setVal(b)
_pdf = ws.pdf('bgPol')
ws.var('mass').setBins(nbins)
_ds = _pdf.generateBinned(RooArgSet(ws.var('mass')), nev)
_h = _ds.createHistogram('hBgpol', ws.var('mass'))
return _h
def _runPath(self):
# Hook to registerd file
fileName = self.cfg.get('fileName', "")
if os.path.exists(fileName):
self.ifile = self.process.filemanager.open(str(hex(id(self))), fileName, 'READ') #Open Wspace File #Pritam
# Load wspace
wspaceName = "wspace.{0}.{1}".format(str(self.process.cfg['args'].Year), self.cfg['wspaceTag'])
# print("sourcemanager: ", self.process.sourcemanager.keys())
if wspaceName in self.process.sourcemanager.keys():
self.wspace = self.process.sourcemanager.get(wspaceName)
elif not self.ifile == None:
self.wspace = self.ifile.Get(wspaceName)
# Load desired objects
if self.wspace == None:
self.logger.logINFO("RooWorkspace {0} not found. Create new workspace without booking target objects.".format(wspaceName))
self.wspace = RooWorkspace(wspaceName)
self.cfg['source'][wspaceName] = self.wspace
# Update results only for new workspace.
# By design you CANNOT overwrite items in RooWorkspace by `Write` or `writeToFile`.
self.bookEndSeq()
else:
self.logger.logINFO("RooWorkspace {0} found. Loading objects...".format(wspaceName))
self.cfg['source'][wspaceName] = self.wspace
def readObjs():
"""Exactly define how to read varaiables, read `wspace_key` from wspace and book as `source_key`"""
for source_key, wspace_key in self.cfg['obj'].items():
if source_key in self.process.sourcemanager.keys():
self.cfg['source'][source_key] = self.process.sourcemanager.get(source_key)
else:
obj = self.wspace.obj(wspace_key)
if obj == None:
self.logger.logWARNING("No Variable {0} is found.".format(wspace_key))
else:
self.cfg['source'][source_key] = obj
def readAll(iArgs):
def bookOne(arg):
argName = arg.GetName()
if argName in self.process.sourcemanager:
self.cfg['source'][argName] = self.process.sourcemanager.get(argName)
else:
self.cfg['source'][argName+".{0}".format(str(self.process.cfg['args'].Year))] = arg
FitterCore.ArgLooper(iArgs, bookOne)
if self.cfg['obj']:
readObjs()
else:
readAll(self.wspace.allFunctions())
readAll(self.wspace.allPdfs())
readAll(self.wspace.allVars())
def generate_bw(self, peak, width, low, high, nbins, nev):
ws = RooWorkspace('ws', 'ws')
ws.factory(
'BreitWigner::bw(mass[200, 600], peak[350, 200, 700], width[50])')
ws.var('mass').setRange(low, high)
ws.var('peak').setVal(peak)
ws.var('width').setVal(width)
_bw = ws.pdf('bw')
ws.var('mass').setBins(nbins)
_ds = _bw.generateBinned(RooArgSet(ws.var('mass')), nev)
_h = _ds.createHistogram('hBw', ws.var('mass'))
return _h
def rooFit510():
print ">>> create and fill workspace..."
workspace = RooWorkspace("workspace") # RooWorkspace
fillWorkspace(workspace)
print ">>>\n>>> retrieve model from workspace..."
# Exploit convention encoded in named set "parameters" and "observables"
# to use workspace contents w/o need for introspected
model = workspace.pdf("model") # RooAbsPdf
print ">>> generate and fit data in given observables"
data = model.generate(workspace.set("observables"), 1000) # RooDataSet
model.fitTo(data)
print ">>> plot model and data of first observables..."
frame1 = workspace.set("observables").first().frame() # RooPlot
data.plotOn(frame1, Name("data"), Binning(50))
model.plotOn(frame1, Name("model"))
print ">>> overlay plots with reference parameters as stored in snapshots..."
workspace.loadSnapshot("reference_fit")
model.plotOn(frame1, LineColor(kRed), Name("model_ref"))
workspace.loadSnapshot("reference_fit_bkgonly")
model.plotOn(frame1, LineColor(kRed), LineStyle(kDashed), Name("bkg_ref"))
print "\n>>> draw on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600)
legend = TLegend(0.2, 0.8, 0.4, 0.6)
legend.SetTextSize(0.032)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
gPad.SetLeftMargin(0.15)
gPad.SetRightMargin(0.02)
frame1.GetYaxis().SetLabelOffset(0.008)
frame1.GetYaxis().SetTitleOffset(1.5)
frame1.GetYaxis().SetTitleSize(0.045)
frame1.GetXaxis().SetTitleSize(0.045)
frame1.Draw()
legend.AddEntry("data", "data", 'LEP')
legend.AddEntry("model", "model", 'L')
legend.AddEntry("model_ref", "model fit", 'L')
legend.AddEntry("bkg_ref", "background only fit", 'L')
legend.Draw()
canvas.SaveAs("rooFit510.png")
# Print workspace contents
workspace.Print()
# Workspace will remain in memory after macro finishes
gDirectory.Add(workspace)
def update(self,wsname,fname,storelist=[]):
"""Stores the current model (and observable) into a ROOT.Workspace,
persistified in a root file
Parameters
----------
wsname: str
Name to be given to the ROOT.Workspace
fname: str
Name to be given to the ROOT file
storelist: list(ROOT.RooDataSet,ROOT.RooRealVar,...), optional
List of RooFit objects to be stored in the same workspace
"""
import os
from ROOT import RooWorkspace, TFile
# Create the ws with the model and data available
w = RooWorkspace(wsname,'Workspace')
wsImport = getattr(w,'import')
# Put the models
for rawtuple in self.__models.values():
model = rawtuple[0]
anythingelse = rawtuple[1:]
wsImport(model)
# Put whatever the user want to store
for _item in storelist:
wsImport(_item)
# Check if the fname already exist
file_exist = os.path.isfile(fname)
if file_exist:
# Create a new auxiliar file, to provisionaly
# store the ws
auxfname = '_auxroot.root'
w.writeToFile(auxfname)
# Previously delete the old Workspace, if any
_rootfile = TFile(fname)
_rootfile.Delete(wsname+';*')
_rootfile.Delete("ProcessID*;*")
_rootfile.Close()
# Open the auxiliary file
_aux = TFile(auxfname)
# and copy the ws to the rootfile
w = _aux.Get(wsname)
# and copy the ws to the rootfile
w.writeToFile(fname,False)
if file_exist:
# And closing and removing auxfile
_aux.Close()
os.remove(auxfname)
def rooFit511(compact=kFALSE):
workspace = RooWorkspace("workspace")
print ">>> creating and adding basic pdfs..."
# Remake example pdf of tutorial rf502_wspacewrite.C:
#
# Basic pdf construction: ClassName::ObjectName(constructor arguments)
# Variable construction: VarName[x,xlo,xhi], VarName[xlo,xhi], VarName[x]
# pdf addition: SUM::ObjectName(coef1*pdf1,...coefM*pdfM,pdfN)
if not compact:
# Use object factory to build pdf of tutorial rooFit502_wspacewrite
workspace.factory("Gaussian::sig1(x[-10,10],mean[5,0,10],0.5)")
workspace.factory("Gaussian::sig2(x,mean,1)")
workspace.factory("Chebychev::bkg(x,{a0[0.5,0.,1],a1[-0.2,0.,1.]})")
workspace.factory("SUM::sig(sig1frac[0.8,0.,1.]*sig1,sig2)")
workspace.factory("SUM::model(bkgfrac[0.5,0.,1.]*bkg,sig)")
else:
# Use object factory to build pdf of tutorial rf502_wspacewrite but
# - Contracted to a single line recursive expression,
# - Omitting explicit names for components that are not referred to explicitly later
workspace.factory("SUM::model(bkgfrac[0.5,0.,1.]*Chebychev::bkg(x[-10,10],{a0[0.5,0.,1],a1[-0.2,0.,1.]}),"+\
"SUM(sig1frac[0.8,0.,1.]*Gaussian(x,mean[5,0,10],0.5),Gaussian(x,mean,1)))")
print ">>> advanced pdf constructor arguments..."
# pdf constructor arguments may by any type of RooAbsArg, but also the follow conversion are made:
# Double_t --> RooConst(...)
# {a,b,c} --> RooArgSet() or RooArgList() depending on required ctor arg
# dataset name --> RooAbsData reference for any dataset residing in the workspace
# enum --> any enum label that belongs to an enum defined in the (base) class
print ">>> generate a dummy dataset from 'model' pdf and import it in the workspace..."
data = workspace.pdf("model").generate(RooArgSet(workspace.var("x")),
1000) # RooDataSet
getattr(workspace, "import")(data, Rename("data"))
print ">>> construct keys pdf..."
# Construct a KEYS pdf passing a dataset name and an enum type defining the
# mirroring strategy
workspace.factory("KeysPdf::k(x,data,NoMirror,0.2)")
print ">>> workspace contents:"
workspace.Print()
print ">>> save workspace in memory (gDirectory)..."
gDirectory.Add(workspace)
def fit(self, save_to, signal_name=None, fix_p3=False, fit_range=[300., 1200.], fit_strategy=1):
# Run a RooFit fit
# Create background PDF
p1 = RooRealVar('p1','p1',args.p1,0.,100.)
p2 = RooRealVar('p2','p2',args.p2,0.,60.)
p3 = RooRealVar('p3','p3',args.p3,-10.,10.)
if args.fix_p3:
p3.setConstant()
background_pdf = RooGenericPdf('background_pdf','(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(self.collision_energy,self.collision_energy,self.collision_energy),RooArgList(self.mjj_,p1,p2,p3))
background_pdf.Print()
data_integral = data_histogram.Integral(data_histogram.GetXaxis().FindBin(float(fit_range[0])),data_histogram.GetXaxis().FindBin(float(fit_range[1])))
background_norm = RooRealVar('background_norm','background_norm',data_integral,0.,1e+08)
background_norm.Print()
# Create signal PDF and fit model
if signal_name:
signal_pdf = RooHistPdf('signal_pdf', 'signal_pdf', RooArgSet(self.mjj_), self.signal_roohistograms_[signal_name])
signal_pdf.Print()
signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+05,1e+05)
signal_norm.Print()
model = RooAddPdf("model","s+b",RooArgList(background_pdf,signal_pdf),RooArgList(background_norm,signal_norm))
else:
model = RooAddPdf("model","b",RooArgList(background_pdf),RooArgList(background_norm))
# Run fit
res = model.fitTo(data_, RooFit.Save(kTRUE), RooFit.Strategy(fit_strategy))
# Save to workspace
self.workspace_ = RooWorkspace('w','workspace')
#getattr(w,'import')(background,ROOT.RooCmdArg())
getattr(self.workspace_,'import')(background_pdf,RooFit.Rename("background"))
getattr(self.workspace_,'import')(background_norm,ROOT.RooCmdArg())
getattr(self.workspace_,'import')(self.data_roohistogram_,RooFit.Rename("data_obs"))
getattr(self.workspace_, 'import')(model, RooFit.Rename("model"))
if signal_name:
getattr(self.workspace_,'import')(signal_roohistogram,RooFit.Rename("signal"))
getattr(self.workspace_,'import')(signal_pdf,RooFit.Rename("signal_pdf"))
getattr(self.workspace_,'import')(signal_norm,ROOT.RooCmdArg())
self.workspace_.Print()
self.workspace_.writeToFile(save_to)
if signal_name:
roofit_results[signal_name] = save_to
else:
roofit_results["background"] = save_to
def getWspace(self):
"""Read workspace"""
wspaceName = "wspace.{0}".format(self.cfg.get('wspaceTag', "DEFAULT"))
if wspaceName in self.process.sourcemanager.keys():
wspace = self.process.sourcemanager.get(wspaceName)
else:
if not isDEBUG:
self.logger.logERROR(
"RooWorkspace '{0}' not found".format(wspaceName))
self.logger.logDEBUG(
"Please access RooWorkspace with WspaceReader")
raise RuntimeError
wspace = RooWorkspace(wspaceName)
self.process.sourcemanager.update(wspaceName, wspace)
wspace.addClassDeclImportDir(modulePath) #+ '/cpp')
wspace.addClassImplImportDir(modulePath) #+ '/cpp')
return wspace
def create_master_workspaces(meta_data):
pwd = gDirectory.GetPath()
ws_list = {}
for (sample,chanlist) in meta_data.getAssociation().iteritems():
ws_list[sample] = {}
for (channel,proclist) in chanlist.iteritems():
#make the workspace we're going to use
this_ws = RooWorkspace('%s-%s'%(channel,sample))
initialize_workspace(this_ws)
ws_list[sample][channel] = {}
for (process,subproclist) in proclist.iteritems():
print sample, channel, process
if 'HToZG'in process:
ws_list[sample][channel][process] = []
for (subproc,info) in subproclist.iteritems():
print '\tprocessing: %s'%subproc
input_file = info['input_file']
info['num_mc_events'] = -1
if input_file == '':
print '\t no input file found! Skipping!'
continue
if 'data' not in process:
print '\t mc input = %s'%input_file.split('/')[-1]
if 'HToZG' in subproc:
info['num_mc_events'] = \
extract_higgs_data_in_categories(subproc,
input_file,
this_ws)
ws_list[sample][channel][process].append(subproc)
else:
info['num_mc_events'] = \
extract_bkg_data_in_categories(subproc,
input_file,
this_ws)
else:
print '\t data input = %s'%input_file.split('/')[-1]
extract_data_in_categories(channel,input_file,this_ws)
#end loop over processes and data
fout_name = '%s_%s_master_workspace.root'%(channel,sample)
fout = TFile.Open(fout_name,'recreate')
fout.cd()
this_ws.Write()
fout.Close()
gDirectory.cd(pwd)
ws_list[sample][channel]['filename'] = fout_name
return ws_list
def Split_DS(Save_DS=False):
ws_file = TFile(
"/afs/cern.ch/user/" + prefix + "/" + user + "/eos/lhcb/user/" +
prefix + "/" + user +
"/WrongSign/2015/WorkSpaces/Merged_Merged_WS.root", "read")
wsp = ws_file.Get("wspace")
ws_file.Close()
LOG_D0_IPCHI2_OWNPV = wsp.var("LOG_D0_IPCHI2_OWNPV")
Dst_DTF_D0_CTAU = wsp.var("Dst_DTF_D0_CTAU")
Dst_DTF_D0_M = wsp.var("Dst_DTF_D0_M")
DTF_D0sPi_M = wsp.var("DTF_D0sPi_M")
DTF_D0sPi_M.setMax(2020)
DTF_D0sPi_M.setMin(2000)
dataset_RS_tot = wsp.data("dataset_RS")
dataset_RS_tot.SetName("dataset_RS_tot")
varset = RooArgSet("varset")
varset.add(LOG_D0_IPCHI2_OWNPV)
varset.add(DTF_D0sPi_M)
varset.add(Dst_DTF_D0_CTAU)
varset.add(Dst_DTF_D0_M)
for i, bin in enumerate(decaytime_binnning):
start = datetime.now()
dataset_RS_dtb_init = RooDataSet(
"dataset_RS_dtb_init", "Decaytime bin" + str(i), dataset_RS_tot,
varset, "Dst_DTF_D0_CTAU>" + str(bin[0] * ctau) +
"&&Dst_DTF_D0_CTAU<" + str(bin[1] * ctau) + "&&" + offline_cut)
dataset_RS = Subtract_Distribution(dataset_RS_dtb_init, DTF_D0sPi_M,
LOG_D0_IPCHI2_OWNPV, str(i))
dataset_RS.SetName("dataset_RS")
wspace = RooWorkspace("wspace")
wsfile2 = TFile(
"~/eos/lhcb/user/" + prefix + "/" + user +
"/WrongSign/2015/WorkSpaces/Merged_WS_Bin_" + str(i) + ".root",
"recreate")
wspace.rfimport(varset)
wspace.rfimport(dataset_RS)
wspace.Write("wspace")
wsfile2.Close()
print "Dataset " + str(i) + " creation took " + str(datetime.now() -
start) + " \n"
return True
def main(options, args):
gROOT.Reset()
#load our super special Polarization PDF
gROOT.ProcessLine('.L RooPolarizationPdf.cxx+')
gROOT.ProcessLine('.L RooPolarizationConstraint.cxx+')
#setup integration
intConf = ROOT.RooAbsReal.defaultIntegratorConfig()
#intConf.Print('v')
# intConf.method1D().setLabel('RooAdaptiveGaussKronrodIntegrator1D')
intConf.setEpsAbs(1e-13)
intConf.setEpsRel(1e-13)
print intConf.epsAbs()
print intConf.epsRel()
# intConf.method2D().setLabel('RooMCIntegrator')
# intConf.methodND().setLabel('RooMCIntegrator')
output = TFile.Open(options.workspaceName + '.root', 'RECREATE')
theWS = RooWorkspace(options.workspaceName, 1)
#save the polarization PDF code in the RooWorkspace
theWS.importClassCode('RooPolarization*', True)
buildDataAndCategories(theWS, options, args)
buildMassAndLifetimePDF(theWS)
# if options.fitFrame is not None:
# buildPolarizationPDF(theWS,options)
#root is stupid
output.cd()
theWS.Print('v')
ROOT.RooMsgService.instance().Print()
doFit(theWS, options)
theWS.Write()
output.Close()
def __init__ (self, pars):
self.pars = pars
self.ws = RooWorkspace('wjj2dfitter')
self.utils = Wjj2DFitterUtils(self.pars)
self.useImportPars = False
self.rangeString = None
obs = []
for v in self.pars.var:
try:
vName = self.pars.varNames[v]
except AttributeError:
vName = v
obs.append(vName)
var1 = self.ws.factory('%s[%f,%f]' % (vName,
self.pars.varRanges[v][1],
self.pars.varRanges[v][2])
)
var1.setUnit('GeV')
try:
var1.SetTitle(self.pars.varTitles[v])
except AttributeError:
var1.SetTitle('m_{jj}')
var1.setPlotLabel(var1.GetTitle())
if len(self.pars.varRanges[v][3]) > 1:
vbinning = RooBinning(len(self.pars.varRanges[v][3]) - 1,
array('d', self.pars.varRanges[v][3]),
'%sBinning' % vName)
var1.setBinning(vbinning)
else:
var1.setBins(self.pars.varRanges[v][0])
var1.Print()
if v in self.pars.exclude:
var1.setRange('signalRegion', self.pars.exclude[v][0],
self.pars.exclude[v][1])
var1.setRange('lowSideband', var1.getMin(),
self.pars.exclude[v][0])
var1.setRange('highSideband', self.pars.exclude[v][1],
var1.getMax())
self.rangeString = 'lowSideband,highSideband'
self.ws.defineSet('obsSet', ','.join(obs))
def rooFit502():
print ">>> setup model components..."
x = RooRealVar("x", "x", 0, 10)
mean = RooRealVar("mean", "mean of gaussians", 5, 0, 10)
sigma1 = RooRealVar("sigma1", "width of gaussians", 0.5)
sigma2 = RooRealVar("sigma2", "width of gaussians", 1)
sig1 = RooGaussian("sig1", "Signal component 1", x, mean, sigma1)
sig2 = RooGaussian("sig2", "Signal component 2", x, mean, sigma2)
a0 = RooRealVar("a0", "a0", 0.5, 0., 1.)
a1 = RooRealVar("a1", "a1", -0.2, 0., 1.)
bkg = RooChebychev("bkg", "Background", x, RooArgList(a0, a1))
print ">>> sum model components..."
sig1frac = RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8,
0., 1.)
sig = RooAddPdf("sig", "Signal", RooArgList(sig1, sig2),
RooArgList(sig1frac))
bkgfrac = RooRealVar("bkgfrac", "fraction of background", 0.5, 0., 1.)
model = RooAddPdf("model", "g1+g2+a", RooArgList(bkg, sig),
RooArgList(bkgfrac))
print ">>> generate data..."
data = model.generate(RooArgSet(x), 1000) # RooDataSet
print ">>> create workspace, import data and model..."
workspace = RooWorkspace("workspace", "workspace") # empty RooWorkspace
getattr(workspace, 'import')(model) # import model and all its components
getattr(workspace, 'import')(data) # import data
#workspace.import(model) # causes synthax error in python
#workspace.import(data) # causes synthax error in python
print "\n>>> print workspace contents:"
workspace.Print()
print "\n>>> save workspace in file..."
workspace.writeToFile("rooFit502_workspace.root")
print ">>> save workspace in memory (gDirectory)..."
gDirectory.Add(workspace)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c2.Print(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_SignalNorm.pdf")
c2.Print(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("Zprime_"+YEAR, "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m], RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile(WORKDIR+"MC_signal_%s_%s.root" % (YEAR, category), True)
print "Workspace", WORKDIR+"MC_signal_%s_%s.root" % (YEAR, category), "saved successfully"
def drawPlot(name, channel, variable, model, dataset, fitRes=[], norm=-1, reg=None, cat="", alt=None, anorm=-1, signal=None, snorm=-1):
isData = norm>0
isMass = "Mass" in name
isSignal = '_M' in name
mass = name[8:12]
isCategory = reg is not None
#isBottomPanel = not isSignal
vtb = 0.999105 # http://pdg.lbl.gov/2018/reviews/rpp2018-rev-ckm-matrix.pdf
vts_term_bWsW = 2 * (vts**2) * (vtb**2) # + vts**4
vts_term_bWbW = (vtb**2)**2
f = ROOT.TFile(
"/cms/ldap_home/wjjang/wj_nanoAOD_CMSSW_9_4_4/src/nano/analysis/test/vts/tmva/output/vts_dR_04_Jet.root"
)
t = f.Get(str(sys.argv[1]) + "/Method_BDT/BDT")
t2 = f.Get(str(sys.argv[2]) + "/Method_BDT/BDT")
h_sig = t.Get("MVA_BDT_S")
h_bkg = t.Get("MVA_BDT_B")
h_sig2 = t2.Get("MVA_BDT_S")
h_bkg2 = t2.Get("MVA_BDT_B")
w = RooWorkspace()
c = ROOT.TCanvas("plots", "plots", 1920, 1080)
c.Divide(4, 2)
w.factory("bdt[-1,1]")
rooh_sig = ROOT.RooDataHist("sigHist", "sigHist",
ROOT.RooArgList(w.var("bdt")), h_sig)
rooh_bkg = ROOT.RooDataHist("bkgHist", "bkgHist",
ROOT.RooArgList(w.var("bdt")), h_bkg)
getattr(w, 'import')(rooh_sig)
getattr(w, 'import')(rooh_bkg)
if t.GetPath().find("_JKS_") is not -1 or t.GetPath().find("JKS_") is not -1:
w.factory(
"Gaussian::sig_1(bdt, meanSig1[-0.0317964,-1, 1.], sigmaSig1[0.0654243,0, 1])"
)
def main(o, args):
variables = options.variables
procs = options.procs
cats = options.cats
print "****variables***"
print variables
#from configfile import variables, procs, cats
if 0 and options.files.startswith("/store"):
filepath = "root://eoscms/" + str(options.files)
else:
filepath = options.files
print 'filepath is ' + str(filepath)
infile = TFile.Open(filepath)
infile.Print()
wspace = infile.Get("tagsDumper/cms_hgg_13TeV")
wspace.Print()
isData = False
dname = ""
print variables
for varset in variables:
print 'we look at the variable ...'
print varset
if not varset[-1]:
#varset[-1]=varset[-2]
varset = (varset[0], varset[1], varset[2], varset[2])
if not varset[1]:
varset = (varset[0], varset[0], varset[2], [1, 1])
isData = True
datasetsReduced = {}
c = TCanvas("c", "", 1)
binning1 = varset[-2]
binning1[-1] = binning1[-2] * 1.2
print binning1
binning2 = varset[-1]
binning2[-1] = binning2[-2] * 1.2
print binning2
th2sigtot = TH2D(
"signalEv_" + str(varset[0]) + "_vs_" + str(varset[1]) + "_all",
"signalEv_" + str(varset[0]) + "_vs_" + str(varset[1]) + "_all",
len(binning1) - 1, np.asarray(binning1),
len(binning2) - 1, np.asarray(binning2))
th2sigtot.GetXaxis().SetTitle(str(varset[0]))
th2sigtot.GetYaxis().SetTitle(str(varset[1]))
for proc in procs:
for cat in cats:
print "reading ds " + str(proc) + "_" + str(cat) + " from ws"
dataset = wspace.data(str(proc) + "_" + str(cat))
dataset.Print()
th2sig = TH2D(
"signalEv_" + str(varset[0]) + "_vs_" + str(varset[1]) +
"_" + str(proc) + "_" + str(cat),
"signalEv_" + str(varset[0]) + "_vs_" + str(varset[1]) +
"_" + str(proc) + "_" + str(cat),
len(binning1) - 1, np.asarray(binning1),
len(binning2) - 1, np.asarray(binning2))
th2sig.GetXaxis().SetTitle(str(varset[0]))
th2sig.GetYaxis().SetTitle(str(varset[1]))
for bound1 in range(len(varset[-2]) - 1):
for bound2 in range(len(varset[-1]) - 1):
if isData:
dname = str(proc) + "_" + str(
cat) + "_" + varset[0] + "_" + str(
varset[-2][bound1]) + "to" + str(
varset[-2][bound1 + 1])
else:
# dname = str(proc)+"_"+str(cat)+"_"+varset[0]+"_"+str(varset[-2][bound1])+"to"+str(varset[-2][bound1+1])+"_Vs_"+varset[1]+"_"+str(varset[-1][bound2])+"to"+str(varset[-1][bound2+1])
dname = str(
proc.split('_', 1)[0]
) + "_" + varset[0] + "_" + str(
varset[-2][bound1]
) + "to" + str(varset[-2][bound1 + 1]) + "_" + str(
proc.split('_', 1)[1]) + "_" + str(
cat) + "_" + varset[1] + "_" + str(
varset[-1][bound2]) + "to" + str(
varset[-1][bound2 + 1])
datRed = dataset.Clone(dname)
datRed.reset()
datasetsReduced[dname] = datRed
val = 0.
maxEntries = dataset.numEntries()
if options.maxEntries != -1:
maxEntries = options.maxEntries
for i in range(maxEntries):
if i % 10000 == 0:
print i
iset = dataset.get(i)
val1 = iset.getRealValue(varset[0])
val2 = iset.getRealValue(varset[1])
# print val1,val2
for bound1, bound2 in (
(b1, b2) for b1 in range(len(varset[-2]) - 1)
for b2 in range(len(varset[-1]) - 1)):
if isData:
selCondition = (val1 > varset[-2][bound1]) and (
val1 < varset[-2][bound1 + 1])
dname = str(proc) + "_" + str(
cat) + "_" + varset[0] + "_" + str(
varset[-2][bound1]) + "to" + str(
varset[-2][bound1 + 1])
else:
selCondition = (val1 > varset[-2][bound1]) and (
val1 < varset[-2][bound1 + 1]) and (
val2 > varset[-1][bound2]) and (
val2 < varset[-1][bound2 + 1])
# dname = str(proc)+"_"+str(cat)+"_"+varset[0]+"_"+str(varset[-2][bound1])+"to"+str(varset[-2][bound1+1])+"_Vs_"+varset[1]+"_"+str(varset[-1][bound2])+"to"+str(varset[-1][bound2+1])
dname = str(
proc.split('_', 1)[0]
) + "_" + varset[0] + "_" + str(
varset[-2][bound1]
) + "to" + str(varset[-2][bound1 + 1]) + "_" + str(
proc.split('_', 1)[1]) + "_" + str(
cat) + "_" + varset[1] + "_" + str(
varset[-1][bound2]) + "to" + str(
varset[-1][bound2 + 1])
if selCondition:
# print "filling dataset"
## print dataset.weight()
datasetsReduced[dname].add(iset, dataset.weight())
break
for bound1 in range(len(varset[-2]) - 1):
for bound2 in range(len(varset[-1]) - 1):
if isData:
dname = str(proc) + "_" + str(
cat) + "_" + varset[0] + "_" + str(
varset[-2][bound1]) + "to" + str(
varset[-2][bound1 + 1])
else:
dname = str(
proc.split('_', 1)[0]
) + "_" + varset[0] + "_" + str(
varset[-2][bound1]
) + "to" + str(varset[-2][bound1 + 1]) + "_" + str(
proc.split('_', 1)[1]) + "_" + str(
cat) + "_" + varset[1] + "_" + str(
varset[-1][bound2]) + "to" + str(
varset[-1][bound2 + 1])
print "binning1 ", float(binning1[bound1]) + (
float(binning1[bound1 + 1]) -
float(binning1[bound1])) / 2.
print binning1[bound1 + 1]
print binning1[bound1]
print "cat1 ", (float(varset[-2][bound1 + 1]) -
float(varset[-2][bound1])) / 2.
print "binning2 ", float(binning2[bound2]) + (
float(binning2[bound2 + 1]) -
float(binning2[bound2])) / 2.
print binning2[bound2 + 1]
print binning2[bound2]
print "cat2 ", (float(varset[-1][bound2 + 1]) -
float(varset[-1][bound2])) / 2.
print "sumentries ", datasetsReduced[
dname].sumEntries()
th2sig.Fill(
float(binning1[bound1]) +
(float(binning1[bound1 + 1]) -
float(binning1[bound1])) / 2.,
float(binning2[bound2]) +
(float(binning2[bound2 + 1]) -
float(binning2[bound2])) / 2.,
datasetsReduced[dname].sumEntries())
th2sig.Print("V")
c.cd()
if options.logz:
c.SetLogz(1)
th2sigtot.Add(th2sig)
th2sig.Draw("colz")
for fmt in savefmts:
savename = th2sig.GetName()
if options.logz:
savename = str(savename) + "_logz"
c.SaveAs(str(savename) + str(fmt))
c.cd()
if options.logz:
c.SetLogz(1)
th2sigtot.Draw("colz")
savename = th2sigtot.GetName()
if options.logz:
savename = str(savename) + "_logz"
for fmt in savefmts:
c.SaveAs(str(savename) + str(fmt))
# print "#### Compare reductions ####"
new_wspace = RooWorkspace("cms_hgg_13TeV")
getattr(new_wspace, 'import')(wspace.var("CMS_hgg_mass"))
getattr(new_wspace, 'import')(wspace.var("IntLumi"))
if not isData:
getattr(new_wspace, 'import')(wspace.var("dZ"))
getattr(new_wspace, 'import')(wspace.var("weight"))
# alldata = new_wspace.allData()
# for ds in alldata :
# new_wspace.removeSet(str(ds.GetName()))
outfilename = options.outfile.split('.')[0] + '_' + str(
varset[0]) + '_3th2.root'
outfile = TFile(outfilename, 'RECREATE')
stepsize = int(len(datasetsReduced.keys()) / 10)
iteration = 0
while (len(datasetsReduced.keys()) > 0):
print 'iteration ' + str(iteration)
iteration = iteration + 1
try:
outfile
except NameError:
outfile = TFile(outfilename, 'UPDATE')
try:
new_wspace
except NameError:
outfile.ls()
new_wspace = outfile.Get("cms_hgg_13TeV")
print "cms_hgg_13TeV;1"
gDirectory.Delete("cms_hgg_13TeV;1")
outfile.ls()
# if not 'outfile' in globals():
# print 'outfile is not defined'
# outfile = TFile(options.outfile, 'UPDATE')
# if not 'new_wspace' in globals():
# new_wspace = outfile.Get("cms_hgg_13TeV")
# print 'newspace is not defined'
written = []
if stepsize > len(datasetsReduced.keys()):
stepsize = len(datasetsReduced.keys())
for ikey in range(stepsize):
# for ikey in range(len(datasetsReduced.keys())):
# dataset.Print("V")
# if dataset.numEntries() > 100:
getattr(new_wspace, 'import')(
datasetsReduced[datasetsReduced.keys()[ikey]])
datasetsReduced[datasetsReduced.keys()[ikey]].Write()
written.append(datasetsReduced.keys()[ikey])
print 'Number of data in ws '
print len(new_wspace.allData())
#new_wspace.Print()
new_wspace.Write()
outfile.Write()
outfile.Close()
for wkey in written:
del datasetsReduced[wkey]
del new_wspace
del outfile
def makeCard(TYPE, mZp, mChi, DIR):
# Setup input and output files
indir = DIR
old_str = ""
new_str = ""
if (TYPE == "BARY"):
old_str = "sig_ZpBaryonic_mZP10_mChi1"
new_str = "sig_ZpBaryonic_mZP" + mZp + "_mChi" + mChi
if (TYPE == "2HDM"):
old_str = "sig_2HDM_mZP600_mA0300"
new_str = "sig_2HDM_mZP" + mZp + "_mA0" + mChi
fin = open(indir + "dataCard_" + old_str + "_13TeV.txt", "r")
fout = open(indir + "dataCard_" + new_str + "_13TeV.txt", "w")
rin = ROOT.TFile(indir + old_str + "_13TeV.root")
rout = ROOT.TFile(indir + new_str + "_13TeV.root", "RECREATE")
# Copy the datacard for new mass point
for line in fin:
if old_str in line:
line = line.replace(old_str, new_str)
fout.write(line)
# Get the old and new eff
mZ = float(mZp)
mDM = float(mChi)
old_efflowMET = 1.0
old_effhighMET = 1.0
new_efflowMET = 1.0
new_effhighMET = 1.0
if (TYPE == "BARY"):
old_efflowMET = getEffBary(0, 10, 1)
old_effhighMET = getEffBary(1, 10, 1)
new_efflowMET = getEffBary(0, mZ, mDM)
new_effhighMET = getEffBary(1, mZ, mDM)
if (TYPE == "2HDM"):
old_efflowMET = getEff2HDM(0, 600, 300)
old_effhighMET = getEff2HDM(1, 600, 300)
new_efflowMET = getEff2HDM(0, mZ, mDM)
new_effhighMET = getEff2HDM(1, mZ, mDM)
scale_lowMET = new_efflowMET / old_efflowMET
scale_highMET = new_effhighMET / old_effhighMET
#print("Old eff: low = %f, high = %f" %(old_efflowMET,old_effhighMET))
#print("New eff: low = %f, high = %f" %(new_efflowMET,new_effhighMET))
#print("Scale: low = %f, high = %f" %(scale_lowMET,scale_highMET))
# Copy the input file
in_TObjString = TObjString(rin.cfg)
out_TObjString = in_TObjString.Clone()
in_RooWorkspace = RooWorkspace(rin.wtemplates)
#in_RooWorkspace.Print() # print obj in input rooWorkspace
w1 = ROOT.RooWorkspace("wtemplates")
w1.rooImport = getattr(w1, 'import')
var1 = in_RooWorkspace.var('mgg')
var2 = in_RooWorkspace.var('model_signal_' + old_str +
'_13TeV_met0-130_norm')
var3 = in_RooWorkspace.var('model_signal_' + old_str +
'_13TeV_met130_norm')
# multiply old normalization by new scale
valnorm_lowMET = scale_lowMET * var2.getValV()
valnorm_highMET = scale_highMET * var3.getValV()
norm1 = RooRealVar("model_signal_" + new_str + "_13TeV_met0-130_norm",
"model_signal" + new_str + "13TeV_met0-130_norm",
valnorm_lowMET)
norm2 = RooRealVar("model_signal_" + new_str + "_13TeV_met130_norm",
"model_signal" + new_str + "13TeV_met130_norm",
valnorm_highMET)
varlist = ROOT.RooArgList(var1, norm1, norm2)
#print("%f * %f" %(scale_lowMET,var2.getValV()))
#print("%f" %valnorm_lowMET)
#print("%f" %norm1.getValV())
# get old pdfs and change names
pdf1 = in_RooWorkspace.pdf('model_signal_' + old_str + '_13TeV_met0-130')
pdf2 = in_RooWorkspace.pdf('model_signal_' + old_str +
'_13TeV_met0-130_energyScalemet0-130Down')
pdf3 = in_RooWorkspace.pdf('model_signal_' + old_str +
'_13TeV_met0-130_energyScalemet0-130Up')
pdf4 = in_RooWorkspace.pdf('model_signal_' + old_str + '_13TeV_met130')
pdf5 = in_RooWorkspace.pdf('model_signal_' + old_str +
'_13TeV_met130_energyScalemet130Down')
pdf6 = in_RooWorkspace.pdf('model_signal_' + old_str +
'_13TeV_met130_energyScalemet130Up')
pdf1new = ROOT.RooHistPdf(pdf1,
"model_signal_" + new_str + "_13TeV_met0-130")
pdf2new = ROOT.RooHistPdf(
pdf2,
"model_signal_" + new_str + "_13TeV_met0-130_energyScalemet0-130Down")
pdf3new = ROOT.RooHistPdf(
pdf3,
"model_signal_" + new_str + "_13TeV_met0-130_energyScalemet0-130Up")
pdf4new = ROOT.RooHistPdf(pdf4,
"model_signal_" + new_str + "_13TeV_met130")
pdf5new = ROOT.RooHistPdf(
pdf5,
"model_signal_" + new_str + "_13TeV_met130_energyScalemet130Down")
pdf6new = ROOT.RooHistPdf(
pdf6, "model_signal_" + new_str + "_13TeV_met130_energyScalemet130Up")
# these are null pointers -- probably don't have right classes (missing from dipho analysis) to read them
# but they are also not needed for running higgs combine so left out for now
dat1 = in_RooWorkspace.data('signal_' + old_str + '_13TeV_met130')
dat2 = in_RooWorkspace.data('signalforPdf_' + old_str + '_13TeV_met130')
dat3 = in_RooWorkspace.data('signal_' + old_str + '_13TeV_met0-130')
dat4 = in_RooWorkspace.data('signalforPdf_' + old_str + '_13TeV_met0-130')
#print("%f" %dat1.sumEntries())
# Write to output file
#out_TObjString.Write()
w1.rooImport(var1)
w1.rooImport(norm1)
w1.rooImport(norm2)
w1.rooImport(pdf1new)
w1.rooImport(pdf2new)
w1.rooImport(pdf3new)
w1.rooImport(pdf4new)
w1.rooImport(pdf5new)
w1.rooImport(pdf6new)
#w1.Print() # print contents of workspace
w1.Write()
rout.Close()
def signal(category):
interPar = True
n = len(genPoints)
cColor = color[category] if category in color else 4
nBtag = category.count('b')
isAH = False #relict from using Alberto's more complex script
if not os.path.exists(PLOTDIR + "MC_signal_" + YEAR):
os.makedirs(PLOTDIR + "MC_signal_" + YEAR)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar("jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV")
j1_pt = RooRealVar("jpt_1", "jet1 pt", 0., 13000., "GeV")
jj_deltaEta = RooRealVar("jj_deltaEta_widejet", "", 0., 5.)
jbtag_WP_1 = RooRealVar("jbtag_WP_1", "", -1., 4.)
jbtag_WP_2 = RooRealVar("jbtag_WP_2", "", -1., 4.)
fatjetmass_1 = RooRealVar("fatjetmass_1", "", -1., 2500.)
fatjetmass_2 = RooRealVar("fatjetmass_2", "", -1., 2500.)
jid_1 = RooRealVar("jid_1", "j1 ID", -1., 8.)
jid_2 = RooRealVar("jid_2", "j2 ID", -1., 8.)
jnmuons_1 = RooRealVar("jnmuons_1", "j1 n_{#mu}", -1., 8.)
jnmuons_2 = RooRealVar("jnmuons_2", "j2 n_{#mu}", -1., 8.)
jnmuons_loose_1 = RooRealVar("jnmuons_loose_1", "jnmuons_loose_1", -1., 8.)
jnmuons_loose_2 = RooRealVar("jnmuons_loose_2", "jnmuons_loose_2", -1., 8.)
nmuons = RooRealVar("nmuons", "n_{#mu}", -1., 10.)
nelectrons = RooRealVar("nelectrons", "n_{e}", -1., 10.)
HLT_AK8PFJet500 = RooRealVar("HLT_AK8PFJet500", "", -1., 1.)
HLT_PFJet500 = RooRealVar("HLT_PFJet500", "", -1., 1.)
HLT_CaloJet500_NoJetID = RooRealVar("HLT_CaloJet500_NoJetID", "", -1., 1.)
HLT_PFHT900 = RooRealVar("HLT_PFHT900", "", -1., 1.)
HLT_AK8PFJet550 = RooRealVar("HLT_AK8PFJet550", "", -1., 1.)
HLT_PFJet550 = RooRealVar("HLT_PFJet550", "", -1., 1.)
HLT_CaloJet550_NoJetID = RooRealVar("HLT_CaloJet550_NoJetID", "", -1., 1.)
HLT_PFHT1050 = RooRealVar("HLT_PFHT1050", "", -1., 1.)
#HLT_DoublePFJets100_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets100_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. )
#HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. )
#HLT_DoublePFJets200_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets200_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets350_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets350_CaloBTagDeepCSV_p71" , "", -1., 1. )
#HLT_DoublePFJets40_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets40_CaloBTagDeepCSV_p71" , "", -1., 1. )
weight = RooRealVar("eventWeightLumi", "", -1.e9, 1.e9)
# Define the RooArgSet which will include all the variables defined before
# there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
variables = RooArgSet(X_mass)
variables.add(
RooArgSet(j1_pt, jj_deltaEta, jbtag_WP_1, jbtag_WP_2, fatjetmass_1,
fatjetmass_2, jnmuons_1, jnmuons_2, weight))
variables.add(
RooArgSet(nmuons, nelectrons, jid_1, jid_2, jnmuons_loose_1,
jnmuons_loose_2))
variables.add(
RooArgSet(HLT_AK8PFJet500, HLT_PFJet500, HLT_CaloJet500_NoJetID,
HLT_PFHT900, HLT_AK8PFJet550, HLT_PFJet550,
HLT_CaloJet550_NoJetID, HLT_PFHT1050))
#variables.add(RooArgSet(HLT_DoublePFJets100_CaloBTagDeepCSV_p71, HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets200_CaloBTagDeepCSV_p71, HLT_DoublePFJets350_CaloBTagDeepCSV_p71, HLT_DoublePFJets40_CaloBTagDeepCSV_p71))
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", X_mass.getMin(), X_mass.getMax())
if VARBINS:
binsXmass = RooBinning(len(abins) - 1, abins)
X_mass.setBinning(binsXmass)
plot_binning = RooBinning(
int((X_mass.getMax() - X_mass.getMin()) / 100.), X_mass.getMin(),
X_mass.getMax())
else:
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin()) / 10))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin()) / 100.),
X_mass.getMin(), X_mass.getMax())
plot_binning = binsXmass
X_mass.setBinning(plot_binning, "PLOT")
#X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/10))
#binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
#X_mass.setBinning(binsXmass, "PLOT")
massArg = RooArgSet(X_mass)
# Cuts
if BTAGGING == 'semimedium':
SRcut = aliasSM[category]
#SRcut = aliasSM[category+"_vetoAK8"]
else:
SRcut = alias[category].format(WP=working_points[BTAGGING])
#SRcut = alias[category+"_vetoAK8"].format(WP=working_points[BTAGGING])
if ADDSELECTION: SRcut += SELECTIONS[options.selection]
print " Cut:\t", SRcut
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
valpha2 = {}
vslope2 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_jes = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p1_scale_jes",
"Variation of the resonance position with the jet energy scale", 0.02,
-1., 1.) #0.001
smean_jes = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p1_jes",
"Change of the resonance position with the jet energy scale", 0., -10,
10)
xsigma_jer = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p2_scale_jer",
"Variation of the resonance width with the jet energy resolution",
0.10, -1., 1.)
ssigma_jer = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p2_jer",
"Change of the resonance width with the jet energy resolution", 0.,
-10, 10)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
for m in massPoints:
signalMass = "%s_M%d" % (stype, m)
signalName = "ZpBB_{}_{}_M{}".format(YEAR, category, m)
sampleName = "ZpBB_M{}".format(m)
signalColor = sample[sampleName][
'linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m,
m * 0.96, m * 1.05)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)",
RooArgList(vmean[m], xmean_jes, smean_jes))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma",
m * 0.0233, m * 0.019, m * 0.025)
ssigma[m] = RooFormulaVar(
signalName + "_sigma", "@0*(1+@1*@2)",
RooArgList(vsigma[m], xsigma_jer, ssigma_jer))
valpha1[m] = RooRealVar(
signalName + "_valpha1", "Crystal Ball alpha 1", 0.2, 0.05, 0.28
) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0",
RooArgList(valpha1[m]))
#vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope 1", 10., 0.1, 20.) # slope of the power tail
vslope1[m] = RooRealVar(signalName + "_vslope1",
"Crystal Ball slope 1", 13., 10.,
20.) # slope of the power tail
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0",
RooArgList(vslope1[m]))
valpha2[m] = RooRealVar(signalName + "_valpha2",
"Crystal Ball alpha 2", 1.)
valpha2[m].setConstant(True)
salpha2[m] = RooFormulaVar(signalName + "_alpha2", "@0",
RooArgList(valpha2[m]))
#vslope2[m] = RooRealVar(signalName + "_vslope2", "Crystal Ball slope 2", 6., 2.5, 15.) # slope of the higher power tail
## FIXME test FIXME
vslope2_estimation = -5.88111436852 + m * 0.00728809389442 + m * m * (
-1.65059568762e-06) + m * m * m * (1.25128996309e-10)
vslope2[m] = RooRealVar(signalName + "_vslope2",
"Crystal Ball slope 2", vslope2_estimation,
vslope2_estimation * 0.9, vslope2_estimation *
1.1) # slope of the higher power tail
## FIXME end FIXME
sslope2[m] = RooFormulaVar(
signalName + "_slope2", "@0",
RooArgList(vslope2[m])) # slope of the higher power tail
signal[m] = RooDoubleCrystalBall(signalName,
"m_{%s'} = %d GeV" % ('X', m), X_mass,
smean[m], ssigma[m], salpha1[m],
sslope1[m], salpha2[m], sslope2[m])
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName + "_yield", "signalYield", 50,
0., 1.e15)
signalNorm[m] = RooRealVar(signalName + "_norm", "signalNorm", 1., 0.,
1.e15)
signalXS[m] = RooRealVar(signalName + "_xs", "signalXS", 1., 0., 1.e15)
signalExt[m] = RooExtendPdf(signalName + "_ext", "extended p.d.f",
signal[m], signalYield[m])
# ---------- if there is no simulated signal, skip this mass point ----------
if m in genPoints:
if VERBOSE: print " - Mass point", m
# define the dataset for the signal applying the SR cuts
treeSign[m] = TChain("tree")
if YEAR == 'run2':
pd = sample[sampleName]['files']
if len(pd) > 3:
print "multiple files given than years for a single masspoint:", pd
sys.exit()
for ss in pd:
if not '2016' in ss and not '2017' in ss and not '2018' in ss:
print "unknown year given in:", ss
sys.exit()
else:
pd = [x for x in sample[sampleName]['files'] if YEAR in x]
if len(pd) > 1:
print "multiple files given for a single masspoint/year:", pd
sys.exit()
for ss in pd:
if options.unskimmed:
j = 0
while True:
if os.path.exists(NTUPLEDIR + ss + "/" + ss +
"_flatTuple_{}.root".format(j)):
treeSign[m].Add(NTUPLEDIR + ss + "/" + ss +
"_flatTuple_{}.root".format(j))
j += 1
else:
print "found {} files for sample:".format(j), ss
break
else:
if os.path.exists(NTUPLEDIR + ss + ".root"):
treeSign[m].Add(NTUPLEDIR + ss + ".root")
else:
print "found no file for sample:", ss
if treeSign[m].GetEntries() <= 0.:
if VERBOSE:
print " - 0 events available for mass", m, "skipping mass point..."
signalNorm[m].setVal(-1)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
salpha1[m].setConstant(True)
sslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
continue
#setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar("eventWeightLumi*BTagAK4Weight_deepJet"), RooFit.Import(treeSign[m]))
setSignal[m] = RooDataSet("setSignal_" + signalName, "setSignal",
variables, RooFit.Cut(SRcut),
RooFit.WeightVar(weight),
RooFit.Import(treeSign[m]))
if VERBOSE:
print " - Dataset with", setSignal[m].sumEntries(
), "events loaded"
# FIT
entries = setSignal[m].sumEntries()
if entries < 0. or entries != entries: entries = 0
signalYield[m].setVal(entries)
# Instead of eventWeightLumi
#signalYield[m].setVal(entries * LUMI / (300000 if YEAR=='run2' else 100000) )
if treeSign[m].GetEntries(SRcut) > 5:
if VERBOSE: print " - Running fit"
frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1),
RooFit.Extended(True),
RooFit.SumW2Error(True),
RooFit.PrintLevel(-1))
if VERBOSE:
print "********** Fit result [", m, "] **", category, "*" * 40, "\n", frSignal[
m].Print(), "\n", "*" * 80
if VERBOSE: frSignal[m].correlationMatrix().Print()
drawPlot(signalMass + "_" + category, stype + category, X_mass,
signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in category", category, "has 0 entries or does not exist"
# Remove HVT cross sections
#xs = getCrossSection(stype, channel, m)
xs = 1.
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(
massArg, RooFit.NormSet(massArg),
RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if boundaryFactor < 0. or boundaryFactor != boundaryFactor:
boundaryFactor = 0
if VERBOSE:
print " - Fit normalization vs integral:", signalYield[
m].getVal(), "/", boundaryFactor, "events"
signalNorm[m].setVal(boundaryFactor * signalYield[m].getVal() /
signalXS[m].getVal()
) # here normalize to sigma(X) x Br = 1 [fb]
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
valpha2[m].setConstant(True)
vslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
#*******************************************************#
# #
# Signal interpolation #
# #
#*******************************************************#
### FIXME FIXME just for a test FIXME FIXME
#print
#print
#print "slope2 fit results:"
#print
#y_vals = []
#for m in genPoints:
# y_vals.append(vslope2[m].getVal())
#print "m =", genPoints
#print "y =", y_vals
#sys.exit()
### FIXME FIXME test end FIXME FIXME
# ====== CONTROL PLOT ======
color_scheme = [
636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635,
634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633,
632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633
]
c_signal = TCanvas("c_signal", "c_signal", 800, 600)
c_signal.cd()
frame_signal = X_mass.frame()
for j, m in enumerate(genPoints):
if m in signalExt.keys():
#print "color:",(j%9)+1
#print "signalNorm[m].getVal() =", signalNorm[m].getVal()
#print "RooAbsReal.NumEvent =", RooAbsReal.NumEvent
signal[m].plotOn(
frame_signal, RooFit.LineColor(color_scheme[j]),
RooFit.Normalization(signalNorm[m].getVal(),
RooAbsReal.NumEvent),
RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 10000)
frame_signal.Draw()
drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
#drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c_signal.SaveAs(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" +
category + "_Signal.pdf")
c_signal.SaveAs(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" +
category + "_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 700, 3000)
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 10000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 10000)
fsigma.SetLineColor(2)
fsigma.SetFillColor(2)
# Alpha1
galpha1 = TGraphErrors()
galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha")
galpha1.SetMarkerStyle(20)
galpha1.SetMarkerColor(cColor)
galpha1.SetLineColor(cColor)
ialpha1 = TGraphErrors()
ialpha1.SetMarkerStyle(24)
falpha1 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha1.SetLineColor(2)
falpha1.SetFillColor(2)
# Slope1
gslope1 = TGraphErrors()
gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)")
gslope1.SetMarkerStyle(20)
gslope1.SetMarkerColor(cColor)
gslope1.SetLineColor(cColor)
islope1 = TGraphErrors()
islope1.SetMarkerStyle(24)
fslope1 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope1.SetLineColor(2)
fslope1.SetFillColor(2)
# Alpha2
galpha2 = TGraphErrors()
galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha")
galpha2.SetMarkerStyle(20)
galpha2.SetMarkerColor(cColor)
galpha2.SetLineColor(cColor)
ialpha2 = TGraphErrors()
ialpha2.SetMarkerStyle(24)
falpha2 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha2.SetLineColor(2)
falpha2.SetFillColor(2)
# Slope2
gslope2 = TGraphErrors()
gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)")
gslope2.SetMarkerStyle(20)
gslope2.SetMarkerColor(cColor)
gslope2.SetLineColor(cColor)
islope2 = TGraphErrors()
islope2.SetMarkerStyle(24)
fslope2 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope2.SetLineColor(2)
fslope2.SetFillColor(2)
n = 0
for i, m in enumerate(genPoints):
if not m in signalNorm.keys(): continue
if signalNorm[m].getVal() < 1.e-6: continue
if gnorm.GetMaximum() < signalNorm[m].getVal():
gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
#gnorm.SetPointError(i, 0, signalNorm[m].getVal()/math.sqrt(treeSign[m].GetEntriesFast()))
gmean.SetPoint(n, m, vmean[m].getVal())
gmean.SetPointError(n, 0,
min(vmean[m].getError(), vmean[m].getVal() * 0.02))
gsigma.SetPoint(n, m, vsigma[m].getVal())
gsigma.SetPointError(
n, 0, min(vsigma[m].getError(), vsigma[m].getVal() * 0.05))
galpha1.SetPoint(n, m, valpha1[m].getVal())
galpha1.SetPointError(
n, 0, min(valpha1[m].getError(), valpha1[m].getVal() * 0.10))
gslope1.SetPoint(n, m, vslope1[m].getVal())
gslope1.SetPointError(
n, 0, min(vslope1[m].getError(), vslope1[m].getVal() * 0.10))
galpha2.SetPoint(n, m, salpha2[m].getVal())
galpha2.SetPointError(
n, 0, min(valpha2[m].getError(), valpha2[m].getVal() * 0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(
n, 0, min(vslope2[m].getError(), vslope2[m].getVal() * 0.10))
#tmpVar = w.var(var+"_"+signalString)
#print m, tmpVar.getVal(), tmpVar.getError()
n = n + 1
gmean.Fit(fmean, "Q0", "SAME")
gsigma.Fit(fsigma, "Q0", "SAME")
galpha1.Fit(falpha1, "Q0", "SAME")
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
# gnorm.Fit(fnorm, "Q0", "", 700, 5000)
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
#gnorm.Fit(fnorm, "Q", "SAME", 700, 6000)
gnorm.Fit(fnorm, "Q", "SAME", 1800, 8000) ## adjusted recently
for m in massPoints:
if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.)
# Interpolation method
syield = gnorm.Eval(m)
spline = gnorm.Eval(m, 0, "S")
sfunct = fnorm.Eval(m)
#delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield))
delta = abs(1. - spline / sfunct) if sfunct > 0 else 0
syield = spline
if interPar:
#jmean = gmean.Eval(m)
#jsigma = gsigma.Eval(m)
#jalpha1 = galpha1.Eval(m)
#jslope1 = gslope1.Eval(m)
#jalpha2 = galpha2.Eval(m)
#jslope2 = gslope2.Eval(m)
jmean = gmean.Eval(m, 0, "S")
jsigma = gsigma.Eval(m, 0, "S")
jalpha1 = galpha1.Eval(m, 0, "S")
jslope1 = gslope1.Eval(m, 0, "S")
jalpha2 = galpha2.Eval(m, 0, "S")
jslope2 = gslope2.Eval(m, 0, "S")
else:
jmean = fmean.GetParameter(
0) + fmean.GetParameter(1) * m + fmean.GetParameter(2) * m * m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(
1) * m + fsigma.GetParameter(2) * m * m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(
1) * m + falpha1.GetParameter(2) * m * m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(
1) * m + fslope1.GetParameter(2) * m * m
jalpha2 = falpha2.GetParameter(0) + falpha2.GetParameter(
1) * m + falpha2.GetParameter(2) * m * m
jslope2 = fslope2.GetParameter(0) + fslope2.GetParameter(
1) * m + fslope2.GetParameter(2) * m * m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(max(0., syield))
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1 == 0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
ialpha2.SetPoint(ialpha2.GetN(), m, jalpha2)
if not jalpha2 == 0: valpha2[m].setVal(jalpha2)
islope2.SetPoint(islope2.GetN(), m, jslope2)
if jslope2 > 0: vslope2[m].setVal(jslope2)
#### newly introduced, not yet sure if helpful:
vmean[m].removeError()
vsigma[m].removeError()
valpha1[m].removeError()
valpha2[m].removeError()
vslope1[m].removeError()
vslope2[m].removeError()
#signalNorm[m].setConstant(False) ## newly put here to ensure it's freely floating in the combine fit
#c1 = TCanvas("c1", "Crystal Ball", 1200, 1200) #if not isAH else 1200
#c1.Divide(2, 3)
c1 = TCanvas("c1", "Crystal Ball", 1800, 800)
c1.Divide(3, 2)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
galpha1.GetYaxis().SetRangeUser(0., 1.1) #adjusted upper limit from 5 to 2
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
gslope1.GetYaxis().SetRangeUser(0.,
150.) #adjusted upper limit from 125 to 60
if True: #isAH:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
galpha2.GetYaxis().SetRangeUser(0., 2.)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(category)
drawCMS(-1, "Simulation Preliminary", year=YEAR) ## new FIXME
gslope2.GetYaxis().SetRangeUser(0., 20.)
c1.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalShape.pdf")
c1.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0] - 100, genPoints[-1] + 100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum() * 1.25)
drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
#drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c2.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalNorm.pdf")
c2.Print(PLOTDIR + "MC_signal_" + YEAR + "/" + stype + "_" + category +
"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("Zprime_" + YEAR, "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m],
RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile(WORKDIR + "MC_signal_%s_%s.root" % (YEAR, category), True)
print "Workspace", WORKDIR + "MC_signal_%s_%s.root" % (
YEAR, category), "saved successfully"
#tree.Add("/afs/cern.ch/user/d/demattia/public/TagAndProbe/TagAndProbe_ZMuMu.root")
tree.Add(
"/afs/cern.ch/user/d/demattia/public/TagAndProbe/TagAndProbe_Run2012A.root"
)
tree.Add(
"/afs/cern.ch/user/d/demattia/public/TagAndProbe/TagAndProbe_Run2012B.root"
)
tree.Add(
"/afs/cern.ch/user/d/demattia/public/TagAndProbe/TagAndProbe_Run2012C.root"
)
tree.Add(
"/afs/cern.ch/user/d/demattia/public/TagAndProbe/TagAndProbe_Run2012D.root"
)
# Prepare the workspace
ws = RooWorkspace("ws", "workspace")
Workspace.buildPdf(ws, p)
mass = ws.var("mass")
sample = ws.cat("sample")
simPdf = ws.pdf("simPdf")
efficiency = ws.var("efficiency")
meanB = ws.var("meanB")
# prepare_datasets(ws, p)
# Prepare datasets
datasetAllMap = {}
datasetPassMap = {}
hAllMap = {}
hPassMap = {}
binWidth = 1.
if opts.sipm:
# maxMip = int(maxPed + pedRms*80) + 0.5
maxMip = int(maxPed + pedRms * 100) + 0.5
binWidth = 5.
else:
maxMip = int(maxPed + pedRms * 20) + 0.5
while int((maxMip - minMip) / binWidth) * binWidth < (maxMip - minMip):
maxMip += 1
Nbins = int((maxMip - minMip) / binWidth + 0.5)
print 'ped min: {0:0.2f} max: {1:0.2f}'.format(minPed,maxPed), \
'sig min: {0:0.1f} max: {1:0.1f}'.format(minMip, maxMip)
ws = RooWorkspace('ws')
x = RooRealVar('x', 'energy', minMip, maxMip, 'fC')
dataTree.Draw(
'{0}>>ped_hist({1},{2:0.1f},{3:0.1f})'.format(HOTower,
int(maxPed - minPed), minPed,
maxPed), pedCut, 'goff')
ped_hist = gDirectory.Get('ped_hist')
ped_hist.SetLineColor(myBlue)
xfped = x.frame(minPed, maxPed, int(maxPed - minPed))
if havePeds:
pedDS = fillDataSet(dataTree.GetV1(), x, dataTree.GetSelectedRows())
getattr(ws, 'import')(pedDS)
if pedDS.numEntries() < 5:
hData)
rooDataHist.Print()
if args.runFit:
res = model.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
if not args.decoBkg: res.Print()
# decorrelated background parameters for Bayesian limits
if args.decoBkg:
signal_norm.setConstant()
res = model.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
res.Print()
## temp workspace for the PDF diagonalizer
w_tmp = RooWorkspace("w_tmp")
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"
#space.loadSnapshot(snapshot)
return None
def recTime(mesg):
global currenttime
rectime = currenttime
currenttime = time.time()
txtRecFile.write('{0} in {1} minutes\n'.format(
mesg, int((currenttime - rectime) / 60)))
# def functions end }}}
recTime('def funcs')
space = RooWorkspace('space', False)
# new parameters
space.factory('lbl0Mass[5.4,5.9]')
space.factory('tktkMass[0.5,2.0]')
space.factory('lbl0Pt[0.,200.]')
########## load workspace ####################
workspaceFile1 = TFile.Open('store_root/workspace_0thStep_LbL0Shape.root')
space1st = workspaceFile1.Get('space')
space1st.SetName('space1st')
spaceExt = space1st
load2016Data = True
if load2016Data:
toyCheck = False
RooRealVar(varName,
tree1.GetBranch(varName).GetTitle(),
tree1.GetMinimum(varName), tree1.GetMaximum(varName))
]
varRenamedList += [RooFit.RenameVariable(varName, tupleDict[varName])]
#RooFit.RenameVariable(varName, tupleDict[varName]);
#varList[i].SetName(tupleDict.keys()[i]);
#tupleDataSet = RooDataSet("treeData","treeData",tree1,RooArgSet(*varList));
weightvar = RooRealVar("weight", "weight", -1e7, 1e7)
tupleDataSet = RooDataSet("tupleDataSet", "tupleDataSet",
RooArgSet(*varList), RooFit.Import(tree1),
RooFit.WeightVar(weightvar))
ws = RooWorkspace('ws_FIT')
tupleDataSet = WS(ws, tupleDataSet, varRenamedList)
tupleDataSet.Print()
sys.exit(0)
#qt needs to be a category?
# Manuel's shit...
#weightvar = RooRealVar("weight", "weight", -1e7, 1e7)
#tupleDataSet = RooDataSet("tupleDataSet", "tupleDataSet",
# RooArgSet(treetime, treeqt, treeqf, treeeta),
# RooFit.Import(tree1), RooFit.WeightVar(weightvar))
#tupleDS = WS(ws, tupleDS, [
# RooFit.RenameVariable("Bs_ctau", "time"), ... ])
# # note: do not forget to add to fitTo options: RooFit.SumW2Error(True)
#ROOT.SetOwnership(tupleDataSet,False);
dcFN = outdir_datacards + 'Qstar' + str(mass) + '_datacard_nuisance.txt'
wsFN = outdir_datacards + 'Qstar' + str(mass) + '_workspace_nuisance.root'
#dcFN = outdir_datacards+'Qstar'+str(mass)+'_datacard_nuisance_testForSignificance.txt'
#wsFN = outdir_datacards+'Qstar'+str(mass)+'_workspace_nuisance_testForSignificance.root'
else:
dcFN = outdir_datacards + 'Qstar' + str(
mass) + '_datacard_' + name + '.txt'
wsFN = outdir_datacards + 'Qstar' + str(
mass) + '_workspace_' + name + '.root'
nObs = dataHist_data.sumEntries()
#nObs = roohistBkg.sumEntries();
#nBkg = roohistBkg.sumEntries();
w = RooWorkspace('w', 'workspace')
getattr(w, 'import')(signal)
getattr(w, 'import')(background)
getattr(w, 'import')(background_norm)
getattr(w, 'import')(roohistBkg, RooFit.Rename("data_obs"))
#getattr(w,'import')(dataHist_data,RooFit.Rename("data_obs"))
#getattr(w,'import')(background_f6)
#getattr(w,'import')(background_f6_norm)
w.Print()
w.writeToFile(wsFN)
# -----------------------------------------
# write a datacard
##not needed
#ExpectedSignalRate = signalCrossSection*LUMI*signalEfficiency
def getCard(sig, ch, ifilename, outdir, mode="histo", unblind=False):
try:
ifile = ROOT.TFile.Open(ifilename)
except IOError:
print "Cannot open ", ifilename
else:
print "Opening file ", ifilename
ifile.cd()
print syst
workdir_ = ifilename.split("/")[:-1]
WORKDIR = "/".join(workdir_) + "/"
carddir = outdir + "/" + sig + "/"
hist_filename = os.getcwd() + "/" + ifilename
hist = getHist(ch, sig, ifile)
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
if (mode == "template"):
histBkgData = getHist(ch, "Bkg", ifile)
histData = histBkgData
if (unblind):
print "BE CAREFULL: YOU ARE UNBLINDING"
histData = getHist(ch, "data_obs", ifile)
print "*********Number of data ", histData.Integral()
histSig = getHist(ch, sig, ifile)
bkgData = RooDataHist("bkgdata", "Data (MC Bkg)", RooArgList(mT),
histBkgData, 1.)
obsData = RooDataHist("data_obs", "(pseudo) Data", RooArgList(mT),
histData, 1.)
sigData = RooDataHist("sigdata", "Data (MC sig)", RooArgList(mT),
histSig, 1.)
print "Bkg Integral: ", histData.Integral()
nBkgEvts = histBkgData.Integral()
print "Bkg Events: ", nBkgEvts
print "Channel: ", ch
modelBkg = fitParam[ch].modelBkg
normzBkg = RooRealVar(modelBkg.GetName() + "_norm",
"Number of background events", nBkgEvts, 0.,
1.e3)
print "NormBkg ", nBkgEvts
modelExt = RooExtendPdf(modelBkg.GetName() + "_ext",
modelBkg.GetTitle(), modelBkg, normzBkg)
# create workspace
w = RooWorkspace("SVJ", "workspace")
# Dataset
# ATT: include isData
getattr(w, "import")(bkgData, RooFit.Rename("Bkg"))
getattr(w, "import")(obsData, RooFit.Rename("data_obs"))
getattr(w, "import")(sigData, RooFit.Rename(sig))
for i in xrange(hist.GetNbinsX()):
mcstatSysName = "mcstat_%s_%s_bin%d" % (ch, sig, i + 1)
#print mcstatSysName
#print sig + "_" + mcstatSysName + "Up"
mcstatSigUp = getHist(ch, sig + "_" + mcstatSysName + "Up", ifile)
#print "Integral ", mcstatSigUp.Integral()
mcstatSigDown = getHist(ch, sig + "_" + mcstatSysName + "Down",
ifile)
mcstatSigHistUp = RooDataHist(sig + "_" + mcstatSysName + "Up",
"Data (MC sig)", RooArgList(mT),
mcstatSigUp, 1.)
mcstatSigHistDown = RooDataHist(sig + "_" + mcstatSysName + "Down",
"Data (MC sig)", RooArgList(mT),
mcstatSigDown, 1.)
getattr(w,
"import")(mcstatSigHistUp,
RooFit.Rename(sig + "_" + mcstatSysName + "Up"))
getattr(w, "import")(mcstatSigHistDown,
RooFit.Rename(sig + "_" + mcstatSysName +
"Down"))
for sysName, sysValue in syst.iteritems():
if (sysValue[0] == "shape" and "mcstat" not in sysName):
sysUp = getHist(ch, sig + "_" + sysName + "Up", ifile)
sysDown = getHist(ch, sig + "_" + sysName + "Down", ifile)
print "==> Trigg sys name: ", sig + "_" + sysName + "Down"
sysSigHistUp = RooDataHist(sig + "_" + sysName + "Up",
sysName + " uncertainty",
RooArgList(mT), sysUp, 1.)
sysSigHistDown = RooDataHist(sig + "_" + sysName + "Down",
sysName + " uncertainty",
RooArgList(mT), sysDown, 1.)
getattr(w, "import")(sysSigHistUp,
RooFit.Rename(sig + "_" + sysName + "Up"))
getattr(w,
"import")(sysSigHistDown,
RooFit.Rename(sig + "_" + sysName + "Down"))
#else: getattr(w, "import")(setToys, RooFit.Rename("data_obs"))
getattr(w, "import")(modelBkg, RooFit.Rename(modelBkg.GetName()))
#getattr(w, "import")(modelAlt, RooFit.Rename(modelAlt.GetName()))
getattr(w, "import")(normzBkg, RooFit.Rename(normzBkg.GetName()))
w.writeToFile("%sws_%s_%s_%s.root" % (carddir, sig, ch, mode), True)
print "Workspace", "%sws_%s_%s_%s.root" % (carddir, sig, ch,
mode), "saved successfully"
workfile = "./ws_%s_%s_%s.root" % (sig, ch, mode)
# ====== END MODEL GENERATION ======
rates = {}
procLine = ""
procNumbLine = ""
rateLine = ""
binString = ""
if (mode == "template"):
processes.append("Bkg")
processes[:-1] = []
rates["Bkg"] = nBkgEvts
procLine += ("%-43s") % ("Bkg")
rateLine += ("%-43s") % (rates["Bkg"])
binString += (("%-43s") % (ch)) * (2)
procNumbLine = 1
else:
i = 1
bkgrate = 0
print "===> Backgrounds: ", processes
nproc = (len(processes) + 1)
for p in processes:
print "======================= p for rate", p, " syst, ", syst
print "ch is ", ch, " process is ", p, " ifile is ", ifile.GetName(
)
rates[p] = getRate(ch, p, ifile)
bkgrate = rates[p]
if (p == "QCD"): print "qcd: ", bkgrate
if (bkgrate == 0):
nproc = nproc - 1
continue
procNumbLine += ("%-43s") % (i)
procLine += ("%-43s") % (p)
rateLine += ("%-43.2f") % (bkgrate)
i += 1
binString += (("%-43s") % (ch)) * (nproc)
if ((not unblind) and (mode == "template")):
print "N.B: We are in blind mode. Using MC bkg data for data_obs"
rates["data_obs"] = getRate(ch, "Bkg", ifile)
print "Pseudo data rate: ", rates["data_obs"]
else:
rates["data_obs"] = getRate(ch, "data_obs", ifile)
rates[sig] = getRate(ch, sig, ifile)
card = "imax 1 number of channels \n"
card += "jmax * number of backgrounds \n"
card += "kmax * number of nuisance parameters\n"
card += "-----------------------------------------------------------------------------------\n"
if (mode == "template"):
# card += "shapes %s %s %s %s %s\n" % (sig, ch, ifilename, "$CHANNEL/$PROCESS", "$CHANNEL/$PROCESS_SYSTEMATIC")
# card += "shapes %-15s %-5s %s%s.root %s\n" % (sig, ch, WORKDIR, ch, "SVJ:$PROCESS")
card += "shapes %s %s %s %s\n" % (modelBkg.GetName(), ch,
workfile, "SVJ:$PROCESS")
card += "shapes %s %s %s %s %s\n" % (
sig, ch, workfile, "SVJ:$PROCESS", "SVJ:$PROCESS_$SYSTEMATIC")
card += "shapes %s %s %s %s\n" % ("data_obs", ch, workfile,
"SVJ:$PROCESS")
else:
card += "shapes * * %s %s %s\n" % (
hist_filename, "$CHANNEL/$PROCESS",
"$CHANNEL/$PROCESS_$SYSTEMATIC")
card += "shapes data_obs * %s %s\n" % (hist_filename,
"$CHANNEL/$PROCESS")
card += "-----------------------------------------------------------------------------------\n"
card += "bin %s\n" % ch
print "===> Observed data: ", rates["data_obs"]
card += "observation %0.d\n" % (rates["data_obs"])
card += "-----------------------------------------------------------------------------------\n"
card += "bin %-43s\n" % (binString)
card += "process %-43s%-43s\n" % (
sig, procLine) #"roomultipdf"
card += "process %-43s%-43s\n" % (
"0", procNumbLine)
card += "rate %-43.6f%-43s\n" % (
rates[sig], rateLine) #signalYield[m].getVal(), nevents
card += "-----------------------------------------------------------------------------------\n"
for sysName, sysValue in syst.iteritems():
print "Systematic Uncertainty: ", sysName
if ("2016" in sysName and "2016" not in ch): continue
elif ("2017" in sysName and "2017" not in ch): continue
elif ("2018" in sysName and "2018" not in ch): continue
if ("mu" in sysName and "mu" not in ch): continue
elif ("ele" in sysName and "ele" not in ch): continue
if (sysValue[0] == "lnN"):
card += "%-20s%-20s" % (sysName, sysValue[0])
if (sysValue[1] == "all" and len(sysValue) > 2):
if (mode == "template"): card += "%-20s" % (sysValue[2]) * (2)
else: card += "%-20s" % (sysValue[2]) * (len(processes) + 1)
elif (sysValue[1] == "QCD" and len(sysValue) > 2):
if (mode == "template"):
card += "%-20s" % (sysValue[2]) * (2)
else:
card += "%-20s" % (sysValue[2]) * (len(processes) + 1)
else:
if (sysValue[1] == "all"):
sysValue[1] = copy.deepcopy(processes)
sysValue[1].append(sig)
hsysName = "_" + sysName
hsysNameUp = "_" + sysName + "Up"
hsysNameDown = "_" + sysName + "Down"
#print "Applying syst on ", sysValue[1]
if ("sig" in sysValue[1]):
if (getRate(ch, sig, ifile) != 0.):
sigSys = abs((getRate(ch, sig + hsysNameUp, ifile) -
getRate(ch, sig + hsysNameDown, ifile)) /
(2 * getRate(ch, sig, ifile)))
else:
sigSys = 1
if (sigSys < 1. and sigSys > 0.): sigSys = sigSys + 1
card += "%-20s" % (sigSys)
else:
card += "%-20s" % ("-")
for p in processes:
if (p in sysValue[1]):
if (getRate(ch, p, ifile) != 0.):
bkgSys = abs(
(getRate(ch, p + hsysNameUp, ifile) -
getRate(ch, p + hsysNameDown, ifile)) /
(2 * getRate(ch, p, ifile)))
else:
bkgSys = 1
if (bkgSys < 1. and bkgSys > 0.): bkgSys = bkgSys + 1
card += "%-20s" % (bkgSys)
else:
card += "%-20s" % ("-")
elif (sysValue[0] == "shape"):
print "sys shape named ", sysName
if ("mcstat" not in sysName and 'autoMCstat' not in sysName):
card += "%-20s shape " % (sysName)
isbogussys = False
#if(getRate(ch, sig+"_"+sysName+"Up", ifile)==0 or getRate(ch, sig+"_"+sysName+"Down", ifile)==0):isbogussys=True
if ("sig" in sysValue[1]) and ((getRate(ch, sig, ifile) != 0.)
and not isbogussys):
#print " signal ",sig," channel, ",ch, " file ",ifile, " rate ",(getRate(ch, sig, ifile))
#print " signal ",sig," channel, ",ch, " file ",ifile, " rate up ",(getRate(ch, sig+"_"+sysName+"Up", ifile))
#print " signal ",sig," channel, ",ch, " file ",ifile, " rate down",(getRate(ch, sig+"_"+sysName+"Down", ifile))
card += "%-20s" % ("1")
else:
card += "%-20s" % ("-")
for p in processes:
if (p in sysValue[1]):
if "q2SingleTop" in sysName: card += "%-20s" % ("1")
else: card += "%-20s" % ("1")
#print "adding to channel ", p
else:
card += "%-20s" % ("-")
elif ("mcstat" in sysName):
# CAMBIARE NOME DELLA SYST
for samp in sysValue[1]:
sampName = ""
line = ""
if (samp == "sig" or samp == "Sig"):
line = "%-20s" % ("1")
line += "%-20s" % ("-") * (len(processes))
sampName = sig
elif (mode != "template"):
line = "%-20s" % ("-")
lineProc = [
"%-20s" % ("-") for x in xrange(len(processes))
]
if samp in processes:
index = processes.index(samp)
lineProc[index] = "1"
lineProc = " ".join(lineProc)
line += lineProc
sampName = samp
else:
continue
for i in xrange(hist.GetNbinsX()):
sysName = "mcstat_%s_%s_bin%d " % (ch, sampName,
i + 1)
card += "%-20s shape " % (sysName)
card += line
card += "\n"
if ('autoMCstat' in sysName):
card += "%-20s%-20s%-20d\n " % (ch, "autoMCStats", 0)
card += "\n"
# End for loop on syst unc.
for k, v in rateParams.items():
for ch_ in v.chs:
if ("2016" in k and "2016" not in ch):
continue
elif ("2017" in k and "2017" not in ch):
continue
elif ("2018" in k and "2018" not in ch):
continue
if ("mu" in k and "mu" not in ch):
continue
elif ("ele" in k and "ele" not in ch):
continue
if (ch_ == ("_").join(ch.split("_")[:-1])):
if (("2016" in k) or ("2017" in k) or ("2018" in k)):
if ('mu' in k and 'mu' in ch):
card += "%-20s%-20s%-20s%-20s%-20d\n" % (
k, "rateParam", ch, v.bkg, 1)
elif ('ele' in k and 'ele' in ch):
card += "%-20s%-20s%-20s%-20s%-20d\n" % (
k, "rateParam", ch, v.bkg, 1)
else:
sameyear = (("2016" in k and "2016" in ch)
or ("2017" in k and "2017" in ch)
or ("2018" in k and "2018" in ch))
if (sameyear):
card += "%-20s%-20s%-20s%-20s%-20d\n" % (
k, "rateParam", ch, v.bkg, 1)
else:
card += "%-20s%-20s%-20s%-20s%-20d\n" % (k, "rateParam",
ch, v.bkg, 1)
card += "\n"
if not os.path.isdir(outdir): os.system('mkdir ' + outdir)
if not os.path.isdir(outdir + "/" + sig):
os.system('mkdir ' + outdir + "/" + sig)
outname = "%s%s_%s_%s.txt" % (carddir, sig, ch, mode)
cardfile = open(outname, 'w')
cardfile.write(card)
cardfile.close()
#print card
return card
def signal(channel, stype):
if 'VBF' in channel:
stype = 'XZHVBF'
else:
stype = 'XZH'
# HVT model
if stype.startswith('X'):
signalType = 'HVT'
genPoints = [800, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000]
massPoints = [x for x in range(800, 5000+1, 100)]
interPar = True
else:
print "Signal type", stype, "not recognized"
return
n = len(genPoints)
category = channel
cColor = color[category] if category in color else 1
nElec = channel.count('e')
nMuon = channel.count('m')
nLept = nElec + nMuon
nBtag = channel.count('b')
if '0b' in channel:
nBtag = 0
X_name = "VH_mass"
if not os.path.exists(PLOTDIR+stype+category): os.makedirs(PLOTDIR+stype+category)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar( "X_mass", "m_{ZH}", XBINMIN, XBINMAX, "GeV")
J_mass = RooRealVar( "H_mass", "jet mass", LOWMIN, HIGMAX, "GeV")
V_mass = RooRealVar( "V_mass", "V jet mass", -9., 1.e6, "GeV")
CSV1 = RooRealVar( "H_csv1", "", -999., 2. )
CSV2 = RooRealVar( "H_csv2", "", -999., 2. )
DeepCSV1= RooRealVar( "H_deepcsv1", "", -999., 2. )
DeepCSV2= RooRealVar( "H_deepcsv2", "", -999., 2. )
H_ntag = RooRealVar( "H_ntag", "", -9., 9. )
H_dbt = RooRealVar( "H_dbt", "", -2., 2. )
H_tau21 = RooRealVar( "H_tau21", "", -9., 2. )
H_eta = RooRealVar( "H_eta", "", -9., 9. )
H_tau21_ddt = RooRealVar( "H_ddt", "", -9., 2. )
MaxBTag = RooRealVar( "MaxBTag", "", -10., 2. )
H_chf = RooRealVar( "H_chf", "", -1., 2. )
MinDPhi = RooRealVar( "MinDPhi", "", -1., 99. )
DPhi = RooRealVar( "DPhi", "", -1., 99. )
DEta = RooRealVar( "DEta", "", -1., 99. )
Mu1_relIso = RooRealVar( "Mu1_relIso", "", -1., 99. )
Mu2_relIso = RooRealVar( "Mu2_relIso", "", -1., 99. )
nTaus = RooRealVar( "nTaus", "", -1., 99. )
Vpt = RooRealVar( "V.Pt()", "", -1., 1.e6 )
V_pt = RooRealVar( "V_pt", "", -1., 1.e6 )
H_pt = RooRealVar( "H_pt", "", -1., 1.e6 )
VH_deltaR=RooRealVar( "VH_deltaR", "", -1., 99. )
isZtoNN = RooRealVar( "isZtoNN", "", 0., 2. )
isZtoEE = RooRealVar( "isZtoEE", "", 0., 2. )
isZtoMM = RooRealVar( "isZtoMM", "", 0., 2. )
isHtobb = RooRealVar( "isHtobb", "", 0., 2. )
isVBF = RooRealVar( "isVBF", "", 0., 2. )
isMaxBTag_loose = RooRealVar( "isMaxBTag_loose", "", 0., 2. )
weight = RooRealVar( "eventWeightLumi", "", -1.e9, 1.e9 )
Xmin = XBINMIN
Xmax = XBINMAX
# Define the RooArgSet which will include all the variables defined before
# there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
variables = RooArgSet(X_mass, J_mass, V_mass, CSV1, CSV2, H_ntag, H_dbt, H_tau21)
variables.add(RooArgSet(DEta, DPhi, MaxBTag, MinDPhi, nTaus, Vpt))
variables.add(RooArgSet(DeepCSV1, DeepCSV2,VH_deltaR, H_tau21_ddt))
variables.add(RooArgSet(isZtoNN, isZtoEE, isZtoMM, isHtobb, isMaxBTag_loose, weight))
variables.add(RooArgSet(isVBF, Mu1_relIso, Mu2_relIso, H_chf, H_pt, V_pt,H_eta))
#X_mass.setRange("X_extended_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", Xmin, Xmax)
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/100))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
X_mass.setBinning(binsXmass, "PLOT")
massArg = RooArgSet(X_mass)
# Cuts
SRcut = selection[category]+selection['SR']
print " Cut:\t", SRcut
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
a1 = {}
a2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_fit = RooRealVar("sig_p1_fit", "Variation of the resonance position with the fit uncertainty", 0.005, -1., 1.)
smean_fit = RooRealVar("CMSRunII_sig_p1_fit", "Change of the resonance position with the fit uncertainty", 0., -10, 10)
xmean_jes = RooRealVar("sig_p1_scale_jes", "Variation of the resonance position with the jet energy scale", 0.010, -1., 1.) #0.001
smean_jes = RooRealVar("CMSRunII_sig_p1_jes", "Change of the resonance position with the jet energy scale", 0., -10, 10)
xmean_e = RooRealVar("sig_p1_scale_e", "Variation of the resonance position with the electron energy scale", 0.001, -1., 1.)
smean_e = RooRealVar("CMSRunII_sig_p1_scale_e", "Change of the resonance position with the electron energy scale", 0., -10, 10)
xmean_m = RooRealVar("sig_p1_scale_m", "Variation of the resonance position with the muon energy scale", 0.001, -1., 1.)
smean_m = RooRealVar("CMSRunII_sig_p1_scale_m", "Change of the resonance position with the muon energy scale", 0., -10, 10)
xsigma_fit = RooRealVar("sig_p2_fit", "Variation of the resonance width with the fit uncertainty", 0.02, -1., 1.)
ssigma_fit = RooRealVar("CMSRunII_sig_p2_fit", "Change of the resonance width with the fit uncertainty", 0., -10, 10)
xsigma_jes = RooRealVar("sig_p2_scale_jes", "Variation of the resonance width with the jet energy scale", 0.010, -1., 1.) #0.001
ssigma_jes = RooRealVar("CMSRunII_sig_p2_jes", "Change of the resonance width with the jet energy scale", 0., -10, 10)
xsigma_jer = RooRealVar("sig_p2_scale_jer", "Variation of the resonance width with the jet energy resolution", 0.020, -1., 1.)
ssigma_jer = RooRealVar("CMSRunII_sig_p2_jer", "Change of the resonance width with the jet energy resolution", 0., -10, 10)
xsigma_e = RooRealVar("sig_p2_scale_e", "Variation of the resonance width with the electron energy scale", 0.001, -1., 1.)
ssigma_e = RooRealVar("CMSRunII_sig_p2_scale_e", "Change of the resonance width with the electron energy scale", 0., -10, 10)
xsigma_m = RooRealVar("sig_p2_scale_m", "Variation of the resonance width with the muon energy scale", 0.040, -1., 1.)
ssigma_m = RooRealVar("CMSRunII_sig_p2_scale_m", "Change of the resonance width with the muon energy scale", 0., -10, 10)
xalpha1_fit = RooRealVar("sig_p3_fit", "Variation of the resonance alpha with the fit uncertainty", 0.03, -1., 1.)
salpha1_fit = RooRealVar("CMSRunII_sig_p3_fit", "Change of the resonance alpha with the fit uncertainty", 0., -10, 10)
xslope1_fit = RooRealVar("sig_p4_fit", "Variation of the resonance slope with the fit uncertainty", 0.10, -1., 1.)
sslope1_fit = RooRealVar("CMSRunII_sig_p4_fit", "Change of the resonance slope with the fit uncertainty", 0., -10, 10)
xmean_fit.setConstant(True)
smean_fit.setConstant(True)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xmean_e.setConstant(True)
smean_e.setConstant(True)
xmean_m.setConstant(True)
smean_m.setConstant(True)
xsigma_fit.setConstant(True)
ssigma_fit.setConstant(True)
xsigma_jes.setConstant(True)
ssigma_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
xsigma_e.setConstant(True)
ssigma_e.setConstant(True)
xsigma_m.setConstant(True)
ssigma_m.setConstant(True)
xalpha1_fit.setConstant(True)
salpha1_fit.setConstant(True)
xslope1_fit.setConstant(True)
sslope1_fit.setConstant(True)
# the alpha method is now done.
for m in massPoints:
signalString = "M%d" % m
signalMass = "%s_M%d" % (stype, m)
signalName = "%s%s_M%d" % (stype, category, m)
signalColor = sample[signalMass]['linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m, m*0.5, m*1.25)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)", RooArgList(vmean[m], xmean_e, smean_e, xmean_m, smean_m, xmean_jes, smean_jes, xmean_fit, smean_fit))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma", m*0.035, m*0.01, m*0.4)
sigmaList = RooArgList(vsigma[m], xsigma_e, ssigma_e, xsigma_m, ssigma_m, xsigma_jes, ssigma_jes, xsigma_jer, ssigma_jer)
sigmaList.add(RooArgList(xsigma_fit, ssigma_fit))
ssigma[m] = RooFormulaVar(signalName + "_sigma", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)*(1+@9*@10)", sigmaList)
valpha1[m] = RooRealVar(signalName + "_valpha1", "Crystal Ball alpha", 1., 0., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0*(1+@1*@2)", RooArgList(valpha1[m], xalpha1_fit, salpha1_fit))
vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope", 10., 1., 60.) # slope of the power tail #10 1 60
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0*(1+@1*@2)", RooArgList(vslope1[m], xslope1_fit, sslope1_fit))
salpha2[m] = RooRealVar(signalName + "_alpha2", "Crystal Ball alpha", 2, 1., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
sslope2[m] = RooRealVar(signalName + "_slope2", "Crystal Ball slope", 10, 1.e-1, 115.) # slope of the power tail
#define polynomial
#a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", m, 0.5*m, 2*m)
a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", 0.001*m, 0.0005*m, 0.01*m)
a2[m] = RooRealVar(signalName + "_a2", "par 2 for polynomial", 0.05, -1.,1.)
#if channel=='nnbbVBF' or channel=='nn0bVBF':
# signal[m] = RooPolynomial(signalName,"m_{%s'} = %d GeV" % (stype[1], m) , X_mass, RooArgList(a1[m],a2[m]))
#else:
# signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName+"_yield", "signalYield", 100, 0., 1.e6)
signalNorm[m] = RooRealVar(signalName+"_norm", "signalNorm", 1., 0., 1.e6)
signalXS[m] = RooRealVar(signalName+"_xs", "signalXS", 1., 0., 1.e6)
signalExt[m] = RooExtendPdf(signalName+"_ext", "extended p.d.f", signal[m], signalYield[m])
vslope1[m].setMax(50.)
vslope1[m].setVal(20.)
#valpha1[m].setVal(1.0)
#valpha1[m].setConstant(True)
if 'bb' in channel and 'VBF' not in channel:
if 'nn' in channel:
valpha1[m].setVal(0.5)
elif '0b' in channel and 'VBF' not in channel:
if 'nn' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.04)
elif 'ee' in channel:
valpha1[m].setVal(0.8)
if m==800:
#valpha1[m].setVal(1.2)
valpha1[m].setVal(2.5)
vslope1[m].setVal(50.)
elif 'mm' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.03)
else:
vmean[m].setVal(m*0.9)
vsigma[m].setVal(m*0.08)
elif 'bb' in channel and 'VBF' in channel:
if 'nn' in channel:
if m!=1800:
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
valpha1[m].setVal(0.7)
elif 'mm' in channel:
if m==800:
vslope1[m].setVal(50.)
valpha1[m].setVal(0.7)
elif '0b' in channel and 'VBF' in channel:
if 'nn' in channel:
valpha1[m].setVal(3.)
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
if m<2500:
valpha1[m].setVal(2.)
if m==800:
vsigma[m].setVal(m*0.05)
elif m==1000:
vsigma[m].setVal(m*0.03)
elif m>1000 and m<1800:
vsigma[m].setVal(m*0.04)
elif 'mm' in channel:
if m<2000:
valpha1[m].setVal(2.)
if m==1000 or m==1800:
vsigma[m].setVal(m*0.03)
elif m==1200 or m==1600:
vsigma[m].setVal(m*0.04)
#if m < 1000: vsigma[m].setVal(m*0.06)
# If it's not the proper channel, make it a gaussian
#if nLept==0 and 'VBF' in channel:
# valpha1[m].setVal(5)
# valpha1[m].setConstant(True)
# vslope1[m].setConstant(True)
# salpha2[m].setConstant(True)
# sslope2[m].setConstant(True)
# ---------- if there is no simulated signal, skip this mass point ----------
if m in genPoints:
if VERBOSE: print " - Mass point", m
# define the dataset for the signal applying the SR cuts
treeSign[m] = TChain("tree")
for j, ss in enumerate(sample[signalMass]['files']):
treeSign[m].Add(NTUPLEDIR + ss + ".root")
if treeSign[m].GetEntries() <= 0.:
if VERBOSE: print " - 0 events available for mass", m, "skipping mass point..."
signalNorm[m].setVal(-1)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
salpha1[m].setConstant(True)
sslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
continue
setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar(weight), RooFit.Import(treeSign[m]))
if VERBOSE: print " - Dataset with", setSignal[m].sumEntries(), "events loaded"
# FIT
signalYield[m].setVal(setSignal[m].sumEntries())
if treeSign[m].GetEntries(SRcut) > 5:
if VERBOSE: print " - Running fit"
frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1), RooFit.Extended(True), RooFit.SumW2Error(True), RooFit.PrintLevel(-1))
if VERBOSE: print "********** Fit result [", m, "] **", category, "*"*40, "\n", frSignal[m].Print(), "\n", "*"*80
if VERBOSE: frSignal[m].correlationMatrix().Print()
drawPlot(signalMass, stype+channel, X_mass, signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in channel", channel, "has 0 entries or does not exist"
# Remove HVT cross section (which is the same for Zlep and Zinv)
if stype == "XZHVBF":
sample_name = 'Zprime_VBF_Zh_Zlephinc_narrow_M-%d' % m
else:
sample_name = 'ZprimeToZHToZlepHinc_narrow_M%d' % m
xs = xsection[sample_name]['xsec']
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(massArg, RooFit.NormSet(massArg), RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if VERBOSE:
print " - Fit normalization vs integral:", signalYield[m].getVal(), "/", boundaryFactor, "events"
if channel=='nnbb' and m==5000:
signalNorm[m].setVal(2.5)
elif channel=='nn0b' and m==5000:
signalNorm[m].setVal(6.7)
else:
signalNorm[m].setVal( boundaryFactor * signalYield[m].getVal() / signalXS[m].getVal()) # here normalize to sigma(X) x Br(X->VH) = 1 [fb]
a1[m].setConstant(True)
a2[m].setConstant(True)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
#*******************************************************#
# #
# Signal interpolation #
# #
#*******************************************************#
# ====== CONTROL PLOT ======
c_signal = TCanvas("c_signal", "c_signal", 800, 600)
c_signal.cd()
frame_signal = X_mass.frame()
for m in genPoints[:-2]:
if m in signalExt.keys():
signal[m].plotOn(frame_signal, RooFit.LineColor(sample["%s_M%d" % (stype, m)]['linecolor']), RooFit.Normalization(signalNorm[m].getVal(), RooAbsReal.NumEvent), RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 6500)
frame_signal.Draw()
drawCMS(-1, YEAR, "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.pdf")
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 800, 5000) #"pol5" if not channel=="XZHnnbb" else "pol6" #pol5*TMath::Floor(x-1800) + ([5]*x + [6]*x*x)*(1-TMath::Floor(x-1800))
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 5000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 5000)
fsigma.SetLineColor(2)
fsigma.SetFillColor(2)
# Alpha1
galpha1 = TGraphErrors()
galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha")
galpha1.SetMarkerStyle(20)
galpha1.SetMarkerColor(cColor)
galpha1.SetLineColor(cColor)
ialpha1 = TGraphErrors()
ialpha1.SetMarkerStyle(24)
falpha1 = TF1("falpha", "pol0", 0, 5000)
falpha1.SetLineColor(2)
falpha1.SetFillColor(2)
# Slope1
gslope1 = TGraphErrors()
gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)")
gslope1.SetMarkerStyle(20)
gslope1.SetMarkerColor(cColor)
gslope1.SetLineColor(cColor)
islope1 = TGraphErrors()
islope1.SetMarkerStyle(24)
fslope1 = TF1("fslope", "pol0", 0, 5000)
fslope1.SetLineColor(2)
fslope1.SetFillColor(2)
# Alpha2
galpha2 = TGraphErrors()
galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha")
galpha2.SetMarkerStyle(20)
galpha2.SetMarkerColor(cColor)
galpha2.SetLineColor(cColor)
ialpha2 = TGraphErrors()
ialpha2.SetMarkerStyle(24)
falpha2 = TF1("falpha", "pol0", 0, 5000)
falpha2.SetLineColor(2)
falpha2.SetFillColor(2)
# Slope2
gslope2 = TGraphErrors()
gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)")
gslope2.SetMarkerStyle(20)
gslope2.SetMarkerColor(cColor)
gslope2.SetLineColor(cColor)
islope2 = TGraphErrors()
islope2.SetMarkerStyle(24)
fslope2 = TF1("fslope", "pol0", 0, 5000)
fslope2.SetLineColor(2)
fslope2.SetFillColor(2)
n = 0
for i, m in enumerate(genPoints):
if not m in signalNorm.keys(): continue
if signalNorm[m].getVal() < 1.e-6: continue
signalString = "M%d" % m
signalName = "%s_M%d" % (stype, m)
if gnorm.GetMaximum() < signalNorm[m].getVal(): gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
gmean.SetPoint(n, m, vmean[m].getVal())
gmean.SetPointError(n, 0, min(vmean[m].getError(), vmean[m].getVal()*0.02))
gsigma.SetPoint(n, m, vsigma[m].getVal())
gsigma.SetPointError(n, 0, min(vsigma[m].getError(), vsigma[m].getVal()*0.05))
galpha1.SetPoint(n, m, valpha1[m].getVal())
galpha1.SetPointError(n, 0, min(valpha1[m].getError(), valpha1[m].getVal()*0.10))
gslope1.SetPoint(n, m, vslope1[m].getVal())
gslope1.SetPointError(n, 0, min(vslope1[m].getError(), vslope1[m].getVal()*0.10))
galpha2.SetPoint(n, m, salpha2[m].getVal())
galpha2.SetPointError(n, 0, min(salpha2[m].getError(), salpha2[m].getVal()*0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(n, 0, min(sslope2[m].getError(), sslope2[m].getVal()*0.10))
n = n + 1
print "fit on gmean:"
gmean.Fit(fmean, "Q0", "SAME")
print "fit on gsigma:"
gsigma.Fit(fsigma, "Q0", "SAME")
print "fit on galpha:"
galpha1.Fit(falpha1, "Q0", "SAME")
print "fit on gslope:"
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
gnorm.Fit(fnorm, "Q", "SAME", 700, 5000)
for m in massPoints:
signalName = "%s_M%d" % (stype, m)
if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.)
# Interpolation method
syield = gnorm.Eval(m)
spline = gnorm.Eval(m, 0, "S")
sfunct = fnorm.Eval(m)
#delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield))
delta = abs(1.-spline/sfunct) if sfunct > 0 else 0
syield = spline
if interPar:
jmean = gmean.Eval(m)
jsigma = gsigma.Eval(m)
jalpha1 = galpha1.Eval(m)
jslope1 = gslope1.Eval(m)
else:
jmean = fmean.GetParameter(0) + fmean.GetParameter(1)*m + fmean.GetParameter(2)*m*m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(1)*m + fsigma.GetParameter(2)*m*m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(1)*m + falpha1.GetParameter(2)*m*m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(1)*m + fslope1.GetParameter(2)*m*m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(syield)
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1==0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
c1 = TCanvas("c1", "Crystal Ball", 1200, 800)
c1.Divide(2, 2)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(channel)
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(channel)
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(channel)
galpha1.GetYaxis().SetRangeUser(0., 5.)
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(channel)
gslope1.GetYaxis().SetRangeUser(0., 125.)
if False:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(channel)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(channel)
gslope2.GetYaxis().SetRangeUser(0., 10.)
c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.pdf")
c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0]-100, genPoints[-1]+100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum()*1.25)
drawCMS(-1,YEAR , "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.pdf")
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("ZH_RunII", "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m], RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile("%s%s.root" % (WORKDIR, stype+channel), True)
print "Workspace", "%s%s.root" % (WORKDIR, stype+channel), "saved successfully"
sys.exit()
def buildTimePdf(config):
"""
build time pdf, return pdf and associated data in dictionary
"""
from B2DXFitters.WS import WS
print 'CONFIGURATION'
for k in sorted(config.keys()):
print ' %32s: %32s' % (k, config[k])
# start building the fit
ws = RooWorkspace('ws_%s' % config['Context'])
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
# start by defining observables
time = WS(ws, RooRealVar('time', 'time [ps]', 0.2, 15.0))
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
qt = WS(ws, RooCategory('qt', 'tagging decision'))
qt.defineType('B+', +1)
qt.defineType('Untagged', 0)
qt.defineType('B-', -1)
# now other settings
Gamma = WS(ws, RooRealVar('Gamma', 'Gamma', 0.661)) # ps^-1
DGamma = WS(ws, RooRealVar('DGamma', 'DGamma', 0.106)) # ps^-1
Dm = WS(ws, RooRealVar('Dm', 'Dm', 17.719)) # ps^-1
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 0.60, 0.0, 1.0))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr', 0.040, 0.001, 0.100))
# now build the PDF
from B2DXFitters.timepdfutils import buildBDecayTimePdf
from B2DXFitters.resmodelutils import getResolutionModel
from B2DXFitters.acceptanceutils import buildSplineAcceptance
obs = [qf, qt, time]
acc, accnorm = buildSplineAcceptance(
ws, time, 'Bs2DsPi_accpetance',
config['SplineAcceptance']['KnotPositions'],
config['SplineAcceptance']['KnotCoefficients'][config['Context']],
'FIT' in config['Context']) # float for fitting
if 'GEN' in config['Context']:
acc = accnorm # use normalised acceptance for generation
# get resolution model
resmodel, acc = getResolutionModel(ws, config, time, timeerr, acc)
# build the time pdf
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[mistag]], [tageff],
Gamma,
DGamma,
Dm,
C=one,
D=zero,
Dbar=zero,
S=zero,
Sbar=zero,
timeresmodel=resmodel,
acceptance=acc)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
def buildTimePdf(config, tupleDataSet, tupleDict):
from B2DXFitters.WS import WS
print 'CONFIGURATION'
for k in sorted(config.keys()):
print ' %32s: %32s' % (k, config[k])
ws = RooWorkspace('ws_%s' % config['Context'])
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
###USE FIT CONTEXT
"""
build time pdf, return pdf and associated data in dictionary
"""
# start by defining observables
time = WS(ws,
tupleDataSet.get().find('ct'))
#qt = WS(ws, tupleDataSet.get().find('ssDecision'));
'''
time = WS(ws, RooRealVar('time', 'time [ps]', 0.2, 15.0))
'''
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
qt = WS(ws, RooCategory('qt', 'tagging decision'))
qt.defineType('B+', +1)
qt.defineType('Untagged', 0)
qt.defineType('B-', -1)
# now other settings
Gamma = WS(ws, RooRealVar('Gamma', 'Gamma', 0.661)) # ps^-1
DGamma = WS(ws, RooRealVar('DGamma', 'DGamma', 0.106)) # ps^-1
Dm = WS(ws, RooRealVar('Dm', 'Dm', 17.719)) # ps^-1
# HACK (1/2): be careful about lower bound on eta, since mistagpdf below
# is zero below a certain value - generation in accept/reject would get
# stuck
if 'GEN' in config['Context'] or 'FIT' in config['Context']:
eta = WS(
ws,
RooRealVar(
'eta', 'eta', 0.35, 0.0 if 'FIT' in config['Context'] else
(1. + 1e-5) *
max(0.0, config['TrivialMistagParams']['omega0']), 0.5))
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 0.60, 0.0, 1.0))
terrpdf = WS(ws,
tupleDataSet.get().find('cterr'))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr', 0.040, 0.001, 0.100))
#MISTAGPDF
# fit average mistag
# add mistagged
#ge rid of untagged events by putting restriction on qf or something when reduceing ds
# now build the PDF
from B2DXFitters.timepdfutils import buildBDecayTimePdf
from B2DXFitters.resmodelutils import getResolutionModel
from B2DXFitters.acceptanceutils import buildSplineAcceptance
obs = [qf, qt, time]
acc, accnorm = buildSplineAcceptance(
ws, time, 'Bs2DsPi_accpetance',
config['SplineAcceptance']['KnotPositions'],
config['SplineAcceptance']['KnotCoefficients'][config['Context'][0:3]],
'FIT' in config['Context']) # float for fitting
# get resolution model
resmodel, acc = getResolutionModel(ws, config, time, timeerr, acc)
mistagpdf = WS(
ws,
RooArgList(tupleDataSet.get().find('ssMistag'),
tupleDataSet.get().find('osMistag')))
#???
'''
if 'GEN' in config['Context']:
# build a (mock) mistag distribution
mistagpdfparams = {} # start with parameters of mock distribution
for sfx in ('omega0', 'omegaavg', 'f'):
mistagpdfparams[sfx] = WS(ws, RooRealVar(
'Bs2DsPi_mistagpdf_%s' % sfx, 'Bs2DsPi_mistagpdf_%s' % sfx,
config['TrivialMistagParams'][sfx]))
# build mistag pdf itself
mistagpdf = WS(ws, [tupleDataSet.reduce('ssMistag'), tupleDataSet.reduce('osMistag')]);
mistagcalibparams = {} # start with parameters of calibration
for sfx in ('p0', 'p1', 'etaavg'):
mistagcalibparams[sfx] = WS(ws, RooRealVar('Bs2DsPi_mistagcalib_%s' % sfx, 'Bs2DsPi_mistagpdf_%s' % sfx,config['MistagCalibParams'][sfx]));
for sfx in ('p0', 'p1'): # float calibration paramters
mistagcalibparams[sfx].setConstant(False)
mistagcalibparams[sfx].setError(0.1)
# build mistag pdf itself
omega = WS(ws, MistagCalibration(
'Bs2DsPi_mistagcalib', 'Bs2DsPi_mistagcalib',
eta, mistagcalibparams['p0'], mistagcalibparams['p1'],
mistagcalibparams['etaavg']))
# build the time pdf
if 'GEN' in config['Context']:
pdf = buildBDecayTimePdf(
config, 'Bs2DsPi', ws,
time, timeerr, qt, qf, [ [ omega ] ], [ tageff ],
Gamma, DGamma, Dm,
C = one, D = zero, Dbar = zero, S = zero, Sbar = zero,
timeresmodel = resmodel, acceptance = acc, timeerrpdf,
mistagpdf = [mistagpdf], mistagobs = eta)
else:
pdf = buildBDecayTimePdf(
config, 'Bs2DsPi', ws,
time, timeerr, qt, qf, [ [ eta ] ], [ tageff ],
Gamma, DGamma, Dm,
C = one, D = zero, Dbar = zero, S = zero, Sbar = zero,
timeresmodel = resmodel, acceptance = acc, timeerrpdf = None)
'''
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[eta]], [tageff],
Gamma,
DGamma,
Dm,
C=one,
D=zero,
Dbar=zero,
S=zero,
Sbar=zero,
timeresmodel=resmodel,
acceptance=acc,
timeerrpdf=terrpdf,
mistagpdf=[mistagpdf],
mistagobs=eta)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
def buildTimePdf(config):
"""
build time pdf, return pdf and associated data in dictionary
"""
from B2DXFitters.WS import WS
print 'CONFIGURATION'
for k in sorted(config.keys()):
print ' %32s: %32s' % (k, config[k])
# start building the fit
ws = RooWorkspace('ws_%s' % config['Context'])
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
# start by defining observables
time = WS(ws, RooRealVar('time', 'time [ps]', 0.2, 15.0))
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
qt = WS(ws, RooCategory('qt', 'tagging decision'))
qt.defineType('B+', +1)
qt.defineType('Untagged', 0)
qt.defineType('B-', -1)
# now other settings
Gamma = WS(ws, RooRealVar('Gamma', 'Gamma', 0.661)) # ps^-1
DGamma = WS(ws, RooRealVar('DGamma', 'DGamma', 0.106)) # ps^-1
Dm = WS(ws, RooRealVar('Dm', 'Dm', 17.719)) # ps^-1
# HACK (1/2): be careful about lower bound on eta, since mistagpdf below
# is zero below a certain value - generation in accept/reject would get
# stuck
eta = WS(
ws,
RooRealVar(
'eta', 'eta', 0.35, 0.0 if 'FIT' in config['Context'] else
(1. + 1e-5) * max(0.0, config['TrivialMistagParams']['omega0']),
0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 0.60, 0.0, 1.0))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr', 0.040, 0.001, 0.100))
# now build the PDF
from B2DXFitters.timepdfutils import buildBDecayTimePdf
from B2DXFitters.resmodelutils import getResolutionModel
from B2DXFitters.acceptanceutils import buildSplineAcceptance
obs = [qt, qf, time, eta, timeerr]
acc, accnorm = buildSplineAcceptance(
ws, time, 'Bs2DsPi_accpetance',
config['SplineAcceptance']['KnotPositions'],
config['SplineAcceptance']['KnotCoefficients'][config['Context']],
'FIT' in config['Context']) # float for fitting
if 'GEN' in config['Context']:
acc = accnorm # use normalised acceptance for generation
# get resolution model
resmodel, acc = getResolutionModel(ws, config, time, timeerr, acc)
# build a (mock) mistag distribution
mistagpdfparams = {} # start with parameters of mock distribution
for sfx in ('omega0', 'omegaavg', 'f'):
mistagpdfparams[sfx] = WS(
ws,
RooRealVar('Bs2DsPi_mistagpdf_%s' % sfx,
'Bs2DsPi_mistagpdf_%s' % sfx,
config['TrivialMistagParams'][sfx]))
# build mistag pdf itself
mistagpdf = WS(
ws,
MistagDistribution('Bs2DsPi_mistagpdf', 'Bs2DsPi_mistagpdf', eta,
mistagpdfparams['omega0'],
mistagpdfparams['omegaavg'], mistagpdfparams['f']))
# build mistag calibration
mistagcalibparams = {} # start with parameters of calibration
for sfx in ('p0', 'p1', 'etaavg'):
mistagcalibparams[sfx] = WS(
ws,
RooRealVar('Bs2DsPi_mistagcalib_%s' % sfx,
'Bs2DsPi_mistagpdf_%s' % sfx,
config['MistagCalibParams'][sfx]))
for sfx in ('p0', 'p1'): # float calibration paramters
mistagcalibparams[sfx].setConstant(False)
mistagcalibparams[sfx].setError(0.1)
# build mistag pdf itself
omega = WS(
ws,
MistagCalibration('Bs2DsPi_mistagcalib', 'Bs2DsPi_mistagcalib', eta,
mistagcalibparams['p0'], mistagcalibparams['p1'],
mistagcalibparams['etaavg']))
# build mock decay time error distribution (~ timeerr^6 * exp(-timerr /
# (timerr_av / 7))
terrpdf_shape = WS(
ws,
RooConstVar('timeerr_ac', 'timeerr_ac',
config['DecayTimeResolutionAvg'] / 7.))
terrpdf_truth = WS(
ws, RooTruthModel('terrpdf_truth', 'terrpdf_truth', timeerr))
terrpdf_i0 = WS(
ws,
RooDecay('terrpdf_i0', 'terrpdf_i0', timeerr, terrpdf_shape,
terrpdf_truth, RooDecay.SingleSided))
terrpdf_i1 = WS(
ws,
RooPolynomial('terrpdf_i1', 'terrpdf_i1', timeerr,
RooArgList(zero, zero, zero, zero, zero, zero, one), 0))
terrpdf = WS(ws, RooProdPdf('terrpdf', 'terrpdf', terrpdf_i0, terrpdf_i1))
# build the time pdf
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[omega]], [tageff],
Gamma,
DGamma,
Dm,
C=one,
D=zero,
Dbar=zero,
S=zero,
Sbar=zero,
timeresmodel=resmodel,
acceptance=acc,
timeerrpdf=terrpdf,
mistagpdf=[mistagpdf],
mistagobs=eta)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
