def tagnprobe_new(): print "gen_tp_debugging.tagnprobe_new()" #rt.gROOT.Reset() rt.gROOT.SetBatch(rt.kTRUE) # dont show the canvases plotfilename = "debugging.root" _fm = fm.FileManager() tstr = str(dt.datetime.now()).replace(" ", "_").replace(":","-").split(".")[0] fs = fm.FileSaver(plotfilename,tstr,_fm) #filename = "myTestSelector_DY_debug_genMatchTagnProbe_1458133145.root" #filename = "myTestSelector_DY_debug_genMatchTagnProbe_1458257495.root" #filename = "myTestSelector_DY_debug_genMatchTagnProbe_1458263936.root" #filename = "myTestSelector_DY_debug_genMatchTagnProbe_1458270353.root" #filename = "myTestSelector_DY_debug_genMatchTagnProbe_1458271176.root" #filename = "myTestSelector_DY_debug_genMatchTagnProbe_1458273497.root" #filename = "myTestSelector_NEW_tnp_forPlots_1460026482.root" #filename = "myTestSelector_NEW_tnp_forPlots_1460033048.root" #filename = "myTestSelector_NEW_tnp_forPlots_1460036199.root" #filename = "myTestSelector_NEW_tnp_forPlots_1460036586.root" #filename = "myTestSelector_NEW_tnp_forPlots_1460040961.root" #filename = "myTestSelector_NEW_tnp_forPlots_1460042765.root" #filename = "myTestSelector_NEW_tnp_forPlots_1460048500.root" #filename = "myTestSelector_NEW_tnp_forPlots_1460109853.root" filename = "myTestSelector_NEW_tnp_forPlots_1460109853.root" h, h2, h3 = _fm.loadfile(filename) tnp_ee = h2["tnp_ee"].Clone() # pt dependency tnp_eg = h2["tnp_eg"].Clone() tnp_ee_ntracks = h2["tnp_ee_ntracks"].Clone() tnp_eg_ntracks = h2["tnp_eg_ntracks"].Clone() tnp_ee_nvtx = h2["tnp_ee_nvtx"].Clone() tnp_eg_nvtx = h2["tnp_eg_nvtx"].Clone() tnp_ee_eta = h2["tnp_ee_eta"].Clone() tnp_eg_eta = h2["tnp_eg_eta"].Clone() samplename = "DYJetsToLL" strClosure = "_perbin" ''' # closure on wgamma sample #filename = "myTestSelector_WG_debug_genMatchTagnProbe_1458286818.root" if "WG" in filename: strClosure = "_perbin_wg" samplename = "WGToLNuG" del h del h2 del h3 h, h2, h3 = _fm.loadfile(filename) # ''' clos_e = h["clos_gen_e"] clos_g = h["clos_gen_g"] clos_e_ntracks = h["clos_gen_e_ntracks"] clos_g_ntracks = h["clos_gen_g_ntracks"] clos_e_nvtx = h["clos_gen_e_nvtx"] clos_g_nvtx = h["clos_gen_g_nvtx"] clos_e_eta = h["clos_gen_e_eta"] clos_g_eta = h["clos_gen_g_eta"] # ''' # BINNINGS #ss = "1bin_" # for the plots ss = "_1bin" #pt_binning = range(40, 60, 5)+range(60, 100, 10)+range(100, 200+1, 20) pt_binning = range(40, 201, 160) print "pt_binning: ", pt_binning #ntracks_binning = range(0, 200+1, 10) ntracks_binning = range(0, 200+1, 200) print "ntracks_binning: ", ntracks_binning #nvtx_binning = range(0, 30+1, 1) nvtx_binning = range(0, 30+1, 30) print "nvtx_binning: ", nvtx_binning #eta_binning = range(0, 150+1, 10) eta_binning = range(0, 150+1, 150) for i in range(len(eta_binning)): eta_binning[i] = eta_binning[i]*0.01 # #eta_binning[i] = round(eta_binning[i]*0.01, 2) # CALCULATIONS # pt: tnp_ee.Add(tnp_eg) tpf = h2f.h2Fakerate("pt", tnp_ee, tnp_eg) tpf.rebin(pt_binning) #tpf.addFitModel(rt.TF1("tpf", "[0]+[1]/x+[2]/(x*x)", 40, 200)) tpf.addFitModel(rt.TF1("tpf", "[0]", 40, 200)) tpf.fit() tpcan = h2f.h2FakerateCanvas(tpf) tpcan.setLabel("Tag n probe") clos_e = aux.rebin(clos_e, pt_binning) clos_g = aux.rebin(clos_g, pt_binning) tpclos = h2f.closure("tagnprobe"+strClosure, clos_e, clos_g) tpclos.setLabel("Closure on "+samplename) #tpclos.addFakerate(tpgenf, "Tag n Probe gen info"+strClosure) tpclos.addFakerate(tpf, "Tag n Probe"+strClosure) tpclos.createFitPrediction() tpclos.createHistoPrediction() # Ntracks: tnp_ee_ntracks.Add(tnp_eg_ntracks) tpf_ntracks = h2f.h2Fakerate("ntracks", tnp_ee_ntracks, tnp_eg_ntracks) tpf_ntracks.rebin(ntracks_binning) tpf_ntracks.addFitModel(rt.TF1("tpf_ntracks", "[0]+[1]*x+[2]*x*x", 0, 200)) tpf_ntracks.fit() tpcan_ntracks = h2f.h2FakerateCanvas(tpf_ntracks) tpcan_ntracks.setLabel("Tag n probe") clos_e_ntracks = aux.rebin(clos_e_ntracks, ntracks_binning) clos_g_ntracks = aux.rebin(clos_g_ntracks, ntracks_binning) tpclos_ntracks = h2f.closure("tnp_ntracks"+strClosure, clos_e_ntracks, clos_g_ntracks) #tpclos_ntracks.addFakerate(tpgenf_ntracks, "Tag n Probe gen info"+strClosure) tpclos_ntracks.addFakerate(tpf_ntracks, "Tag n Probe"+strClosure) tpclos_ntracks.createFitPrediction() tpclos_ntracks.createHistoPrediction() # Nvtx: tnp_ee_nvtx.Add(tnp_eg_nvtx) tpf_nvtx = h2f.h2Fakerate("nvtx", tnp_ee_nvtx, tnp_eg_nvtx) tpf_nvtx.rebin(nvtx_binning) tpf_nvtx.addFitModel(rt.TF1("tpf_nvtx", "[0]+[1]*x", 0, 30)) tpf_nvtx.fit() tpcan_nvtx = h2f.h2FakerateCanvas(tpf_nvtx) tpcan_nvtx.setLabel("Tag n probe") clos_e_nvtx = aux.rebin(clos_e_nvtx, nvtx_binning) clos_g_nvtx = aux.rebin(clos_g_nvtx, nvtx_binning) tpclos_nvtx = h2f.closure("tnp_nvtx"+strClosure, clos_e_nvtx, clos_g_nvtx) #tpclos_nvtx.addFakerate(tpgenf_nvtx, "Tag n Probe gen info"+strClosure) tpclos_nvtx.addFakerate(tpf_nvtx, "Tag n Probe"+strClosure) tpclos_nvtx.createFitPrediction() tpclos_nvtx.createHistoPrediction() # Eta: print "eta_binning: ", eta_binning tnp_ee_eta.Add(tnp_eg_eta) tpf_eta = h2f.h2Fakerate("eta", tnp_ee_eta, tnp_eg_eta) tpf_eta.rebin(eta_binning) tpf_eta.addFitModel(rt.TF1("tpf_eta", "[0]+[1]*x", 0.1, 1.4)) tpf_eta.fit() tpcan_eta = h2f.h2FakerateCanvas(tpf_eta) tpcan_eta.setLabel("Tag n probe") clos_e_eta = aux.rebin(clos_e_eta, eta_binning) clos_g_eta = aux.rebin(clos_g_eta, eta_binning) #clos_e_eta.Rebin(10) #clos_g_eta.Rebin(10) tpclos_eta = h2f.closure("tnp_eta"+strClosure, clos_e_eta, clos_g_eta) #tpclos_eta.addFakerate(tpgenf_eta, "Tag n Probe gen info"+strClosure) tpclos_eta.addFakerate(tpf_eta, "Tag n Probe"+strClosure) tpclos_eta.createFitPrediction() tpclos_eta.createHistoPrediction() # PLOTS and CANVASES tpcan.createCanvas(fs, name="f_tnp_pt"+ss)#, yrng=[0.002, 0.015]) tpcan_ntracks.createCanvas(fs, yrng=[0., .03], name="f_tnp_ntracks"+ss) tpcan_nvtx.createCanvas(fs, name="f_tnp_nvtx"+ss) tpcan_eta.createCanvas(fs, name="f_tnp_eta"+ss) # def histoCanvas(self, fs, name, clabel, logy=True, *opt): tpclos.histoCanvas( fs, "tnp_pt_closure_ownclass"+ss+strClosure, "Closure on "+samplename, logy = True, opt=[0]) h2f.histoCanvas( fs, "tnp_pt_closure"+ss+strClosure, tpclos.target, tpclos.predictionh[0], "Closure on "+samplename, logy = True, yrng = [1000., 10000000.]) h2f.histoCanvas( fs, "tnp_ntracks_closure"+ss+strClosure, tpclos_ntracks.target, tpclos_ntracks.predictionh[0], "Closure on "+samplename) h2f.histoCanvas( fs, "tnp_nvtx_closure"+ss+strClosure, tpclos_nvtx.target, tpclos_nvtx.predictionh[0], "Closure on "+samplename, logy = True, yrng = [1, 10000000]) h2f.histoCanvas( fs, "tnp_eta_closure"+ss+strClosure, tpclos_eta.target, tpclos_eta.predictionh[0], "Closure on "+samplename, logy=True) # ''' #c = rt.TCanvas("c", "c", 600, 600) #c.cd() #tpf.egraph.Draw("ap") #raw_input() #raw_input() #x = 50. #err = [rt.Double(0)] #xerr = tpf.fitres.GetConfidenceIntervals(1, 1, 1, x, err, 0.683, False); #print tpf.fitres.GetConfidenceIntervals(10, 1, 2, x, err, 0.94999, True); #raw_input() return 0
def diphoton(): print "gen_tp_debugging.diphoton()" rt.gROOT.SetBatch(rt.kTRUE) # dont show the canvases plotfilename = "diphoton_reco.root" pdf_str = "diphoton_" _fm = fm.FileManager() tstr = str(dt.datetime.now()).replace(" ", "_").replace(":","-").split(".")[0] fs = fm.FileSaver(plotfilename,tstr,_fm) filename = "myTestSelector_SingleE_diphoton_check_1458295538.root" h, h2, h3 = _fm.loadfile(filename) #print "clos_e xaxis:", clos_e.GetXaxis().GetTitle() #print "clos_g xaxis:", clos_g.GetXaxis().GetTitle() tnp_ee = h2["tnp_ee"].Clone() tnp_eg = h2["tnp_eg"].Clone() tnp_gen_ee = h2["tnp_gen_ee"].Clone() tnp_gen_eg = h2["tnp_gen_eg"].Clone() tnp_gen_ee_ntracks = h2["tnp_gen_ee_ntracks"].Clone() tnp_gen_eg_ntracks = h2["tnp_gen_eg_ntracks"].Clone() tnp_ee_ntracks = h2["tnp_ee_ntracks"].Clone() tnp_eg_ntracks = h2["tnp_eg_ntracks"].Clone() tnp_gen_ee_nvtx = h2["tnp_gen_ee_nvtx"].Clone() tnp_gen_eg_nvtx = h2["tnp_gen_eg_nvtx"].Clone() tnp_ee_nvtx = h2["tnp_ee_nvtx"].Clone() tnp_eg_nvtx = h2["tnp_eg_nvtx"].Clone() tnp_gen_ee_eta = h2["tnp_gen_ee_eta"].Clone() tnp_gen_eg_eta = h2["tnp_gen_eg_eta"].Clone() tnp_ee_eta = h2["tnp_ee_eta"].Clone() tnp_eg_eta = h2["tnp_eg_eta"].Clone() pt_binning = range(40, 60+1, 5)+range(60, 100+1, 10)+range(100, 200+1, 20) tnp_ee.Add(tnp_eg) tpf = h2f.h2Fakerate("pt", tnp_ee, tnp_eg) #, 60, 120) tpf.rebin(pt_binning) tpf.addFitModel(rt.TF1("tpf", "[0]+[1]*x+[2]*(x*x)", 40, 200)) tpf.fit() tpcan = h2f.h2FakerateCanvas(tpf) tpcan.createCanvas(fs, name=pdf_str+"f_tnp_pt") #fs.savePdf(tpcan.c, pdf_str+"f_tnp_pt") # ''' #del tpf #del tpcan ''' tnp_gen_ee.Add(tnp_gen_eg) tpgenf = h2f.h2Fakerate("pt_gen", tnp_gen_ee, tnp_gen_eg) tpgenf.rebin(pt_binning) tpgenf.addFitModel(rt.TF1( "tpgenf", "[0]+[1]/x+[2]/(x*x)", 40, 200)) tpgenf.fit() tpgencan = h2f.h2FakerateCanvas(tpgenf) tpgencan.createCanvas() fs.savePdf(tpgencan.c, "f_tnpgen_pt") clos_e = aux.rebin(clos_e, pt_binning) clos_g = aux.rebin(clos_g, pt_binning) tpclos = h2f.closure("tagnprobe"+strClosure, clos_e, clos_g) tpclos.addFakerate(tpgenf, "Tag n Probe gen info"+strClosure) tpclos.addFakerate(tpf, "Tag n Probe"+strClosure) tpclos.createFitPrediction() tpclos.createHistoPrediction() tpclos.histoCanvas("tnpgen_pt_closure"+strClosure, 0) tpclos.histoCanvas("tnp_pt_closure"+strClosure, 1) fs.savePdf(tpclos.ch["Tag n Probe gen info"+strClosure], "tnpgen_pt_closure_perbin"+strClosure) fs.savePdf(tpclos.ch["Tag n Probe"+strClosure], "tnp_pt_closure_perbin"+strClosure) # ''' ntracks_binning = range(0,200+1,10) ''' tnp_gen_ee_ntracks.Add(tnp_gen_eg_ntracks) tpgenf_ntracks = h2f.h2Fakerate("ntracks_gen", tnp_gen_ee_ntracks, tnp_gen_eg_ntracks) tpgenf_ntracks.rebin(ntracks_binning) tpgenf_ntracks.addFitModel(rt.TF1("tpgenf_ntracks", "[0]+[1]*x+[2]*x*x", 0, 200)) tpgenf_ntracks.fit() tpgencan_ntracks = h2f.h2FakerateCanvas(tpgenf_ntracks) tpgencan_ntracks.createCanvas(fs, yrng=[0., .03], name=pdf_str+"f_tnpgen_ntracks") #fs.savePdf(tpgencan_ntracks.c, pdf_str+"f_tnpgen_ntracks") #''' tnp_ee_ntracks.Add(tnp_eg_ntracks) tpf_ntracks = h2f.h2Fakerate("ntracks", tnp_ee_ntracks, tnp_eg_ntracks) tpf_ntracks.rebin(ntracks_binning) tpf_ntracks.addFitModel(rt.TF1("tpf_ntracks", "[0]+[1]*x+[2]*x*x", 0, 200)) tpf_ntracks.fit() tpcan_ntracks = h2f.h2FakerateCanvas(tpf_ntracks) #tpcan_ntracks.createCanvas([0., .03]) tpcan_ntracks.createCanvas(fs, yrng=[0., .03], name=pdf_str+"f_tnp_ntracks") #fs.savePdf(tpcan_ntracks.c, pdf_str+"f_tnp_ntracks") # ''' ''' clos_e_ntracks = aux.rebin(clos_e_ntracks, ntracks_binning) clos_g_ntracks = aux.rebin(clos_g_ntracks, ntracks_binning) tpclos_ntracks = h2f.closure("tnp_ntracks"+strClosure, clos_e_ntracks, clos_g_ntracks) tpclos_ntracks.addFakerate(tpgenf_ntracks, "Tag n Probe gen info"+strClosure) tpclos_ntracks.addFakerate(tpf_ntracks, "Tag n Probe"+strClosure) tpclos_ntracks.createFitPrediction() tpclos_ntracks.createHistoPrediction() tpclos_ntracks.histoCanvas("tnpgen_ntracks_closure"+strClosure, 0) tpclos_ntracks.histoCanvas("tnp_ntracks_closure"+strClosure, 1) fs.savePdf(tpclos_ntracks.ch["Tag n Probe gen info"+strClosure], "tnpgen_ntracks_closure_perbin"+strClosure) fs.savePdf(tpclos_ntracks.ch["Tag n Probe"+strClosure], "tnp_ntracks_closure_perbin"+strClosure) #''' nvtx_binning = range(30) ''' tnp_gen_ee_nvtx.Add(tnp_gen_eg_nvtx) tpgenf_nvtx = h2f.h2Fakerate("nvtx_gen", tnp_gen_ee_nvtx, tnp_gen_eg_nvtx) tpgenf_nvtx.addFitModel(rt.TF1("tpgenf_nvtx", "[0]+[1]*x", 0, 30)) tpgenf_nvtx.fit() tpgencan_nvtx = h2f.h2FakerateCanvas(tpgenf_nvtx) tpgencan_nvtx.createCanvas(fs, name=pdf_str+"f_tnpgen_nvtx") #fs.savePdf(tpgencan_nvtx.c, "f_tnpgen_nvtx") #''' tnp_ee_nvtx.Add(tnp_eg_nvtx) tpf_nvtx = h2f.h2Fakerate("nvtx", tnp_ee_nvtx, tnp_eg_nvtx) tpf_nvtx.addFitModel(rt.TF1("tpf_nvtx", "[0]+[1]*x", 0, 30)) tpf_nvtx.fit() tpcan_nvtx = h2f.h2FakerateCanvas(tpf_nvtx) tpcan_nvtx.createCanvas(fs, yrng=[0., 0.04], name=pdf_str+"f_tnp_nvtx") #fs.savePdf(tpcan_nvtx.c, pdf_str+"f_tnp_nvtx") #''' ''' clos_e_nvtx = aux.rebin(clos_e_nvtx, nvtx_binning) clos_g_nvtx = aux.rebin(clos_g_nvtx, nvtx_binning) tpclos_nvtx = h2f.closure("tnp_nvtx"+strClosure, clos_e_nvtx, clos_g_nvtx) tpclos_nvtx.addFakerate(tpgenf_nvtx, "Tag n Probe gen info"+strClosure) tpclos_nvtx.addFakerate(tpf_nvtx, "Tag n Probe"+strClosure) tpclos_nvtx.createFitPrediction() tpclos_nvtx.createHistoPrediction() tpclos_nvtx.histoCanvas("tnpgen_nvtx_closure"+strClosure, 0) tpclos_nvtx.histoCanvas("tnp_nvtx_closure"+strClosure, 1) fs.savePdf(tpclos_nvtx.ch["Tag n Probe gen info"+strClosure], "tnpgen_nvtx_closure_perbin"+strClosure) fs.savePdf(tpclos_nvtx.ch["Tag n Probe"+strClosure], "tnp_nvtx_closure_perbin"+strClosure) #''' eta_binning = range(0, 150+1, 10) for i in range(len(eta_binning)): eta_binning[i] = eta_binning[i]*0.01 ''' tnp_gen_ee_eta.Add(tnp_gen_eg_eta) tpgenf_eta = h2f.h2Fakerate("eta_gen", tnp_gen_ee_eta, tnp_gen_eg_eta) tpgenf_eta.rebin(eta_binning) tpgenf_eta.addFitModel(rt.TF1("tpgenf_eta", "[0]+[1]*x", 0.1, 1.4)) tpgenf_eta.fit() tpgencan_eta = h2f.h2FakerateCanvas(tpgenf_eta) tpgencan_eta.createCanvas() fs.savePdf(tpgencan_eta.c, "f_tnpgen_eta") #''' tnp_ee_eta.Add(tnp_eg_eta) tpf_eta = h2f.h2Fakerate("eta", tnp_ee_eta, tnp_eg_eta) tpf_eta.rebin(eta_binning) tpf_eta.addFitModel(rt.TF1("tpf_eta", "[0]+[1]*x", 0.2, 1.4)) tpf_eta.fit() tpcan_eta = h2f.h2FakerateCanvas(tpf_eta) tpcan_eta.createCanvas(fs, name=pdf_str+"f_tnp_eta") #fs.savePdf(tpcan_eta.c, pdf_str+"f_tnp_eta") #''' ''' clos_e_eta = aux.rebin(clos_e_eta, eta_binning) clos_g_eta = aux.rebin(clos_g_eta, eta_binning) tpclos_eta = h2f.closure("tnp_eta"+strClosure, clos_e_eta, clos_g_eta) tpclos_eta.addFakerate(tpgenf_eta, "Tag n Probe gen info"+strClosure) tpclos_eta.addFakerate(tpf_eta, "Tag n Probe"+strClosure) tpclos_eta.createFitPrediction() tpclos_eta.createHistoPrediction() tpclos_eta.histoCanvas("tnpgen_eta_closure"+strClosure, 0) tpclos_eta.histoCanvas("tnp_eta_closure"+strClosure, 1) fs.savePdf(tpclos_eta.ch["Tag n Probe gen info"+strClosure], "tnpgen_eta_closure_perbin"+strClosure) fs.savePdf(tpclos_eta.ch["Tag n Probe"+strClosure], "tnp_eta_closure_perbin"+strClosure) # ''' return 0