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