startpars = totalPdf.getParameters(fitter.ws.set('obsSet'))
fitter.ws.defineSet("params", startpars)
fitter.ws.saveSnapshot("initPars", startpars)
if opts.toy:
#generate toy dataset
print 'Generated parameters'
fitter.ws.set('params').Print('v')
fitter.ws.saveSnapshot("genPars", startpars)
data = totalPdf.generate(fitter.ws.set('obsSet'), RooFit.Name('data_obs'),
RooFit.Extended())
if fitter.pars.binData:
data = RooDataHist('data_obs', 'data_obs', fitter.ws.set('obsSet'),
data)
data.Print('v')
getattr(fitter.ws, 'import')(data)
else:
data = fitter.loadData()
fitter.setMultijetYield()
data.Print()
startpars.IsA().Destructor(startpars)
print 'Time elapsed: %.1f sec' % timer.RealTime()
print 'CPU time used: %.1f sec' % timer.CpuTime()
print 'starting fitting routine'
timer.Continue()
#fitter.ws.var('top_nrm').setConstant()
fr = None
fr = fitter.fit()
class MjjFit:
def __init__(self, mass_range=[0., 2000.]):
self.data_histogram_ = None
self.data_roohistogram_ = None
self.luminosity_ = 0.
self.collision_energy_ = 8000.
self.signal_histograms_ = {}
self.signal_roohistograms_ = {}
self.signal_names_ = []
# Fit storage
self.simple_fit_ = None
self.mjj_ = RooRealVar('mjj','mjj',float(mass_range[0]),float(mass_range[1]))
self.workspace_ = None
def add_data(self, data_histogram):
print "[MjFit.add_data] INFO : Adding data histogram"
# Add a data histogram
self.data_histogram_ = data_histogram.Clone()
self.data_histogram_.SetDirectory(0)
self.data_roohistogram_ = RooDataHist('data_roohistogram','data_roohistogram',RooArgList(self.mjj_),self.data_histogram_)
self.data_roohistogram_.Print()
def add_signal(self, signal_name, signal_histogram):
print "[MjjFit.add_signal] INFO : Adding signal histogram " + signal_name
# Add a signal histogram.
# Scale to sigma=1 (in whatever units the data luminosity is given in), so the 'r' parameter corresponds to the limit on the cross section.
if self.luminosity_ == 0:
print "[MjjFit.add_signal] ERROR : Please set luminosity first (MjjFit.set_luminosity(###))."
sys.exit(1)
self.signal_names_.append(signal_name)
self.signal_histograms_[signal_name] = signal_histogram.Clone()
self.signal_histograms_[signal_name].SetDirectory(0)
self.signal_histograms_[signal_name].Scale(1. * self.luminosity_ / self.signal_histograms_[signal_name].Integral())
self.signal_roohistograms_[signal_name] = RooDataHist(signal_histogram.GetName() + "_rdh", signal_histogram.GetName() + "_rdh", RooArgList(self.mjj_), signal_histograms[signal_name])
self.signal_roohistograms_[signal_name].Print()
def simple_fit_B(self, fit_range=None):
# Run a simple ROOT fit (B only)
# - No S+B equivalent. This is done with RooFit.
if fit_range:
fit_min = fit_range[0]
fit_max = fit_range[1]
else:
fit_min = data_histogram.GetXaxis().GetXmin()
fit_max = data_histogram.GetXaxis().GetXmax()
fit = TF1(data_histogram.GetName() + "_fit_B", BackgroundFit, fit_min, fit_max, 4)
fit.SetParameter(0, 2.e-4)
fit.SetParameter(1, 3)
fit.SetParameter(2, 10)
fit.SetParameter(3, 1)
fit.SetParLimits(0, 1.e-6, 1.e2)
fit.SetParLimits(1, -25., 25.)
fit.SetParLimits(2, -25., 25.)
fit.SetParLimits(3, -5., 5.)
data_histogram.Fit(fit, "ER0I")
fit_ratio = self.make_fit_pull_histogram(data_histogram, fit)
print "Fit chi2/ndf = " + str(fit.GetChisquare()) + " / " + str(fit.GetNDF()) + " = " + str(fit.GetChisquare() / fit.GetNDF())
self.simple_fit_ = {"fit":fit, "fit_ratio":fit_ratio}
return self.simple_fit_
def make_fit_pull_histogram(self, hist, fit):
#print "Fit xmin = " + str(fit.GetXmin())
hist_ratio = hist.Clone()
hist_ratio.SetName(hist.GetName() + "_fit_ratio")
for bin in xrange(1, hist_ratio.GetNbinsX() + 1):
xmin = hist_ratio.GetXaxis().GetBinLowEdge(bin)
xmax = hist_ratio.GetXaxis().GetBinUpEdge(bin)
if xmax < fit.GetXmin() or xmin > fit.GetXmax():
hist_ratio.SetBinContent(bin, 0.)
hist_ratio.SetBinError(bin, 0.)
continue
fit_integral = fit.Integral(xmin, xmax)
if hist.GetBinError(bin) > 0:
hist_ratio.SetBinContent(bin, (hist.GetBinContent(bin) * hist.GetBinWidth(bin) - fit_integral) / (hist.GetBinError(bin) * hist.GetBinWidth(bin)))
hist_ratio.SetBinError(bin, 0.)
else:
hist_ratio.SetBinContent(bin, 0.)
hist_ratio.SetBinError(bin, 0.)
return hist_ratio
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
# fitted_signal_shapes = list of fitted S(+B) shapes to plot.
# expected_signal_shapes = list of S shapes to plot, scaled to cross sections taken from configuration file
def plot(self, save_tag, fitted_signal_shapes=None, expected_signal_shapes=None, log=False, x_range=None):
c = TCanvas("c_" + save_tag, "c_" + save_tag, 800, 1600)
l = TLegend(0.55, 0.6, 0.88, 0.88)
l.SetFillColor(0)
l.SetBorderSize(0)
top = TPad("top", "top", 0., 0.5, 1., 1.)
top.SetBottomMargin(0.03)
top.Draw()
if log:
top.SetLogy()
c.cd()
bottom = TPad("bottom", "bottom", 0., 0., 1., 0.5)
bottom.SetTopMargin(0.02)
bottom.SetBottomMargin(0.2)
bottom.Draw()
ROOT.SetOwnership(c, False)
ROOT.SetOwnership(top, False)
ROOT.SetOwnership(bottom, False)
top.cd()
# Frame from background fit
if not roofit_results.has_key("background"):
print "[MjjFit.plot] ERROR : Please run background-only fit before plotting (e.g. MjjFit.plot(\"bkgd_file.root\"))"
f_background = TFile(roofit_results["background"], "READ")
w_background = f_background.Get("w")
data_rdh = w_background.data("data_obs")
if x_range:
x_min = x_range[0]
x_max = x_range[1]
else:
x_min = self.mjj_.GetMin()
x_max = self.mjj_.GetMax()
frame_top = self.mjj_.frame(x_min, x_max)
frame_top.GetYaxis().SetTitle("Events / " + str(int(data_histogram.GetXaxis().GetBinWidth(1))) + " GeV")
frame_top.GetXaxis().SetTitleSize(0)
frame_top.GetXaxis().SetLabelSize(0)
#if log:
# frame_top.SetMaximum(y_max * 10.)
# frame_top.SetMinimum(y_min / 100.)
#else:
# frame_top.SetMaximum(y_max * 1.3)
# frame_top.SetMinimum(0.)
frame_top.Draw()
data_rdh.plotOn(frame_top, RooFit.Name("Data"))
l.AddEntry(frame_top.findObject("Data"), "Data", "pl")
background_pdf = w_background.pdf("background")
background_pdf.plotOn(frame_top, RooFit.Name("Background Fit"), RooFit.LineColor(seaborn.GetColorRoot("default", 0)), RooFit.LineStyle(1), RooFit.LineWidth(2))
style_counter = 1
if fitted_signal_shapes:
for signal_name in fitted_signal_shapes:
# Load from workspace
if not roofit_results.has_key(signal_name):
print "[MjjFit.plot] ERROR : Signal name " + signal_name + " does not exist in roofit_results. Did you run the fit first?"
sys.exit(1)
f_in = TFile(roofit_results[signal_name], "READ")
w = f_in.Get("workspace")
fit_pdf = w.pdf("model")
fit_pdf_name = "Fit (" + signal_name + ")"
fit_pdf.SetName(fit_pdf_name)
fit_pdf.plotOn(frame_top, RooFit.Name(fit_pdf_name), RooFit.LineColor(seaborn.GetColorRoot("default", style_counter)), RooFit.LineStyle(1), RooFit.LineWidth(2))
l.AddEntry(frame_top.findObject(signal_name), signal_name, "l")
style_counter += 1
f_in.Close()
if expected_signal_shapes:
for signal_name in expected_signal_shapes:
self.signal_roohistograms_[signal_name].plotOn(frame_top, RooFit.Name(signal_name), RooFit.Rescale(cross_section * self.luminosity_ / self.signal_roohistograms_[signal_name].sum()), RooFit.LineColor(seaborn.GetColorRoot("pastel", style_counter)), RooFit.LineStyle(2), RooFit.LineWidth(2))
l.AddEntry(frame_top.findObject(signal_name), signal_name, "l")
style_counter += 1
frame_top.Draw()
l.Draw()
# Pull histogram
c.cd()
bottom.cd()
pull_histogram = frame_top.pullHist("Data", "Background Fit")
frame_bottom = self.mjj_.frame(x_min, x_max)
pull_histogram.plotOn(frame_bottom, RooFit.Name(fit_pdf_name))
frame_bottom.GetXaxis().SetTitle("m_{jj} [GeV]")
frame_bottom.GetYaxis().SetTitle("#frac{Data - Fit}{#sigma(Fit)}")
frame_bottom.Draw()
c.cd()
c.SaveAs("/uscms/home/dryu/Dijets/data/EightTeeEeVeeBee/Results/figures/c_" + save_tag + ".pdf")
bkg = RooGenericPdf('bkg', '1/(exp(pow(@0/@1,@2))+1)',
RooArgList(x, x0, p))
fsig = RooRealVar('fsig', 'fsig', 0.5, 0., 1.)
signal = RooAddPdf('signal', 'signal', sig, bkg, fsig)
# -----------------------------------------
# fit signal
canSname = 'can_Mjj' + str(mass)
canS = TCanvas(canSname, canSname, 900, 600)
gPad.SetLogy()
roohistSig = RooDataHist('roohist', 'roohist', RooArgList(x), hSig)
roohistSig.Print()
res_sig = signal.fitTo(roohistSig, RooFit.Save(ROOT.kTRUE))
res_sig.Print()
frame = x.frame()
roohistSig.plotOn(frame, RooFit.Binning(166))
signal.plotOn(frame)
signal.plotOn(frame, RooFit.Components('bkg'), RooFit.LineColor(ROOT.kRed),
RooFit.LineWidth(2), RooFit.LineStyle(ROOT.kDashed))
#frame.GetXaxis().SetRangeUser(1118,6099)
frame.GetXaxis().SetRangeUser(minX_mass, maxX_mass)
frame.GetXaxis().SetTitle('m_{jj} (GeV)')
frame.Draw()
parsSig = signal.getParameters(roohistSig)
parsSig.setAttribAll('Constant', True)
print ">> Creating datacard and workspace for %s resonance with m = %i GeV..." % (
args.signal_model, int(mass))
# get signal shape
hSig = signal_shapes_file.Get("h_" + args.signal_model + "_" +
str(int(mass)))
# normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
hSig.Scale(
signalCrossSection * lumi / hSig.Integral()
) # divide by a number that provides roughly an r value of 1-10
print "[debug] hSig.Integral() = " + str(hSig.Integral())
print "[debug] hSig.GetMean() = " + str(hSig.GetMean())
print "[debug] hSig.GetRMS() = " + str(hSig.GetRMS())
rooSigHist = RooDataHist('rooSigHist', 'rooSigHist', RooArgList(mjj),
hSig)
rooSigHist.Print()
signal = RooHistPdf('signal', 'signal', RooArgSet(mjj), rooSigHist)
signal.Print()
signal_norm = RooRealVar('signal_norm', 'signal_norm', 0, -1e+05,
1e+05)
if args.fitBonly: signal_norm.setConstant()
signal_norm.Print()
p1 = RooRealVar('p1', 'p1', args.p1, 0., 100.)
p2 = RooRealVar('p2', 'p2', args.p2, 0., 60.)
p3 = RooRealVar('p3', 'p3', args.p3, -10., 10.)
if args.fixP3: p3.setConstant()
background = RooGenericPdf(
'background',
'(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))' %
def main():
global beamEnergy
# Parse all command line arguments using the argparse module.
parser = argparse.ArgumentParser(
description='PyRoot analysis demostrating the us of a DST.')
parser.add_argument("dst_file", help="ROOT DST file to process")
parser.add_argument("-o", "--output", help="Name of output pdf file")
parser.add_argument("-m", "--mc", help="is MonteCarlo")
parser.add_argument("-p", "--pulser", help="is Pulser")
parser.add_argument("-e", "--energy", help="beam energy")
args = parser.parse_args()
# If an output file name was not specified, set a default name and warn
# the user
if args.output:
output_file = args.output
else:
output_file = "analysis_output.root"
print "[ HPS ANALYSIS ]: An output file name was not specified. Setting the name to "
print output_file
print "[ HPS ANALYSIS ]: Output file is " + output_file
isMC = False
if args.mc:
print "[ HPS ANALYSIS ]: Setting to run as MC"
isMC = True
isPulser = False
if args.pulser:
print "[ HPS ANALYSIS ]: Setting to run from a pulser file"
isPulser = True
if args.energy:
print 'Setting beam energy to ' + args.energy
beamEnergy = float(args.energy)
myhist.setEnergyScales(beamEnergy)
#################################
# Event Selection
################################
#clean up event first
#### nominal selection
nTrkMax = 10
nTrkMin = 2
nPosMax = 3
###### two tracks (e+/e-) exactly
# nTrkMax=2
# nTrkMin=2
# nPosMax=1
###### more than 1 electron
# nTrkMax=10
# nTrkMin=3
# nPosMax=1
###################
#v0 cuts
v0Chi2 = 10
#ESum -- full region
v0PzMax = 1.2 * beamEnergy
v0PzMin = 0.55 * beamEnergy
#ESum -- Radiative region
# v0PzMax=1.2
# v0PzMin=0.80
v0PyMax = 0.2 #absolute value
v0PxMax = 0.2 #absolute value
v0VzMax = 25.0 # mm from target
v0VyMax = 1.0 # mm from target
v0VxMax = 2.0 # mm from target
# track quality cuts
trkChi2 = 10
beamCut = 0.8 * beamEnergy
minPCut = 0.05
trkPyMax = 0.2
trkPxMax = 0.2
# slopeCut=0.03
slopeCut = 0.0
trkDeltaT = 4 #ns
cluDeltaT = 2 #ns
cluTrkDeltaT = 4 #ns
##############
# ESum slices; upper limits
nSlicesESum = 5
esumMin = 0.55
esumMax = 1.2
sliceSizeESum = 0.1 #100MeV starting at esumMin
##############
trackKiller = False
tkThreshold = 0.5 #GeV, below this start killing tracks
tkThreshEff = 1.0
tkLowPoint = 0.20
tkLowPointEff = 0.40
# tkSlope=2.6
# tkIntercept=-0.04
#calculate tkSlope and Intercept
tkSlope = (tkThreshEff - tkLowPointEff) / (tkThreshold - tkLowPoint)
tkIntercept = tkThreshEff - tkSlope * tkThreshold
##############
requireECalMatch = True
requireECalFiducial = False
requireECalSuperFiducial = False
useGBL = True
# Open the ROOT file
# root_file = ROOT.TFile(str(args.dst_file))
# Get the TTree "HPS_EVENT" containing the HpsEvent branch and all
# other colletions
# tree = root_file.Get("HPS_Event")
#use a TChain
print "[ HPS ANALYSIS ]: Reading in root chain from " + args.dst_file
tree = ROOT.TChain("HPS_Event")
tree.Add(str(args.dst_file) + "*")
# Create an HpsEvent object in order to read the TClonesArray
# collections
hps_event = HpsEvent()
b_hps_event = tree.SetBranchAddress("Event", ROOT.AddressOf(hps_event))
#--- Analysis ---#
#----------------#
#counters
nEvents = 0
nPassBasicCuts = 0
nPassV0Cuts = 0
nPassTrkCuts = 0
nPassNCand = 0
nPassECalMatch = 0
nFakeTri = 0
if 1 == 0:
extraElectronProb = 0.0
dataNele = 170.0
btNele = 1.4
hf = ROOT.TFile(
"OutputHistograms/Data/hps_005772_pass6_useGBL_ECalMatch_SuperFiducialCut.root"
)
hfMC = ROOT.TFile(
"OutputHistograms/MC/beam-tri_HPS-EngRun2015-Nominal-v4-4_pass6_useGBL_ECalMatch_SuperFiducialCut.root"
)
pele = ROOT.RooRealVar("pele", "pele", 0, 1)
peleHist = hf.Get("raweleMom")
peleMCHist = hfMC.Get("raweleMom")
peleHist.Scale(1 / dataNele)
peleMCHist.Scale(1 / btNele)
peleHist.Add(
peleMCHist, -1.0
) #subtract the MC from the data to get the distribution of extra tracks...this is cheating!
for i in xrange(0, peleHist.GetNbinsX()):
print "Electron Momentum bin i = " + str(peleHist.GetBinContent(i))
if peleHist.GetBinContent(i) < 0:
peleHist.SetBinContent(i, 0)
peleHist.Print("V")
peleDH = RooDataHist("peleDH", "peleDH", ROOT.RooArgList(pele),
peleHist)
peleDH.Print("V")
extraElectronPdf = RooHistPdf("extraElectronPdf", "extraElectronPdf",
ROOT.RooArgSet(pele), peleDH)
print 'pdf is made...printing info'
extraElectronPdf.Print("V")
# if isMC and random.random()<extraElectronProb :
#add an extra electron based on electron momentum
print 'generating events'
newElePData = extraElectronPdf.generate(ROOT.RooArgSet(pele),
1000000.0, True, False)
newElePData.Print("V")
seedCnt = 0
# Loop over all events in the file
for entry in xrange(0, tree.GetEntries()):
# Print the event number every 500 events
if (entry + 1) % 10000 == 0: print "Event " + str(entry + 1)
tree.GetEntry(entry)
if not hps_event.isPair1Trigger() and not isMC and not isPulser:
continue
nEvents += 1
addFakeEle = False
if 1 == 0:
if isMC and random.random() < extraElectronProb:
#add an extra electron based on electron momentum
addFakeEle = True
newEleP = newElePData.get(seedCnt).find("pele").getVal()
seedCnt = seedCnt + 1
# print 'Inserting an electron with momentum = '+str(newEleP)
# newEleCluster=hps_event.addEcalCluster();
# newEleTrack=hps_event.addTrack();
# print 'numbe of HpsParticles before = '+str(hps_event.getNumberOfParticles(HpsParticle.FINAL_STATE_PARTICLE))
# newEle=hps_event.addParticle(HpsParticle.FINAL_STATE_PARTICLE)
# print 'numbe of HpsParticles after = '+str(hps_event.getNumberOfParticles(HpsParticle.FINAL_STATE_PARTICLE))
newEle = HpsParticle()
newEle.setCharge(-1)
newEle.setPDG(11)
newEle.setEnergy(newEleP)
sign = 1
if random.random() < 0.5:
sign = -1
newEleMom = [
math.sin(0.03) * newEleP, sign * math.sin(0.04) * newEleP,
math.cos(0.04) * newEleP
]
newEle.setMomentum(np.array(newEleMom))
#fake track info
newEleTrack = SvtTrack()
newEleTrack.setTrackParameters(0.0, 0.03, 0.0001, sign * 0.04,
0.0)
newEleTrack.setTrackTime(40.0)
newEleTrack.setParticle(newEle)
newEleTrack.setChi2(3.0)
newEleTrack.setPositionAtEcal(
np.array([300.0, sign * 50.0, 1350.0]))
#fake cluster info
newEleCluster = EcalCluster()
newEleCluster.setEnergy(newEleP)
foobar = [300.0, sign * 50.0, 1350.0]
newEleCluster.setPosition(np.array(foobar, dtype='float32'))
newEle.addTrack(newEleTrack)
newEle.addCluster(newEleCluster)
# print 'fake electron cluster x ' + str(newEle.getClusters().First().getPosition()[0])
# Loop over all tracks in the event
npositrons = 0
n_tracks = 0
for track_n in xrange(0, hps_event.getNumberOfTracks()):
track = hps_event.getTrack(track_n)
if track is None:
continue
# if useGBL and track.getParticle().getType()<32 : continue
# if not useGBL and track.getParticle().getType()>31 : continue
if trkMatchAndFiducial(
track.getParticle(),
requireECalSuperFiducial) and trkMomentum(
track, minPCut, beamCut
): # count only matched tracks in defined fiducial region
n_tracks += 1
if track.getCharge() > 0:
npositrons += 1
myhist.rawposMom.Fill(pMag(track.getMomentum()))
else:
myhist.raweleMom.Fill(pMag(track.getMomentum()))
# findWABPair(track.getParticle(),hps_event)
if addFakeEle:
myhist.raweleMom.Fill(newEle.getEnergy())
n_tracks = n_tracks + 1
# print "nTracks = "+str(n_tracks)+"; nPositrons = "+str(npositrons)
# if n_tracks/2.0>nTrkMax : continue #do this very dumb thing (divide by 2 to un-double count GBL tracks)
# if n_tracks/2.0<2: continue
myhist.nTrk.Fill(n_tracks)
myhist.nPos.Fill(npositrons)
myhist.nEle.Fill(n_tracks - npositrons)
myhist.nClust.Fill(hps_event.getNumberOfEcalClusters())
if n_tracks > nTrkMax: continue
if n_tracks < nTrkMin: continue
if npositrons < 1 or npositrons > nPosMax: continue
nPassBasicCuts += 1
# print "passed basic cuts"
candidateList = []
bestCandidate = -99
nCandidate = 0
# loop over all v0 candidates...
for uc_index in xrange(
0,
hps_event.getNumberOfParticles(HpsParticle.UC_V0_CANDIDATE)):
particle = hps_event.getParticle(HpsParticle.UC_V0_CANDIDATE,
uc_index)
if useGBL and particle.getType() < 32: continue
if not useGBL and particle.getType() > 31: continue
# print "found one..."
vchi2 = particle.getVertexFitChi2()
vposition = particle.getVertexPosition()
vmomentum = particle.getMomentum()
if vchi2 > v0Chi2: continue
# use the measured sum of momentum
# if vmomentum[2]>v0PzMax : continue
# if vmomentum[2]<v0PzMin : continue
#recon'ed vertex position cuts
if abs(vposition[0]) > v0VxMax: continue
if abs(vposition[1]) > v0VyMax: continue
# if abs(vposition[2])>v0VzMax :continue
# Only look at particles that have two daugther particles...
daughter_particles = particle.getParticles()
if daughter_particles.GetSize() != 2: continue
# Only look at particles that are composed of e+e- pairs
if daughter_particles.At(0).getCharge() * daughter_particles.At(
1).getCharge() > 0:
continue
# print "Passed daughter number cuts"
electron = daughter_particles.At(0)
positron = daughter_particles.At(1)
if daughter_particles.At(0).getCharge() > 0:
electron = daughter_particles.At(1)
positron = daughter_particles.At(0)
pEle = electron.getMomentum()
pPos = positron.getMomentum()
v0Sum = pMag(pSum(pEle, pPos))
#total momentum sum cuts
if v0Sum > v0PzMax: continue
if v0Sum < v0PzMin: continue
nPassV0Cuts += 1
# print "Passed v0 cuts"
############# tracking cuts
#momentum cuts...get rid of very soft or very hard tracks
if pMag(pEle) > beamCut or pMag(pPos) > beamCut: continue
if pMag(pEle) < minPCut or pMag(pPos) < minPCut: continue
#top+bottom requirement
if pEle[1] * pPos[1] > 0: continue
# print 'looking at tracks now'
# print len(electron.getTracks())
if len(electron.getTracks()) == 0 or len(
positron.getTracks()) == 0:
continue
eleTrk = electron.getTracks().At(0)
posTrk = positron.getTracks().At(0)
if eleTrk is None or posTrk is None: continue
# eleTrk.Print("v")
#track timing
if eleTrk.getTrackTime() - posTrk.getTrackTime() > trkDeltaT:
continue
#track slope (if any cut)
if abs(eleTrk.getTanLambda()) < slopeCut or abs(
posTrk.getTanLambda()) < slopeCut:
continue
# print 'satisfied timing cuts...'
##############
# track killer part
if isMC and trackKiller:
if pMag(pEle) < tkThreshold: #electron
tkEff = tkSlope * pMag(pEle) + tkIntercept
if random.random() > tkEff:
continue
elif random.random(
) > tkThreshEff: #allow for a flat killer above threshold
continue
if pMag(pPos) < tkThreshold: # positron
tkEff = tkSlope * pMag(pPos) + tkIntercept
if random.random() > tkEff:
continue
elif random.random(
) > tkThreshEff: #allow for a flat killer above threshold
continue
# end of track killer
##############
nPassTrkCuts += 1
##############
# ECAL matching and timing cuts...also fiducial region cuts...
if requireECalMatch:
if positron.getClusters().GetEntries() == 0:
continue
if electron.getClusters().GetEntries() == 0:
continue
posCluster = positron.getClusters().First()
eleCluster = electron.getClusters().First()
if eleCluster.getClusterTime() - posCluster.getClusterTime(
) > cluDeltaT:
continue
if eleTrk.getTrackTime() - eleCluster.getClusterTime(
) + 43.5 > cluTrkDeltaT:
continue
if posTrk.getTrackTime() - posCluster.getClusterTime(
) + 43.5 > cluTrkDeltaT:
continue
if requireECalFiducial:
#ANTI-fiducial cut
# if myhist.inFiducialRegion(posCluster.getPosition()[0],posCluster.getPosition()[1]) :
# continue
# if myhist.inFiducialRegion(eleCluster.getPosition()[0],eleCluster.getPosition()[1]) :
# continue
#Fiducial cut
if not myhist.inFiducialRegion(
posCluster.getPosition()[0],
posCluster.getPosition()[1]):
continue
if not myhist.inFiducialRegion(
eleCluster.getPosition()[0],
eleCluster.getPosition()[1]):
continue
if requireECalSuperFiducial:
if not myhist.inSuperFiducialRegion(
posCluster.getPosition()[0],
posCluster.getPosition()[1]):
continue
if not myhist.inSuperFiducialRegion(
eleCluster.getPosition()[0],
eleCluster.getPosition()[1]):
continue
nPassECalMatch += 1
##############
#Passed the cuts..append the candidate index
findWABPair(electron, hps_event)
candidateList.append(uc_index)
numCands = len(candidateList)
if addFakeEle:
for track_n in xrange(0, hps_event.getNumberOfTracks()):
track = hps_event.getTrack(track_n)
if track is None:
continue
if trkMatchAndFiducial(track.getParticle()) and trkMomentum(
track, minPCut, beamCut
) and track.getCharge > 0 and newEle.getMomentum(
)[1] * track.getMomentum(
)[1] < 0: # get positron in fudicial region; make sure it's in opposite quadrant
myhist.eSum.Fill(newEle.getEnergy() +
pMag(track.getMomentum()))
numCands += 1
if len(candidateList) == 0:
# print 'made a new trident event'
nFakeTri += 1
myhist.nCand.Fill(numCands)
#########################
# found some candidates...lets fill plots...
#########################
for index in range(0, len(candidateList)):
particle = hps_event.getParticle(HpsParticle.TC_V0_CANDIDATE,
candidateList[index])
myhist.fillCandidateHistograms(particle)
myhist.nTrkCand.Fill(n_tracks)
myhist.nPosCand.Fill(npositrons)
myhist.nEleCand.Fill(n_tracks - npositrons)
myhist.nClustCand.Fill(hps_event.getNumberOfEcalClusters())
# if(nPassTrkCuts>0):
myhist.saveHistograms(output_file)
print "******************************************************************************************"
print "Number of Events:\t\t", nEvents, "\t\t\t", float(
nEvents) / nEvents, "\t\t\t", float(nEvents) / nEvents
print "N(particle) Cuts:\t\t", nPassBasicCuts, "\t\t\t", float(
nPassBasicCuts) / nEvents, "\t\t\t", float(nPassBasicCuts) / nEvents
print "V0 Vertex Cuts:\t\t", nPassV0Cuts, "\t\t\t", float(
nPassV0Cuts) / nPassBasicCuts, "\t\t\t", float(nPassV0Cuts) / nEvents
print "Tracking Cuts:\t\t", nPassTrkCuts, "\t\t\t", float(
nPassTrkCuts) / nPassV0Cuts, "\t\t\t", float(nPassTrkCuts) / nEvents
print "ECal Match Cuts:\t\t", nPassECalMatch, "\t\t\t", float(
nPassECalMatch) / nPassTrkCuts, "\t\t\t", float(
nPassECalMatch) / nEvents
print "Number of Fake Events Added: \t\t", nFakeTri, "\t\t\t", float(
nFakeTri) / nPassECalMatch
def shapeCards( process, isData, datahistosFile, histosFile, signalHistosFile, signalSample, hist, signalMass, minMass, maxMass, jesValue, jerValue, lumiUnc, outputName ):
"""function to run Roofit and save workspace for RooStats"""
warnings.filterwarnings( action='ignore', category=RuntimeWarning, message='.*class stack<RooAbsArg\*,deque<RooAbsArg\*> >' )
hSignal = signalHistosFile.Get(hist+'_'+signalSample)
hSignal.Rebin(10)
htmpSignal = hSignal.Clone()
#htmpSignal.Scale(100)
signalXS = search(dictXS, 'RPVStopStopToJets_UDD312_M-'+str(signalMass) )
#hSignal.Scale( lumi*signalXS / hSignal.Integral())
massAve = RooRealVar( 'massAve', 'massAve', minMass, maxMass )
rooSigHist = RooDataHist( 'rooSigHist', 'rooSigHist', RooArgList(massAve), hSignal )
rooSigHist.Print()
signal = RooHistPdf('signal','signal',RooArgSet(massAve),rooSigHist)
signal.Print()
signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+04,1e+04)
#if args.fitBonly: signal_norm.setConstant()
signal_norm.Print()
hBkg = datahistosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
#hBkg = histosFile.Get(hist+'_QCDPtAll_BCD')
bkgAcc = round(hBkg.Integral( hBkg.GetXaxis().FindBin( minMass ), hBkg.GetXaxis().FindBin( maxMass )))
#hBkg.Scale(1/hBkg.Integral())
hPseudo = hBkg.Clone()
hPseudo.Reset()
#background_norm = RooRealVar('background_norm','background_norm',bkgAcc,0.,1e+07)
background_norm = RooRealVar('background_norm','background_norm',1.,0.,1e+07)
background_norm.Print()
if 'template' in process:
rooBkgHist = RooDataHist( 'rooBkgHist', 'rooBkgHist', RooArgList(massAve), hBkg )
rooBkgHist.Print()
background = RooHistPdf('background','background',RooArgSet(massAve),rooBkgHist)
background.Print()
else:
p1 = RooRealVar('p1','p1', 1 ,0.,100.)
p2 = RooRealVar('p2','p2', 1 ,0.,60.)
p3 = RooRealVar('p3','p3', 1 , -10.,10.)
background = RooGenericPdf('background','(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(1300,1300,1300),RooArgList(massAve,p1,p2,p3))
background.Print()
### S+B model
if not isData:
newNumEvents = random.randint( bkgAcc-round(TMath.Sqrt(bkgAcc)), bkgAcc+round(TMath.Sqrt(bkgAcc)) )
print 'Events in MC:', bkgAcc, ', in PseudoExperiment:', newNumEvents
hPseudo.FillRandom( hBkg, newNumEvents )
#hPseudo.Scale(1/hPseudo.Integral())
#hData = histosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_ABCDProj')
hData = datahistosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
#hData = histosFile.Get(hist+'_QCDPtAll_A')
#hData.Add(htmpSignal)
#hData.Scale(1/hData.Integral())
rooDataHist = RooDataHist('rooDatahist','rooDatahist',RooArgList(massAve), hData if isData else hPseudo )
rooDataHist.Print()
#model = RooAddPdf("model","s+b",RooArgList(background,signal),RooArgList(background_norm,signal_norm))
#res = model.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(0))
#res.Print()
############# JES and JER uncertainties
hSigSyst = {}
hSigSystDataHist = {}
signalCDF = TGraph(hSignal.GetNbinsX()+1)
# JES and JER uncertainties
if args.jesUnc or args.jerUnc:
signalCDF.SetPoint(0,0.,0.)
integral = 0.
for i in range(1, hSignal.GetNbinsX()+1):
x = hSignal.GetXaxis().GetBinLowEdge(i+1)
integral = integral + hSignal.GetBinContent(i)
signalCDF.SetPoint(i,x,integral)
if args.jesUnc:
print ' |---> Adding JES'
hSigSyst['JESUp'] = hSignal.Clone()
hSigSyst['JESDown'] = hSignal.Clone()
if args.jerUnc:
print ' |---> Adding JER'
hSigSyst['JERUp'] = hSignal.Clone()
hSigSyst['JERDown'] = hSignal.Clone()
# reset signal histograms
for key in hSigSyst:
hSigSyst[key].Reset()
hSigSyst[key].SetName(hSigSyst[key].GetName() + '_' + key)
# produce JES signal shapes
if args.jesUnc:
for q in range(1, hSignal.GetNbinsX()+1):
xLow = hSignal.GetXaxis().GetBinLowEdge(q)
xUp = hSignal.GetXaxis().GetBinLowEdge(q+1)
jes = 1. - jesValue
xLowPrime = jes*xLow
xUpPrime = jes*xUp
hSigSyst['JESUp'].SetBinContent(q, signalCDF.Eval(xUpPrime) - signalCDF.Eval(xLowPrime))
jes = 1. + jesValue
xLowPrime = jes*xLow
xUpPrime = jes*xUp
hSigSyst['JESDown'].SetBinContent(q, signalCDF.Eval(xUpPrime) - signalCDF.Eval(xLowPrime))
hSigSystDataHist['JESUp'] = RooDataHist('hSignalJESUp','hSignalJESUp',RooArgList(massAve),hSigSyst['JESUp'])
hSigSystDataHist['JESDown'] = RooDataHist('hSignalJESDown','hSignalJESDown',RooArgList(massAve),hSigSyst['JESDown'])
# produce JER signal shapes
if args.jerUnc:
for i in range(1, hSignal.GetNbinsX()+1):
xLow = hSignal.GetXaxis().GetBinLowEdge(i)
xUp = hSignal.GetXaxis().GetBinLowEdge(i+1)
jer = 1. - jerValue
xLowPrime = jer*(xLow-float(signalMass))+float(signalMass)
xUpPrime = jer*(xUp-float(signalMass))+float(signalMass)
hSigSyst['JERUp'].SetBinContent(i, signalCDF.Eval(xUpPrime) - signalCDF.Eval(xLowPrime))
jer = 1. + jerValue
xLowPrime = jer*(xLow-float(signalMass))+float(signalMass)
xUpPrime = jer*(xUp-float(signalMass))+float(signalMass)
hSigSyst['JERDown'].SetBinContent(i, signalCDF.Eval(xUpPrime) - signalCDF.Eval(xLowPrime))
hSigSystDataHist['JERUp'] = RooDataHist('hSignalJERUp','hSignalJERUp',RooArgList(massAve),hSigSyst['JERUp'])
hSigSystDataHist['JERDown'] = RooDataHist('hSignalJERDown','hSignalJERDown',RooArgList(massAve),hSigSyst['JERDown'])
myWS = RooWorkspace("myWS")
getattr(myWS,'import')(rooSigHist,RooFit.Rename("signal"))
getattr(myWS,'import')(rooBkgHist,RooFit.Rename("background"))
#getattr(myWS,'import')(signal_norm)
getattr(myWS,'import')(background_norm)
if args.jesUnc:
getattr(myWS,'import')(hSigSystDataHist['JESUp'],RooFit.Rename("signal__JESUp"))
getattr(myWS,'import')(hSigSystDataHist['JESDown'],RooFit.Rename("signal__JESDown"))
if args.jerUnc:
getattr(myWS,'import')(hSigSystDataHist['JERUp'],RooFit.Rename("signal__JERUp"))
getattr(myWS,'import')(hSigSystDataHist['JERDown'],RooFit.Rename("signal__JERDown"))
getattr(myWS,'import')(rooDataHist,RooFit.Rename("data_obs"))
myWS.Print()
outputRootFile = currentDir+'/Rootfiles/workspace_'+outputName+'.root'
myWS.writeToFile(outputRootFile, True)
print ' |----> Workspace created in root file:\n', outputRootFile
# -----------------------------------------
# write a datacard
dataCardName = currentDir+'/Datacards/datacard_'+outputName+'.txt'
datacard = open( dataCardName ,'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if args.jesUnc or args.jerUnc or args.lumiUnc or args.normUnc or args.unc:
datacard.write('shapes * * '+outputRootFile+' myWS:$PROCESS myWS:$PROCESS__$SYSTEMATIC\n')
else: datacard.write("shapes * * "+outputRootFile+" myWS:$PROCESS \n")
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background\n')
datacard.write('process 0 1\n')
datacard.write('rate -1 -1\n')
datacard.write('------------------------------\n')
if args.lumiUnc: datacard.write('lumi lnN %f -\n'%(lumiUnc))
if args.jesUnc: datacard.write('JES shape 1 -\n')
if args.jerUnc: datacard.write('JER shape 1 -\n')
#flat parameters --- flat prior
if args.normUnc: datacard.write('background_norm flatParam\n')
#datacard.write('p1 flatParam\n')
datacard.close()
print ' |----> Datacard created:\n', dataCardName
def shapeCards(datahistosFile, histosFile, signalFile, signalSample, hist,
signalMass, minMass, maxMass, outputName, outputFileTheta):
"""function to run Roofit and save workspace for RooStats"""
warnings.filterwarnings(
action='ignore',
category=RuntimeWarning,
message='.*class stack<RooAbsArg\*,deque<RooAbsArg\*> >')
############################################################# DATA
#hData = histosFile.Get('massAve_deltaEtaDijet_QCDPtAll')
#hData = datahistosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
dataFile = TFile(datahistosFile)
hData = dataFile.Get(hist + '_DATA')
hData.Rebin(args.reBin)
#hData = hData.Rebin( len( boostedMassAveBins )-1, hData.GetName(), boostedMassAveBins )
#hData = histosFile.Get(hist+'_QCDPtAll_A')
#hData.Add(htmpSignal)
#hData.Scale(1/hData.Integral())
#maxMass = boostedMassAveBins[ hData.FindLastBinAbove( 0, 1) ]
#minMass = signalMass-30
#maxMass = signalMass+30
massAve = RooRealVar('massAve', 'massAve', minMass, maxMass)
#massAveData = RooRealVar( 'massAveData', 'massAveData', minMass, maxMass )
rooDataHist = RooDataHist('rooDatahist', 'rooDatahist',
RooArgList(massAve),
hData) # if isData else hPseudo )
rooDataHist.Print()
############################################################################################
####################### Signal
if 'gaus' in args.job:
hSignal = TH1F('massAve_RPVStop', 'massAve_RPVStop',
maxMass / args.reBin, minMass, maxMass)
for q in range(hSignal.GetNbinsX() + 1):
gausEval = signalFile.Eval(hSignal.GetXaxis().GetBinCenter(q))
hSignal.SetBinContent(q, gausEval)
#meanSig = RooRealVar( 'meanSig', 'mean of signal', sigGaus.GetParameter( 1 ) )
#sigmaSig = RooRealVar( 'sigmaSig', 'sigma of signal', sigGaus.GetParameter( 2 ) )
#signalPdf = RooGaussian( 'signal', 'signal', massAve, meanSig, sigmaSig )
#signalPdf.Print()
else:
signalHistosFile = TFile(signalFile)
hSignal = signalHistosFile.Get(hist + '_' + signalSample)
hSignal.Rebin(args.reBin)
hSignal.Scale(twoProngSF)
signalXS = search(dictXS, 'RPVStopStopToJets_UDD312_M-' + str(signalMass))
rooSigHist = RooDataHist('rooSigHist', 'rooSigHist', RooArgList(massAve),
hSignal)
sigAcc = rooSigHist.sumEntries(
) #round(hSignal.Integral( hSignal.GetXaxis().FindBin( minMass ), hSignal.GetXaxis().FindBin( maxMass )), 2)
rooSigHist.Print()
#signal = RooHistPdf('signal','signal',RooArgSet(massAve),rooSigHist)
#signal.Print()
#signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+04,1e+04)
#if args.fitBonly: signal_norm.setConstant()
#signal_norm.Print()
#####################################################################
hSigSyst = signalUnc(hSignal, signalMass)
hSigSystDataHist = {}
if args.jesUnc:
hSigSystDataHist['JESUp'] = RooDataHist('hSignalJESUp', 'hSignalJESUp',
RooArgList(massAve),
hSigSyst['JESUp'])
hSigSystDataHist['JESDown'] = RooDataHist('hSignalJESDown',
'hSignalJESDown',
RooArgList(massAve),
hSigSyst['JESDown'])
if args.jerUnc:
hSigSystDataHist['JERUp'] = RooDataHist('hSignalJERUp', 'hSignalJERUp',
RooArgList(massAve),
hSigSyst['JERUp'])
hSigSystDataHist['JERDown'] = RooDataHist('hSignalJERDown',
'hSignalJERDown',
RooArgList(massAve),
hSigSyst['JERDown'])
#################################### Background
if args.altBkg:
newBkgHistoFile = datahistosFile.replace('DATA', 'DATA_ABCDBkg')
newBkgFile = TFile(newBkgHistoFile)
htmpBkg = newBkgFile.Get(
'massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
if (htmpBkg.GetBinWidth(1) != args.reBin):
print '|----- Bin size in DATA_C histogram is different than rest.'
sys.exit(0)
else:
htmpBkg = dataFile.Get(
'massAve_prunedMassAsymVsdeltaEtaDijet_DATA_ABCDProj')
htmpBkg.Rebin(args.reBin)
#hBkg = histosFile.Get('massAve_prunedMassAsymVsdeltaEtaDijet_QCDPtAll_ABCDProj')
#hBkg = histosFile.Get(hist+'_QCDPtAll_BCD')
#htmpBkg = htmpBkg.Rebin( len( boostedMassAveBins )-1, htmpBkg.GetName(), boostedMassAveBins )
hBkg = htmpBkg.Clone()
hBkg.Reset()
for ibin in range(htmpBkg.GetNbinsX()):
binCont = htmpBkg.GetBinContent(ibin)
binErr = htmpBkg.GetBinError(ibin)
if binCont == 0:
hBkg.SetBinContent(ibin, 0)
hBkg.SetBinError(ibin, 1.8)
else:
hBkg.SetBinContent(ibin, binCont)
hBkg.SetBinError(ibin, binErr)
#hBkg.Scale(1/hBkg.Integral())
hPseudo = createPseudoExperiment(hBkg, bkgAcc)
###### Adding statistical uncertanty
hBkgStatUncUp = hBkg.Clone()
hBkgStatUncUp.Reset()
hBkgStatUncDown = hBkg.Clone()
hBkgStatUncDown.Reset()
for i in range(hBkg.GetNbinsX() + 1):
cont = hBkg.GetBinContent(i)
contErr = hBkg.GetBinError(i)
hBkgStatUncUp.SetBinContent(i, cont + (1 * contErr))
hBkgStatUncDown.SetBinContent(i, cont - (1 * contErr))
hBkgStatUncUpDataHist = RooDataHist('hBkgStatUncUp', 'hBkgStatUncUp',
RooArgList(massAve), hBkgStatUncUp)
hBkgStatUncDownDataHist = RooDataHist('hBkgStatUncDown', 'hBkgStatUncDown',
RooArgList(massAve), hBkgStatUncDown)
if 'template' in args.job:
rooBkgHist = RooDataHist('rooBkgHist', 'rooBkgHist',
RooArgList(massAve), hBkg)
bkgAcc = rooBkgHist.sumEntries()
rooBkgHist.Print()
background = RooHistPdf('background', 'background', RooArgSet(massAve),
rooBkgHist)
background.Print()
else:
massAveBkg = RooRealVar('massAveBkg', 'massAveBkg', minMass, maxMass)
p1 = RooRealVar('p1', 'p1', 1, 0., 100.)
p2 = RooRealVar('p2', 'p2', 1, 0., 60.)
p3 = RooRealVar('p3', 'p3', 1, -10., 10.)
bkgAcc = round(
hBkg.Integral(hBkg.GetXaxis().FindBin(minMass),
hBkg.GetXaxis().FindBin(maxMass)), 2)
background = RooGenericPdf(
'background',
'(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))' %
(1300, 1300, 1300), RooArgList(massAveBkg, p1, p2, p3))
background.Print()
hBkgSyst = {}
hBkgSystDataHist = {}
if args.bkgUnc:
print ' |---> Adding bkg unc'
hBkgSyst['BkgUncUp'] = hBkg.Clone()
hBkgSyst['BkgUncDown'] = hBkg.Clone()
for key in hBkgSyst:
hBkgSyst[key].Reset()
hBkgSyst[key].SetName(hBkgSyst[key].GetName() + '_' + key)
for q in range(0, hBkg.GetNbinsX()):
binCont = hBkg.GetBinContent(q)
bkgUncUp = 1. + (args.bkgUncValue / 100.)
hBkgSyst['BkgUncUp'].SetBinContent(q, binCont * bkgUncUp)
bkgUncDown = 1. - (args.bkgUncValue / 100.)
hBkgSyst['BkgUncDown'].SetBinContent(q, binCont * bkgUncDown)
hBkgSystDataHist['BkgUncUp'] = RooDataHist('hBkgBkgUncUp',
'hBkgBkgUncUp',
RooArgList(massAve),
hBkgSyst['BkgUncUp'])
hBkgSystDataHist['BkgUncDown'] = RooDataHist('hBkgBkgUncDown',
'hBkgBkgUncDown',
RooArgList(massAve),
hBkgSyst['BkgUncDown'])
############################################################################################
#model = RooAddPdf("model","s+b",RooArgList(background,signal),RooArgList(background_norm,signal_norm))
#res = model.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(0))
#res.Print()
############################ Create Workspace
myWS = RooWorkspace("myWS")
getattr(myWS, 'import')(rooBkgHist, RooFit.Rename("background"))
#getattr(myWS,'import')(background,RooFit.Rename("background"))
#getattr(myWS,'import')(signal_norm)
#getattr(myWS,'import')(background_norm)
'''
if 'gaus' in args.job:
getattr(myWS,'import')(signalPdf,RooFit.Rename("signal"))
if args.jesUnc:
getattr(myWS,'import')(signalPdfJESUp,RooFit.Rename("signal__JESUp"))
getattr(myWS,'import')(signalPdfJESDown,RooFit.Rename("signal__JESDown"))
else:
'''
getattr(myWS, 'import')(rooSigHist, RooFit.Rename("signal"))
if args.jesUnc:
getattr(myWS, 'import')(hSigSystDataHist['JESUp'],
RooFit.Rename("signal__JESUp"))
getattr(myWS, 'import')(hSigSystDataHist['JESDown'],
RooFit.Rename("signal__JESDown"))
if args.jerUnc:
getattr(myWS, 'import')(hSigSystDataHist['JERUp'],
RooFit.Rename("signal__JERUp"))
getattr(myWS, 'import')(hSigSystDataHist['JERDown'],
RooFit.Rename("signal__JERDown"))
if args.bkgUnc:
getattr(myWS, 'import')(hBkgSystDataHist['BkgUncUp'],
RooFit.Rename("background__BkgUncUp"))
getattr(myWS, 'import')(hBkgSystDataHist['BkgUncDown'],
RooFit.Rename("background__BkgUncDown"))
getattr(myWS, 'import')(hBkgStatUncUpDataHist,
RooFit.Rename("background__BkgStatUncUp"))
getattr(myWS, 'import')(hBkgStatUncDownDataHist,
RooFit.Rename("background__BkgStatUncDown"))
getattr(myWS, 'import')(rooDataHist, RooFit.Rename("data_obs"))
myWS.Print()
outputRootFile = currentDir + '/Rootfiles/workspace_' + outputName + '.root'
myWS.writeToFile(outputRootFile, True)
print ' |----> Workspace created in root file:\n', outputRootFile
'''
c1 = TCanvas('c1', 'c1', 10, 10, 750, 500 )
# c1.SetLogy()
xframe = myWS.var("massAve").frame()
signalPdf.plotOn( xframe )
xframe.Draw()
c1.SaveAs('test.png')
del c1
'''
############################################################################################
######################### write a datacard
dataCardName = currentDir + '/Datacards/datacard_' + outputName + '.txt'
datacard = open(dataCardName, 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if args.jesUnc or args.jerUnc or args.lumiUnc or args.bkgUnc or args.unc:
datacard.write('shapes * * ' + outputRootFile +
' myWS:$PROCESS myWS:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write("shapes * * " + outputRootFile + " myWS:$PROCESS \n")
datacard.write('---------------\n')
datacard.write('bin ' + signalSample + '\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin ' + signalSample + ' ' +
signalSample + '\n')
datacard.write('process signal background\n')
datacard.write('process 0 1\n')
#datacard.write('rate -1 -1\n')
datacard.write('rate ' + str(sigAcc) + ' ' + str(bkgAcc) +
'\n')
datacard.write('------------------------------\n')
if args.lumiUnc:
datacard.write('lumi lnN %f -\n' % (lumiValue))
if args.puUnc: datacard.write('pu lnN %f -\n' % (puValue))
if args.jesUnc: datacard.write('JES shape 1 -\n')
if args.jerUnc: datacard.write('JER shape 1 -\n')
#flat parameters --- flat prior
#if args.bkgUnc: datacard.write('BkgUnc shape - '+str( round( 1/ (args.bkgUncValue/34.1), 2 ) )+'\n')
if args.bkgUnc: datacard.write('BkgUnc shape - 1 \n')
#NcombineUnc = ( 1 / TMath.Sqrt( args.bkgUncValue / 100. ) ) - 1
#datacard.write('background_norm gmN '+str(int(round(NcombineUnc)))+' - '+str( round(bkgAcc/NcombineUnc,2) )+'\n')
#datacard.write('p1 flatParam\n')
datacard.write('BkgStatUnc shape - 1 \n')
datacard.close()
print ' |----> Datacard created:\n', dataCardName
############################################################################################
########## Theta
if args.theta:
print ' |----> Creating Theta file\n', outputFileTheta
outFile = TFile(outputFileTheta, 'update')
tmpName = 'rpvstopjj' + str(signalMass)
hSignal.SetName('massAve__' + tmpName)
hSignal.Write()
hSigSyst['JESDown'].SetName('massAve__' + tmpName + '__jes__down')
hSigSyst['JESDown'].Write()
hSigSyst['JESUp'].SetName('massAve__' + tmpName + '__jes__up')
hSigSyst['JESUp'].Write()
hSigSyst['JERDown'].SetName('massAve__' + tmpName + '__jer__down')
hSigSyst['JERDown'].Write()
hSigSyst['JERUp'].SetName('massAve__' + tmpName + '__jer__up')
hSigSyst['JERUp'].Write()
if (signalMass == 100): #or (signalMass == 170):
hBkg.SetName('massAve__background')
hBkg.Write()
hBkgSyst['BkgUncDown'].SetName('massAve__background__unc__down')
hBkgSyst['BkgUncDown'].Write()
hBkgSyst['BkgUncUp'].SetName('massAve__background__unc__up')
hBkgSyst['BkgUncUp'].Write()
hData.SetName('massAve__DATA')
hData.Write()
outFile.Close()
def expectedPlcLimit(obs_, poi_, model, ws, ntoys=30, CL=0.95, binData=False):
# obs : observable variable or RooArgSet of observables
# poi : parameter of interest or RooArgSet of parameters
# model : RooAbsPdf of model to consider including any constraints
# the parameters should have the values corresponding to the
# background-only hypothesis which will be used to estimate the
# expected limit.
# ntoys : number of toy datsets to generate to get expected limit
# CL : confidence level for interval
# returns a dictionary containing the expected limits and their 1 sigma
# errors for the first/only parameter in poi_ and a list of the results
# from the individual toys.
from math import sqrt
obs = RooArgSet(obs_)
obs.setName('observables')
mPars = model.getParameters(obs)
genPars = mPars.snapshot()
print "parameters for generating toy datasets"
genPars.Print("v")
limits = []
upperLimits = TH1F("upperLimits_%s" % poi_.GetName(), "", 100,
poi_.getMin(), poi_.getMax())
lowerLimits = TH1F("lowerLimits_%s" % poi_.GetName(), "", 100,
poi_.getMin(), poi_.getMax())
probs = array('d', [0.022, 0.16, 0.5, 0.84, 0.978])
upperQs = array('d', [0.] * len(probs))
lowerQs = array('d', [0.] * len(probs))
for i in range(0, ntoys):
print 'generate limit of toy %i of %i' % (i + 1, ntoys)
mPars.assignFast(genPars)
toyData = model.generate(obs, RooFit.Extended())
if binData:
toyData = RooDataHist('data_obs_%i' % i, 'data_obs_%i' % i, obs,
toyData)
toyData.SetName('data_obs_%i' % i)
toyData.Print()
limits.append(plcLimit(obs_, poi_, model, ws, toyData, CL))
#print limits[-1]
if limits[-1][poi_.GetName()]['ok'] and \
((poi_.getMax()-limits[-1][poi_.GetName()]['upper']) > 0.001*poi_.getMax()):
upperLimits.Fill(limits[-1][poi_.GetName()]['upper'])
if limits[-1][poi_.GetName()]['ok'] and \
((limits[-1][poi_.GetName()]['lower']-poi_.getMin()) > 0.001*abs(poi_.getMin())):
lowerLimits.Fill(limits[-1][poi_.GetName()]['lower'])
toyData.IsA().Destructor(toyData)
upperLimits.GetQuantiles(len(probs), upperQs, probs)
# upperLimits.Print()
print 'expected upper limit quantiles using %i toys: [' % (
upperLimits.GetEntries()),
for q in upperQs:
print '%0.4f' % q,
print ']'
lowerLimits.GetQuantiles(len(probs), lowerQs, probs)
# lowerLimits.Print()
print 'expected lower limit quantiles using %i toys: [' % (
lowerLimits.GetEntries()),
for q in lowerQs:
print '%0.4f' % q,
print ']'
expLimits = {
'upper': upperQs[2],
'upperErr': sqrt(
(upperQs[2] - upperQs[1]) * (upperQs[3] - upperQs[2])),
'lower': lowerQs[2],
'lowerErr': sqrt(
(lowerQs[2] - lowerQs[1]) * (lowerQs[3] - lowerQs[2])),
'ntoys': upperLimits.GetEntries(),
'upperQuantiles': upperQs,
'lowerQuantiles': lowerQs,
'quantiles': probs
}
return (expLimits, limits)
dspihist = dspi_data.fillHistogram(dspihist, RooArgList(time))
dskhist = dsk_data.fillHistogram(dskhist, RooArgList(time))
ratiohist.Divide(dskhist, dspihist)
# relative normalisation
fintegral = ratio.createIntegral(RooArgSet(time), 'fullrange').getVal()
hintegral = ratiohist.Integral('width') # has weights, use width
norm = fintegral / hintegral
print '=' * 5, ' Integrals ', '=' * 5
print 'Function integral / histogram integral = %g / %g = %g' % (
fintegral, hintegral, norm)
ratiohist.Scale(norm)
# make dataset from histogram
ratiodset = RooDataHist('ratiodset', '', RooArgList(time), ratiohist)
ratiodset.Print('v')
## Plot
tframe = time.frame(RooFit.Title('Time acceptance ratio'))
paramset = RooArgSet(rturnon, roffset, rbeta)
ratio.plotOn(tframe, RooFit.VisualizeError(fitresult, paramset, 1, False))
ratio.plotOn(tframe)
ratiodset.plotOn(tframe, RooFit.MarkerStyle(kFullDotMedium))
tframe.Draw()
# Print
if doPrint:
print 'Plotting to file: plots/DsK_ratio_%s.{png,pdf}' % timestamp
gPad.Print('plots/DsK_ratio_%s.png' % timestamp)
PDF = RooGenericPdf('PDF', '%s ? 0 : %s' % (acc_cond, expr),
RooArgList(time, turnon, offset))
elif accfn == 'erf':
acc_cond = '(@1<0.0002)'
expr = '(0.5*(TMath::Erf((@1-@2)/@0)+1))'
PDF = RooGenericPdf('PDF', '%s ? 0 : %s' % (acc_cond, expr),
RooArgList(turnon, time, offset))
else:
print 'Unknown acceptance type. Aborting'
assert(False)
# Dataset to fit to
datahist = RooDataHist('datahist', 'Dataset from a histogram',
RooArgList(time), RooFit.Import(timehist2, False))
# Debug
datahist.Print('v')
# Fit
PDF.fitTo(datahist, RooFit.SumW2Error(True), RooFit.NumCPU(1),
RooFit.Range(epsilon, 0.005),
RooFit.Optimize(False), RooFit.Verbose(True), RooFit.Strategy(2))
# Plot
tframe1 = time.frame(RooFit.Name('ptime'),
RooFit.Title('Lifetime acceptance fitted to %s' % accfn))
datahist.plotOn(tframe1, RooFit.MarkerStyle(kFullTriangleUp))
PDF.plotOn(tframe1, RooFit.LineColor(kGreen))
# canvas2 = TCanvas('canvas2', 'Acceptance', 800, 600)
tframe1.Draw()
canvas1.Print(fname)