def Fitfcn_max_likelihood(self, npar, gin, fcnVal, par, iflag):
likelihood = 0
mf = ROOT.MyMassSpectrum()
mf.SetParameters(par)
ig = GaussIntegrator()
ig.SetFunction(mf)
ig.SetRelTolerance(0.00001)
for i in range(0, self.num_bins):
for lower, higher in self.exclude_regions:
if lower < self.xmins[i] < higher:
continue
model_val = ig.Integral(self.xmins[i], self.xmaxes[i]) / (
self.xmaxes[i] - self.xmins[i])
self.background_fit_only[i] = model_val
model_val += self.model_scale_values[i] * par[4]
self.data_fits[i] = model_val
mv = model_val
di = self.data[i]
#print("mv", mv, "di", di)
#sys.stdout.flush()
if di > 1e10 or math.isinf(mv) or math.isnan(mv):
self.fit_failed = True
return
likelihood += mv - di
if di > 0 and mv > 0:
likelihood += di * (TMath.Log(di) - TMath.Log(mv))
#sys.stderr.flush()
fcnVal[0] = likelihood
def GetTestStatistics( h_mass_data, h_temp_bgd, h_temp_sig ): # Compute likelihood Loglik_bgr = 0. Loglik_sb = 0. for i in range(1,h_mass_data.GetNbinsX()+1): # \mu = 0 (no signal) Loglik_bgr += TMath.Log( TMath.Poisson( h_mass_data.GetBinContent(i),h_temp_bgd.GetBinContent(i) ) ) # \mu = 1 (signal + background) Loglik_sb += TMath.Log( TMath.Poisson( h_mass_data.GetBinContent(i),h_temp_sig.GetBinContent(i)+h_temp_bgd.GetBinContent(i) ) ) # Get likelihood ratio X = 2*( Loglik_bgr-Loglik_sb ) return X
def __call__(self, x, par):
#Eq. 93.16
#photon energy and angle
w = x[0]
d = x[1]
#initial and final electron E and E'
E = self.gen.Ee_n
Efin = E - w
t1 = 8. * self.gen.Z * self.gen.Z * self.gen.ar2 * (1. / w) * (
Efin / E) * d / ((1 + d**2)**2)
t2 = ((E / Efin) + (Efin / E) - 4 * d * d /
((1 + d**2)**2)) * TMath.Log(2. * E * Efin /
(self.gen.me * w))
t3 = 0.5 * ((E / Efin) + (Efin / E) + 2 - 16 * d * d /
((1 + d**2)**2))
sig = t1 * (t2 - t3)
return sig
def __call__(self, ndim, x):
#Eq. 93.16 from Lifshitz QED for bremsstrahlung, version for TFoam
#photon energy and angle, FOAM input ranges from 0 to 1
w = x[0] * self.gen.Ee
d = x[1] * self.gen.dmax
if w < self.gen.emin: return 0.
#initial and final electron E and E'
E = self.gen.Ee
Efin = E - w
t1 = 8. * self.gen.Z * self.gen.Z * self.gen.ar2 * (1. / w) * (
Efin / E) * d / ((1 + d**2)**2)
t2 = ((E / Efin) + (Efin / E) - 4 * d * d /
((1 + d**2)**2)) * TMath.Log(2. * E * Efin /
(self.gen.me * w))
t3 = 0.5 * ((E / Efin) + (Efin / E) + 2 - 16 * d * d /
((1 + d**2)**2))
sig = t1 * (t2 - t3)
return sig
def __call__(self, x, par):
#formula for dSigma/dy with y = Eg/Ee
y = x[0]
t1 = self.gen.ar2 / y
t2 = 1. + (1. - y)**2 - (2. / 3) * (1 - y)
t3 = TMath.Log(self.gen.s * (1 - y) / (self.gen.mep * y)) - 0.5
return t1 * t2 * t3
def eq1(self, x):
#formula for dSigma/dy with y = Eg/Ee
y = x[0]
t1 = self.ar2 / y
t2 = 1. + (1. - y)**2 - (2. / 3) * (1 - y)
t3 = TMath.Log(self.s * (1 - y) / (self.mep * y)) - 0.5
return t1 * t2 * t3
def LogLikelihood(histo, template, scale, minST, maxST):
result = 0.0
firstbin = histo.FindBin(minST)
lastbin = histo.FindBin(maxST)
for i in range(firstbin, lastbin):
n = histo.GetBinContent(i)
f = scale * template.GetBinContent(i)
#f = scale * template.Eval(histo.GetBinCenter(i))
log_f = TMath.Log(f)
result += n * log_f - f
return result
def eq1(self, x):
#E_gamma
Eg = x[0]
#electron and proton energy
Ee = self.Ee
Ep = self.Ep
#scattered electron Ee'
Escat = Ee - Eg
#if Escat < 1e-5: return 0.
t1 = Escat / (Eg * Ee)
t2 = (Ee / Escat) + (Escat / Ee) - 2. / 3
t3 = TMath.Log(4 * Ep * Ee * Escat / (self.mep * Eg)) - 1. / 2
return self.ar2 * t1 * t2 * t3
def BackgroundFit_f1(x, par): return par[0] * (1. - (x[0] / 8.e3))**par[1] / ((x[0] / 8.e3)**(par[2] + par[3] * TMath.Log((x[0] / 8.e3))))
def __init__(self, parse, tree, hepmc_attrib):
print("Quasi-real configuration:")
#electron and proton beam energy, GeV
self.Ee = parse.getfloat("main", "Ee")
self.Ep = parse.getfloat("main", "Ep")
print("Ee =", self.Ee, "GeV")
print("Ep =", self.Ep, "GeV")
#electron and proton mass
self.me = TDatabasePDG.Instance().GetParticle(11).Mass()
mp = TDatabasePDG.Instance().GetParticle(2212).Mass()
#boost vector pbvec of proton beam
pbeam = TLorentzVector()
pbeam.SetPxPyPzE(0, 0, TMath.Sqrt(self.Ep**2-mp**2), self.Ep)
self.pbvec = pbeam.BoostVector()
#electron beam energy Ee_p in proton beam rest frame
ebeam = TLorentzVector()
ebeam.SetPxPyPzE(0, 0, -TMath.Sqrt(self.Ee**2-self.me**2), self.Ee)
ebeam.Boost(-self.pbvec.x(), -self.pbvec.y(), -self.pbvec.z()) # transform to proton beam frame
self.Ee_p = ebeam.E()
#center-of-mass squared s, GeV^2
self.s = self.get_s(self.Ee, self.Ep)
print("s =", self.s, "GeV^2")
print("sqrt(s) =", TMath.Sqrt(self.s), "GeV")
#range in x
xmin = parse.getfloat("main", "xmin")
xmax = parse.getfloat("main", "xmax")
print("xmin =", xmin)
print("xmax =", xmax)
#range in u = log_10(x)
umin = TMath.Log10(xmin)
umax = TMath.Log10(xmax)
print("umin =", umin)
print("umax =", umax)
#range in y
ymin = parse.getfloat("main", "ymin")
ymax = parse.getfloat("main", "ymax")
#range in W
wmin = -1.
wmax = -1.
if parse.has_option("main", "Wmin"):
wmin = parse.getfloat("main", "Wmin")
print("Wmin =", wmin)
if parse.has_option("main", "Wmax"):
wmax = parse.getfloat("main", "Wmax")
print("Wmax =", wmax)
#adjust range in y according to W
if wmin > 0 and ymin < wmin**2/self.s:
ymin = wmin**2/self.s
if wmax > 0 and ymax > wmax**2/self.s:
ymax = wmax**2/self.s
print("ymin =", ymin)
print("ymax =", ymax)
#range in v = log_10(y)
vmin = TMath.Log10(ymin)
vmax = TMath.Log10(ymax)
print("vmin =", vmin)
print("vmax =", vmax)
#range in Q2
self.Q2min = parse.getfloat("main", "Q2min")
self.Q2max = parse.getfloat("main", "Q2max")
print("Q2min =", self.Q2min)
print("Q2max =", self.Q2max)
#constant term in the cross section
self.const = TMath.Log(10)*TMath.Log(10)*(1./137)/(2.*math.pi)
#cross section formula for d^2 sigma / dxdy, Eq. II.6
#transformed as x -> u = log_10(x) and y -> v = log_10(y)
self.eq_II6_uv_par = self.eq_II6_uv(self)
self.eq = TF2("d2SigDuDvII6", self.eq_II6_uv_par, umin, umax, vmin, vmax)
self.eq.SetNpx(1000)
self.eq.SetNpy(1000)
#uniform generator for azimuthal angles
self.rand = TRandom3()
self.rand.SetSeed(5572323)
#generator event variables in output tree
tnam = ["gen_u", "gen_v", "true_x", "true_y", "true_Q2", "true_W2"]
tnam += ["true_el_Q2"]
tnam += ["true_el_pT", "true_el_theta", "true_el_phi", "true_el_E"]
#create the tree variables
tcmd = "struct gen_out { Double_t "
for i in tnam:
tcmd += i + ", "
tcmd = tcmd[:-2] + ";};"
gROOT.ProcessLine( tcmd )
self.out = rt.gen_out()
#put zero to all variables
for i in tnam:
exec("self.out."+i+"=0")
#set the variables in the tree
if tree is not None:
for i in tnam:
tree.Branch(i, addressof(self.out, i), i+"/D")
#event attributes for hepmc
self.hepmc_attrib = hepmc_attrib
#counters for all generated and selected events
self.nall = 0
self.nsel = 0
#print generator statistics at the end
atexit.register(self.show_stat)
#total integrated cross section
self.sigma_tot = self.eq.Integral(umin, umax, vmin, vmax)
print("Total integrated cross section for a given x and y range:", self.sigma_tot, "mb")
print("Quasi-real photoproduction initialized")
def MuFit(Nbins,irebin=1.):
# Get histrograms
h_bgr = GetMassDistribution(1)
h_data = GetMassDistribution(2)
h_sig = GetMassDistribution(125)
h_bgr.Rebin(irebin)
h_data.Rebin(irebin)
h_sig.Rebin(irebin)
h_sf = TH2D("scalefactor","title",Nbins,0.5,2.,Nbins,0.,5.)
for i in range(1,h_sf.GetNbinsX()+1):
for j in range(1,h_sf.GetNbinsY()+1):
sf_bgr = h_sf.GetXaxis().GetBinCenter(i)
sf_sig = h_sf.GetYaxis().GetBinCenter(j)
# Loop over bins, compute likelihood
loglik = 0.
for iDataBin in range(1,h_data.GetNbinsX()+1):
m4lepBin = h_data.GetBinCenter(iDataBin)
NObsBin = h_data.GetBinContent(iDataBin)
MeanBin = sf_bgr*h_bgr.GetBinContent(iDataBin) + sf_sig*h_sig.GetBinContent(iDataBin)
if (MeanBin>0): loglik += TMath.Log( TMath.Poisson(NObsBin,MeanBin) )
h_sf.SetBinContent(i,j,-2.*loglik)
# Get best SF parameters
x=ctypes.c_int(0)
y=ctypes.c_int(0)
z=ctypes.c_int(0)
h_sf.GetBinXYZ(h_sf.GetMinimumBin(),x,y,z)
Minimum = h_sf.GetBinContent(x.value,y.value)
best_alpha = h_sf.GetXaxis().GetBinCenter(x.value)
best_mu = h_sf.GetYaxis().GetBinCenter(y.value)
print(Minimum,best_alpha,best_mu)
# Rescale histogram
for i in range(1,h_sf.GetNbinsX()+1):
for j in range(1,h_sf.GetNbinsY()+1):
h_sf.SetBinContent( i,j, h_sf.GetBinContent(i,j)-Minimum )
canvas = TCanvas("canvas","Standard Canvas",600,400)
canvas.cd()
Min = TMarker(best_alpha,best_mu,29)
Min.SetMarkerSize(2)
h_sf.SetStats(kFALSE)
h_sf.SetTitle("HIGGS PRODUCTION CROSS SECTION - PARAMETERS")
h_sf.GetXaxis().SetTitle(r"#alpha")
h_sf.GetYaxis().SetTitle(r"#mu")
h_sf.Draw("COLZ")
h_sigma = h_sf.Clone("h_sigma")
h_sigma.Reset()
for i in range(1,h_sigma.GetNbinsX()+1):
for j in range(1,h_sigma.GetNbinsY()+1):
if( h_sf.GetBinContent(i,j)<=1.): h_sigma.SetBinContent(i,j, 1.)
h_sigma.SetMarkerColorAlpha(kRed,0.40)
h_sigma.SetMarkerSize(10)
h_sigma.Draw("same L")
Min.Draw()
leg1 = TLegend(0.65,0.85,0.85,0.75)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
leg1a = leg1.AddEntry(Min, r"optimal (#alpha,#mu)", "p"); leg1a.SetTextSize(0.04);
leg1.Draw()
canvas.Print("Plots/MuFit.pdf")
def myAnalyzer( dictSamples, listCuts, signalName, RANGE, UNC ):
outputFileName = 'Rootfiles/RUNMiniBoostedAnalysis_'+grooming+'_'+signalName+UNC+'_'+RANGE+'_'+args.version+'p7.root'
outputFile = TFile( outputFileName, 'RECREATE' )
###################################### output Tree
#tree = TTree('RUNAFinTree'+grooming, 'RUNAFinTree'+grooming)
#AvgMass = array( 'f', [ 0. ] )
#tree.Branch( 'AvgMass', AvgMass, 'AvgMass/F' )
#Scale = array( 'f', [ 0. ] )
#tree.Branch( 'Scale', Scale, 'Scale/F' )
################################################################################################## Histos
massBins = 100
massXmin = 0.
massXmax = 500.
listOfOptions = [ [ j,k] for j in range(len(listCuts)-1) for k in range(1, len(listCuts) ) if k > j ]
for sam in dictSamples:
allHistos[ "cutFlow_"+sam ] = TH1F( "cutflow_"+sam, "cutflow_"+sam, len(listCuts), 0., len(listCuts) )
allHistos[ "cutFlow_Scaled_"+sam ] = TH1F( "cutflow_scaled_"+sam, "cutflow_scaled_"+sam, len(listCuts), 0., len(listCuts) )
allHistos[ "cutFlow_Scaled_Weights_"+sam ] = TH1F( "cutflow_scaled_weights_"+sam, "cutflow_scaled_weights_"+sam, len(listCuts), 0., len(listCuts) )
allHistos[ "HT_"+sam ] = TH1F( "HT_"+sam, "HT_"+sam, 5000, 0., 5000 )
allHistos[ "HT_"+sam ].Sumw2()
allHistos[ "MET_"+sam ] = TH1F( "MET_"+sam, "MET_"+sam, 500, 0., 500 )
allHistos[ "MET_"+sam ].Sumw2()
allHistos[ "massAve_"+sam ] = TH1F( "massAve_"+sam, "massAve_"+sam, 500, 0., 500 )
allHistos[ "massAve_"+sam ].Sumw2()
allHistos[ "numJets_"+sam ] = TH1F( "numJets_"+sam, "numJets_"+sam, 20, 0., 20 )
allHistos[ "numJets_"+sam ].Sumw2()
allHistos[ "jet1Pt_"+sam ] = TH1F( "jet1Pt_"+sam, "jet1Pt_"+sam, 2000, 0., 2000 )
allHistos[ "jet1Pt_"+sam ].Sumw2()
allHistos[ "jet2Pt_"+sam ] = TH1F( "jet2Pt_"+sam, "jet2Pt_"+sam, 2000, 0., 2000 )
allHistos[ "jet2Pt_"+sam ].Sumw2()
allHistos[ "jet1CosThetaStar_"+sam ] = TH1F( "jet1CosThetaStar_"+sam, "jet1CosThetaStar_"+sam, 20, 0., 1 )
allHistos[ "jet1CosThetaStar_"+sam ].Sumw2()
allHistos[ "jet2CosThetaStar_"+sam ] = TH1F( "jet2CosThetaStar_"+sam, "jet2CosThetaStar_"+sam, 20, 0., 1 )
allHistos[ "jet2CosThetaStar_"+sam ].Sumw2()
allHistos[ "jet1Tau32_"+sam ] = TH1F( "jet1Tau32_"+sam, "jet1Tau32_"+sam, 20, 0., 1 )
allHistos[ "jet1Tau32_"+sam ].Sumw2()
allHistos[ "jet2Tau32_"+sam ] = TH1F( "jet2Tau32_"+sam, "jet2Tau32_"+sam, 20, 0., 1 )
allHistos[ "jet2Tau32_"+sam ].Sumw2()
allHistos[ "jet1RhoDDT_"+sam ] = TH1F( "jet1RhoDDT_"+sam, "jet1RhoDDT_"+sam, 200, -10, 10 )
allHistos[ "jet1RhoDDT_"+sam ].Sumw2()
allHistos[ "jet2RhoDDT_"+sam ] = TH1F( "jet2RhoDDT_"+sam, "jet2RhoDDT_"+sam, 200, -10, 10 )
allHistos[ "jet2RhoDDT_"+sam ].Sumw2()
allHistos[ "jet1Tau21VsRhoDDT_"+sam ] = TH2F( "jet1Tau21VsRhoDDT_"+sam, "jet1Tau21VsRhoDDT_"+sam, 20, 0., 1., 200, -10, 10 )
allHistos[ "jet1Tau21VsRhoDDT_"+sam ].Sumw2()
allHistos[ "jet2Tau21VsRhoDDT_"+sam ] = TH2F( "jet2Tau21VsRhoDDT_"+sam, "jet2Tau21VsRhoDDT_"+sam, 20, 0., 1., 200, -10, 10 )
allHistos[ "jet2Tau21VsRhoDDT_"+sam ].Sumw2()
allHistos[ "jet1Tau21DDT_"+sam ] = TH1F( "jet1Tau21DDT_"+sam, "jet1Tau21DDT_"+sam, 30, -1, 2 )
allHistos[ "jet1Tau21DDT_"+sam ].Sumw2()
allHistos[ "jet2Tau21DDT_"+sam ] = TH1F( "jet2Tau21DDT_"+sam, "jet2Tau21DDT_"+sam, 30, -1, 2 )
allHistos[ "jet2Tau21DDT_"+sam ].Sumw2()
allHistos[ "jet1Tau21DDTVsRhoDDT_"+sam ] = TH2F( "jet1Tau21DDTVsRhoDDT_"+sam, "jet1Tau21DDTVsRhoDDT_"+sam, 20, 0., 1., 200, -10, 10 )
allHistos[ "jet1Tau21DDTVsRhoDDT_"+sam ].Sumw2()
allHistos[ "jet2Tau21DDTVsRhoDDT_"+sam ] = TH2F( "jet2Tau21DDTVsRhoDDT_"+sam, "jet2Tau21DDTVsRhoDDT_"+sam, 20, 0., 1., 200, -10, 10 )
allHistos[ "jet2Tau21DDTVsRhoDDT_"+sam ].Sumw2()
#if 'high' in args.RANGE:
allHistos[ "jet1Tau31_"+sam ] = TH1F( "jet1Tau31_"+sam, "jet1Tau31_"+sam, 20, 0., 1 )
allHistos[ "jet1Tau31_"+sam ].Sumw2()
allHistos[ "jet2Tau31_"+sam ] = TH1F( "jet2Tau31_"+sam, "jet2Tau31_"+sam, 20, 0., 1 )
allHistos[ "jet2Tau31_"+sam ].Sumw2()
allHistos[ "jet1SubjetPtRatio_"+sam ] = TH1F( "jet1SubjetPtRatio_"+sam, "jet1SubjetPtRatio_"+sam, 20, 0., 1 )
allHistos[ "jet1SubjetPtRatio_"+sam ].Sumw2()
allHistos[ "jet2SubjetPtRatio_"+sam ] = TH1F( "jet2SubjetPtRatio_"+sam, "jet2SubjetPtRatio_"+sam, 20, 0., 1 )
allHistos[ "jet2SubjetPtRatio_"+sam ].Sumw2()
allHistos[ "jet1BtagCSV_"+sam ] = TH1F( "jet1BtagCSV_"+sam, "jet1BtagCSV_"+sam, 5, 0., 5 )
allHistos[ "jet1BtagCSV_"+sam ].Sumw2()
allHistos[ "jet2BtagCSV_"+sam ] = TH1F( "jet2BtagCSV_"+sam, "jet2BtagCSV_"+sam, 5, 0., 5 )
allHistos[ "jet2BtagCSV_"+sam ].Sumw2()
allHistos[ "jetsBtagCSV_"+sam ] = TH1F( "jetsBtagCSV_"+sam, "jetsBtagCSV_"+sam, 5, 0., 5 )
allHistos[ "jetsBtagCSV_"+sam ].Sumw2()
allHistos[ "deltaEtaDijet_n-1_"+sam ] = TH1F( "deltaEtaDijet_n-1_"+sam, "deltaEtaDijet_n-1_"+sam, 50, 0., 5 )
allHistos[ "deltaEtaDijet_n-1_"+sam ].Sumw2()
allHistos[ "prunedMassAsym_n-1_"+sam ] = TH1F( "prunedMassAsym_n-1_"+sam, "prunedMassAsym_n-1_"+sam, 20, 0., 1 )
allHistos[ "prunedMassAsym_n-1_"+sam ].Sumw2()
allHistos[ "jet1Tau21_n-1_"+sam ] = TH1F( "jet1Tau21_n-1_"+sam, "jet1Tau21_n-1_"+sam, 20, 0., 1 )
allHistos[ "jet1Tau21_n-1_"+sam ].Sumw2()
allHistos[ "jet2Tau21_n-1_"+sam ] = TH1F( "jet2Tau21_n-1_"+sam, "jet2Tau21_n-1_"+sam, 20, 0., 1 )
allHistos[ "jet2Tau21_n-1_"+sam ].Sumw2()
'''
if 'low' in args.RANGE:
allHistos[ "jet1Tau31_n-1_"+sam ] = TH1F( "jet1Tau31_n-1_"+sam, "jet1Tau31_n-1_"+sam, 20, 0., 1 )
allHistos[ "jet1Tau31_n-1_"+sam ].Sumw2()
allHistos[ "jet2Tau31_n-1_"+sam ] = TH1F( "jet2Tau31_n-1_"+sam, "jet2Tau31_n-1_"+sam, 20, 0., 1 )
allHistos[ "jet2Tau31_n-1_"+sam ].Sumw2()
'''
listCuts.append( [ 'btag' ] )
for var in listCuts:
if 'deltaEta' in var[0]:
allHistos[ var[0]+'_'+sam ] = TH1F( var[0]+'_'+sam, var[0]+'_'+sam, 50, 0., 5. )
for var1 in listCuts: allHistos[ var[0]+'_'+var1[0]+"_"+sam ] = TH1F( var[0]+'_'+var1[0]+"_"+sam, var[0]+'_'+var1[0]+"_"+sam, 50, 0., 5. )
else:
allHistos[ var[0]+'_'+sam ] = TH1F( var[0]+'_'+sam, var[0]+'_'+sam, 20, 0., 1. )
for var1 in listCuts: allHistos[ var[0]+'_'+var1[0]+"_"+sam ] = TH1F( var[0]+'_'+var1[0]+"_"+sam, var[0]+'_'+var1[0]+"_"+sam, 20, 0., 1. )
allHistos[ var[0]+'_'+sam ].Sumw2()
allHistos[ "massAve_"+var[0]+'_'+sam ] = TH1F( "massAve_"+var[0]+'_'+sam, "massAve_"+var[0]+'_'+sam, massBins, massXmin, massXmax )
allHistos[ "massAve_"+var[0]+'_'+sam ].Sumw2()
allHistos[ "HT_"+var[0]+"_"+sam ] = TH1F( "HT_"+var[0]+"_"+sam, "HT_"+var[0]+"_"+sam, 5000, 0., 5000 )
allHistos[ "HT_"+var[0]+"_"+sam ].Sumw2()
allHistos[ "MET_"+var[0]+"_"+sam ] = TH1F( "MET_"+var[0]+"_"+sam, "MET_"+var[0]+"_"+sam, 500, 0., 500 )
allHistos[ "MET_"+var[0]+"_"+sam ].Sumw2()
allHistos[ "numJets_"+var[0]+"_"+sam ] = TH1F( "numJets_"+var[0]+"_"+sam, "numJets_"+var[0]+"_"+sam, 20, 0., 20 )
allHistos[ "numJets_"+var[0]+"_"+sam ].Sumw2()
allHistos[ "jet1Pt_"+var[0]+"_"+sam ] = TH1F( "jet1Pt_"+var[0]+"_"+sam, "jet1Pt_"+var[0]+"_"+sam, 2000, 0., 2000 )
allHistos[ "jet1Pt_"+var[0]+"_"+sam ].Sumw2()
allHistos[ "jet2Pt_"+var[0]+"_"+sam ] = TH1F( "jet2Pt_"+var[0]+"_"+sam, "jet2Pt_"+var[0]+"_"+sam, 2000, 0., 2000 )
allHistos[ "jet2Pt_"+var[0]+"_"+sam ].Sumw2()
listCuts.remove( ['btag'] )
for ind in listOfOptions:
tmpName = listCuts[ind[0]][0]+'Vs'+listCuts[ind[1]][0]+'_'+sam
allHistos[ tmpName ] = TH2F( tmpName, tmpName,
(50 if 'deltaEta' in listCuts[ind[0]][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[ind[0]][0] else 1. ),
(50 if 'deltaEta' in listCuts[ind[1]][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[ind[1]][0] else 1. )
)
allHistos[ tmpName ].Sumw2()
#tmpNameSam = listCuts[-2][0]+'Vs'+listCuts[-1][0]+'_'+sam
tmpNameSam = tmpName #listCuts[-2][0]+'Vs'+listCuts[-1][0]+'_'+sam
#if 'RPV' in sam: massBins = 50
#allHistos[ "massAve_"+tmpNameSam+'_ABCDProj' ] = TH1F( "massAve_"+tmpNameSam+'_ABCDProj', "massAve_"+tmpNameSam+'_ABCDProj', len(boostedMassAveBins)-1, boostedMassAveBins)
#allHistos[ "massAve_"+tmpNameSam+'_BC' ] = TH1F( "massAve_"+tmpNameSam+'_BC', "massAve_"+tmpNameSam+'_BC', len(boostedMassAveBins)-1, boostedMassAveBins )
#else:
allHistos[ "massAve_"+tmpNameSam+'_ABCDProj' ] = TH1F( "massAve_"+tmpNameSam+'_ABCDProj', "massAve_"+tmpNameSam+'_ABCDProj', massBins, massXmin, massXmax )
allHistos[ "massAve_"+tmpNameSam+'_BC' ] = TH1F( "massAve_"+tmpNameSam+'_BC', "massAve_"+tmpNameSam+'_BC', massBins, massXmin, massXmax )
allHistos[ "massAve_"+tmpNameSam+'_ABCDProj' ].Sumw2()
allHistos[ "massAve_"+tmpNameSam+'_BC' ].Sumw2()
allHistos[ tmpNameSam+'_Bkg' ] = TH2F( tmpNameSam+'_Bkg', tmpNameSam+'_Bkg',
#(50 if 'deltaEta' in listCuts[-2][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[-2][0] else 1. ),
#(50 if 'deltaEta' in listCuts[-1][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[-1][0] else 1. )
(50 if 'deltaEta' in listCuts[ind[0]][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[ind[0]][0] else 1. ),
(50 if 'deltaEta' in listCuts[ind[1]][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[ind[1]][0] else 1. )
)
allHistos[ tmpNameSam+'_Bkg' ].Sumw2()
for k in [ 'A', 'B', 'C', 'D' ]:
#allHistos[ "massAve_"+tmpNameSam+'_'+k ] = TH1F( "massAve_"+tmpNameSam+'_'+k, "massAve_"+tmpNameSam+'_'+k, len(boostedMassAveBins)-1, boostedMassAveBins )
allHistos[ "massAve_"+tmpNameSam+'_'+k ] = TH1F( "massAve_"+tmpNameSam+'_'+k, "massAve_"+tmpNameSam+'_'+k, massBins, massXmin, massXmax )
allHistos[ "massAve_"+tmpNameSam+'_'+k ].Sumw2()
allHistos[ tmpNameSam+'_'+k ] = TH2F( tmpNameSam+'_'+k, tmpNameSam+'_'+k,
#(50 if 'deltaEta' in listCuts[-2][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[-2][0] else 1. ),
#(50 if 'deltaEta' in listCuts[-1][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[-1][0] else 1. )
(50 if 'deltaEta' in listCuts[ind[0]][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[ind[0]][0] else 1. ),
(50 if 'deltaEta' in listCuts[ind[1]][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[ind[1]][0] else 1. )
)
allHistos[ tmpNameSam+'_'+k ].Sumw2()
allHistos[ "massAve_"+tmpNameSam+'_btag_'+k ] = TH1F( "massAve_"+tmpNameSam+'_btag_'+k, "massAve_"+tmpNameSam+'_btag_'+k, massBins, massXmin, massXmax )
allHistos[ "massAve_"+tmpNameSam+'_btag_'+k ].Sumw2()
allHistos[ tmpNameSam+'_btag_'+k ] = TH2F( tmpNameSam+'_btag_'+k, tmpNameSam+'_btag_'+k,
#(50 if 'deltaEta' in listCuts[-2][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[-2][0] else 1. ),
#(50 if 'deltaEta' in listCuts[-1][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[-1][0] else 1. )
(50 if 'deltaEta' in listCuts[ind[0]][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[ind[0]][0] else 1. ),
(50 if 'deltaEta' in listCuts[ind[1]][0] else 20 ), 0., (5. if 'deltaEta' in listCuts[ind[1]][0] else 1. )
)
allHistos[ tmpNameSam+'_btag_'+k ].Sumw2()
#print allHistos
################################################################################################## Running the Analysis
for sample in dictSamples:
####### Get GenTree
inputFile, events, numEntries = getTree( dictSamples[ sample ], ('BoostedAnalysisPlotsPuppi'+( '' if 'PDF' in UNC else UNC)+'/RUNATree' if 'Puppi' in args.grooming else 'BoostedAnalysisPlots'+( '' if 'PDF' in UNC else UNC)+'/RUNATree' ) )
print '-'*40
print '------> ', sample
print '------> Number of events: '+str(numEntries)
d = 0
cutFlowList = OrderedDict()
cutFlowScaledList = OrderedDict()
cutFlowScaledListWeights = OrderedDict()
cutFlowList[ 'Process' ] = 0
cutFlowList[ 'Preselection' ] = 0
cutFlowScaledList[ 'Process' ] = 0
cutFlowScaledList[ 'Preselection' ] = 0
cutFlowScaledListWeights[ 'Process' ] = 0
cutFlowScaledListWeights[ 'Preselection' ] = 0
for k in listCuts:
cutFlowList[ k[0] ] = 0
cutFlowScaledList[ k[0] ] = 0
cutFlowScaledListWeights[ k[0] ] = 0
cutFlowList[ 'btag' ] = 0
cutFlowScaledList[ 'btag' ] = 0
cutFlowScaledListWeights[ 'btag' ] = 0
for i in xrange(numEntries):
events.GetEntry(i)
#---- progress of the reading --------
fraction = 10.*i/(1.*numEntries)
if TMath.FloorNint(fraction) > d: print str(10*TMath.FloorNint(fraction))+'%'
d = TMath.FloorNint(fraction)
#if ( i > 100000 ): break
Run = events.run
Lumi = events.lumi
NumEvent = events.event
puWeight = events.puWeight
if 'v06' in args.version: pdfWeight = events.pdfWeight
lumiWeight = events.lumiWeight
HT = events.HT
MET = events.MET
numJets = events.numJets
massAve = getattr( events, (args.grooming+"MassAve").replace('Puppi','') )
jet1Mass = getattr( events, 'jet1'+(args.grooming+"Mass").replace('pruned','Pruned').replace('soft','Soft').replace('Puppi',''))
jet2Mass = getattr( events, 'jet2'+(args.grooming+"Mass").replace('pruned','Pruned').replace('soft','Soft').replace('Puppi',''))
jet1Pt = events.jet1Pt
jet2Pt = events.jet2Pt
jet1Eta = events.jet1Eta
jet2Eta = events.jet2Eta
jet1CosThetaStar = events.jet1CosThetaStar
jet2CosThetaStar = events.jet2CosThetaStar
jet1BtagCSV = ( events.jet1btagCSVv2 > 0.800 )
jet2BtagCSV = ( events.jet2btagCSVv2 > 0.800 )
#print 'Entry ', Run, ':', Lumi, ':', NumEvent
if 'DATA' in sample: scale = 1
#elif 'RPV' in sample: scale = 2606 * puWeight * SF
else: scale = 2666 * puWeight * lumiWeight
if 'PDF' in UNC:
if 'Up' in UNC: scale = scale*(1+pdfWeight)
else: scale = scale*(1-pdfWeight)
cutFlowList[ 'Process' ] += 1
cutFlowScaledList[ 'Process' ] += scale
cutFlowScaledList[ 'Process' ] += (puWeight*puWeight)
########## DDT
jet1RhoDDT = TMath.Log( jet1Mass*jet1Mass/jet1Pt )
jet2RhoDDT = TMath.Log( jet2Mass*jet2Mass/jet2Pt )
jet1Tau21DDT = events.jet1Tau21 + 0.063 * jet1RhoDDT
jet2Tau21DDT = events.jet2Tau21 + 0.063 * jet2RhoDDT
#### Pre-selection
HTCut = ( HT > 900 )
dijetCut = ( numJets > 1 )
#jetPtCut = ( jet1Pt > 500 ) and ( jet2Pt > 450 )
jetPtCut = ( jet1Pt > 150 ) and ( jet2Pt > 150 )
#if HTCut and dijetCut and jetPtCut:
if HTCut and dijetCut :
cutFlowList[ 'Preselection' ] += 1
cutFlowScaledList[ 'Preselection' ] += scale
cutFlowScaledList[ 'Preselection' ] += (puWeight*puWeight)
sigCutsList = []
allHistos[ "HT_"+sam ].Fill( HT, scale )
allHistos[ "MET_"+sam ].Fill( MET, scale )
allHistos[ "massAve_"+sam ].Fill( massAve, scale )
allHistos[ "numJets_"+sam ].Fill( numJets, scale )
allHistos[ "jet1Pt_"+sam ].Fill( jet1Pt, scale )
allHistos[ "jet2Pt_"+sam ].Fill( jet2Pt, scale )
allHistos[ "jet1RhoDDT_"+sam ].Fill( jet1RhoDDT, scale )
allHistos[ "jet2RhoDDT_"+sam ].Fill( jet2RhoDDT, scale )
allHistos[ "jet1Tau21VsRhoDDT_"+sam ].Fill( events.jet1Tau21, jet1RhoDDT, scale )
allHistos[ "jet2Tau21VsRhoDDT_"+sam ].Fill( events.jet2Tau21, jet2RhoDDT, scale )
allHistos[ "jet1Tau21DDT_"+sam ].Fill( jet1Tau21DDT, scale )
allHistos[ "jet2Tau21DDT_"+sam ].Fill( jet2Tau21DDT, scale )
allHistos[ "jet1Tau21DDTVsRhoDDT_"+sam ].Fill( jet1Tau21DDT, jet1RhoDDT, scale )
allHistos[ "jet2Tau21DDTVsRhoDDT_"+sam ].Fill( jet2Tau21DDT, jet2RhoDDT, scale )
allHistos[ "prunedMassAsym_"+sam ].Fill( events.prunedMassAsym, scale )
allHistos[ "deltaEtaDijet_"+sam ].Fill( events.deltaEtaDijet, scale )
allHistos[ "jet1CosThetaStar_"+sam ].Fill( jet1CosThetaStar, scale )
allHistos[ "jet2CosThetaStar_"+sam ].Fill( jet2CosThetaStar, scale )
allHistos[ "jet1Tau21_"+sam ].Fill( events.jet1Tau21, scale )
allHistos[ "jet2Tau21_"+sam ].Fill( events.jet2Tau21, scale )
allHistos[ "jet1Tau31_"+sam ].Fill( events.jet1Tau31, scale )
allHistos[ "jet2Tau31_"+sam ].Fill( events.jet2Tau31, scale )
allHistos[ "jet1Tau32_"+sam ].Fill( events.jet1Tau32, scale )
allHistos[ "jet2Tau32_"+sam ].Fill( events.jet2Tau32, scale )
allHistos[ "jet1SubjetPtRatio_"+sam ].Fill( events.jet1SubjetPtRatio, scale )
allHistos[ "jet2SubjetPtRatio_"+sam ].Fill( events.jet2SubjetPtRatio, scale )
allHistos[ "jet1BtagCSV_"+sam ].Fill( 1 if jet1BtagCSV else 0 )
allHistos[ "jet2BtagCSV_"+sam ].Fill( 1 if jet2BtagCSV else 0 )
bothBtag = ( jet1BtagCSV and jet2BtagCSV )
oneBtag = ( jet1BtagCSV or jet2BtagCSV )
if bothBtag: allHistos[ "jetsBtagCSV_"+sam ].Fill( 2 )
elif oneBtag: allHistos[ "jetsBtagCSV_"+sam ].Fill( 1 )
else: allHistos[ "jetsBtagCSV_"+sam ].Fill( 0 )
for var in listCuts:
#allHistos[ var[0]+'_'+sample ].Fill( getattr( events, var[0] ), scale )
nextCut = False
if ( getattr( events, var[0] ) < var[1] ): nextCut = True
else: nextCut = False
sigCutsList.append( nextCut )
if all(sigCutsList):
allHistos[ 'massAve_'+var[0]+'_'+sample ].Fill( massAve, scale ) ### adding two prong scale factor
allHistos[ 'jet1Tau21_'+var[0]+'_'+sample ].Fill( events.jet1Tau21, scale )
allHistos[ 'jet2Tau21_'+var[0]+'_'+sample ].Fill( events.jet2Tau21, scale )
#if 'low' in args.RANGE: allHistos[ 'jet1Tau31_'+var[0]+'_'+sample ].Fill( events.jet1Tau31, scale )
#if 'low' in args.RANGE: allHistos[ 'jet2Tau31_'+var[0]+'_'+sample ].Fill( events.jet2Tau31, scale )
allHistos[ 'prunedMassAsym_'+var[0]+'_'+sample ].Fill( events.prunedMassAsym, scale )
allHistos[ 'deltaEtaDijet_'+var[0]+'_'+sample ].Fill( events.deltaEtaDijet, scale )
allHistos[ "HT_"+var[0]+"_"+sam ].Fill( HT, scale )
allHistos[ "MET_"+var[0]+"_"+sam ].Fill( MET, scale )
allHistos[ "numJets_"+var[0]+"_"+sam ].Fill( numJets, scale )
allHistos[ "jet1Pt_"+var[0]+"_"+sam ].Fill( jet1Pt, scale )
allHistos[ "jet2Pt_"+var[0]+"_"+sam ].Fill( jet2Pt, scale )
cutFlowList[ var[0] ] += 1
cutFlowScaledList[ var[0] ] += scale
cutFlowScaledList[ var[0] ] += (puWeight*puWeight)
#if oneBtag and all(sigCutsList):
if bothBtag and all(sigCutsList):
allHistos[ 'massAve_btag_'+sample ].Fill( massAve, scale ) ### adding two prong scale factor
allHistos[ 'jet1Tau21_btag_'+sample ].Fill( events.jet1Tau21, scale )
allHistos[ 'jet2Tau21_btag_'+sample ].Fill( events.jet2Tau21, scale )
#if 'low' in args.RANGE: allHistos[ 'jet1Tau31_btag_'+sample ].Fill( events.jet1Tau31, scale )
#if 'low' in args.RANGE: allHistos[ 'jet2Tau31_btag_'+sample ].Fill( events.jet2Tau31, scale )
allHistos[ 'prunedMassAsym_btag_'+sample ].Fill( events.prunedMassAsym, scale )
allHistos[ 'deltaEtaDijet_btag_'+sample ].Fill( events.deltaEtaDijet, scale )
allHistos[ "HT_btag_"+sam ].Fill( HT, scale )
allHistos[ "MET_btag_"+sam ].Fill( MET, scale )
allHistos[ "numJets_btag_"+sam ].Fill( numJets, scale )
allHistos[ "jet1Pt_btag_"+sam ].Fill( jet1Pt, scale )
allHistos[ "jet2Pt_btag_"+sam ].Fill( jet2Pt, scale )
cutFlowList[ 'btag' ] += 1
cutFlowScaledList[ 'btag' ] += scale
cutFlowScaledList[ 'btag' ] += (puWeight*puWeight)
#### n-1 plots
'''
if ( 'low' in args.RANGE ):
if ( getattr( events, listCuts[0][0] ) < listCuts[0][1] ) and ( getattr( events, listCuts[1][0] ) < listCuts[1][1] ) and ( getattr( events, listCuts[2][0] ) < listCuts[2][1] ) and ( getattr( events, listCuts[3][0] ) < listCuts[3][1] ) and ( getattr( events, listCuts[4][0] ) < listCuts[4][1] ): allHistos[ 'deltaEtaDijet_n-1_'+sample ].Fill( events.deltaEtaDijet, scale )
if ( getattr( events, listCuts[0][0] ) < listCuts[0][1] ) and ( getattr( events, listCuts[1][0] ) < listCuts[1][1] ) and ( getattr( events, listCuts[2][0] ) < listCuts[2][1] ) and ( getattr( events, listCuts[3][0] ) < listCuts[3][1] ) and ( getattr( events, listCuts[5][0] ) < listCuts[5][1] ): allHistos[ 'prunedMassAsym_n-1_'+sample ].Fill( events.prunedMassAsym, scale )
if ( getattr( events, listCuts[2][0] ) < listCuts[2][1] ) and ( getattr( events, listCuts[3][0] ) < listCuts[3][1] ) and ( getattr( events, listCuts[4][0] ) < listCuts[4][1] ) and ( getattr( events, listCuts[5][0] ) < listCuts[5][1] ):
allHistos[ 'jet1Tau21_n-1_'+sample ].Fill( events.jet1Tau21, scale )
allHistos[ 'jet2Tau21_n-1_'+sample ].Fill( events.jet2Tau21, scale )
if ( getattr( events, listCuts[0][0] ) < listCuts[0][1] ) and ( getattr( events, listCuts[1][0] ) < listCuts[1][1] ) and ( getattr( events, listCuts[4][0] ) < listCuts[4][1] ) and ( getattr( events, listCuts[5][0] ) < listCuts[5][1] ):
allHistos[ 'jet1Tau31_n-1_'+sample ].Fill( events.jet1Tau31, scale )
allHistos[ 'jet2Tau31_n-1_'+sample ].Fill( events.jet2Tau31, scale )
else:
'''
if ( getattr( events, listCuts[0][0] ) < listCuts[0][1] ) and ( getattr( events, listCuts[1][0] ) < listCuts[1][1] ) and ( getattr( events, listCuts[3][0] ) < listCuts[3][1] ): allHistos[ 'prunedMassAsym_n-1_'+sample ].Fill( events.prunedMassAsym, scale )
if ( getattr( events, listCuts[0][0] ) < listCuts[0][1] ) and ( getattr( events, listCuts[1][0] ) < listCuts[1][1] ) and ( getattr( events, listCuts[2][0] ) < listCuts[2][1] ): allHistos[ 'deltaEtaDijet_n-1_'+sample ].Fill( events.deltaEtaDijet, scale )
if ( getattr( events, listCuts[2][0] ) < listCuts[2][1] ) and ( getattr( events, listCuts[3][0] ) < listCuts[3][1] ):
allHistos[ 'jet1Tau21_n-1_'+sample ].Fill( events.jet1Tau21, scale )
allHistos[ 'jet2Tau21_n-1_'+sample ].Fill( events.jet2Tau21, scale )
##########
for Ind in listOfOptions:
allHistos[ listCuts[Ind[0]][0]+'Vs'+listCuts[Ind[1]][0]+'_'+sample ].Fill( getattr( events, listCuts[Ind[0]][0] ), getattr( events, listCuts[Ind[1]][0] ), scale )
tmpSigCutsList = [ x for i,x in enumerate(sigCutsList) if i not in Ind ]
##### Bkg estimation/ABCD method
if ( all(sigCutsList[:-2]) ): # and ( getattr( events, listCuts[5][0] ) > (listCuts[5][1]*2) )):
allHistos[ listCuts[-2][0]+'Vs'+listCuts[-1][0]+'_'+sample+'_Bkg' ].Fill( getattr( events, listCuts[0][0] ), getattr( events, listCuts[1][0] ), scale )
plotABCD( [ ( getattr( events, listCuts[-2][0] ) < listCuts[-2][1] ), ( getattr( events, listCuts[-1][0] ) < listCuts[-1][1] ) ], [ listCuts[-2][0], listCuts[-1][0] ], events, massAve, scale, sample )
if bothBtag: plotABCD( [ ( getattr( events, listCuts[-2][0] ) < listCuts[-2][1] ), ( getattr( events, listCuts[-1][0] ) < listCuts[-1][1] ) ], [ listCuts[-2][0], listCuts[-1][0] ], events, massAve, scale, sample+'_btag' )
####### bkg estimation alternatives
if sigCutsList[2]:
allHistos[ 'jet1Tau21VsdeltaEtaDijet_'+sample+'_Bkg' ].Fill( getattr( events, 'jet1Tau21' ), getattr( events, 'deltaEtaDijet' ), scale )
allHistos[ 'jet2Tau21VsdeltaEtaDijet_'+sample+'_Bkg' ].Fill( getattr( events, 'jet2Tau21' ), getattr( events, 'deltaEtaDijet' ), scale )
plotABCDv2( [ ( getattr( events, listCuts[0][0] ) < listCuts[0][1] ), ( getattr( events, listCuts[1][0] ) < listCuts[1][1] ), ( getattr( events, listCuts[-1][0] ) < listCuts[-1][1] ) ], [ listCuts[0][0], listCuts[-1][0] ], events, massAve, scale, sample )
plotABCDv2( [ ( getattr( events, listCuts[0][0] ) < listCuts[0][1] ), ( getattr( events, listCuts[1][0] ) < listCuts[1][1] ), ( getattr( events, listCuts[-1][0] ) < listCuts[-1][1] ) ], [ listCuts[1][0], listCuts[-1][0] ], events, massAve, scale, sample )
'''
if sigCutsList[-1]:
allHistos[ 'jet1Tau21VsprunedMassAsym_'+sample+'_Bkg' ].Fill( getattr( events, 'jet1Tau21' ), getattr( events, 'prunedMassAsym' ), scale )
allHistos[ 'jet2Tau21VsprunedMassAsym_'+sample+'_Bkg' ].Fill( getattr( events, 'jet2Tau21' ), getattr( events, 'prunedMassAsym' ), scale )
plotABCDv2( [ ( getattr( events, listCuts[0][0] ) < listCuts[0][1] ), ( getattr( events, listCuts[1][0] ) < listCuts[1][1] ), ( getattr( events, listCuts[-2][0] ) < listCuts[-2][1] ) ], [ listCuts[0][0], listCuts[-2][0] ], events, massAve, scale, sample )
plotABCDv2( [ ( getattr( events, listCuts[0][0] ) < listCuts[0][1] ), ( getattr( events, listCuts[1][0] ) < listCuts[1][1] ), ( getattr( events, listCuts[-2][0] ) < listCuts[-2][1] ) ], [ listCuts[1][0], listCuts[-2][0] ], events, massAve, scale, sample )
'''
dummy = 1
for q in cutFlowList:
allHistos[ 'cutFlow_'+sample ].SetBinContent( dummy, cutFlowList[q] )
allHistos[ 'cutFlow_'+sample ].GetXaxis().SetBinLabel( dummy, q )
allHistos[ 'cutFlow_Scaled_'+sample ].SetBinContent( dummy, cutFlowScaledList[q] )
allHistos[ 'cutFlow_Scaled_'+sample ].GetXaxis().SetBinLabel( dummy, q )
allHistos[ 'cutFlow_Scaled_Weights_'+sample ].SetBinContent( dummy, cutFlowScaledListWeights[q] )
allHistos[ 'cutFlow_Scaled_Weights_'+sample ].GetXaxis().SetBinLabel( dummy, q )
dummy+=1
for sample in dictSamples:
nameABCD = listCuts[-2][0]+'Vs'+listCuts[-1][0]+'_'+sample
allHistos[ 'massAve_'+nameABCD+'_BC' ].Multiply( allHistos[ 'massAve_'+nameABCD+'_B' ], allHistos[ 'massAve_'+nameABCD+'_C' ], 1, 1, '')
allHistos[ 'massAve_'+nameABCD+'_ABCDProj' ].Divide( allHistos[ 'massAve_'+nameABCD+'_BC' ], allHistos[ 'massAve_'+nameABCD+'_D' ], 1, 1, '')
'''
### The two lines above are doing exactly the following:
for ibin in range( 0, allHistos[ 'massAve_'+nameABCD+'_B' ].GetNbinsX() ):
Bcont = allHistos[ 'massAve_'+nameABCD+'_B' ].GetBinContent( ibin )
Berr = allHistos[ 'massAve_'+nameABCD+'_B' ].GetBinError( ibin )
Ccont = allHistos[ 'massAve_'+nameABCD+'_C' ].GetBinContent( ibin )
Cerr = allHistos[ 'massAve_'+nameABCD+'_C' ].GetBinError( ibin )
Dcont = allHistos[ 'massAve_'+nameABCD+'_D' ].GetBinContent( ibin )
Derr = allHistos[ 'massAve_'+nameABCD+'_D' ].GetBinError( ibin )
try: Nbkg = ( Bcont * Ccont ) / Dcont
except ZeroDivisionError: Nbkg = 0
allHistos[ "massAve_"+nameABCD+'_ABCDProj' ].SetBinContent( ibin, Nbkg )
#try: NbkgErr = Nbkg * TMath.Sqrt( TMath.Power( Berr / Bcont, 2 ) + TMath.Power( Cerr / Ccont, 2 ) + TMath.Power( Derr / Dcont, 2 ) )
try: NbkgErr = Nbkg * TMath.Sqrt( TMath.Power( TMath.Sqrt(Bcont) / Bcont, 2 ) + TMath.Power( TMath.Sqrt(Ccont) / Ccont, 2 ) + TMath.Power( TMath.Sqrt(Dcont) / Dcont, 2 ) )
except ZeroDivisionError: NbkgErr = 0
allHistos[ "massAve_"+nameABCD+'_ABCDProj' ].SetBinError( ibin, NbkgErr )
'''
outputFile.Write()
##### Closing
print 'Writing output file: '+ outputFileName
outputFile.Close()
def PoissonError(nObs, ErrorType, plot=""):
# Prepare histograms
LambdaMin = 0.
LambdaMax = nObs + 6*np.sqrt(nObs)
Nsteps = 1000
h_likelihood = TH1D( "h_likelihood","",Nsteps,LambdaMin,LambdaMax )
h_2loglik = TH1D( "h_2loglik","",Nsteps,LambdaMin,LambdaMax )
h_pdf_full = TH1D( "h_pdf_full","",Nsteps,LambdaMin,LambdaMax )
IntFraction =ROOT.Math.gaussian_cdf(-1,1,0)
# loop over possible Lambda values
for iBin in range(1,Nsteps+1):
Lambda=h_likelihood.GetBinCenter(iBin)
PoissonProb = TMath.Poisson(nObs,Lambda)
LogLikelihood = -2.*TMath.Log(PoissonProb)
h_likelihood.Fill( Lambda,PoissonProb )
h_2loglik.Fill( Lambda, LogLikelihood )
h_pdf_full.Fill( Lambda, PoissonProb*1. )
# get characteristic values
bin_central = h_2loglik.GetMinimumBin()
LoglikMin = h_2loglik.GetBinContent(bin_central)
Lambda_central = h_2loglik.GetBinCenter(bin_central)
LambdaLow = -1.
LambdaUp = -1.
if ErrorType=="ClassicalCentral": # Frequentist
NobsMax = nObs+100
for iBin in range(1,h_pdf_full.GetNbinsX()+1):
Lambda = h_pdf_full.GetBinCenter(iBin)
PoissonSumLow = 0.
PoissonSumUp = 0.
for i in range(nObs,NobsMax+1):
PoissonSumLow += TMath.Poisson(i,Lambda)
if( PoissonSumLow>IntFraction and LambdaLow<0 ):
LambdaLow = Lambda
for i in range(0,nObs+1):
PoissonSumUp += TMath.Poisson(i,Lambda)
if( PoissonSumUp<IntFraction and LambdaUp<0 ):
LambdaUp = Lambda
cvs = TCanvas("Standard Canvas","",600,600)
cvs.Divide(1,2)
cvs.cd(1)
hLow = TH1D("hLow","CLASSICAL CENTRAL",1000,0,17)
for i in range(1,hLow.GetNbinsX()+1):
hLow.SetBinContent(i, TMath.Poisson( hLow.GetBinCenter(i),LambdaLow ))
hLow.SetLineColor(1)
hLow.GetXaxis().SetTitle(r"#mu")
h_conf = hLow.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)<=nObs): h_conf.SetBinContent(i, 0)
h_conf.SetFillColorAlpha(9,0.5)
h_conf.SetLineWidth(1)
h_conf.SetLineColor(1)
LowLine = TLine(LambdaLow,0,LambdaLow,hLow.GetBinContent(hLow.FindBin(LambdaLow)))
hLow.SetStats(kFALSE)
hLow.Draw("l")
h_conf.Draw("l same")
LowLine.Draw()
text = TLatex()
text.SetTextSize(0.5)
text.SetTextColor(1)
text.DrawLatex(0.5,0.8, "LOL")
cvs.cd(2)
hUp = TH1D("hUp","",1000,0,17)
for i in range(1,hUp.GetNbinsX()+1):
hUp.SetBinContent(i, TMath.Poisson( hLow.GetBinCenter(i),LambdaUp ))
hUp.SetLineColor(1)
hUp.GetXaxis().SetTitle(r"#mu")
h_conf1 = hUp.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>=nObs): h_conf1.SetBinContent(i, 0)
h_conf1.SetFillColorAlpha(9,0.5)
h_conf1.SetLineWidth(1)
h_conf1.SetLineColor(1)
UpLine = TLine(LambdaUp,0,LambdaUp,hUp.GetBinContent(hUp.FindBin(LambdaUp)))
hUp.SetStats(kFALSE)
hUp.Draw("l")
h_conf1.Draw("l same")
UpLine.Draw()
cvs.cd()
leg1 = TLegend(0.35,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
if ErrorType=="LikelihoodRatio":
for i in range(1,h_2loglik.GetNbinsX()+1):
if (h_2loglik.GetBinCenter(i)<LoglikMin and h_2loglik.GetBinContent(i)-LoglikMin>=1.): LambdaLow=h_2loglik.GetBinCenter(i)
if (h_2loglik.GetBinCenter(i)>LoglikMin and h_2loglik.GetBinContent(i)-LoglikMin<=1.): LambdaUp=h_2loglik.GetBinCenter(i)
cvs = TCanvas("Standard Canvas","",600,400)
cvs.cd()
LowLine = TLine(LambdaLow,2,LambdaLow,h_2loglik.GetBinContent(h_2loglik.FindBin(LambdaLow))); LowLine.SetLineWidth(1)
UpLine = TLine(LambdaUp,2,LambdaUp,h_2loglik.GetBinContent(h_2loglik.FindBin(LambdaUp))); UpLine.SetLineWidth(1)
ObsLine = TLine(nObs,2,nObs,h_2loglik.GetBinContent(h_2loglik.FindBin(nObs))); ObsLine.SetLineWidth(1); ObsLine.SetLineColor(2); ObsLine.SetLineStyle(7)
h_2loglik.SetFillColor(0)
h_2loglik.SetStats(kFALSE)
h_2loglik.SetTitle("LIKELIHOOD RATIO")
h_2loglik.SetAxisRange(0.8,8,"X")
h_2loglik.SetAxisRange(2,7,"Y")
h_2loglik.Draw("hist lp")
h_2loglik.GetXaxis().SetTitle(r"#mu")
h_conf = h_2loglik.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>LambdaLow and h_conf.GetBinCenter(i)<LambdaUp): h_conf.SetBinContent(i,0)
h_conf.SetFillColor(9)
h_conf.Draw("hist same")
leg1 = TLegend(0.55,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
LowLine.Draw()
UpLine.Draw()
ObsLine.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
if ErrorType=="BayesCentral":
# Work on likelihood as PDF
Integral = h_likelihood.Integral()
for i in range(1,h_likelihood.GetNbinsX()+1):
h_likelihood.SetBinContent(i,h_likelihood.GetBinContent(i)/Integral)
# Sum over bins until reached CL
Sum_low = 0.
Sum_up = 0.
for i in range(1,bin_central+1):
if Sum_low<=IntFraction:
Sum_low += h_likelihood.GetBinContent(i)
LambdaLow = h_likelihood.GetBinCenter(i)
for i in range(h_likelihood.GetNbinsX(),bin_central,-1):
if Sum_up<=IntFraction:
Sum_up += h_likelihood.GetBinContent(i)
LambdaUp = h_likelihood.GetBinCenter(i)
cvs = TCanvas("Standard Canvas","",600,400)
cvs.cd()
LowLine = TLine(LambdaLow,0,LambdaLow,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaLow))); LowLine.SetLineWidth(1)
UpLine = TLine(LambdaUp,0,LambdaUp,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaUp))); UpLine.SetLineWidth(1)
ObsLine = TLine(nObs,0,nObs,h_likelihood.GetBinContent(h_2loglik.FindBin(nObs))); ObsLine.SetLineWidth(1); ObsLine.SetLineColor(2); ObsLine.SetLineStyle(7)
h_likelihood.SetFillColor(0)
h_likelihood.SetStats(kFALSE)
h_likelihood.SetTitle("BAYES CENTRAL")
#h_likelihood.SetAxisRange(0.8,8,"X")
#h_likelihood.SetAxisRange(2,7,"Y")
h_likelihood.Draw("hist lp")
h_likelihood.GetXaxis().SetTitle(r"#mu")
h_conf = h_likelihood.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>LambdaLow and h_conf.GetBinCenter(i)<LambdaUp): h_conf.SetBinContent(i,0)
h_conf.SetFillColor(9)
h_conf.Draw("hist same")
leg1 = TLegend(0.55,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
LowLine.Draw()
UpLine.Draw()
ObsLine.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
if ErrorType=="BayesHDI":
# Work on likelihood as PDF
Integral = h_likelihood.Integral()
for i in range(1,h_likelihood.GetNbinsX()+1):
h_likelihood.SetBinContent(i,h_likelihood.GetBinContent(i)/Integral)
Area = 0.
RightIndex=bin_central
while Area<=(1-2*IntFraction):
RightIndex+=1
# find corresponding bin on left side
for i in range(1,bin_central):
if h_likelihood.GetBinContent(i)<=h_likelihood.GetBinContent(RightIndex):
LeftIndex=i
Area = h_likelihood.Integral( LeftIndex,RightIndex )
LambdaLow = h_likelihood.GetBinCenter(LeftIndex)
LambdaUp = h_likelihood.GetBinCenter(RightIndex)
cvs = TCanvas("Standard Canvas","",600,400)
cvs.cd()
LowLine = TLine(LambdaLow,0,LambdaLow,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaLow))); LowLine.SetLineWidth(1)
UpLine = TLine(LambdaUp,0,LambdaUp,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaUp))); UpLine.SetLineWidth(1)
ObsLine = TLine(nObs,0,nObs,h_likelihood.GetBinContent(h_2loglik.FindBin(nObs))); ObsLine.SetLineWidth(1); ObsLine.SetLineColor(2); ObsLine.SetLineStyle(7)
h_likelihood.SetFillColor(0)
h_likelihood.SetStats(kFALSE)
h_likelihood.SetTitle("BAYES SHORTEST")
h_likelihood.Draw("hist lp")
h_likelihood.GetXaxis().SetTitle(r"#mu")
h_conf = h_likelihood.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>LambdaLow and h_conf.GetBinCenter(i)<LambdaUp): h_conf.SetBinContent(i,0)
h_conf.SetFillColor(9)
h_conf.Draw("hist same")
leg1 = TLegend(0.55,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
LowLine.Draw()
UpLine.Draw()
ObsLine.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
return [LambdaLow, LambdaUp]
return
def BackgroundFit_f1_trigcorr_bbh(x, par): return par[0] * (1. - (x[0] / 8.e3))**par[1] / ((x[0] / 8.e3)**(par[2] + par[3] * TMath.Log((x[0] / 8.e3)))) * trigger_efficiency.trigger_efficiency_bbh(x[0])
def create_off_interpolations(OffDir, plot=False):
global log_ene_off
global numtotoff
global inter_func
off_en_edges = []
off_en_center = []
log_off_en_center = np.empty(shape=(numsamples, config.NumBckgEnBins),
dtype='double')
y_log_off_en_center = np.empty(shape=(numsamples, config.NumBckgEnBins),
dtype='double')
binsize = math.log(
config.EnCutHighBG / config.EnCutLowBG) / config.NumBckgEnBins
for ebin in range(0, config.NumBckgEnBins + 1):
off_en_edges.append(config.EnCutLowBG * math.exp(binsize * ebin))
for ebin in range(0, config.NumBckgEnBins):
off_en_center.append(
math.sqrt(off_en_edges[ebin + 1] * off_en_edges[ebin]))
# create here offbins with the (larger) background limits
offbins = array('d', off_en_edges)
for k in range(0, numsamples):
print("\n\n--- INITIALIZING VALUES from:", OffDir[k], "\n")
DoHisto = TFile.Open(OffDir[k], "READ")
print(DoHisto)
log_ene_off.append(np.empty(
shape=(config.EvtMaxOff),
dtype='double')) # array of log(energies) from all OFF events
noff = np.array([DoHisto.Get("fOffSample")])
numtotoff[k] = noff[0].GetEntries()
print("The total number of entries in the normalization histogram is:",
numtotoff[k])
isto = ROOT.TH1F("interpolation", "interpolation",
config.NumBckgEnBins, offbins)
num = 0
norm = 0.
for i in range(0, numtotoff[k]):
noff[0].GetEntry(i)
energy = noff[0].GetArgs()[0]
isto.Fill(energy)
if (not pass_energy_cuts(energy)):
continue
log_ene_off[k][num] = math.log(energy)
num += 1
numtotoff[k] = num
log_ene_off[k] = np.resize(log_ene_off[k], num)
print('The number of off entries (after energy cuts) is:', num)
print(log_ene_off[k])
print('The sum of ', np.sum(log_ene_off[k]))
ebin_cnt = 0
for ebin in range(0, config.NumBckgEnBins):
bin = isto.GetXaxis().FindBin(off_en_center[ebin])
bin_content = isto.GetBinContent(bin)
if (bin_content < 1.):
print('ebin: ', ebin, ' bin: ', bin,
'skipped because of zero entries')
continue
# calculate here the logarithm of dN/dE (which normalized will provide dP/dE)
log_off_en_center[k][ebin_cnt] = math.log(off_en_center[ebin])
y_log_off_en_center[k][ebin_cnt] = math.log(
bin_content / (off_en_edges[ebin + 1] - off_en_edges[ebin]))
print('ebin: ', ebin, ' ebin_cnt=', ebin_cnt, ' log_off_center: ',
log_off_en_center[k][ebin_cnt], ' content: ',
y_log_off_en_center[k][ebin_cnt], 'bin= ', bin, ' content=',
bin_content)
ebin_cnt += 1
inter_func.append(
interp1d(
log_off_en_center[k], y_log_off_en_center[k],
kind='linear')) #inter_func = interp1d(x, y, kind='linear')
print(math.exp(inter_func[k](math.log(90))))
print('found sum: ', np.sum(inter_func[k](log_ene_off[k])))
if plot:
xnew = np.linspace(TMath.Log(config.energy_cut),
TMath.Log(config.energy_upper_cut),
num=200,
endpoint=True)
plt.plot(log_off_en_center[k], y_log_off_en_center[k], 'o', xnew,
inter_func[k](xnew), '-')
plt.xlabel('Log Eest')
plt.ylabel('Log events')
plt.legend(['data', 'linear'], loc='best')
plt.show()
plt.savefig('interpolation_{}.png'.format(k))
# for k in range (0, 2): #k in range 0,2 ???
## if k < NumSubSamples:
# hOffHeight[k] = ROOT.TH1F(ROOT.Form("hOffHeight_%s" % SampleName[k]), ROOT.Form("Background Level_%s" % SampleName[k]), config.NumBckgEnBins, off_en_edges)
## else:
## hOffHeight[k] = ROOT.TH1F("hOffHeight", "Background Level", config.NumBckgEnBins, off_en_edges)
## if k < NumSubSamples:
## hOnHeight[k] = ROOT.TH1F(ROOT.Form("hOnHeight_%s" % SampleName[k]), ROOT.Form("Signal Level_%s" % SampleName[k]), config.NumBckgEnBins, off_en_edges)
## else:
## hOnHeight[k] = ROOT.TH1F("hOnHeight", "Signal Level", config.NumBckgEnBins, off_en_edges)
return
def calc_doubling(slope):
return 1. / (slope * 24 * 3600) * TMath.Log(2.)
def main():
if len(sys.argv) < 3:
print("Usage: ToyMC [numberEvents] [randomSeed]")
return
numberEvents = int(sys.argv[1])
seed = int(sys.argv[2])
print(
"==================================== TRAIN ===================================="
)
f = root_open(
"legotrain_350_20161117-2106_LHCb4_fix_CF_pPb_MC_ptHardMerged.root", "read"
)
hJetPt = f.Get("AliJJetJtTask/AliJJetJtHistManager/JetPt/JetPtNFin{:02d}".format(2))
hZ = f.Get("AliJJetJtTask/AliJJetJtHistManager/Z/ZNFin{:02d}".format(2))
FillFakes = False
dummy_variable = True
weight = True
NBINS = 50
LimL = 0.1
LimH = 500
logBW = (TMath.Log(LimH) - TMath.Log(LimL)) / NBINS
LogBinsX = [LimL * math.exp(ij * logBW) for ij in range(0, NBINS + 1)]
hJetPtMeas = Hist(LogBinsX)
hJetPtTrue = Hist(LogBinsX)
myRandom = TRandom3(seed)
fEff = TF1("fEff", "1-0.5*exp(-x)")
jetBinBorders = [5, 10, 20, 30, 40, 60, 80, 100, 150, 500]
hJetPtMeasCoarse = Hist(jetBinBorders)
hJetPtTrueCoarse = Hist(jetBinBorders)
NBINSJt = 64
low = 0.01
high = 10
BinW = (TMath.Log(high) - TMath.Log(low)) / NBINSJt
LogBinsJt = [low * math.exp(i * BinW) for i in range(NBINSJt + 1)]
hJtTrue = Hist(LogBinsJt)
hJtMeas = Hist(LogBinsJt)
hJtFake = Hist(LogBinsJt)
LogBinsPt = jetBinBorders
jetPtBins = [(a, b) for a, b in zip(jetBinBorders, jetBinBorders[1:])]
hJtTrue2D = Hist2D(LogBinsJt, LogBinsPt)
hJtMeas2D = Hist2D(LogBinsJt, LogBinsPt)
hJtFake2D = Hist2D(LogBinsJt, LogBinsPt)
hJtMeasBin = [Hist(LogBinsJt) for i in jetBinBorders]
hJtTrueBin = [Hist(LogBinsJt) for i in jetBinBorders]
response = RooUnfoldResponse(hJtMeas, hJtTrue)
response2D = RooUnfoldResponse(hJtMeas2D, hJtTrue2D)
responseBin = [RooUnfoldResponse(hJtMeas, hJtTrue) for i in jetBinBorders]
responseJetPt = RooUnfoldResponse(hJetPtMeas, hJetPtTrue)
responseJetPtCoarse = RooUnfoldResponse(hJetPtMeasCoarse, hJetPtTrueCoarse)
# Histogram index is jet pT index, Bin 0 is 5-10 GeV
# Histogram X axis is observed jT, Bin 0 is underflow
# Histogram Y axis is observed jet Pt, Bin 0 is underflow
# Histogram Z axis is True jT, Bin 0 is underflow
responses = [Hist3D(LogBinsJt, LogBinsPt, LogBinsJt) for i in jetPtBins]
misses = Hist2D(LogBinsJt, LogBinsPt)
fakes2D = Hist2D(LogBinsJt, LogBinsPt)
outFile = TFile("tuple.root", "recreate")
responseTuple = TNtuple(
"responseTuple", "responseTuple", "jtObs:ptObs:jtTrue:ptTrue"
)
hMultiTrue = Hist(50, 0, 50)
hMultiMeas = Hist(50, 0, 50)
hZMeas = Hist(50, 0, 1)
hZTrue = Hist(50, 0, 1)
hZFake = Hist(50, 0, 1)
responseMatrix = Hist2D(LogBinsJt, LogBinsJt)
numberJets = 0
numberFakes = 0
numberJetsMeasBin = [0 for i in jetBinBorders]
numberJetsTrueBin = [0 for i in jetBinBorders]
numberFakesBin = [0 for i in jetBinBorders]
ieout = numberEvents / 10
if ieout > 10000:
ieout = 10000
fakeRate = 1
start_time = datetime.now()
print("Processing Training Events")
for ievt in range(numberEvents):
tracksTrue = []
tracksMeas = [0 for x in range(100)]
if ievt % ieout == 0 and ievt > 0:
time_elapsed = datetime.now() - start_time
time_left = timedelta(
seconds=time_elapsed.total_seconds()
* 1.0
* (numberEvents - ievt)
/ ievt
)
print(
"Event {} [{:.2f}%] Time Elapsed: {} ETA: {}".format(
ievt,
100.0 * ievt / numberEvents,
fmtDelta(time_elapsed),
fmtDelta(time_left),
)
)
jetTrue = TVector3(0, 0, 0)
jetMeas = TVector3(0, 0, 0)
jetPt = hJetPt.GetRandom()
remainder = jetPt
if jetPt < 5:
continue
nt = 0
nt_meas = 0
while remainder > 0:
trackPt = hZ.GetRandom() * jetPt
if trackPt < remainder:
track = TVector3()
remainder = remainder - trackPt
else:
trackPt = remainder
remainder = -1
if trackPt > 0.15:
track.SetPtEtaPhi(
trackPt, myRandom.Gaus(0, 0.1), myRandom.Gaus(math.pi, 0.2)
)
tracksTrue.append(track)
jetTrue += track
if fEff.Eval(trackPt) > myRandom.Uniform(0, 1):
tracksMeas[nt] = 1
jetMeas += track
nt_meas += 1
else:
tracksMeas[nt] = 0
nt += 1
fakes = []
for it in range(fakeRate * 100):
if myRandom.Uniform(0, 1) > 0.99:
fake = TVector3()
fake.SetPtEtaPhi(
myRandom.Uniform(0.15, 1),
myRandom.Gaus(0, 0.1),
myRandom.Gaus(math.pi, 0.2),
)
fakes.append(fake)
jetMeas += fake
hJetPtMeas.Fill(jetMeas.Pt())
hJetPtTrue.Fill(jetTrue.Pt())
responseJetPt.Fill(jetMeas.Pt(), jetTrue.Pt())
responseJetPtCoarse.Fill(jetMeas.Pt(), jetTrue.Pt())
hMultiTrue.Fill(nt)
hMultiMeas.Fill(nt_meas)
ij_meas = GetBin(jetBinBorders, jetMeas.Pt())
ij_true = GetBin(jetBinBorders, jetTrue.Pt())
if nt < 5 or nt_meas < 5:
continue
numberJets += 1
if ij_meas >= 0:
numberJetsMeasBin[ij_meas] += 1
hJetPtMeasCoarse.Fill(jetMeas.Pt())
if ij_true >= 0:
numberJetsTrueBin[ij_true] += 1
hJetPtTrueCoarse.Fill(jetTrue.Pt())
for track, it in zip(tracksTrue, range(100)):
zTrue = (track * jetTrue.Unit()) / jetTrue.Mag()
jtTrue = (track - scaleJet(jetTrue, zTrue)).Mag()
hZTrue.Fill(zTrue)
if ij_true >= 0:
if weight:
hJtTrue.Fill(jtTrue, 1.0 / jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue, 1.0 / jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt(), 1.0 / jtTrue)
else:
hJtTrue.Fill(jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt())
if ij_meas >= 0:
if tracksMeas[it] == 1:
zMeas = (track * jetMeas.Unit()) / jetMeas.Mag()
jtMeas = (track - scaleJet(jetMeas, zMeas)).Mag()
hZMeas.Fill(zMeas)
if weight:
hJtMeasBin[ij_meas].Fill(jtMeas, 1.0 / jtMeas)
hJtMeas.Fill(jtMeas, 1.0 / jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt(), 1.0 / jtMeas)
else:
hJtMeas.Fill(jtMeas)
hJtMeasBin[ij_meas].Fill(jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt())
response.Fill(jtMeas, jtTrue)
responseBin[ij_true].Fill(jtMeas, jtTrue)
response2D.Fill(jtMeas, jetMeas.Pt(), jtTrue, jetTrue.Pt())
responseMatrix.Fill(jtMeas, jtTrue)
responses[ij_true].Fill(jtMeas, jetMeas.Pt(), jtTrue)
responseTuple.Fill(jtMeas, jetMeas.Pt(), jtTrue, jetTrue.Pt())
else:
response.Miss(jtTrue)
responseBin[ij_true].Miss(jtTrue)
response2D.Miss(jtTrue, jetTrue.Pt())
misses.Fill(jtTrue, jetTrue.Pt())
responseTuple.Fill(-1, -1, jtTrue, jetTrue.Pt())
if ij_meas >= 0:
for fake in fakes:
zFake = (fake * jetMeas.Unit()) / jetMeas.Mag()
jtFake = (fake - scaleJet(jetMeas, zFake)).Mag()
hZMeas.Fill(zFake)
hZFake.Fill(zFake)
if weight:
hJtMeas.Fill(jtFake, 1.0 / jtFake)
hJtMeasBin[ij_meas].Fill(jtFake, 1.0 / jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake.Fill(jtFake, 1.0 / jtFake)
else:
hJtMeas.Fill(jtFake)
hJtMeasBin[ij_meas].Fill(jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt())
hJtFake2D.Fill(jtFake, jetMeas.Pt())
hJtFake.Fill(jtFake)
if FillFakes:
response.Fake(jtFake)
responseBin[ij_true].Fake(jtFake)
response2D.Fake(jtFake, jetMeas.Pt())
fakes2D.Fill(jtFake, jetMeas.Pt())
responseTuple.Fill(jtFake, jetMeas.Pt(), -1, -1)
numberFakes += 1
numberFakesBin[ij_true] += 1
response2Dtest = make2Dresponse(
responses, jetPtBins, hJtMeas2D, hJtTrue2D, misses=misses, fakes=fakes2D
)
if dummy_variable:
hJetPtMeas.Reset()
hJetPtTrue.Reset()
hMultiTrue.Reset()
hMultiMeas.Reset()
hJetPtMeasCoarse.Reset()
hJetPtTrueCoarse.Reset()
hZTrue.Reset()
hZMeas.Reset()
hJtTrue.Reset()
hJtTrue2D.Reset()
hJtMeas.Reset()
hJtMeas2D.Reset()
hJtFake.Reset()
hJtFake2D.Reset()
for h, h2 in zip(hJtTrueBin, hJtMeasBin):
h.Reset()
h2.Reset()
numberJetsMeasBin = [0 for i in jetBinBorders]
numberJetsTrueBin = [0 for i in jetBinBorders]
numberJets = 0
print("Create testing data")
start_time = datetime.now()
numberEvents = numberEvents / 2
for ievt in range(numberEvents):
tracksTrue = []
tracksMeas = [0 for x in range(100)]
if ievt % ieout == 0 and ievt > 0:
time_elapsed = datetime.now() - start_time
time_left = timedelta(
seconds=time_elapsed.total_seconds()
* 1.0
* (numberEvents - ievt)
/ ievt
)
print(
"Event {} [{:.2f}%] Time Elapsed: {} ETA: {}".format(
ievt,
100.0 * ievt / numberEvents,
fmtDelta(time_elapsed),
fmtDelta(time_left),
)
)
jetTrue = TVector3(0, 0, 0)
jetMeas = TVector3(0, 0, 0)
jetPt = hJetPt.GetRandom()
remainder = jetPt
if jetPt < 5:
continue
nt = 0
nt_meas = 0
while remainder > 0:
trackPt = hZ.GetRandom() * jetPt
if trackPt < remainder:
track = TVector3()
remainder = remainder - trackPt
else:
trackPt = remainder
remainder = -1
if trackPt > 0.15:
track.SetPtEtaPhi(
trackPt, myRandom.Gaus(0, 0.1), myRandom.Gaus(math.pi, 0.2)
)
tracksTrue.append(track)
jetTrue += track
if fEff.Eval(trackPt) > myRandom.Uniform(0, 1):
tracksMeas[nt] = 1
jetMeas += track
nt_meas += 1
else:
tracksMeas[nt] = 0
nt += 1
fakes = []
for it in range(fakeRate * 100):
if myRandom.Uniform(0, 1) > 0.99:
fake = TVector3()
fake.SetPtEtaPhi(
myRandom.Uniform(0.15, 1),
myRandom.Gaus(0, 0.1),
myRandom.Gaus(math.pi, 0.2),
)
fakes.append(fake)
jetMeas += fake
hJetPtMeas.Fill(jetMeas.Pt())
hJetPtTrue.Fill(jetTrue.Pt())
hMultiTrue.Fill(nt)
hMultiMeas.Fill(nt_meas)
ij_meas = GetBin(jetBinBorders, jetMeas.Pt())
ij_true = GetBin(jetBinBorders, jetTrue.Pt())
if nt < 5 or nt_meas < 5:
continue
numberJets += 1
if ij_meas >= 0:
numberJetsMeasBin[ij_meas] += 1
hJetPtMeasCoarse.Fill(jetMeas.Pt())
if ij_true >= 0:
numberJetsTrueBin[ij_true] += 1
hJetPtTrueCoarse.Fill(jetTrue.Pt())
for track, it in zip(tracksTrue, range(100)):
zTrue = (track * jetTrue.Unit()) / jetTrue.Mag()
jtTrue = (track - scaleJet(jetTrue, zTrue)).Mag()
hZTrue.Fill(zTrue)
if ij_true >= 0:
if weight:
hJtTrue.Fill(jtTrue, 1.0 / jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue, 1.0 / jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt(), 1.0 / jtTrue)
else:
hJtTrue.Fill(jtTrue)
hJtTrueBin[ij_true].Fill(jtTrue)
hJtTrue2D.Fill(jtTrue, jetTrue.Pt())
if ij_meas >= 0:
if tracksMeas[it] == 1:
zMeas = (track * jetMeas.Unit()) / jetMeas.Mag()
jtMeas = (track - scaleJet(jetMeas, zMeas)).Mag()
hZMeas.Fill(zMeas)
if weight:
hJtMeasBin[ij_meas].Fill(jtMeas, 1.0 / jtMeas)
hJtMeas.Fill(jtMeas, 1.0 / jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt(), 1.0 / jtMeas)
else:
hJtMeas.Fill(jtMeas)
hJtMeasBin[ij_meas].Fill(jtMeas)
hJtMeas2D.Fill(jtMeas, jetMeas.Pt())
if ij_meas >= 0:
for fake in fakes:
zFake = (fake * jetMeas.Unit()) / jetMeas.Mag()
jtFake = (fake - scaleJet(jetMeas, zFake)).Mag()
hZMeas.Fill(zFake)
hZFake.Fill(zFake)
if weight:
hJtMeas.Fill(jtFake, 1.0 / jtFake)
hJtMeasBin[ij_meas].Fill(jtFake, 1.0 / jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake2D.Fill(jtFake, jetMeas.Pt(), 1.0 / jtFake)
hJtFake.Fill(jtFake, 1.0 / jtFake)
else:
hJtMeas.Fill(jtFake)
hJtMeasBin[ij_meas].Fill(jtFake)
hJtMeas2D.Fill(jtFake, jetMeas.Pt())
hJtFake2D.Fill(jtFake, jetMeas.Pt())
hJtFake.Fill(jtFake)
time_elapsed = datetime.now() - start_time
print(
"Event {} [{:.2f}%] Time Elapsed: {}".format(
numberEvents, 100.0, fmtDelta(time_elapsed)
)
)
if not FillFakes:
hJtMeas.Add(hJtFake, -1)
hJtMeas2D.Add(hJtFake2D, -1)
responseTuple.Print()
outFile.Write()
# printTuple(responseTuple)
hJtMeasProjBin = [
makeHist(hJtMeas2D.ProjectionX("histMeas{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
hJtMeasProj = makeHist(hJtMeas2D.ProjectionX("histMeas"), bins=LogBinsJt)
hJtTrueProjBin = [
makeHist(hJtTrue2D.ProjectionX("histTrue{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
hJtTrueProj = makeHist(hJtTrue2D.ProjectionX("histTrue"), bins=LogBinsJt)
hJtFakeProjBin = [
makeHist(hJtFake2D.ProjectionX("histFake{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
if not FillFakes:
for h, h2 in zip(hJtMeasBin, hJtFakeProjBin):
h.Add(h2, -1)
for h in (
hJtMeasProj,
hJtTrueProj,
hJtMeas,
hJtTrue,
hJtFake,
hZFake,
hZMeas,
hZTrue,
):
h.Scale(1.0 / numberJets, "width")
for meas, true, n_meas, n_true in zip(
hJtMeasBin, hJtTrueBin, numberJetsMeasBin, numberJetsTrueBin
):
if n_meas > 0:
meas.Scale(1.0 / n_meas, "width")
if n_true > 0:
true.Scale(1.0 / n_true, "width")
numberJetsMeasFromHist = [
hJetPtMeasCoarse.GetBinContent(i)
for i in range(1, hJetPtMeasCoarse.GetNbinsX() + 1)
]
numberJetsTrueFromHist = [
hJetPtTrueCoarse.GetBinContent(i)
for i in range(1, hJetPtTrueCoarse.GetNbinsX() + 1)
]
print("Total number of jets: {}".format(numberJets))
print("Total number of fakes: {}".format(numberFakes))
print("Measured jets by bin")
print(numberJetsMeasBin)
print(numberJetsMeasFromHist)
print("True jets by bin")
print(numberJetsTrueBin)
print(numberJetsTrueFromHist)
hRecoJetPtCoarse = unfoldJetPt(hJetPtMeasCoarse, responseJetPtCoarse, jetBinBorders)
numberJetsFromReco = [
hRecoJetPtCoarse.GetBinContent(i)
for i in range(1, hRecoJetPtCoarse.GetNbinsX())
]
print("Unfolded jet numbers by bin:")
print(numberJetsFromReco)
print("Fakes by bin")
print(numberFakesBin)
print(
"==================================== UNFOLD ==================================="
)
unfold = RooUnfoldBayes(response, hJtMeas, 4) # OR
unfoldSVD = RooUnfoldSvd(response, hJtMeas, 20) # OR
unfold2D = RooUnfoldBayes(response2D, hJtMeas2D, 4)
for u in (unfold, unfoldSVD, unfold2D):
u.SetVerbose(0)
# response2Dtest = makeResponseFromTuple(responseTuple,hJtMeas2D,hJtTrue2D)
unfold2Dtest = RooUnfoldBayes(response2Dtest, hJtMeas2D, 4)
unfoldBin = [
RooUnfoldBayes(responseBin[i], hJtMeasBin[i]) for i in range(len(jetBinBorders))
]
for u in unfoldBin:
u.SetVerbose(0)
hRecoBayes = makeHist(unfold.Hreco(), bins=LogBinsJt)
hRecoSVD = makeHist(unfoldSVD.Hreco(), bins=LogBinsJt)
hRecoBin = [
makeHist(unfoldBin[i].Hreco(), bins=LogBinsJt)
for i in range(len(jetBinBorders))
]
hReco2D = make2DHist(unfold2D.Hreco(), xbins=LogBinsJt, ybins=LogBinsPt)
hReco2Dtest = make2DHist(unfold2Dtest.Hreco(), xbins=LogBinsJt, ybins=LogBinsPt)
hRecoJetPt = unfoldJetPt(hJetPtMeas, responseJetPt, LogBinsX)
hReco2DProjBin = [
makeHist(hReco2D.ProjectionX("histReco{}".format(i), i, i), bins=LogBinsJt)
for i in range(1, len(jetBinBorders))
]
hReco2DTestProjBin = [
makeHist(
hReco2Dtest.ProjectionX("histRecoTest{}".format(i), i, i), bins=LogBinsJt
)
for i in range(1, len(jetBinBorders))
]
hReco2DProj = makeHist(hReco2D.ProjectionX("histReco"), bins=LogBinsJt)
hReco2DProj.Scale(1.0 / numberJets, "width")
for h, h2, n in zip(hReco2DProjBin, hReco2DTestProjBin, numberJetsFromReco):
if n > 0:
h.Scale(1.0 / n, "width")
h2.Scale(1.0 / n, "width")
# unfold.PrintTable (cout, hJtTrue)
for h, h2, nj in zip(hJtMeasProjBin, hJtFakeProjBin, numberJetsMeasBin):
if nj > 0:
h.Scale(1.0 / nj, "width")
h2.Scale(1.0 / nj, "width")
# else:
# print("nj is 0 for {}".format(h.GetName()))
for h, nj in zip(hJtTrueProjBin, numberJetsTrueBin):
if nj > 0:
h.Scale(1.0 / nj, "width")
# draw8grid(hJtMeasBin[1:],hJtTrueBin[1:],jetPtBins[1:],xlog = True,ylog = True,name="newfile.pdf",proj = hJtMeasProjBin[2:], unf2d = hReco2DProjBin[2:], unf=hRecoBin[1:])
if numberEvents > 1000:
if numberEvents > 1000000:
filename = "ToyMC_{}M_events.pdf".format(numberEvents / 1000000)
else:
filename = "ToyMC_{}k_events.pdf".format(numberEvents / 1000)
else:
filename = "ToyMC_{}_events.pdf".format(numberEvents)
draw8gridcomparison(
hJtMeasBin,
hJtTrueBin,
jetPtBins,
xlog=True,
ylog=True,
name=filename,
proj=None,
unf2d=hReco2DProjBin,
unf2dtest=hReco2DTestProjBin,
unf=hRecoBin,
fake=hJtFakeProjBin,
start=1,
stride=1,
)
drawQA(
hJtMeas,
hJtTrue,
hJtFake,
hRecoBayes,
hRecoSVD,
hReco2DProj,
hZ,
hZTrue,
hZMeas,
hZFake,
hMultiMeas,
hMultiTrue,
hJetPt,
hJetPtTrue,
hJetPtMeas,
hRecoJetPt,
responseMatrix,
)
outFile.Close()
def integrand_init(interp, mass, Elow, Eup):
print("\n\n--- INITIALIZING MASS:", mass, " Low Limit ", Elow,
" , Up Lim: ", Eup, "\n")
global inter_mean1
global inter_mean2
global inter_sigma1
global inter_sigma2
global inter_part
global ene_on
global integrand
global normon
global numtoton
# integrand = []
for k in range(0, config.nsamples): # k= number of samples
integrand.append(
np.empty(shape=(numtoton[k], config.BinsIntegrand),
dtype=config.ArrayDataType))
pbar = tqdm(total=numtoton[k] * config.BinsIntegrand)
for i in range(0, numtoton[k]):
energy = ene_on[k][i]
mean1 = energy - inter_mean1[k](
energy) * energy # pEnBiasVsEnEst = ( E' - E ) / E'
mean2 = energy - inter_mean1[k](
energy) * energy # pEnBiasVsEnEst = ( E' - E ) / E'
sigma1 = inter_sigma1[k](energy) * energy
sigma2 = inter_sigma2[k](energy) * energy
part = inter_part[k](energy)
# define the integration limits for true energy
lowl = mean2 - config.SigmaLimits * sigma2
highl = mean2 + config.SigmaLimits * sigma2
if (lowl < config.energy_absmin):
lowl = config.energy_absmin
# define the bins in true energy
binwidth = (highl - lowl) / config.BinsIntegrand
for j in range(0, config.BinsIntegrand):
entrue = lowl + (j + 0.5) * binwidth
pbar.update(1)
area = get_area(entrue, k)
flux = 0.
if (Elow < entrue < Eup):
flux = get_phi(entrue, interp, mass)
enmig = part * gaussian(entrue, mean1, sigma1) + (
1. - part) * gaussian(entrue, mean2, sigma2)
integrand[k][i][j] = flux * area * enmig * binwidth
pbar.close()
print("Starting Normalization\n")
integrand_norm.append(
np.empty(shape=(config.BinsIntegrandNorm, config.BinsIntegrand),
dtype=config.ArrayDataType))
on_en_edges = []
binsize = TMath.Log(config.energy_upper_cut /
config.energy_cut) / config.BinsIntegrandNorm
for ebin in range(0, config.BinsIntegrandNorm + 1):
on_en_edges.append(config.energy_cut * TMath.Exp(binsize * ebin))
for ebin in range(0, config.BinsIntegrandNorm):
energy = math.sqrt(on_en_edges[ebin + 1] * on_en_edges[ebin])
mean1 = energy - inter_mean1[k](
energy) * energy # pEnBiasVsEnEst = ( E' - E ) / E'
mean2 = energy - inter_mean1[k](
energy) * energy # pEnBiasVsEnEst = ( E' - E ) / E'
sigma1 = inter_sigma1[k](energy) * energy
sigma2 = inter_sigma2[k](energy) * energy
part = inter_part[k](energy)
# define the integration limits for true energy
lowl = mean2 - config.SigmaLimits * sigma2
highl = mean2 + config.SigmaLimits * sigma2
if (lowl < config.energy_absmin):
lowl = config.energy_absmin
# define the bins in true energy
binwidth = (highl - lowl) / config.BinsIntegrand
for j in range(0, config.BinsIntegrand):
entrue = lowl + (j + 0.5) * binwidth
pbar.update(1)
area = get_area(entrue, k)
flux = 0.
if ((Elow < entrue) and (entrue < Eup)):
flux = get_phi(entrue, interp, mass)
enmig = part * gaussian(entrue, mean1, sigma1) + (
1. - part) * gaussian(entrue, mean2, sigma2)
integrand_norm[k][ebin][j] = flux * area * enmig * (
on_en_edges[ebin + 1] - on_en_edges[ebin])
normon[k] = np.sum(integrand_norm[k])
print("Normalization=", normon[k])
# enmig.append(part*gaussian(entrue,mean1,sigma1)+(1.-part)*gaussian(entrue,mean2,sigma2))
# print ('mean1=', mean1, ' mean2= ', mean2, ' sigma1= ', sigma1, ' sigma2= ', sigma2, ' part=',part)
print("--- INITIALIZATION FINISHED!\n")
return
def off_energy_distr(OffDir,
NumBckgEnBins=40,
EnCutHighBG=8000,
EnCutLowBG=100):
"""
this function does:
- read the OFF sample input files
-
"""
global normoff
global numtotoff
global numsamples
for k in range(0, numsamples):
print("\n\n--- INITIALIZING VALUES from:", OffDir[k], "\n")
DoHisto = TFile.Open(OffDir[k], "READ")
print(DoHisto)
noff = np.array([DoHisto.Get("fOffSample")])
numtotoff[k] = noff[0].GetEntries()
print("The total number of entries in the normalization histogram is:",
numtotoff[k])
off_en_edges = []
binsize = TMath.Log(EnCutHighBG / EnCutLowBG) / config.NumBckgEnBins
for ebin in range(0, config.NumBckgEnBins + 1):
off_en_edges.append(EnCutLowBG * TMath.Exp(binsize * ebin))
# create here offbins with the (larger) background limits
offbins = array('d', off_en_edges)
isto = ROOT.TH1F("integral", "integral", config.NumBckgEnBins, offbins)
for i in range(0, numtotoff[k]):
noff[0].GetEntry(i)
energy = noff[0].GetArgs()[0]
isto.Fill(energy)
f = TFile('normalization_histogram_sample_{}.root'.format(k),
"RECREATE")
gramma = TCanvas("EOff_Histogram",
'EOFF_Histogram sample {}'.format(k))
gramma.SetCanvasSize(800, 800)
gramma.SetWindowSize(1000, 1000)
gramma.SetLogx()
gramma.SetLogy()
isto.SetXTitle("Log Eest")
isto.SetYTitle("Log events")
isto.Draw()
ll = isto.GetXaxis().FindBin(EnCutLowBG)
hh = isto.GetXaxis().FindBin(EnCutHighBG)
line = TLine()
line.SetLineColor(TColor.kRed)
line.DrawLine(ll, isto.GetMinimum(), ll, isto.GetMaximum())
line.DrawLine(hh, isto.GetMinimum(), hh, isto.GetMaximum())
# gPad.Update()
normoff[k] = isto.Integral(ll, hh)
print("The integral from ", ll, " to ", hh,
" of the normalization histogram is:", normoff[k])
latex = TLatex()
latex.DrawLatex(
ll,
isto.GetMaximum() / 2,
"The integral of the normalization histogram is: {}".format(
normoff[k]))
gramma.Write()
f.Close()
return
def createToyTraining(self, rootFile, numberEvents):
self._f = root_open(rootFile, "read")
self._hJetPtIn = self._f.Get(
"AliJJetJtTask/AliJJetJtHistManager/JetPt/JetPtNFin{:02d}".format(
2))
self._hZIn = self._f.Get(
"AliJJetJtTask/AliJJetJtHistManager/Z/ZNFin{:02d}".format(2))
LimL = 0.1
LimH = 500
logBW = (TMath.Log(LimH) - TMath.Log(LimL)) / self._NBINS
self._LogBinsX = [
LimL * math.exp(ij * logBW) for ij in range(0, self._NBINS + 1)
]
self._hJetPtMeas = Hist(self._LogBinsX)
self._hJetPtTrue = Hist(self._LogBinsX)
self._myRandom = TRandom3(self._randomSeed)
if self._fEff is None:
self._fEff = TF1("fEff", "1-0.5*exp(-x)")
if self._jetBinBorders is None:
self._jetBinBorders = [5, 10, 20, 30, 40, 60, 80, 100, 150, 500]
self._hJetPtMeasCoarse = Hist(self._jetBinBorders)
self._hJetPtTrueCoarse = Hist(self._jetBinBorders)
low = 0.01
high = 10
BinW = (TMath.Log(high) - TMath.Log(low)) / self._NBINSJt
self._LogBinsJt = [
low * math.exp(i * BinW) for i in range(self._NBINSJt + 1)
]
self._jetBinBorders = self._jetBinBorders
self._jetPtBins = [
(a, b)
for a, b in zip(self._jetBinBorders, self._jetBinBorders[1:])
]
self._hJtTrue2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._hJtMeas2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._hJtFake2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
# Histogram to store jT with respect to the leading hadron
self._hJtTestMeas2D = Hist2D(
self._LogBinsJt, self._jetBinBorders) # Needs a better name
self._hJtTestTrue2D = Hist2D(
self._LogBinsJt, self._jetBinBorders) # Needs a better name
self._responseJetPt = RooUnfoldResponse(self._hJetPtMeas,
self._hJetPtTrue)
self._responseJetPtCoarse = RooUnfoldResponse(self._hJetPtMeasCoarse,
self._hJetPtTrueCoarse)
self._hresponseJetPt = Hist2D(self._jetBinBorders, self._jetBinBorders)
self._hresponseJetPtCoarse = Hist2D(self._jetBinBorders,
self._jetBinBorders)
# Histogram index is jet pT index, Bin 0 is 5-10 GeV
# Histogram X axis is observed jT, Bin 0 is underflow
# Histogram Y axis is observed jet Pt, Bin 0 is underflow
# Histogram Z axis is True jT, Bin 0 is underflow
self._2Dresponses = [
Hist3D(self._LogBinsJt, self._jetBinBorders, self._LogBinsJt)
for i in self._jetPtBins
]
self._misses2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._fakes2D = Hist2D(self._LogBinsJt, self._jetBinBorders)
self._numberJetsMeasBin = [0 for i in self._jetBinBorders]
self._numberJetsTrueBin = [0 for i in self._jetBinBorders]
self._numberJetsTestMeasBin = [0 for i in self._jetBinBorders]
self._numberJetsTestTrueBin = [0 for i in self._jetBinBorders]
self._numberFakesBin = [0 for i in self._jetBinBorders]
ieout = numberEvents / 10
if ieout > 20000:
ieout = 20000
start_time = datetime.now()
print("Processing MC Training Events")
for ievt in range(numberEvents):
tracksTrue = []
tracksMeas = [0 for x in range(100)]
if ievt % ieout == 0 and ievt > 0:
time_elapsed = datetime.now() - start_time
time_left = timedelta(seconds=time_elapsed.total_seconds() *
1.0 * (numberEvents - ievt) / ievt)
print("Event {} [{:.2f}%] Time Elapsed: {} ETA: {}".format(
ievt,
100.0 * ievt / numberEvents,
fmtDelta(time_elapsed),
fmtDelta(time_left),
))
jetTrue = TVector3(0, 0, 0)
jetMeas = TVector3(0, 0, 0)
jetPt = self._hJetPtIn.GetRandom()
remainder = jetPt
if jetPt < 5:
continue
nt = 0
nt_meas = 0
while remainder > 0:
trackPt = self._hZIn.GetRandom() * jetPt
if trackPt < remainder:
track = TVector3()
remainder = remainder - trackPt
else:
trackPt = remainder
remainder = -1
if trackPt > 0.15:
track.SetPtEtaPhi(
trackPt,
self._myRandom.Gaus(0, 0.1),
self._myRandom.Gaus(math.pi, 0.2),
)
tracksTrue.append(track)
jetTrue += track
if self._fEff.Eval(trackPt) > self._myRandom.Uniform(0, 1):
tracksMeas[nt] = 1
jetMeas += track
nt_meas += 1
else:
tracksMeas[nt] = 0
nt += 1
fakes = []
for it in range(self._fakeRate * 100):
if self._myRandom.Uniform(0, 1) > 0.99:
fake = TVector3()
fake.SetPtEtaPhi(
self._myRandom.Uniform(0.15, 1),
self._myRandom.Gaus(0, 0.1),
self._myRandom.Gaus(math.pi, 0.2),
)
fakes.append(fake)
jetMeas += fake
self._responseJetPt.Fill(jetMeas.Pt(), jetTrue.Pt())
self._responseJetPtCoarse.Fill(jetMeas.Pt(), jetTrue.Pt())
self._hresponseJetPt.Fill(jetMeas.Pt(), jetTrue.Pt())
self._hresponseJetPtCoarse.Fill(jetMeas.Pt(), jetTrue.Pt())
ij_meas = GetBin(self._jetBinBorders, jetMeas.Pt())
ij_true = GetBin(self._jetBinBorders, jetTrue.Pt())
if nt < 5 or nt_meas < 5:
continue
if ij_meas >= 0:
self._numberJetsMeasBin[ij_meas] += 1
if ij_true >= 0:
self._numberJetsTrueBin[ij_true] += 1
for track, it in zip(tracksTrue, range(100)):
zTrue = (track * jetTrue.Unit()) / jetTrue.Mag()
jtTrue = (track - scaleJet(jetTrue, zTrue)).Mag()
if ij_meas >= 0:
if tracksMeas[it] == 1:
zMeas = (track * jetMeas.Unit()) / jetMeas.Mag()
jtMeas = (track - scaleJet(jetMeas, zMeas)).Mag()
self._2Dresponses[ij_true].Fill(
jtMeas, jetMeas.Pt(), jtTrue)
else:
self._misses2D.Fill(jtTrue, jetTrue.Pt())
if ij_meas >= 0:
for fake in fakes:
zFake = (fake * jetMeas.Unit()) / jetMeas.Mag()
jtFake = (fake - scaleJet(jetMeas, zFake)).Mag()
if self._weight:
self._hJtFake2D.Fill(jtFake, jetMeas.Pt(),
1.0 / jtFake)
else:
self._hJtFake2D.Fill(jtFake, jetMeas.Pt())
if self._fillFakes:
self._fakes2D.Fill(jtFake, jetMeas.Pt())
print("Event {} [{:.2f}%] Time Elapsed: {}".format(
numberEvents, 100.0, fmtDelta(time_elapsed)))
self._numberJetsMeasTrain = sum(self._numberJetsMeasBin)
def SideBandFit(irebin=1):
print("\n Side-band fit \n\n")
# prepare histograms
h_bgr = GetMassDistribution(1)
h_data = GetMassDistribution(2)
# rebin
h_bgr.Rebin(irebin)
h_data.Rebin(irebin)
print("INFO: Rebinning histograms with factor",irebin,". Binwidth: ",h_data.GetBinWidth(1))
# Loop over scalefactor (alpha_bgr)
h_sf_bgr = TH1D("h_sf_bgr","",100,0.8,1.5)
for i in range(1,h_sf_bgr.GetNbinsX()+1):
sf_bgr = h_sf_bgr.GetBinCenter(i)
# Loop over bins, compute loglikelihood, save in histogram
loglik = 0.
for j in range(1,h_data.GetNbinsX()+1):
# Signal-free region
m4lepBin = h_data.GetBinCenter(j)
NObsBin = h_data.GetBinContent(j)
if ( m4lepBin>=150. and m4lepBin<=400.):
MeanBgdBin = sf_bgr * h_bgr.GetBinContent(j)
if MeanBgdBin>0.: loglik += TMath.Log( TMath.Poisson(NObsBin,MeanBgdBin) )
h_sf_bgr.SetBinContent(i,-2.*loglik)
# Interpret the likelihood distribution
h_sf_bgr_rescaled = h_sf_bgr.Clone("h_sf_bgr_rescaled")
# find minimum
MinBin = h_sf_bgr.GetMinimumBin()
Minimum = h_sf_bgr.GetBinContent(MinBin)
BestSF_bgd = h_sf_bgr.GetBinCenter(MinBin)
# Rescale and find \pm 1\sigma errors
for i in range(1,h_sf_bgr.GetNbinsX()+1):
h_sf_bgr_rescaled.SetBinContent(i,Minimum-h_sf_bgr.GetBinContent(i))
LeftLim = h_sf_bgr.GetBinCenter( h_sf_bgr_rescaled.FindFirstBinAbove(-1)-1 )
RightLim = h_sf_bgr.GetBinCenter( h_sf_bgr_rescaled.FindLastBinAbove(-1)+1 )
LeftError = BestSF_bgd - LeftLim
RightError = RightLim - BestSF_bgd
# Print summary
print(" ----------\n"," Result fit: \n"," ----------","Background scale factor from sideband fit: ",BestSF_bgd," - ",LeftError," + ",RightError)
# Plot histogram
canvas1 = TCanvas("canvas1","Standard Canvas",600,400)
canvas1.SetLeftMargin(0.175)
canvas1.SetBottomMargin(0.125)
canvas1.cd()
h_sf_bgr_rescaled.SetStats(kFALSE)
h_sf_bgr_rescaled.SetTitle("Background scale factor")
h_sf_bgr_rescaled.GetXaxis().SetTitle("scale factor")
h_sf_bgr_rescaled.GetYaxis().SetTitle("2 log L")
#h_sf_bgr_rescaled.SetAxisRange(1,-1 ,"Y")
h_sf_bgr_rescaled.Draw("C")
CenterLine = TLine(BestSF_bgd,0,BestSF_bgd,-31)
CenterLine.SetLineColor(2)
LeftLine = TLine(LeftLim,Minimum-h_sf_bgr.GetBinContent( h_sf_bgr_rescaled.FindFirstBinAbove(-1)-1 ),LeftLim,-31)
LeftLine.SetLineColor(40)
RightLine = TLine(RightLim,Minimum-h_sf_bgr.GetBinContent( h_sf_bgr_rescaled.FindLastBinAbove(-1)+1 ),RightLim,-31)
RightLine.SetLineColor(40)
CenterLine.Draw("same axis")
LeftLine.Draw("same axis")
RightLine.Draw("same axis")
canvas1.Print("Plots/SideBandFit.pdf")
# Find expected background
bgr = h_bgr.Integral(h_bgr.FindBin(125.-0.5*7.15),h_bgr.FindBin(125+0.5*7.15))
sig = GetMassDistribution(125).Integral(h_bgr.FindBin(125.-0.5*7.15),h_bgr.FindBin(125+0.5*7.15))
obs = h_data.Integral(h_bgr.FindBin(125.-0.5*7.15),h_bgr.FindBin(125+0.5*7.15))
print("BACKGROUND - without rescaling : ", bgr)
print("Best scalefactor: ",BestSF_bgd)
print("BACKGROUND - with rescaling : ", BestSF_bgd*bgr," - ",LeftError*bgr,' + ',RightError*bgr)
print("SIGNAL EVENTS: ",sig)
print("OBSERVED EVENTS: ",obs)
return
