chiTests1d.append(
Proj2D_Y(Reco2d, minMass, maxMass, Reco2d.GetName(), True))
## now do unfolding
print "CCLA: Unfolding ", Reco2d, "using matix ", response
if DAgostini == True:
unfold2d = RooUnfoldBayes(response, Reco2d, NITER)
else:
unfold2d = RooUnfoldInvert(response, Reco2d)
unfold2d.Print()
new_hists.append(unfold2d)
unfold2d.SetNToys(30000)
hUnf2d = unfold2d.Hreco(2)
hErr2d = unfold2d.Ereco(2)
#chi2 = RooUnfold.Chi2 (unfold2d,2)
chi2comp = Proj2D_Y(Gen2d, minMass, maxMass, Gen2d.GetName(), True)
hraw = Proj2D_Y(Reco2d, minMass, maxMass, Gen2d.GetName(), True)
hUnf1d = Proj2D_Y(hUnf2d, minMass, maxMass, Gen2d.GetName(), True)
print "------", hraw.Integral(), hUnf1d.Integral(), hUnf2d.Integral(
), chi2comp.Integral()
chi2comp.Scale(hUnf1d.Integral() / chi2comp.Integral())
#chi2 = unfold2d.Chi2 (hUnf1d,2)
chi2 = hUnf2d.Chi2(hUnf1d, 2)
print "---chi square:", chi2
def MyRooUnfold(matrix_name=args.h_matrix, h_reco_getG0_name=args.h_data, h_ptcl_getG0_name = args.h_particle,h_reco_get_bkg_name = args.h_background,outputname=args.h_data+"_unfolded",nrebin = args.nrebin):
rfile_data = TFile(args.rfile_data, 'read')
rfile_particle = TFile(args.rfile_particle, 'read')
rfile_matrix = TFile(args.rfile_matrix, 'read')
rfile_background = TFile(args.rfile_background, 'read')
myfbu = fbu.PyFBU()
myfbu.verbose = True
#GET DATA
h_reco_get = rfile_data.Get(h_reco_getG0_name)
h_reco_get.Rebin(nrebin)
#GET PARTICLE
h_ptcl_get = rfile_particle.Get(h_ptcl_getG0_name)
h_ptcl_get.Rebin(nrebin)
#GET MATRIX
h_response_unf = rfile_matrix.Get(matrix_name)
h_response_unf.ClearUnderflowAndOverflow()
h_response_unf.GetXaxis().SetRange(1, h_response_unf.GetXaxis().GetNbins() )
h_response_unf.GetYaxis().SetRange(1, h_response_unf.GetYaxis().GetNbins() )
h_response_unf.Rebin2D(nrebin,nrebin)
h_response_unf.SetName("Migration_Matrix_simulation")
########### ACCEPTANCY
h_acc = h_response_unf.ProjectionX("reco_recoandparticleX") # Reco M
h_acc.Divide(h_reco_get)
########### AKCEPTANCE saved in h_acc #############
########### EFFICIENCY
h_eff = h_response_unf.ProjectionY("reco_recoandparticleY") # Ptcl M
h_eff.Divide(h_ptcl_get)
h_reco_get_input = rfile_data.Get(h_reco_getG0_name)
h_reco_get_bkg = rfile_background.Get(h_reco_get_bkg_name)
h_reco_get_bkg.Rebin(nrebin)
h_reco_get_input_clone=h_reco_get_input.Clone("")
h_reco_get_input_clone.Add(h_reco_get_bkg,-1)
h_reco_get_input_clone.Multiply(h_acc)
h_reco_or = rfile_data.Get(h_reco_getG0_name)
h_ptcl_or = rfile_particle.Get(h_ptcl_getG0_name)
h_ptcl_or.SetMaximum(h_ptcl_or.GetMaximum()*1.5)
### ROOUNFOLD METHOD ###
m_RooUnfold = RooUnfoldBayes()
m_RooUnfold.SetRegParm( 4 )
m_RooUnfold.SetNToys( 10000 )
m_RooUnfold.SetVerbose( 0 )
m_RooUnfold.SetSmoothing( 0 )
response = RooUnfoldResponse(None, None, h_response_unf, "response", "methods")
m_RooUnfold.SetResponse( response )
m_RooUnfold.SetMeasured( h_reco_get_input_clone )
### SVD METHOD ###
m_RooUnfold_svd = RooUnfoldSvd (response, h_reco_get_input_clone, int(round(h_reco_get_input_clone.GetNbinsX()/2.0,0))) #8
svd_par = int(round(h_reco_get_input_clone.GetNbinsX()/2.0,0))
m_RooUnfold_T = RooUnfoldTUnfold (response, h_reco_get_input_clone) # OR
m_RooUnfold_Ids= RooUnfoldIds (response, h_reco_get_input_clone,int(round(h_reco_get_input_clone.GetNbinsX()/12.0,0))) ## TO DO, SET PARAMETERS TO THE BINNING
Ids_par = int(round(h_reco_get_input_clone.GetNbinsX()/12.0,0))
### FBU METHOD ###
h_response_unf_fbu = TransposeMatrix(h_response_unf)
h_response_unf_fbu_norm = NormalizeResponse(h_response_unf_fbu)
h_response_unf_fbu_norm.SetName("Migration_Matrix_simulation_transpose")
histograms.append(h_response_unf_fbu_norm)
myfbu.response = MakeListResponse(h_response_unf_fbu_norm)
myfbu.data = MakeListFromHisto(h_reco_get_input_clone)
myfbu.lower = []
myfbu.upper = []
h_det_div_ptcl=h_reco_get_input_clone.Clone("")
h_det_div_ptcl.Divide(h_ptcl_or)
h_det_div_ptcl.Divide(h_eff)
h_det_div_ptcl.SetName("det_div_ptcl")
histograms.append(h_det_div_ptcl)
for l in range(len(myfbu.data)):
if ( args.SplitFromBinLow != 0) and ( l+1 <= args.SplitFromBinLow ):
myfbu.lower.append(h_reco_get_input_clone.GetBinContent(l+1)*(2-args.ParameterSplitFromBinLow)*h_det_div_ptcl.GetBinContent(l+1))
myfbu.upper.append(h_reco_get_input_clone.GetBinContent(l+1)*args.ParameterSplitFromBinLow*h_det_div_ptcl.GetBinContent(l+1))
elif ( args.SplitFromBinHigh != 0 ) and ( l+1 >= args.SplitFromBinHigh ):
myfbu.lower.append(h_reco_get_input_clone.GetBinContent(l+1)*(2-args.ParameterSplitFromBinHigh)*h_det_div_ptcl.GetBinContent(l+1))
myfbu.upper.append(h_reco_get_input_clone.GetBinContent(l+1)*args.ParameterSplitFromBinHigh*h_det_div_ptcl.GetBinContent(l+1))
else:
myfbu.lower.append(h_reco_get_input_clone.GetBinContent(l+1)*(2-args.par)*h_det_div_ptcl.GetBinContent(l+1))
myfbu.upper.append(h_reco_get_input_clone.GetBinContent(l+1)*args.par*h_det_div_ptcl.GetBinContent(l+1))
#myfbu.regularization = Regularization('Tikhonov',parameters=[{'refcurv':0.1,'alpha':0.2}]) works for old FBU package and python2.7 and old pymc
myfbu.run()
trace = myfbu.trace
traceName = 'Posterior_1_iteration'
posteriors_diag = MakeTH1Ds(trace, traceName)
h_reco_unfolded, h_reco_unfolded_Mean = MakeUnfoldedHisto(h_reco_or, posteriors_diag)
PlotPosteriors(posteriors_diag,outputname+'_iteration_1')
# EFFICIENCY AND ACCEPTANCY CORRECTIONS
h_reco_unfolded.Divide(h_eff)
h_reco_unfolded_Mean.Divide(h_eff)
h_reco_unfolded_roof = m_RooUnfold.Hreco()
h_reco_unfolded_roof.Divide(h_eff)
h_reco_unfolded_svd = m_RooUnfold_svd.Hreco()
h_reco_unfolded_svd.Divide(h_eff)
h_reco_unfolded_T = m_RooUnfold_T.Hreco()
h_reco_unfolded_T.Divide(h_eff)
h_reco_unfolded_Ids = m_RooUnfold_Ids.Hreco()
h_reco_unfolded_Ids.Divide(h_eff)
PlotRatio(h_reco_unfolded_Mean, h_ptcl_or, h_reco_unfolded_roof, h_reco_unfolded_svd, h_reco_unfolded_T,h_reco_unfolded_Ids, svd_par, Ids_par, outputname+'_iteration_1')
Repeat = True
j = 2
while Repeat:
print("Runnig iteration number: ",j)
myfbu.lower = []
myfbu.upper = []
for l in range(len(myfbu.data)):
posteriors_diag[l].Fit("gaus")
fit = posteriors_diag[l].GetFunction("gaus")
p1 = fit.GetParameter(1)
p2 = fit.GetParameter(2)
myfbu.lower.append(p1-4*p2)
myfbu.upper.append(p1+4*p2)
myfbu.run()
trace = myfbu.trace
traceName = 'Posterior_'+str(j)+'_iteration'
posteriors_diag = MakeTH1Ds(trace, traceName)
h_reco_unfolded, h_reco_unfolded_Mean = MakeUnfoldedHisto(h_reco_or, posteriors_diag)
Repeat = PlotPosteriors(posteriors_diag,outputname+'_iteration_'+str(j))
# EFFICIENCY AND ACCEPTANCY CORRECTIONS
h_reco_unfolded.Divide(h_eff)
h_reco_unfolded_Mean.Divide(h_eff)
h_reco_unfolded_roof = m_RooUnfold.Hreco()
h_reco_unfolded_roof.Divide(h_eff)
h_reco_unfolded_svd = m_RooUnfold_svd.Hreco()
h_reco_unfolded_svd.Divide(h_eff)
h_reco_unfolded_T = m_RooUnfold_T.Hreco()
h_reco_unfolded_T.Divide(h_eff)
h_reco_unfolded_Ids = m_RooUnfold_Ids.Hreco()
h_reco_unfolded_Ids.Divide(h_eff)
PlotRatio(h_reco_unfolded_Mean, h_ptcl_or, h_reco_unfolded_roof, h_reco_unfolded_svd, h_reco_unfolded_T,h_reco_unfolded_Ids, svd_par, Ids_par, outputname+'_iteration_'+str(j))
if j == args.maxiterations:
break
j = j+1
h_reco_unfolded.SetName("result_fbu_fit")
histograms.append(h_reco_unfolded)
h_reco_unfolded_Mean.SetName("result_fbu_Mean")
histograms.append(h_reco_unfolded_Mean)
h_reco_unfolded_roof.SetName("result_roof")
histograms.append(h_reco_unfolded_roof)
h_reco_unfolded_svd.SetName("result_svd")
histograms.append(h_reco_unfolded_svd)
h_reco_unfolded_T.SetName("result_T")
histograms.append(h_reco_unfolded_T)
h_reco_unfolded_Ids.SetName("result_Ids")
histograms.append(h_reco_unfolded_Ids)
h_eff.SetName("efficiency")
histograms.append(h_eff)
h_acc.SetName("acceptancy")
histograms.append(h_acc)
h_reco_or.SetName("reco")
histograms.append(h_reco_or)
h_ptcl_or.SetName("ptcl_simulation")
histograms.append(h_ptcl_or)
h_ratio = h_reco_unfolded.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_fbu_fit")
histograms.append(h_ratio)
h_ratio = h_reco_unfolded_Mean.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_fbu_Mean")
histograms.append(h_ratio)
h_ratio = h_reco_unfolded_roof.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_roof")
histograms.append(h_ratio)
h_ratio = h_reco_unfolded_svd.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_svd")
histograms.append(h_ratio)
m_RooUnfold_svd.PrintTable (cout, h_ptcl_or)
m_RooUnfold.PrintTable (cout, h_ptcl_or)
# CORRECTIONS TO GET CROSS SECTION
#DivideBinWidth(h_reco_unfolded_Mean)
#DivideBinWidth(h_reco_unfolded_roof)
#DivideBinWidth(h_reco_unfolded_svd)
#DivideBinWidth(h_reco_unfolded_T)
#DivideBinWidth(h_reco_unfolded_Ids)
#DivideBinWidth(h_ptcl_or)
#Lumi = 36.1e3
#for j in range(1,h_reco_unfolded_Mean.GetXaxis().GetNbins()+1):
# h_reco_unfolded_Mean.SetBinContent(j,h_reco_unfolded_Mean.GetBinContent(j)/(Lumi))
# h_reco_unfolded_Mean.SetBinError(j,h_reco_unfolded_Mean.GetBinError(j)/(Lumi))
# h_reco_unfolded_roof.SetBinContent(j,h_reco_unfolded_roof.GetBinContent(j)/(Lumi))
# h_reco_unfolded_roof.SetBinError(j,h_reco_unfolded_roof.GetBinError(j)/(Lumi))
# h_reco_unfolded_svd.SetBinContent(j,h_reco_unfolded_svd.GetBinContent(j)/(Lumi))
# h_reco_unfolded_svd.SetBinError(j,h_reco_unfolded_svd.GetBinError(j)/(Lumi))
# h_reco_unfolded_T.SetBinContent(j,h_reco_unfolded_T.GetBinContent(j)/(Lumi))
# h_reco_unfolded_T.SetBinError(j,h_reco_unfolded_T.GetBinError(j)/(Lumi))
# h_reco_unfolded_Ids.SetBinContent(j,h_reco_unfolded_Ids.GetBinContent(j)/(Lumi))
# h_reco_unfolded_Ids.SetBinError(j,h_reco_unfolded_Ids.GetBinError(j)/(Lumi))
# h_ptcl_or.SetBinContent(j,h_ptcl_or.GetBinContent(j)/(Lumi))
# h_ptcl_or.SetBinError(j,h_ptcl_or.GetBinError(j)/(Lumi))
#h_reco_unfolded_Mean_clone=h_reco_unfolded_Mean.Clone("FBU_cross_section")
#h_reco_unfolded_roof_clone=h_reco_unfolded_roof.Clone("DAgostini_cross_section")
#h_reco_unfolded_svd_clone=h_reco_unfolded_svd.Clone("SVD_cross_section")
#h_reco_unfolded_T_clone=h_reco_unfolded_T.Clone("TUnfold_cross_section")
#h_reco_unfolded_Ids_clone=h_reco_unfolded_Ids.Clone("Ids_cross_section")
#h_ptcl_or_clone=h_ptcl_or.Clone("Truth_cross_section")
#
#print("CONTROL*******************************************************************: ",h_reco_unfolded_Mean_clone.GetXaxis().GetNbins(),h_reco_unfolded_roof_clone.GetXaxis().GetNbins(),h_reco_unfolded_svd_clone.GetXaxis().GetNbins(),h_reco_unfolded_T_clone.GetXaxis().GetNbins(),h_reco_unfolded_Ids_clone.GetXaxis().GetNbins(),h_ptcl_or_clone.GetXaxis().GetNbins())
#histograms.append(h_reco_unfolded_Mean_clone)
#histograms.append(h_reco_unfolded_roof_clone)
#histograms.append(h_reco_unfolded_svd_clone)
#histograms.append(h_reco_unfolded_T_clone)
#histograms.append(h_reco_unfolded_Ids_clone)
#histograms.append(h_ptcl_or_clone)
SaveHistograms(outputname)
