def main():
process_mit = ["Wen_data","Wen_wjets","Wmn_data","Wmn_wjets","signal_data","signal_zjets","signal_wjets"]
filefolder = "/home/zdemirag/cms_2017/zpt/CMSSW_8_1_0/src/analysis/zpt_fit/"
for proc in range(0,len(process_mit)):
h1 = makehistogram(filefolder+"mono-x.root" ,"category_monojet",process_mit[proc])
h2 = makehistogram(filefolder+"mono-x_sync.root" ,"category_monojet",process_mit[proc])
c = ROOT.TCanvas("c","c",600,700)
#c.SetLogy()
c.SetBottomMargin(0.3)
c.SetRightMargin(0.06)
c.SetTitle("")
c.cd()
h1.SetLineColor(1)
h1_clone = h1.Clone()
h1.GetYaxis().SetTitle("Events")
h1.GetXaxis().SetTitle("")
h1.GetXaxis().SetTitleOffset(1.15)
h1.GetXaxis().SetTitleSize(0)
h1.GetXaxis().SetLabelSize(0)
h1.Draw("hist")
h2.SetLineColor(2)
h2.SetLineWidth(2)
h2.Draw("histsame")
leg = ROOT.TLegend(.45,.7,.9,.9)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.AddEntry(h1 ,"zeynep","l")
leg.AddEntry(h2 ,"sid","l")
leg.Draw("same")
Pad = ROOT.TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
ratio = plot_ratio(False,h1_clone,h2,"met","zeynep/sid",0.95,1.05,5)
ratio.SetLineColor(2)
ratio.SetMarkerColor(2)
ratio.GetXaxis().SetTitle("met [GeV]")
ratio.SetMarkerStyle(20)
ratio.Draw("ep")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/sync/bkg_"+process_mit[proc]+".pdf")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/sync/bkg_"+process_mit[proc]+".png")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/sync/bkg_"+process_mit[proc]+".C")
leg = TLegend(.45, .7, .9, .9)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.AddEntry(hb, "Data driven QCD estimate", "l")
leg.AddEntry(hd, "QCD estimate from MC", "l")
leg.Draw("same")
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(0)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
hd.Sumw2()
ratio = plot_ratio(False, hb_clone, hd, "p_{T}^{miss} [GeV]", "ratio", 0, 5, 5)
ratio.GetXaxis().SetTitle("p_{T}^{miss} [GeV]")
ratio.SetMarkerStyle(20)
ratio.Draw("ep")
#ratio.Fit("pol0","","",250,600)
gStyle.SetOptStat(0)
gStyle.SetOptFit(0)
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/qcd2/estimation.png")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/qcd2/estimation.pdf")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/qcd2/estimation.C")
def scale(model):
#"Zvv_signal"
infile_central = TFile(folder + "ZtoNuNu_pt.root", "read")
tree_central = infile_central.Get("events")
h = TH1D("h", "h", len(ptbins) - 1, array('d', ptbins))
cuts = "metFilter==1&&pfmet>250 && dphipfmet>0.5 && nLooseLep==0 && fabs(calomet-pfmet)/pfmet<0.5&&jet1Pt>100 && fabs(jet1Eta)<2.4 && jet1IsTight==1 && nTau==0 && jetNMBtags==0"
weights = "(sf_pu*sf_tt*normalizedWeight*sf_lepID*sf_lepIso*sf_lepTrack*sf_ewkV*sf_metTrig*35900.0)"
tree_central.Draw("pfmet>>h",
"(" + cuts + " && nLoosePhoton==0)*" + weights)
h.Scale(1, "width")
print h.Integral()
hph = TH1D("hph", "hph", len(ptbins) - 1, array('d', ptbins))
tree_central.Draw("pfmet>>hph", "(" + cuts + ")*" + weights)
hph.Scale(1, "width")
c = TCanvas("c", "c", 700, 800)
c.SetLogy()
c.SetBottomMargin(0.3)
c.SetRightMargin(0.06)
h_clone = h.Clone()
h.GetYaxis().SetTitle("Events/GeV")
h.GetXaxis().SetTitle(" ")
h.GetXaxis().SetTitleOffset(1.15)
h.GetXaxis().SetTitleSize(0)
h.GetXaxis().SetLabelSize(0)
h.Draw("hist")
hph.SetLineColor(2)
hph.Draw("histsame")
leg = TLegend(.35, .8, .9, .9)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.AddEntry(h, "Zvv estimates with photon veto", "l")
leg.AddEntry(hph, "Zvv estimates without photon veto", "l")
leg.Draw("same")
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(0)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
ratio = plot_ratio(False, h_clone, hph, "p_{T}^{miss} [GeV]",
"Uncertainty", 0.93, 1.07, 5)
ratio.GetXaxis().SetTitle("p_{T}^{miss} [GeV]")
ratio.Draw("hist")
f1 = TF1("f1", "pol0", 250, 1500)
f1.SetParameter(0, 1)
f1.SetLineColor(kGray)
f1.SetLineStyle(2)
f1.Draw("same")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/zvv_phoveto.png")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/zvv_phoveto.pdf")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/zvv_phoveto.C")
def makevariations(model):
#bins = [ 250,350,500,700,1000,1500 ]
#bins = [ 250.0, 275.0, 300.0, 325.0, 350.0, 375.0, 400.0, 450.0, 500.0, 750.0, 1000.0, 1500.0, 2000.0]
bins = [ 250.0, 300.0, 350.0, 400.0, 500.0, 750.0, 1000.0, 1500.0]
if model == "pdf":
infile = TFile("/afs/cern.ch/user/z/zdemirag/public/forRaffaele/new_sys/out_pdf.root","READ")
f_out = TFile("wtow_pdf_sys_plot.root","recreate")
h_orig = {}
h = {}
hmj_orig = {}
hmj = {}
hv_orig = {}
hv = {}
i = 0
samplehistos = infile.GetListOfKeys()
for s in samplehistos:
obj = s.ReadObj()
if type(obj)!=type(TH1F()): continue
samplehist = obj
#print obj.GetTitle(), obj.GetName()
h_orig[i] = obj.Clone();
h[i] = obj.Clone(); h[i].SetName(obj.GetName()+"_Down")
hmj[i] = TH1F(h[i].GetName(),h[i].GetName(),len(bins)-1,array('d',bins))
hmj_orig[i] = TH1F(h_orig[i].GetName(),h_orig[i].GetName(),len(bins)-1,array('d',bins))
print i, h_orig[i].GetName()
for b in range(0,len(bins)):
hmj_orig[i].SetBinContent(b,h[i].GetBinContent(h[i].FindBin(hmj[i].GetBinCenter(b))))
hmj[i].SetBinContent(b,2-h[i].GetBinContent(h[i].FindBin(hmj[i].GetBinCenter(b))))
#print b, bins[b], hmj_orig[i].GetBinContent(b), hmj[i].GetBinContent(b)
#hmj[i].SetMaximum(1.1)
#hmj[i].SetMinimum(0.9)
#hmj_orig[i].SetMaximum(1.1)
#hmj_orig[i].SetMinimum(0.9)
f_out.cd()
hmj_orig[i].Write()
hmj[i].Write()
i+1
f_out.Close()
fnew = TFile("wtow_pdf_sys_plot.root","read")
h1 = fnew.Get("pdfvariationsignal")
h2 = fnew.Get("pdfvariationsinglemuon")
c_sr = TCanvas("c_sr","c_sr", 600, 700)
c_sr.SetBottomMargin(0.3)
c_sr.SetRightMargin(0.06)
c_sr.cd()
gStyle.SetOptStat(False)
htemp = h1.Clone("htemp")
h1.SetLineColor(4)
h1.SetLineWidth(2)
h1.Draw("hist")
h2.SetLineColor(2)
h2.SetLineWidth(2)
h2.Draw("histsame")
h1.GetYaxis().SetTitle("Variation")
h1.GetYaxis().CenterTitle()
h1.GetXaxis().SetTitle("")
h2.SetTitle("")
h1.SetTitle("")
h1.GetXaxis().SetLabelSize(0)
h1.SetMaximum(h1.GetMaximum()*1.2)
#legend_sr = TLegend(.60,.65,.82,.92)
legend_sr = TLegend(.20,.75,.62,.92)
legend_sr.AddEntry(h1, "W(l#nu) signal region","l")
legend_sr.AddEntry(h2, "W(#mu#nu) control region","l")
legend_sr.SetShadowColor(0);
legend_sr.SetFillColor(0);
legend_sr.SetLineColor(0);
legend_sr.Draw("same")
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
pull = plot_ratio(False,htemp,h2,len(bins),"E_{T}^{miss} [GeV]","Uncert. %",0.95,1.05,5)
pull.SetLineColor(2)
pull.Draw("HIST0")
c_sr.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/v2/wpdf.pdf")
c_sr.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/v2/wpdf.png")
c_sr.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/v2/wpdf.C")
def scale(model):
#"Zvv_signal"
infile_central = TFile(folder + "fittingForest_signal.root", "read")
tree_central = infile_central.Get("Zvv_signal")
h = TH1D("h", "h", len(ptbins) - 1, array('d', ptbins))
tree_central.Draw("met>>h", "weight")
#h.SetDirectory(0)
h.Scale(1, "width")
print h.Integral()
infile_up = TFile(folder + "fittingForest_Up_signal.root", "read")
tree_up = infile_up.Get(model + "_signal")
hup = TH1D("hup", "hup", len(ptbins) - 1, array('d', ptbins))
#hup.SetDirectory(0)
tree_up.Draw("met>>hup", "weight", "goff")
hup.Scale(1, "width")
infile_down = TFile(folder + "fittingForest_Down_signal.root", "read")
tree_down = infile_down.Get(model + "_signal")
hdw = TH1D("hdw", "hdw", len(ptbins) - 1, array('d', ptbins))
#hdw.SetDirectory(0)
tree_down.Draw("met>>hdw", "weight", "goff")
hdw.Scale(1, "width")
c = TCanvas("c", "c", 700, 800)
c.SetLogy()
c.SetBottomMargin(0.3)
c.SetRightMargin(0.06)
h_clone = h.Clone()
h.GetYaxis().SetTitle("Events/GeV")
h.GetXaxis().SetTitle(" ")
h.GetXaxis().SetTitleOffset(1.15)
h.GetXaxis().SetTitleSize(0)
h.GetXaxis().SetLabelSize(0)
#h.SetLineWidth(2)
h.Draw("hist")
#hup.SetLineWidth(2)
hup.SetLineColor(2)
hup.Draw("histsame")
#hdw.SetLineWidth(2)
hdw.SetLineColor(2)
hdw.Draw("histsame")
leg = TLegend(.35, .8, .9, .9)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.AddEntry(h, "Zvv central estimates", "l")
leg.AddEntry(hdw, "+/- Scale and resolution variations", "l")
leg.Draw("same")
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(0)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
ratio = plot_ratio(False, h_clone, hup, "p_{T}^{miss} [GeV]",
"Uncertainty", 0.93, 1.07, 5)
ratio.GetXaxis().SetTitle("p_{T}^{miss} [GeV]")
ratio.Draw("hist")
ratio2 = plot_ratio(False, h_clone, hdw, "p_{T}^{miss} [GeV]",
"Uncertainty", 0.93, 1.07, 5)
ratio2.Draw("histsame")
ratio2.Draw("histsame")
f1 = TF1("f1", "pol0", 250, 1500)
f1.SetParameter(0, 1)
f1.SetLineColor(kGray)
f1.SetLineStyle(2)
f1.Draw("same")
f2 = TF1("f2", "pol0", 250, 1500)
f2.SetParameter(0, 1.04)
f2.SetLineColor(kGray)
f2.SetLineStyle(2)
f2.Draw("same")
f3 = TF1("f3", "pol0", 250, 1500)
f3.SetParameter(0, 0.96)
f3.SetLineColor(kGray)
f3.SetLineStyle(2)
f3.Draw("same")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/zvv_variation2.png")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/zvv_variation2.pdf")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/zvv_variation2.C")
def scale(model):
#"Zvv_signal"
infile_central = TFile(folder + "fittingForest_all.root", "read")
tree_central = infile_central.Get(model + "_nlo_signal")
h = TH1D("h", "h", len(ptbins) - 1, array('d', ptbins))
tree_central.Draw("met>>h", "weight")
#h.SetDirectory(0)
h.Scale(1, "width")
print h.Integral()
infile_npv = TFile(folder + "fittingForest_signal_pv.root", "read")
tree_npv = infile_npv.Get(model + "_nlo_signal")
hnpv = TH1D("hnpv", "hnpv", len(ptbins) - 1, array('d', ptbins))
#hnpv.SetDirectory(0)
tree_npv.Draw("met>>hnpv", "weight", "goff")
hnpv.Scale(1, "width")
infile_npv_lo = TFile(folder + "fittingForest_signal_pv_lo.root", "read")
tree_npv_lo = infile_npv_lo.Get(model + "_signal")
hnpv_lo = TH1D("hnpv_lo", "hnpv_lo", len(ptbins) - 1, array('d', ptbins))
#hdw.SetDirectory(0)
tree_npv_lo.Draw("met>>hnpv_lo", "weight", "goff")
hnpv_lo.Scale(1, "width")
c = TCanvas("c", "c", 700, 800)
c.SetLogy()
c.SetBottomMargin(0.3)
c.SetRightMargin(0.06)
h_clone = h.Clone()
h_clone2 = hnpv.Clone()
h.GetYaxis().SetTitle("Events/GeV")
h.GetXaxis().SetTitle(" ")
h.GetXaxis().SetTitleOffset(1.15)
h.GetXaxis().SetTitleSize(0)
h.GetXaxis().SetLabelSize(0)
#h.SetLineWidth(2)
h.Draw("hist")
#hnpv.SetLineWidth(2)
hnpv.SetLineColor(2)
hnpv.Draw("histsame")
#hdw.SetLineWidth(2)
hnpv_lo.SetLineColor(4)
hnpv_lo.Draw("histsame")
leg = TLegend(.35, .8, .9, .9)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.AddEntry(h, "NLO samples, pu reweighting", "l")
leg.AddEntry(hnpv, "NLO samples, pv reweighting", "l")
leg.AddEntry(hnpv_lo, "LO samples, pv reweighting", "l")
leg.Draw("same")
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(0)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
ratio = plot_ratio(False, h_clone, hnpv_lo, "p_{T}^{miss} [GeV]",
"#frac{NLO with pu}{LO with pv}", 0.90, 1.10, 5)
ratio.GetXaxis().SetTitle("p_{T}^{miss} [GeV]")
ratio.GetYaxis().SetTitleSize(0.03)
ratio.GetYaxis().SetTitleOffset(1.6)
ratio.SetLineColor(1)
ratio.Draw("hist")
#ratio2 = plot_ratio(False,h_clone,hnpv_lo,"p_{T}^{miss} [GeV]","Difference",0.93,1.07,5)
#ratio2 = plot_ratio(False,h_clone2,hnpv_lo,"p_{T}^{miss} [GeV]","Difference",0.90,1.10,5)
#ratio.SetLineColor(4)
#ratio2.Draw("histsame")
f1 = TF1("f1", "pol0", 250, 1500)
f1.SetParameter(0, 1)
f1.SetLineColor(kGray)
f1.SetLineStyle(2)
f1.Draw("same")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/" + model +
"_difference.png")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/" + model +
"_difference.pdf")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/" + model +
"_difference.C")
def plot_stack(channel, name,var, bin, low, high, ylabel, xlabel, setLog = False):
yield_dic = {}
stack = THStack('a', 'a')
if var is 'met':
binLowE = [200,250,300,350,400,500,600,1000]
# binLowE = [250,300,350,400,500,600,1000]
added = TH1D('added','added',len(binLowE)-1,array('d',binLowE))
else:
added = TH1D('added', 'added',bin,low,high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard= build_selection(channel,metcut)
if var is 'met':
if channel is not 'signal':
xlabel = 'U [GeV]'
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard,"\n"
print 'INFO time is:', datetime.datetime.fromtimestamp( time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
#reordered_physics_processes = ordered_physics_processes
for Type in ordered_physics_processes:
yield_dic[physics_processes[Type]['datacard']] = 0
for Type in reordered_physics_processes:
# Create the Histograms
histName = Type+'_'+name+'_'+channel
if var is 'met':
#binLowE = [200,250,300,350,400,500,600,900,1500]
binLowE = [200,250,300,350,400,500,600,1000]
# binLowE = [250,300,350,400,500,600,1000]
Variables[Type] = TH1F(histName,histName,len(binLowE)-1,array('d',binLowE))
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
#print "\n"
# this right now breaks the tchain logic!
# if we have more than 1 file, this will break!!!!
if Type is not 'data':
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0],"read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
#total = h1[Type].GetEntries()
f[Type].Close()
else:
total = 1.0
input_tree = makeTrees(Type,"events",channel)
n_entries = input_tree.GetEntries()
#Incase you want to apply event by event re-weighting
w = "((0.0*(npv>-0.5&&npv<=0.5)+3.30418257204*(npv>0.5&&npv<=1.5)+2.59691269521*(npv>1.5&&npv<=2.5)+2.44251087681*(npv>2.5&&npv<=3.5)+2.42846225153*(npv>3.5&&npv<=4.5)+2.40062512591*(npv>4.5&&npv<=5.5)+2.30279811595*(npv>5.5&&npv<=6.5)+2.12054720297*(npv>6.5&&npv<=7.5)+1.9104708827*(npv>7.5&&npv<=8.5)+1.67904936047*(npv>8.5&&npv<=9.5)+1.43348925382*(npv>9.5&&npv<=10.5)+1.17893952713*(npv>10.5&&npv<=11.5)+0.940505177881*(npv>11.5&&npv<=12.5)+0.740901867872*(npv>12.5&&npv<=13.5)+0.56877478036*(npv>13.5&&npv<=14.5)+0.433148655714*(npv>14.5&&npv<=15.5)+0.325343558476*(npv>15.5&&npv<=16.5)+0.241688459349*(npv>16.5&&npv<=17.5)+0.180491032782*(npv>17.5&&npv<=18.5)+0.136993937378*(npv>18.5&&npv<=19.5)+0.104859480066*(npv>19.5&&npv<=20.5)+0.0768271030309*(npv>20.5&&npv<=21.5)+0.0563426184938*(npv>21.5&&npv<=22.5)+0.0454037058117*(npv>22.5&&npv<=23.5)+0.0359945616383*(npv>23.5&&npv<=24.5)+0.0286879205085*(npv>24.5&&npv<=25.5)+0.0208185595478*(npv>25.5&&npv<=26.5)+0.0170977379612*(npv>26.5&&npv<=27.5)+0.0122446391898*(npv>27.5&&npv<=28.5)+0.0148028308301*(npv>28.5&&npv<=29.5)+0.0120527550003*(npv>29.5&&npv<=30.5)+0.00402643194054*(npv>30.5&&npv<=31.5)+0.00981143754301*(npv>31.5&&npv<=32.5)+0.0*(npv>32.5&&npv<=33.5)+0.0155664899019*(npv>33.5&&npv<=34.5)+0.0*(npv>34.5&&npv<=35.5)+0.0*(npv>35.5&&npv<=36.5)+0.0*(npv>36.5&&npv<=37.5)+0.0*(npv>37.5&&npv<=38.5)+0.0*(npv>38.5&&npv<=39.5)))"
if channel is 'signal' or channel is 'Zmm' or channel is 'Wmn':
w_trig = '((met < 250)*0.97 + (met >=250 && met<350)* 0.987 + (met>=350)* 1.0 )'
else:
w_trig = '(1.0)'
anlo1_over_alo = "(1.24087232993*(genBos_pt>100.0&&genBos_pt<=150.0)+1.55807026252*(genBos_pt>150.0&&genBos_pt<=200.0)+1.51043242876*(genBos_pt>200.0&&genBos_pt<=250.0)+1.47333461572*(genBos_pt>250.0&&genBos_pt<=300.0)+1.43497331471*(genBos_pt>300.0&&genBos_pt<=350.0)+1.37846354687*(genBos_pt>350.0&&genBos_pt<=400.0)+1.2920177717*(genBos_pt>400.0&&genBos_pt<=500.0)+1.31414429236*(genBos_pt>500.0&&genBos_pt<=600.0)+1.20453974747*(genBos_pt>600.0))"
a_ewkcorr = "(0.998568444581*(genBos_pt>100.0&&genBos_pt<=150.0)+0.992098286517*(genBos_pt>150.0&&genBos_pt<=200.0)+0.986010290609*(genBos_pt>200.0&&genBos_pt<=250.0)+0.980265498435*(genBos_pt>250.0&&genBos_pt<=300.0)+0.974830448283*(genBos_pt>300.0&&genBos_pt<=350.0)+0.969676202351*(genBos_pt>350.0&&genBos_pt<=400.0)+0.962417128177*(genBos_pt>400.0&&genBos_pt<=500.0)+0.953511139209*(genBos_pt>500.0&&genBos_pt<=600.0)+0.934331895615*(genBos_pt>600.0))"
w_ewkcorr = "(0.980859240872*(genBos_pt>100.0&&genBos_pt<=150.0)+0.962118764182*(genBos_pt>150.0&&genBos_pt<=200.0)+0.944428528597*(genBos_pt>200.0&&genBos_pt<=250.0)+0.927685912907*(genBos_pt>250.0&&genBos_pt<=300.0)+0.911802238928*(genBos_pt>300.0&&genBos_pt<=350.0)+0.896700388113*(genBos_pt>350.0&&genBos_pt<=400.0)+0.875368225896*(genBos_pt>400.0&&genBos_pt<=500.0)+0.849096933047*(genBos_pt>500.0&&genBos_pt<=600.0)+0.792158791839*(genBos_pt>600.0))"
wnlo012_over_wlo = "(1.89123123702*(genBos_pt>100.0&&genBos_pt<=150.0)+1.70414182145*(genBos_pt>150.0&&genBos_pt<=200.0)+1.60726459197*(genBos_pt>200.0&&genBos_pt<=250.0)+1.57205818769*(genBos_pt>250.0&&genBos_pt<=300.0)+1.51688539716*(genBos_pt>300.0&&genBos_pt<=350.0)+1.41090079307*(genBos_pt>350.0&&genBos_pt<=400.0)+1.30757555038*(genBos_pt>400.0&&genBos_pt<=500.0)+1.32046236765*(genBos_pt>500.0&&genBos_pt<=600.0)+1.26852513234*(genBos_pt>600.0))"
z_ewkcorr = "(0.984525344338*(genBos_pt>100.0&&genBos_pt<=150.0)+0.969078612189*(genBos_pt>150.0&&genBos_pt<=200.0)+0.954626582726*(genBos_pt>200.0&&genBos_pt<=250.0)+0.941059330021*(genBos_pt>250.0&&genBos_pt<=300.0)+0.92828367065*(genBos_pt>300.0&&genBos_pt<=350.0)+0.916219976557*(genBos_pt>350.0&&genBos_pt<=400.0)+0.89931198024*(genBos_pt>400.0&&genBos_pt<=500.0)+0.878692669663*(genBos_pt>500.0&&genBos_pt<=600.0)+0.834717745177*(genBos_pt>600.0))"
znlo012_over_zlo = "(1.68500099066*(genBos_pt>100.0&&genBos_pt<=150.0)+1.55256109189*(genBos_pt>150.0&&genBos_pt<=200.0)+1.52259467479*(genBos_pt>200.0&&genBos_pt<=250.0)+1.52062313572*(genBos_pt>250.0&&genBos_pt<=300.0)+1.4322825541*(genBos_pt>300.0&&genBos_pt<=350.0)+1.45741443405*(genBos_pt>350.0&&genBos_pt<=400.0)+1.36849777989*(genBos_pt>400.0&&genBos_pt<=500.0)+1.3580214432*(genBos_pt>500.0&&genBos_pt<=600.0)+1.16484769869*(genBos_pt>600.0))"
wm_postfit = "(1.0)"
zm_postfit = "(1.0)"
g_postfit = "(1.0)"
# this is the scale using the total number of effective events
scale = 1.0;
scale = float(lumi)*physics_processes[Type]['xsec']/total
#print '\n'
# print "type: ", Type, "scale", scale, "lumi", lumi, physics_processes[Type]['xsec'], total
if Type is not 'data' and Type is not 'signal_dm' and Type is not 'signal_dm_s' and Type is not 'signal_dm_ps' and Type is not 'signal_dm_v' and Type is not 'signal_dm_av_1_2':
Variables[Type].SetFillColor(physics_processes[Type]['color'])
Variables[Type].SetLineColor(physics_processes[Type]['color'])
if Type.startswith('GJets') :
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*0.98*"+str(g_postfit)+"*"+str(anlo1_over_alo)+"*"+str(a_ewkcorr)+"*" +str(w)+"*"+str(w_trig),"goff")
elif (Type.startswith('Zvv') or Type.startswith('Zll')) :
if channel is 'signal' :
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(zm_postfit)+"*"+str(z_ewkcorr)+"*"+str(znlo012_over_zlo)+"*"+str(w)+"*"+str(w_trig),"goff")
else:
#SF explicitly written for the leading tight lepton from the root files
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*(0.98 *(lep1Eta<2.1)+0.91*(lep1Eta>=2.1))*"+str(zm_postfit)+"*"+str(z_ewkcorr)+"*"+str(znlo012_over_zlo)+"*"+str(w)+"*"+str(w_trig),"goff")
elif Type.startswith('Wlv'):
if channel is 'signal' :
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(wm_postfit)+"*"+str(wnlo012_over_wlo)+"*"+str(w_ewkcorr)+"*" +str(w)+"*"+str(w_trig),"goff")
else:
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(wm_postfit)+"*"+str(wnlo012_over_wlo)+"*"+str(w_ewkcorr)+"*" +str(w)+"*"+str(w_trig),"goff")
else:
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ") *mcWeight*" +str(w)+"*"+str(w_trig),"goff")
Variables[Type].Scale(scale,"width")
stack.Add(Variables[Type],"hist")
added.Add(Variables[Type])
if Type.startswith('signal_h_ggf'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(1)
makeTrees(Type,"events",channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(w),"goff")
Variables[Type].Scale(scale,"width")
if Type.startswith('signal_dm_av_1_2'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(8)
makeTrees(Type,"events",channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(w),"goff")
Variables[Type].Scale(scale,"width")
if Type.startswith('data'):
Variables[Type].SetMarkerStyle(20)
if channel is 'signal' or channel is 'Zmm' or channel is 'Wmn':
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard + " && (triggerFired[0]==1 || triggerFired[1]==1 || triggerFired[2]==1) )", "goff")
else:
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard +" )", "goff")
Variables[Type].Scale(1,"width")
yield_dic[physics_processes[Type]['datacard']] += round(Variables[Type].Integral("width"),3)
dump_datacard(channel,yield_dic)
#added.Write()
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp( time.time())
# if channel is 'Zmm' or channel is 'Zee':
# legend = TLegend(.60,.65,.92,.92)
# elif channel is 'gjets':
# legend = TLegend(.60,.65,.82,.92)
# else:
# legend = TLegend(.60,.60,.92,.92)
legend = TLegend(.60,.70,.92,.92)
lastAdded = ''
for process in ordered_physics_processes:
#print process
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
if process is not 'data' and process is not 'signal_dm' and process is not 'signal_dm_av_1_2' and process is not 'signal_h_ggf' and process is not 'signal_h_vbf':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "f")
if process is 'data':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "p")
c4 = TCanvas("c4","c4", 900, 1000)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.01)
#stack.SetMinimum(10)
if setLog:
c4.SetLogy()
if var is 'fatjet1tau21' or var is 'fatjet1PrunedM':
stack.SetMaximum( stack.GetMaximum() + 1e5*stack.GetMaximum() )
else:
stack.SetMaximum( stack.GetMaximum() + 1e2*stack.GetMaximum() )
else:
stack.SetMaximum( stack.GetMaximum() + 0.5*stack.GetMaximum() )
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetYaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
Variables['data'].Draw("Esame")
legend.SetShadowColor(0);
legend.SetFillColor(0);
legend.SetLineColor(0);
legend.Draw("same")
plot_cms(True,lumi_str)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
Pad.SetTopMargin(0.7)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
Pad.SetRightMargin(0.06)
data = Variables['data'].Clone()
plot_ratio(False,data,added,bin,xlabel,0,2.0,5)
f1 = TF1("f1","1",-5000,5000);
f1.SetLineColor(4);
f1.SetLineStyle(2);
f1.SetLineWidth(2);
f1.Draw("same")
outputName = folder+'/mj_Histo_' + name + '_'+channel
c4.SaveAs(outputName+'.pdf')
c4.SaveAs(outputName+'.png')
c4.SaveAs(outputName+'.C')
del Variables
del var
del f
del h1
c4.IsA().Destructor( c4 )
stack.IsA().Destructor( stack )
leg.SetBorderSize(0)
leg.AddEntry(hb, "Data driven QCD estimate", "l")
leg.AddEntry(hb_down, "+/- Scale and resolution variations", "l")
leg.Draw("same")
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(0)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
hd.Sumw2()
ratio = plot_ratio(False, hb_clone, hb_up, "p_{T}^{miss} [GeV]", "ratio", 0, 3,
5)
ratio.GetXaxis().SetTitle("p_{T}^{miss} [GeV]")
ratio.SetMarkerStyle(20)
ratio.Draw("ep")
ratio2 = plot_ratio(False, hb_clone, hb_down, "p_{T}^{miss} [GeV]", "ratio", 0,
3, 5)
ratio2.Draw("sameep")
#ratio.Fit("pol0","","",250,600)
#gStyle.SetOptStat(0)
#gStyle.SetOptFit(0)
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/qcd2/Up/estimation.png")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/qcd2/Up/estimation.pdf")
c.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/qcd2/Up/estimation.C")
def plot_stack(channel,
name,
var,
bin,
low,
high,
ylabel,
xlabel,
setLog=False):
folder = '/afs/cern.ch/user/z/zdemirag/www/monojet/panda/v1'
if not os.path.exists(folder):
os.mkdir(folder)
yield_dic = {}
stack = THStack('a', 'a')
if var.startswith('pfmet') or var.startswith('pfU'):
binLowE = [
250.0, 280.0, 310.0, 340.0, 370.0, 400.0, 430.0, 470.0, 510.0,
550.0, 590.0, 640.0, 690.0, 740.0, 790.0, 840.0, 900.0, 960.0,
1020.0, 1090.0, 1160.0, 1250.0, 1400.0
]
nb = len(binLowE) - 1
added = TH1D('added', 'added', nb, array('d', binLowE))
else:
added = TH1D('added', 'added', bin, low, high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard = build_selection(channel)
if var.startswith('pfU'):
if channel is not 'signal':
xlabel = 'Hadronic Recoil p_{T} [GeV]'
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard, "\n"
print 'INFO time is:', datetime.datetime.fromtimestamp(time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
for Type in ordered_physics_processes:
yield_dic[physics_processes[Type]['datacard']] = 0
for Type in reordered_physics_processes:
histName = Type + '_' + name + '_' + channel
if var.startswith('pfmet') or var.startswith('pfU'):
binLowE = [
250.0, 280.0, 310.0, 340.0, 370.0, 400.0, 430.0, 470.0, 510.0,
550.0, 590.0, 640.0, 690.0, 740.0, 790.0, 840.0, 900.0, 960.0,
1020.0, 1090.0, 1160.0, 1250.0, 1400.0
]
n2 = len(binLowE) - 1
Variables[Type] = TH1F(histName, histName, n2, array('d', binLowE))
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
Variables[Type].StatOverflows(kTRUE)
input_tree = makeTrees(Type, "events", channel)
n_entries = input_tree.GetEntries()
scale = float(lumi) # *physics_processes[Type]['xsec']/total
common_weight = build_weights(channel, Type)
print Type, common_weight, scale
#if Type is not 'data' and Type is not 'signal_ggf' and Type is not 'signal_vbf':
if Type is not 'data' and Type is not 'signal_ggf':
Variables[Type].SetFillColor(
TColor.GetColor(physics_processes[Type]['color']))
Variables[Type].SetLineColor(1)
makeTrees(Type, 'events',
channel).Draw(var + " >> " + histName,
"(" + cut_standard + " )" + common_weight,
"goff")
if var == "pfmet" or var.startswith('pfU'):
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(
Variables[Type].GetNbinsX(),
Variables[Type].GetBinContent(nbins) +
Variables[Type].GetBinContent(nbins + 1))
Variables[Type].Scale(scale, "width")
stack.Add(Variables[Type], "hist")
added.Add(Variables[Type])
if Type.startswith('signal'):
makeTrees(Type, 'events',
channel).Draw(var + " >> " + histName,
"(" + cut_standard + ")" + common_weight,
"goff")
if var == "pfmet" or var.startswith('pfU'):
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(
Variables[Type].GetNbinsX(),
Variables[Type].GetBinContent(nbins) +
Variables[Type].GetBinContent(nbins + 1))
Variables[Type].Scale(scale, "width")
if Type.startswith('data'):
Variables[Type].SetMarkerStyle(20)
if channel in 'signal' or channel in 'Zmm' or channel in 'Wmn':
#met trigger
#makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard + "&& (trigger&1)!=0)", "goff")
makeTrees(Type, "events",
channel).Draw(var + " >> " + histName, cut_standard,
"goff")
elif channel in 'GJets':
#photon trigger - no need, the flag is not working properly
makeTrees(Type, "events",
channel).Draw(var + " >> " + histName, cut_standard,
"goff")
else:
#ele trigger
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName,
"(" + cut_standard + "&& (trigger&2)!=0)", "goff")
if var == "pfmet" or var.startswith('pfU'):
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(
Variables[Type].GetNbinsX(),
Variables[Type].GetBinContent(nbins) +
Variables[Type].GetBinContent(nbins + 1))
Variables[Type].Scale(1, "width")
yield_dic[physics_processes[Type]['datacard']] += round(
Variables[Type].Integral("width"), 3)
dump_datacard(channel, yield_dic)
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp(
time.time())
if channel is 'GJets':
legend = TLegend(.60, .65, .82, .92)
else:
legend = TLegend(.60, .55, .92, .92)
lastAdded = ''
for process in ordered_physics_processes:
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
#if process is not 'data' and process is not 'signal_vbf' and process is not 'signal_ggf':
if process is not 'data' and process is not 'signal_ggf':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "f")
if process is 'data':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "p")
c4 = TCanvas("c4", "c4", 600, 700)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.01)
if setLog:
c4.SetLogy()
if "eta" in var:
stack.SetMaximum(Variables['data'].GetMaximum() +
1e8 * Variables['data'].GetMaximum())
else:
stack.SetMaximum(Variables['data'].GetMaximum() +
1e2 * Variables['data'].GetMaximum())
else:
stack.SetMaximum(Variables['data'].GetMaximum() +
0.5 * Variables['data'].GetMaximum())
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetYaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
if channel is 'signal' and blind:
for b in range(Variables['data'].GetNbinsX()):
Variables['data'].SetBinContent(b + 1, 0.0)
Variables['data'].Draw("Esame")
#Variables['signal_vbf'].SetLineWidth(2)
Variables['signal_ggf'].SetLineWidth(2)
#Variables['signal_vbf'].SetLineColor(1)
Variables['signal_ggf'].SetLineColor(4)
#Variables['signal_vbf'].Draw("samehist")
#Variables['signal_ggf'].Draw("samehist")
#legend . AddEntry(Variables['signal_vbf'],physics_processes['signal_vbf']['label'] , "l")
#legend . AddEntry(Variables['signal_ggf'],physics_processes['signal_ggf']['label'] , "l")
legend.SetShadowColor(0)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.Draw("same")
plot_cms(True, lumi_str, c4)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
data = Variables['data'].Clone()
plot_ratio(False, data, added, bin, xlabel, 0.5, 1.5, 5)
f1 = TF1("f1", "1", -5000, 5000)
f1.SetLineColor(4)
f1.SetLineStyle(2)
f1.SetLineWidth(2)
f1.Draw("same")
Pad.Update()
Pad.RedrawAxis()
c4.SaveAs(folder + '/Histo_' + category + '_' + name + '_' + channel +
'.pdf')
c4.SaveAs(folder + '/Histo_' + category + '_' + name + '_' + channel +
'.png')
c4.SaveAs(folder + '/Histo_' + category + '_' + name + '_' + channel +
'.C')
del Variables
del var
del f
del h1
c4.IsA().Destructor(c4)
stack.IsA().Destructor(stack)
def plot_stack(channel, name,var, bin, low, high, ylabel, xlabel, setLog = False):
folder = '/afs/cern.ch/user/z/zdemirag/www/Monojet/frozen/'
if not os.path.exists(folder):
os.mkdir(folder)
yield_dic = {}
stack = THStack('a', 'a')
added = TH1D('a', 'a',bin,low,high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
#cut_standard= build_selection(channel,0) ### Fix this back to 200 ###
cut_standard= build_selection(channel,200) ### Fix this back to 200 ###
if var is 'met':
if channel is not 'signal':
xlabel = 'U [GeV]'
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard,"\n"
print 'INFO time is:', datetime.datetime.fromtimestamp( time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
#reordered_physics_processes = ordered_physics_processes
for Type in ordered_physics_processes:
yield_dic[physics_processes[Type]['datacard']] = 0
for Type in reordered_physics_processes:
# Create the Histograms
histName = Type+'_'+name+'_'+channel
if var is 'met':
binLowE = [200,250,300,350,400,500,600,1000]
Variables[Type] = TH1F(histName,histName,7,array('d',binLowE))
Variables[Type].Scale(1,"width");
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
#print "\n"
# this right now breaks the tchain logic!
# if we have more than 1 file, this will break!!!!
#print physics_processes[Type]['files'][0]
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0],"read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
f[Type].Close()
input_tree = makeTrees(Type,"events",channel)
n_entries = input_tree.GetEntries()
#Incase you want to apply event by event re-weighting
#w = 1.0;
w = '(3.57041 + -1.49846*npv + 0.515829*npv*npv + -0.0839209*npv*npv*npv + 0.00719964*npv*npv*npv*npv + -0.000354548*npv*npv*npv*npv*npv + 1.01544e-05*npv*npv*npv*npv*npv*npv + -1.58019e-07*npv*npv*npv*npv*npv*npv*npv + 1.03663e-09*npv*npv*npv*npv*npv*npv*npv*npv)'
# this is the scale using the total number of effective events
scale = 1.0;
scale = float(lumi)*physics_processes[Type]['xsec']/total
#print '\n'
print "type: ", Type, "scale", scale, "lumi", lumi, physics_processes[Type]['xsec'], total
if(shapelimits is True):
# WRITING OUT A TREE
file_out = ROOT.TFile("monojet_"+channel+".root", "update")
if Type is 'data':
selectedTree = input_tree.CopyTree( cut_standard );
else:
selectedTree = input_tree.CopyTree( cut_standard );
scale_w = n.zeros(1,dtype=float)
selectedTree.Branch('scale_w',scale_w,'scale_w/D')
for i in range(selectedTree.GetEntries()):
selectedTree.GetEntry(i)
if Type is 'data':
scale_w[0] = 1.0
else:
scale_w[0] = scale
selectedTree.Fill()
selectedTree.Write(Type+"_"+channel)
file_out.Close()
os.system('root -l -q -b "addMCWeight.C(\\"monojet_'+channel+'.root\\",\\"'+Type+'_'+channel+'\\")"')
if Type is not 'data' and Type is not 'signal_dm':
Variables[Type].SetFillColor(physics_processes[Type]['color'])
Variables[Type].SetLineColor(physics_processes[Type]['color'])
if Type.startswith('GJets'):
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*kfactor*" +str(w),"goff")
else:
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*" +str(w),"goff")
Variables[Type].Scale(scale)
stack.Add(Variables[Type],"hist")
added.Add(Variables[Type])
if Type.startswith('signal_dm'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(8)
makeTrees(Type,"events",channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(w),"goff")
Variables[Type].Scale(scale)
if Type.startswith('data'):
Variables[Type].SetMarkerStyle(20)
if channel is 'signal':
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard + "&& (triggerFired[1]==1 || triggerFired[0]==1 || triggerFired[2]==1 ))", "goff")
else:
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard + ")", "goff")
yield_dic[physics_processes[Type]['datacard']] += round(Variables[Type].Integral(),3)
#print Type, round(Variables[Type].Integral(),3), "total in: ", physics_processes[Type]['datacard'], yield_dic[physics_processes[Type]['datacard']]
dump_datacard(channel,yield_dic)
#added.Write()
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp( time.time())
legend = TLegend(.60,.60,.92,.92)
lastAdded = ''
for process in ordered_physics_processes:
#print process
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
if process is not 'data' and process is not 'signal_dm':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "f")
if process is 'data':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "p")
if process is 'signal_dm':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "l")
c4 = TCanvas("c4","c4", 900, 1000)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.1)
if setLog:
c4.SetLogy()
stack.SetMaximum( stack.GetMaximum() + 1000*stack.GetMaximum() )
else:
stack.SetMaximum( stack.GetMaximum() + stack.GetMaximum() )
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
Variables['data'].Draw("Esame")
Variables['signal_dm'].Draw("same")
legend.SetShadowColor(0);
legend.SetFillColor(0);
legend.SetLineColor(0);
legend.Draw("same")
plot_cms(True,lumi)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
Pad.SetTopMargin(0.7)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
Pad.SetRightMargin(0.06)
data = Variables['data'].Clone()
plot_ratio(False,data,added,bin,xlabel)
f1 = TF1("f1","1",-5000,5000);
f1.SetLineColor(4);
f1.SetLineStyle(2);
f1.SetLineWidth(2);
f1.Draw("same")
c4.SaveAs(folder+'/'+channel+'/Histo_' + name + '_'+channel+'.pdf')
c4.SaveAs(folder+'/'+channel+'/Histo_' + name + '_'+channel+'.png')
c4.SaveAs(folder+'/'+channel+'/Histo_' + name + '_'+channel+'.C')
del Variables
del var
del f
del h1
c4.IsA().Destructor( c4 )
stack.IsA().Destructor( stack )
def makevariations(model):
bins = [250.0, 300.0, 350.0, 400.0, 500.0, 750.0, 1000.0, 1500.0]
if model == "trigger":
infile = TFile(
"/afs/cern.ch/user/z/zdemirag/public/forRaffaele/new_sys/out_triggersys.root",
"READ")
f_out = TFile("trigger_sys.root", "recreate")
h_orig = {}
h = {}
hmj_orig = {}
hmj = {}
hv_orig = {}
hv = {}
i = 0
samplehistos = infile.GetListOfKeys()
for s in samplehistos:
obj = s.ReadObj()
if type(obj) != type(TH1F()): continue
samplehist = obj
print obj.GetTitle(), obj.GetName()
h_orig[i] = obj.Clone()
h[i] = obj.Clone()
h[i].SetName(obj.GetName() + "_Down")
hmj[i] = TH1F(h[i].GetName(), h[i].GetName(),
len(bins) - 1, array('d', bins))
hmj_orig[i] = TH1F(h_orig[i].GetName(), h_orig[i].GetName(),
len(bins) - 1, array('d', bins))
for b in range(0, len(bins)):
hmj_orig[i].SetBinContent(
b, h[i].GetBinContent(h[i].FindBin(hmj[i].GetBinCenter(b))))
hmj[i].SetBinContent(
b,
2 - h[i].GetBinContent(h[i].FindBin(hmj[i].GetBinCenter(b))))
print b, bins[b], hmj_orig[i].GetBinContent(
b), hmj[i].GetBinContent(b)
hmj[i].SetMaximum(1.1)
hmj[i].SetMinimum(0.9)
hmj_orig[i].SetMaximum(1.1)
hmj_orig[i].SetMinimum(0.9)
f_out.cd()
hmj_orig[i].Write()
hmj[i].Write()
i + 1
f_out.Close()
fnew = TFile("trigger_sys.root", "read")
h1 = fnew.Get("zmm_sys_Down")
h2 = fnew.Get("zvv_sys_Down")
c_sr = TCanvas("c_sr", "c_sr", 600, 700)
c_sr.SetBottomMargin(0.3)
c_sr.SetRightMargin(0.06)
c_sr.cd()
gStyle.SetOptStat(False)
h1.GetXaxis().SetRangeUser(250, 800)
h2.GetXaxis().SetRangeUser(250, 800)
htemp = h1.Clone("htemp")
h1.SetLineColor(4)
h1.SetLineWidth(2)
h1.Draw("hist")
h2.SetLineColor(2)
h2.SetLineWidth(2)
h2.Draw("histsame")
h1.GetYaxis().SetTitle("Efficiency")
h1.GetYaxis().CenterTitle()
h1.GetXaxis().SetTitle("")
h2.SetTitle("")
h1.SetTitle("")
h1.GetXaxis().SetLabelSize(0)
func2 = TF1("func2", "1", 0, 3000)
func2.SetLineColor(kGray + 2)
func2.SetLineStyle(2)
func2.Draw("same")
h1.SetMaximum(1.1)
h1.SetMinimum(0.85)
#legend_sr = TLegend(.60,.65,.82,.92)
legend_sr = TLegend(.20, .75, .62, .92)
legend_sr.AddEntry(h1, "Using Z(#mu#mu) events", "l")
legend_sr.AddEntry(h2, "Using W(#mu#nu) events", "l")
legend_sr.SetShadowColor(0)
legend_sr.SetFillColor(0)
legend_sr.SetLineColor(0)
legend_sr.Draw("same")
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
pull = plot_ratio(False, htemp, h2, len(bins),
"Hadronic recoil p_{T} [GeV]", "Uncert. %", 0.95, 1.05,
5)
pull.SetLineColor(4)
pull.GetXaxis().SetRangeUser(250, 800)
pull.Draw("HIST0")
func = TF1("func", "1", 0, 3000)
func.SetLineColor(kGray + 2)
func.SetLineStyle(2)
func.Draw("same")
c_sr.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/v2/trigger.pdf")
c_sr.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/v2/trigger.png")
c_sr.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/v2/trigger.C")
h_bkg_pre[0].GetXaxis().SetLabelSize(0)
h_bkg_pre[0].GetXaxis().SetTitle('')
h_bkg_pre[1].SetLineColor(2)
h_bkg_pre[1].SetLineWidth(2)
h_bkg_pre[1].Draw("histsame")
legend = TLegend(.60,.65,.82,.92)
legend.SetShadowColor(0);
legend.SetFillColor(0);
legend.SetLineColor(0);
legend.AddEntry(h_bkg_pre[0],"2017 Analysis")
legend.AddEntry(h_bkg_pre[1],"2016 Analysis")
legend.Draw("same")
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
ratio = plot_ratio(False,plot_zeynep,h_bkg_pre[1],"Recoil",'2017/2016',0,2,5)
ratio.Draw("HIST")
c.SaveAs(folder+"/"+zeynep+"_"+p_s+"_"+status+".pdf")
c.SaveAs(folder+"/"+zeynep+"_"+p_s+"_"+status+".png")
del h_bkg_pre[0], h_bkg_pre[1]
def plot_stack(channel, name,var, bin, low, high, ylabel, xlabel, setLog = False):
folder = '/afs/cern.ch/user/d/dabercro/www/plots/' + os.environ.get('CROMBIEDATE') + '_monojet/'
if not os.path.exists(folder):
os.mkdir(folder)
yield_dic = {}
stack = THStack('a', 'a')
if var.startswith('met'):
if vtag or vbf:
binLowE = [250,300,350,400,500,600,750,1000]
else:
binLowE = [200., 230., 260.0, 290.0, 320.0, 350.0, 390.0, 430.0, 470.0, 510.0, 550.0, 590.0, 640.0, 690.0, 740.0, 790.0, 840.0, 900.0, 960.0, 1020.0, 1090.0, 1160.0, 1250.0]
nb = len(binLowE)-1
added = TH1D('added','added',nb,array('d',binLowE))
else:
added = TH1D('added', 'added',bin,low,high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard= build_selection(channel,metcut)
if var.startswith('met'):
if channel is not 'signal':
xlabel = 'U [GeV]'
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard,"\n"
print 'INFO time is:', datetime.datetime.fromtimestamp( time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
for Type in ordered_physics_processes:
yield_dic[physics_processes[Type]['datacard']] = 0
for Type in reordered_physics_processes:
if not vbf:
if Type.startswith('EWK') :
continue
#Create the Histograms
histName = Type+'_'+name+'_'+channel
if var.startswith('met'):
if vtag or vbf :
binLowE = [250,300,350,400,500,600,750,1000]
else:
binLowE = [200., 230., 260.0, 290.0, 320.0, 350.0, 390.0, 430.0, 470.0, 510.0, 550.0, 590.0, 640.0, 690.0, 740.0, 790.0, 840.0, 900.0, 960.0, 1020.0, 1090.0, 1160.0, 1250.0]
n2 = len(binLowE)-1
Variables[Type] = TH1F(histName,histName,n2,array('d',binLowE))
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
Variables[Type].StatOverflows(kTRUE)
if Type is not 'data':
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0],"read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
f[Type].Close()
else:
total = 1.0
input_tree = makeTrees(Type,"events",channel)
n_entries = input_tree.GetEntries()
w_trig = '(1.0)'
# this is the scale using the total number of effective events
scale = 1.0;
scale = float(lumi)*physics_processes[Type]['xsec']/total
#print Type, scale, total
##Common weights used for the analysis
triggercut = "(1.0)"
if channel is 'signal':
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[10]==1 || triggerFired[11] == 1 || triggerFired[12] || triggerFired[13] == 1)"
else:
common_weight = "*mcWeight*lepton_SF1*lepton_SF2*METTrigger*puWeight*topPtReweighting"
elif channel is 'Zmm':
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[10]==1 || triggerFired[11] == 1 || triggerFired[12] || triggerFired[13] == 1)"
else:
common_weight = "*mcWeight*lepton_SF1*lepton_SF2*METTrigger*puWeight*topPtReweighting*tracking_SF1*tracking_SF2"
elif channel is 'Wmn':
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[10]==1 || triggerFired[11] == 1 || triggerFired[12] || triggerFired[13] == 1)"
else:
common_weight = "*mcWeight*lepton_SF1*lepton_SF2*METTrigger*puWeight*topPtReweighting*tracking_SF1"
elif channel is 'Wen':
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[0] || triggerFired[1] || triggerFired[2] || triggerFired[3] || triggerFired[4] || triggerFired[5] || triggerFired[26])"
else:
common_weight = "*mcWeight*puWeight*EleTrigger_w1*EleTrigger_w2*lepton_SF1*topPtReweighting*gsfTracking_SF1"
elif channel is 'Zee':
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[0] || triggerFired[1] || triggerFired[2] || triggerFired[3] || triggerFired[4] || triggerFired[5] || triggerFired[26])"
else:
common_weight = "*mcWeight*puWeight*lepton_SF1*lepton_SF2*topPtReweighting*gsfTracking_SF1*gsfTracking_SF2"
else:
if Type is 'data':
common_weight = "*(1.0)"
triggercut = "(triggerFired[18] || triggerFired[19] || triggerFired[17] || triggerFired[5] || triggerFired[15] || triggerFired[16])"
else:
common_weight = "*mcWeight*PhoTrigger*topPtReweighting*photon_SF*puWeight"
#common_weight = "*mcWeight*lepton_SF1*lepton_SF2*puWeight_true"
if Type is not 'data' and Type is not 'signal_ggf' and Type is not 'signal_vbf':
Variables[Type].SetFillColor(TColor.GetColor(physics_processes[Type]['color']))
Variables[Type].SetLineColor(TColor.GetColor(physics_processes[Type]['color']))
if Type.startswith('GJets') :
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_a*akfactor","goff")
#makeTrees(Type,'events',channel).Draw("photonPt*0.98 >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_a*akfactor","goff")
elif (Type.startswith('Zvv')):
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_z*zkfactor","goff")
elif (Type.startswith('Zll')) :
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_z*zkfactor","goff")
#makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_z","goff")
elif Type.startswith('Wlv'):
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_w","goff")
elif Type.startswith('QCD') and channel is 'signal':
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight,"goff")
else:
if Type.startswith('QCD') and channel is 'gjets':
# qcd is from the purity study
purity = "((0.04802 * (photonPt >= 175 && photonPt < 200 )) + \
(0.04241 * (photonPt >= 200 && photonPt < 250 )) + \
(0.0364 * (photonPt >= 250 && photonPt < 300 )) + \
(0.0333 * (photonPt >= 300 && photonPt < 350 )) + \
(0.02544 * (photonPt >= 350)))"
#purity = "((0.0320 * (photonPt >= 175 && photonPt < 200 )) + \
# (0.0253 * (photonPt >= 200 && photonPt < 250 )) + \
# (0.0211 * (photonPt >= 250 && photonPt < 300 )) + \
# (0.0223 * (photonPt >= 300 && photonPt < 350 )) + \
# (0.0187 * (photonPt >= 350)))"
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight+"*ewk_a*akfactor*"+str(purity),"goff")
#makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*"+str(purity),"goff")
else:
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight,"goff")
if var == "met":
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(Variables[Type].GetNbinsX(),Variables[Type].GetBinContent(nbins)+Variables[Type].GetBinContent(nbins+1))
Variables[Type].Scale(scale,"width")
stack.Add(Variables[Type],"hist")
added.Add(Variables[Type])
if Type.startswith('signal'):
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")"+common_weight,"goff")
if var == "met":
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(Variables[Type].GetNbinsX(),Variables[Type].GetBinContent(nbins)+Variables[Type].GetBinContent(nbins+1))
Variables[Type].Scale(scale,"width")
if Type.startswith('data'):
Variables[Type].SetMarkerStyle(20)
#makeTrees(Type,'events',channel).Draw("photonPt*1.02 >> " + histName,"(" + cut_standard +" && metfilter==1 && filterbadChCandidate && filterbadPFMuon && "+triggercut+")","goff")
#makeTrees(Type,'events',channel).Draw("transverseMass(lep1Pt*1.02,lep1Phi,trueMet,trueMetPhi) >> " + histName,"(" + cut_standard +" && metfilter==1 && filterbadChCandidate && filterbadPFMuon && "+triggercut+")","goff")
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard +" && metfilter==1 && filterbadChCandidate && filterbadPFMuon && "+triggercut+")", "goff")
if var == "met":
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(Variables[Type].GetNbinsX(),Variables[Type].GetBinContent(nbins)+Variables[Type].GetBinContent(nbins+1))
Variables[Type].Scale(1,"width")
yield_dic[physics_processes[Type]['datacard']] += round(Variables[Type].Integral("width"),3)
#yield_dic[physics_processes[Type]['datacard']] += round(Variables[Type].Integral(),3)
#print Type, round(Variables[Type].Integral(),3), "total in: ", physics_processes[Type]['datacard'], yield_dic[physics_processes[Type]['datacard']], round(Variables[Type].Integral("width"),3)
dump_datacard(channel,yield_dic)
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp( time.time())
if channel is 'gjets':
legend = TLegend(.60,.65,.82,.92)
else:
legend = TLegend(.60,.55,.92,.92)
lastAdded = ''
for process in ordered_physics_processes:
#print process
if not vbf:
if process.startswith('EWK'):
continue
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
if process is not 'data' and process is not 'signal_vbf' and process is not 'signal_ggf':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "f")
if process is 'data':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "p")
#c4 = TCanvas("c4","c4", 900, 1000)
c4 = TCanvas("c4","c4", 600, 700)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
#stack.Add(h_temp)
stack.SetMinimum(0.01)
#stack.SetMinimum(1)
if setLog:
c4.SetLogy()
if var == "ht_cleanedjets":
#stack.SetMaximum( stack.GetMaximum() + 1e1*stack.GetMaximum() )
stack.SetMaximum( Variables['data'].GetMaximum() + 1e3*Variables['data'].GetMaximum() )
else:
stack.SetMaximum( Variables['data'].GetMaximum() + 1e1*Variables['data'].GetMaximum() )
#stack.SetMaximum( Variables['data'].GetMaximum() + 1e4*Variables['data'].GetMaximum() )
else:
#stack.SetMaximum( stack.GetMaximum() + 0.5*stack.GetMaximum() )
#stack.SetMaximum( stack.GetMaximum() + 1*stack.GetMaximum() )
stack.SetMaximum( Variables['data'].GetMaximum() + 0.5*Variables['data'].GetMaximum() )
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetYaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
if channel is 'signal' and blind:
for b in range(Variables['data'].GetNbinsX()):
Variables['data'].SetBinContent(b+1,0.0)
Variables['data'].Draw("Esame")
Variables['signal_vbf'].SetLineWidth(2)
#Variables['signal_ggf'].SetLineWidth(2)
Variables['signal_vbf'].SetLineColor(1)
#Variables['signal_ggf'].SetLineColor(4)
Variables['signal_vbf'].Draw("samehist")
#Variables['signal_ggf'].Draw("samehist")
legend . AddEntry(Variables['signal_vbf'],physics_processes['signal_vbf']['label'] , "l")
#legend . AddEntry(Variables['signal_ggf'],physics_processes['signal_ggf']['label'] , "l")
legend.SetShadowColor(0);
legend.SetFillColor(0);
legend.SetLineColor(0);
legend.Draw("same")
plot_cms(True,lumi_str,c4)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
data = Variables['data'].Clone()
if vtag is True:
plot_ratio(False,data,added,bin,xlabel,0.25,1.75,5)
else:
if channel is 'Zmm' and var.startswith('met'):
plot_ratio(False,data,added,bin,xlabel,0.5,1.5,5)
else:
plot_ratio(False,data,added,bin,xlabel,0.5,1.5,5)
f1 = TF1("f1","1",-5000,5000);
f1.SetLineColor(4);
f1.SetLineStyle(2);
f1.SetLineWidth(2);
f1.Draw("same")
Pad.Update()
Pad.RedrawAxis()
if vtag is True:
c4.SaveAs(folder+'/Histo_vtag_' + name + '_'+channel+'.pdf')
c4.SaveAs(folder+'/Histo_vtag_' + name + '_'+channel+'.png')
c4.SaveAs(folder+'/Histo_vtag_' + name + '_'+channel+'.C')
elif vbf is True:
c4.SaveAs(folder+'/Histo_vbf_' + name + '_'+channel+'.pdf')
c4.SaveAs(folder+'/Histo_vbf_' + name + '_'+channel+'.png')
c4.SaveAs(folder+'/Histo_vbf_' + name + '_'+channel+'.C')
else:
#c4.SaveAs(folder+'/Histo_notrig_monojet_'+ name + '_'+channel+'.pdf')
#c4.SaveAs(folder+'/Histo_notrig_monojet_'+ name + '_'+channel+'.png')
#c4.SaveAs(folder+'/Histo_notrig_monojet_'+ name + '_'+channel+'.C')
c4.SaveAs(folder+'/Histo_monojet_'+ name + '_'+channel+'.pdf')
c4.SaveAs(folder+'/Histo_monojet_'+ name + '_'+channel+'.png')
c4.SaveAs(folder+'/Histo_monojet_'+ name + '_'+channel+'.C')
#c4.SaveAs(folder+'/Histo_monojet_'+str(lumi_str)+'_' + name + '_'+channel+'.pdf')
#c4.SaveAs(folder+'/Histo_monojet_'+str(lumi_str)+'_' + name + '_'+channel+'.png')
#c4.SaveAs(folder+'/Histo_monojet_'+str(lumi_str)+'_' + name + '_'+channel+'.C')
###### Create a root file and save the histograms individually
#if var == 'photonPt' or var=='dilepPt':
if var=='met':
fhist = ROOT.TFile(channel+"_fullhist.root","recreate")
fhist.cd();
for Type in reordered_physics_processes:
Variables[Type].Write()
fhist.Write()
fhist.Close()
del Variables
del var
del f
del h1
c4.IsA().Destructor( c4 )
stack.IsA().Destructor( stack )
def plot_stack(channel,
name,
var,
bin,
low,
high,
ylabel,
xlabel,
setLog=False):
lumi = 41500.
lumi_str = 41.5
if not os.path.exists(folder):
os.mkdir(folder)
yield_dic = {}
## setup stack plot
stack = THStack('a', 'a')
if var.startswith('mjj'):
added = TH1D('added', 'added', nb, array('d', binLowE))
else:
added = TH1D('added', 'added', bin, low, high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard = build_selection(channel)
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard, "\n"
print 'INFO time is:', datetime.datetime.fromtimestamp(time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
for Type in reordered_physics_processes:
histName = Type + '_' + name + '_' + channel
if var.startswith('mjj'):
Variables[Type] = TH1F(histName, histName, nb, array('d', binLowE))
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
Variables[Type].StatOverflows(kTRUE)
input_tree = makeTrees(Type, "sr_vbf", channel)
scale = float(lumi * physics_processes[Type]['xsec'])
common_weight = build_weights(channel, Type)
makeTrees(Type, 'sr_vbf',
channel).Draw(var + " >> " + histName,
"(" + cut_standard + " )" + common_weight,
"goff")
print Type, scale, common_weight
if var == "mjj":
nbins = Variables[Type].GetNbinsX()
Variables[Type].SetBinContent(
Variables[Type].GetNbinsX(),
Variables[Type].GetBinContent(nbins) +
Variables[Type].GetBinContent(nbins + 1))
if Type is 'data':
Variables[Type].SetMarkerStyle(20)
Variables[Type].Scale(1, "width")
else:
Variables[Type].SetFillColor(
TColor.GetColor(physics_processes[Type]['color']))
Variables[Type].SetLineColor(
TColor.GetColor(physics_processes[Type]['color']))
Variables[Type].Scale(scale, "width")
if Type is not 'signal_vbf':
stack.Add(Variables[Type], "hist")
added.Add(Variables[Type])
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp(
time.time())
legend = TLegend(.60, .55, .92, .92)
lastAdded = ''
for process in ordered_physics_processes:
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
if process is not 'data' and process is not 'signal_vbf' and process is not 'signal_ggf':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "f")
if process is 'data':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "p")
c4 = TCanvas("c4", "c4", 600, 700)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.01)
if setLog:
c4.SetLogy()
if "eta" in var:
stack.SetMaximum(Variables['data'].GetMaximum() +
1e8 * Variables['data'].GetMaximum())
else:
stack.SetMaximum(Variables['data'].GetMaximum() +
1e2 * Variables['data'].GetMaximum())
else:
stack.SetMaximum(Variables['data'].GetMaximum() +
0.5 * Variables['data'].GetMaximum())
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
#stack.GetYaxis().CenterTitle()
stack.GetYaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
if channel is 'signal' and blind:
for b in range(Variables['data'].GetNbinsX()):
Variables['data'].SetBinContent(b + 1, 0.0)
Variables['data'].Draw("Esame")
#Variables['signal_vbf'].SetLineWidth(2)
#Variables['signal_ggf'].SetLineWidth(2)
#Variables['signal_vbf'].SetLineColor(1)
#Variables['signal_ggf'].SetLineColor(4)
#Variables['signal_vbf'].Draw("samehist")
#Variables['signal_ggf'].Draw("samehist")
#legend . AddEntry(Variables['signal_vbf'],physics_processes['signal_vbf']['label'] , "l")
#legend . AddEntry(Variables['signal_ggf'],physics_processes['signal_ggf']['label'] , "l")
legend.SetShadowColor(0)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.Draw("same")
plot_cms(True, lumi_str, c4)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
data = Variables['data'].Clone()
plot_ratio(False, data, added, bin, xlabel, 0.5, 1.5, 5)
f1 = TF1("f1", "1", -5000, 5000)
f1.SetLineColor(4)
f1.SetLineStyle(2)
f1.SetLineWidth(2)
f1.Draw("same")
Pad.Update()
Pad.RedrawAxis()
c4.SaveAs(folder + '/Histo_vbf_' + name + '_' + channel + '.pdf')
c4.SaveAs(folder + '/Histo_vbf_' + name + '_' + channel + '.png')
c4.SaveAs(folder + '/Histo_vbf_' + name + '_' + channel + '.C')
del Variables
del var
del f
del h1
c4.IsA().Destructor(c4)
stack.IsA().Destructor(stack)
def plot_stack(channel, name,var, bin, low, high, ylabel, xlabel, setLog = False):
folder = 'test'
yield_Zll = {}
yield_dic = {}
yield_Wln = {}
yield_signal = {}
stack = THStack('a', 'a')
added = TH1D('a', 'a',bin,low,high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard= build_selection(channel,200)
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard,"\n"
print 'INFO time is:', datetime.datetime.fromtimestamp( time.time())
reordered_physics_processes = []
if channel == 'Zll': reordered_physics_processes = reversed(ordered_physics_processes)
else: reordered_physics_processes = ordered_physics_processes
for Type in reordered_physics_processes:
# Create the Histograms
histName = Type+'_'+name+'_'+channel
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
print "\n"
# this right now breaks the tchain logic!
# if we have more than 1 file, this will break!!!!
print physics_processes[Type]['files'][0]
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0],"read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
f[Type].Close()
input_tree = makeTrees(Type,"events",channel)
n_entries = input_tree.GetEntries()
#Incase you want to apply event by event re-weighting
w = 1.0;
# this is the scale using the total number of effective events
scale = 1.0;
scale = float(lumi)*physics_processes[Type]['xsec']/total
#print "type: ", Type, "weight", w, "scale", scale, "lumi", lumi, physics_processes[Type]['xsec'], total
if Type.startswith('QCD') or Type.startswith('Zll') or \
Type.startswith('others') or Type.startswith('Wlv') or \
Type.startswith('Zvv'):
Variables[Type].SetFillColor(physics_processes[Type]['color'])
Variables[Type].SetLineColor(physics_processes[Type]['color'])
makeTrees(Type,'events',channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*" +str(w),"goff")
Variables[Type].Scale(scale)
#print "Type: ", Type, "Total Events:", scale* Variables[Type].GetEntries() , "scale", scale, "raw events", Variables[Type].GetEntries()
stack.Add(Variables[Type],"hist")
added.Add(Variables[Type])
if Type.startswith('signal_higgs'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(8)
makeTrees(Type,"events",channel).Draw(var + " >> " + histName,"(" + cut_standard + ")*mcWeight*"+str(w),"goff")
Variables[Type].Scale(scale)
if Type.startswith("data"):
Variables[Type].SetMarkerStyle(20)
makeTrees(Type,"events",channel).Draw(var + " >> " + histName, "(" + cut_standard + " && triggerFired[0]==1)*mcWeight*"+str(w), "goff")
yield_dic[Type] = round(Variables[Type].Integral(),3)
dump_datacard(channel,yield_dic)
#added.Write()
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp( time.time())
legend = TLegend(.60,.60,.92,.92)
for process in ordered_physics_processes:
Variables[process].SetTitle(process)
#Variables[process].Write()
if process is not 'data' and process is not 'Zvv_ht200' and process is not 'Zvv_ht400' and process is not 'Zvv_ht600':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "f")
if process is 'data':
legend . AddEntry(Variables[process],physics_processes[process]['label'] , "p")
c4 = TCanvas("c4","c4", 900, 1000)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.1)
if setLog:
c4.SetLogy()
stack.SetMaximum( stack.GetMaximum() + 1000*stack.GetMaximum() )
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
Variables['data'].Draw("Esame")
Variables['signal_higgs'].Draw("same")
legend.SetShadowColor(0);
legend.SetFillColor(0);
legend.SetLineColor(0);
legend.Draw("same")
plot_cms(True,lumi)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
Pad.SetTopMargin(0.7)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
Pad.SetRightMargin(0.06)
data = Variables['data'].Clone()
plot_ratio(False,data,added,bin,xlabel)
f1 = TF1("f1","1",-5000,5000);
f1.SetLineColor(4);
f1.SetLineStyle(2);
f1.SetLineWidth(2);
f1.Draw("same")
c4.SaveAs(folder+'/Histo_' + name + '_'+channel+'.pdf')
del Variables
del var
del f
del h1
c4.IsA().Destructor( c4 )
stack.IsA().Destructor( stack )
leg.SetBorderSize(0)
leg.AddEntry(hb, "Data driven QCD estimate", "l")
leg.AddEntry(hb_down, "+/- Scale and resolution variations", "l")
leg.Draw("same")
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetGridy(0)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
hd.Sumw2()
ratio = plot_ratio(False, hb_clone, hb_up, "p_{T}^{miss} [GeV]", "ratio", -2,
4, 5)
ratio.GetXaxis().SetTitle("p_{T}^{miss} [GeV]")
#ratio.SetMarkerStyle(20)
ratio.Draw("hist")
#ratio2 = plot_ratio(False,hb_clone,hb_down,"p_{T}^{miss} [GeV]","ratio",0,4,5)
#ratio2.Draw("histsame")
ratio2 = ratio.Clone()
for i in range(ratio2.GetNbinsX()):
print ratio2.GetBinContent(i + 1)
ratio2.SetBinContent(i + 1, 2 - ratio2.GetBinContent(i + 1))
ratio2.Draw("histsame")
f1 = TF1("f1", "pol0", 250, 1500)
f1.SetParameter(0, 1)
def plot_stack(channel,
name,
var,
bin,
low,
high,
ylabel,
xlabel,
setLog=False):
yield_dic = {}
stack = THStack('a', 'a')
if var is 'met':
binLowE = [200, 250, 300, 350, 400, 500, 600, 1000]
# binLowE = [250,300,350,400,500,600,1000]
added = TH1D('added', 'added', len(binLowE) - 1, array('d', binLowE))
else:
added = TH1D('added', 'added', bin, low, high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard = build_selection(channel, metcut)
if var is 'met':
if channel is not 'signal':
xlabel = 'U [GeV]'
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard, "\n"
print 'INFO time is:', datetime.datetime.fromtimestamp(time.time())
reordered_physics_processes = []
if channel == 'Zmm' or channel == 'Zee':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
#reordered_physics_processes = ordered_physics_processes
for Type in ordered_physics_processes:
yield_dic[physics_processes[Type]['datacard']] = 0
for Type in reordered_physics_processes:
# Create the Histograms
histName = Type + '_' + name + '_' + channel
if var is 'met':
#binLowE = [200,250,300,350,400,500,600,900,1500]
binLowE = [200, 250, 300, 350, 400, 500, 600, 1000]
# binLowE = [250,300,350,400,500,600,1000]
Variables[Type] = TH1F(histName, histName,
len(binLowE) - 1, array('d', binLowE))
else:
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
#print "\n"
# this right now breaks the tchain logic!
# if we have more than 1 file, this will break!!!!
if Type is not 'data':
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0], "read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
#total = h1[Type].GetEntries()
f[Type].Close()
else:
total = 1.0
input_tree = makeTrees(Type, "events", channel)
n_entries = input_tree.GetEntries()
#Incase you want to apply event by event re-weighting
w = "((0.0*(npv>-0.5&&npv<=0.5)+3.30418257204*(npv>0.5&&npv<=1.5)+2.59691269521*(npv>1.5&&npv<=2.5)+2.44251087681*(npv>2.5&&npv<=3.5)+2.42846225153*(npv>3.5&&npv<=4.5)+2.40062512591*(npv>4.5&&npv<=5.5)+2.30279811595*(npv>5.5&&npv<=6.5)+2.12054720297*(npv>6.5&&npv<=7.5)+1.9104708827*(npv>7.5&&npv<=8.5)+1.67904936047*(npv>8.5&&npv<=9.5)+1.43348925382*(npv>9.5&&npv<=10.5)+1.17893952713*(npv>10.5&&npv<=11.5)+0.940505177881*(npv>11.5&&npv<=12.5)+0.740901867872*(npv>12.5&&npv<=13.5)+0.56877478036*(npv>13.5&&npv<=14.5)+0.433148655714*(npv>14.5&&npv<=15.5)+0.325343558476*(npv>15.5&&npv<=16.5)+0.241688459349*(npv>16.5&&npv<=17.5)+0.180491032782*(npv>17.5&&npv<=18.5)+0.136993937378*(npv>18.5&&npv<=19.5)+0.104859480066*(npv>19.5&&npv<=20.5)+0.0768271030309*(npv>20.5&&npv<=21.5)+0.0563426184938*(npv>21.5&&npv<=22.5)+0.0454037058117*(npv>22.5&&npv<=23.5)+0.0359945616383*(npv>23.5&&npv<=24.5)+0.0286879205085*(npv>24.5&&npv<=25.5)+0.0208185595478*(npv>25.5&&npv<=26.5)+0.0170977379612*(npv>26.5&&npv<=27.5)+0.0122446391898*(npv>27.5&&npv<=28.5)+0.0148028308301*(npv>28.5&&npv<=29.5)+0.0120527550003*(npv>29.5&&npv<=30.5)+0.00402643194054*(npv>30.5&&npv<=31.5)+0.00981143754301*(npv>31.5&&npv<=32.5)+0.0*(npv>32.5&&npv<=33.5)+0.0155664899019*(npv>33.5&&npv<=34.5)+0.0*(npv>34.5&&npv<=35.5)+0.0*(npv>35.5&&npv<=36.5)+0.0*(npv>36.5&&npv<=37.5)+0.0*(npv>37.5&&npv<=38.5)+0.0*(npv>38.5&&npv<=39.5)))"
if channel is 'signal' or channel is 'Zmm' or channel is 'Wmn':
w_trig = '((met < 250)*0.97 + (met >=250 && met<350)* 0.987 + (met>=350)* 1.0 )'
else:
w_trig = '(1.0)'
anlo1_over_alo = "(1.24087232993*(genBos_pt>100.0&&genBos_pt<=150.0)+1.55807026252*(genBos_pt>150.0&&genBos_pt<=200.0)+1.51043242876*(genBos_pt>200.0&&genBos_pt<=250.0)+1.47333461572*(genBos_pt>250.0&&genBos_pt<=300.0)+1.43497331471*(genBos_pt>300.0&&genBos_pt<=350.0)+1.37846354687*(genBos_pt>350.0&&genBos_pt<=400.0)+1.2920177717*(genBos_pt>400.0&&genBos_pt<=500.0)+1.31414429236*(genBos_pt>500.0&&genBos_pt<=600.0)+1.20453974747*(genBos_pt>600.0))"
a_ewkcorr = "(0.998568444581*(genBos_pt>100.0&&genBos_pt<=150.0)+0.992098286517*(genBos_pt>150.0&&genBos_pt<=200.0)+0.986010290609*(genBos_pt>200.0&&genBos_pt<=250.0)+0.980265498435*(genBos_pt>250.0&&genBos_pt<=300.0)+0.974830448283*(genBos_pt>300.0&&genBos_pt<=350.0)+0.969676202351*(genBos_pt>350.0&&genBos_pt<=400.0)+0.962417128177*(genBos_pt>400.0&&genBos_pt<=500.0)+0.953511139209*(genBos_pt>500.0&&genBos_pt<=600.0)+0.934331895615*(genBos_pt>600.0))"
w_ewkcorr = "(0.980859240872*(genBos_pt>100.0&&genBos_pt<=150.0)+0.962118764182*(genBos_pt>150.0&&genBos_pt<=200.0)+0.944428528597*(genBos_pt>200.0&&genBos_pt<=250.0)+0.927685912907*(genBos_pt>250.0&&genBos_pt<=300.0)+0.911802238928*(genBos_pt>300.0&&genBos_pt<=350.0)+0.896700388113*(genBos_pt>350.0&&genBos_pt<=400.0)+0.875368225896*(genBos_pt>400.0&&genBos_pt<=500.0)+0.849096933047*(genBos_pt>500.0&&genBos_pt<=600.0)+0.792158791839*(genBos_pt>600.0))"
wnlo012_over_wlo = "(1.89123123702*(genBos_pt>100.0&&genBos_pt<=150.0)+1.70414182145*(genBos_pt>150.0&&genBos_pt<=200.0)+1.60726459197*(genBos_pt>200.0&&genBos_pt<=250.0)+1.57205818769*(genBos_pt>250.0&&genBos_pt<=300.0)+1.51688539716*(genBos_pt>300.0&&genBos_pt<=350.0)+1.41090079307*(genBos_pt>350.0&&genBos_pt<=400.0)+1.30757555038*(genBos_pt>400.0&&genBos_pt<=500.0)+1.32046236765*(genBos_pt>500.0&&genBos_pt<=600.0)+1.26852513234*(genBos_pt>600.0))"
z_ewkcorr = "(0.984525344338*(genBos_pt>100.0&&genBos_pt<=150.0)+0.969078612189*(genBos_pt>150.0&&genBos_pt<=200.0)+0.954626582726*(genBos_pt>200.0&&genBos_pt<=250.0)+0.941059330021*(genBos_pt>250.0&&genBos_pt<=300.0)+0.92828367065*(genBos_pt>300.0&&genBos_pt<=350.0)+0.916219976557*(genBos_pt>350.0&&genBos_pt<=400.0)+0.89931198024*(genBos_pt>400.0&&genBos_pt<=500.0)+0.878692669663*(genBos_pt>500.0&&genBos_pt<=600.0)+0.834717745177*(genBos_pt>600.0))"
znlo012_over_zlo = "(1.68500099066*(genBos_pt>100.0&&genBos_pt<=150.0)+1.55256109189*(genBos_pt>150.0&&genBos_pt<=200.0)+1.52259467479*(genBos_pt>200.0&&genBos_pt<=250.0)+1.52062313572*(genBos_pt>250.0&&genBos_pt<=300.0)+1.4322825541*(genBos_pt>300.0&&genBos_pt<=350.0)+1.45741443405*(genBos_pt>350.0&&genBos_pt<=400.0)+1.36849777989*(genBos_pt>400.0&&genBos_pt<=500.0)+1.3580214432*(genBos_pt>500.0&&genBos_pt<=600.0)+1.16484769869*(genBos_pt>600.0))"
wm_postfit = "(1.0)"
zm_postfit = "(1.0)"
g_postfit = "(1.0)"
# this is the scale using the total number of effective events
scale = 1.0
scale = float(lumi) * physics_processes[Type]['xsec'] / total
#print '\n'
# print "type: ", Type, "scale", scale, "lumi", lumi, physics_processes[Type]['xsec'], total
if Type is not 'data' and Type is not 'signal_dm' and Type is not 'signal_dm_s' and Type is not 'signal_dm_ps' and Type is not 'signal_dm_v' and Type is not 'signal_dm_av_1_2':
Variables[Type].SetFillColor(physics_processes[Type]['color'])
Variables[Type].SetLineColor(physics_processes[Type]['color'])
if Type.startswith('GJets'):
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*0.98*" + str(g_postfit) +
"*" + str(anlo1_over_alo) + "*" + str(a_ewkcorr) + "*" +
str(w) + "*" + str(w_trig), "goff")
elif (Type.startswith('Zvv') or Type.startswith('Zll')):
if channel is 'signal':
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(zm_postfit) +
"*" + str(z_ewkcorr) + "*" + str(znlo012_over_zlo) +
"*" + str(w) + "*" + str(w_trig), "goff")
else:
#SF explicitly written for the leading tight lepton from the root files
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName, "(" + cut_standard +
")*mcWeight*(0.98 *(lep1Eta<2.1)+0.91*(lep1Eta>=2.1))*"
+ str(zm_postfit) + "*" + str(z_ewkcorr) + "*" +
str(znlo012_over_zlo) + "*" + str(w) + "*" +
str(w_trig), "goff")
elif Type.startswith('Wlv'):
if channel is 'signal':
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(wm_postfit) +
"*" + str(wnlo012_over_wlo) + "*" + str(w_ewkcorr) +
"*" + str(w) + "*" + str(w_trig), "goff")
else:
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(wm_postfit) +
"*" + str(wnlo012_over_wlo) + "*" + str(w_ewkcorr) +
"*" + str(w) + "*" + str(w_trig), "goff")
else:
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName, "(" + cut_standard +
") *mcWeight*" + str(w) + "*" + str(w_trig), "goff")
Variables[Type].Scale(scale, "width")
stack.Add(Variables[Type], "hist")
added.Add(Variables[Type])
if Type.startswith('signal_h_ggf'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(1)
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(w), "goff")
Variables[Type].Scale(scale, "width")
if Type.startswith('signal_dm_av_1_2'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(8)
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(w), "goff")
Variables[Type].Scale(scale, "width")
if Type.startswith('data'):
Variables[Type].SetMarkerStyle(20)
if channel is 'signal' or channel is 'Zmm' or channel is 'Wmn':
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName, "(" + cut_standard +
" && (triggerFired[0]==1 || triggerFired[1]==1 || triggerFired[2]==1) )",
"goff")
else:
makeTrees(Type, "events",
channel).Draw(var + " >> " + histName,
"(" + cut_standard + " )", "goff")
Variables[Type].Scale(1, "width")
yield_dic[physics_processes[Type]['datacard']] += round(
Variables[Type].Integral("width"), 3)
dump_datacard(channel, yield_dic)
#added.Write()
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp(
time.time())
# if channel is 'Zmm' or channel is 'Zee':
# legend = TLegend(.60,.65,.92,.92)
# elif channel is 'gjets':
# legend = TLegend(.60,.65,.82,.92)
# else:
# legend = TLegend(.60,.60,.92,.92)
legend = TLegend(.60, .70, .92, .92)
lastAdded = ''
for process in ordered_physics_processes:
#print process
Variables[process].SetTitle(process)
if physics_processes[process]['label'] != lastAdded:
lastAdded = physics_processes[process]['label']
if process is not 'data' and process is not 'signal_dm' and process is not 'signal_dm_av_1_2' and process is not 'signal_h_ggf' and process is not 'signal_h_vbf':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "f")
if process is 'data':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "p")
c4 = TCanvas("c4", "c4", 900, 1000)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.01)
#stack.SetMinimum(10)
if setLog:
c4.SetLogy()
if var is 'fatjet1tau21' or var is 'fatjet1PrunedM':
stack.SetMaximum(stack.GetMaximum() + 1e5 * stack.GetMaximum())
else:
stack.SetMaximum(stack.GetMaximum() + 1e2 * stack.GetMaximum())
else:
stack.SetMaximum(stack.GetMaximum() + 0.5 * stack.GetMaximum())
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetYaxis().SetTitleOffset(1.2)
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
Variables['data'].Draw("Esame")
legend.SetShadowColor(0)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.Draw("same")
plot_cms(True, lumi_str)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
Pad.SetTopMargin(0.7)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
Pad.SetRightMargin(0.06)
data = Variables['data'].Clone()
plot_ratio(False, data, added, bin, xlabel, 0, 2.0, 5)
f1 = TF1("f1", "1", -5000, 5000)
f1.SetLineColor(4)
f1.SetLineStyle(2)
f1.SetLineWidth(2)
f1.Draw("same")
outputName = folder + '/mj_Histo_' + name + '_' + channel
c4.SaveAs(outputName + '.pdf')
c4.SaveAs(outputName + '.png')
c4.SaveAs(outputName + '.C')
del Variables
del var
del f
del h1
c4.IsA().Destructor(c4)
stack.IsA().Destructor(stack)
def kfactor(model):
infile = TFile("/afs/cern.ch/user/z/zdemirag/lnwork/studies/monojet_kfactors/mcweights/kfactor_vjet_qcd/kfactor_24bins.root","read")
if model == "z":
lo_name = "ZJets_LO/inv_pt"
nlo_qcd = "ZJets_012j_NLO/nominal"
nlo_ewk = "EWKcorr/Z"
if model == "w":
lo_name = "WJets_LO/inv_pt"
nlo_qcd = "WJets_012j_NLO/nominal"
nlo_ewk = "EWKcorr/W"
hlo = infile.Get(lo_name)
hnlo_qcd = infile.Get(nlo_qcd)
hnlo_ewk = infile.Get(nlo_ewk)
hlo.Rebin(2)
hnlo_qcd.Rebin(2)
hnlo_ewk.Rebin(2)
c_sr = TCanvas("c_sr","c_sr", 600, 700)
c_sr.SetBottomMargin(0.3)
c_sr.SetRightMargin(0.06)
c_sr.cd()
c_sr.SetLogy()
gStyle.SetOptStat(False)
htemp = hnlo_qcd.Clone("htemp")
htemp2 = hnlo_ewk.Clone("htemp2")
hlo.SetLineColor(kBlue-7)
hlo.SetLineWidth(2)
hlo.Draw("hist")
hnlo_qcd.SetLineColor(kMagenta+2)
hnlo_qcd.SetLineWidth(2)
hnlo_qcd.Draw("histsame")
hnlo_ewk.SetLineColor(kMagenta-7)
hnlo_ewk.SetLineWidth(2)
hnlo_ewk.Draw("histsame")
hlo.GetYaxis().SetTitle("Events")
hlo.GetYaxis().CenterTitle()
hlo.GetXaxis().SetTitle("")
hnlo_qcd.SetTitle("")
hlo.SetTitle("")
hlo.GetXaxis().SetLabelSize(0)
hlo.SetMaximum(hlo.GetMaximum()*10.0)
legend_sr = TLegend(.50,.65,.82,.92)
legend_sr.AddEntry(hlo, "LO","l")
legend_sr.AddEntry(hnlo_qcd, "NLO QCD","l")
legend_sr.AddEntry(hnlo_ewk, "NLO QCD + EWK","l")
legend_sr.SetShadowColor(0);
legend_sr.SetFillColor(0);
legend_sr.SetLineColor(0);
legend_sr.Draw("same")
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 0.9)
Pad.SetTopMargin(0.7)
Pad.SetRightMargin(0.06)
Pad.SetFillColor(0)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
pull = plot_ratio(False,htemp,hlo,htemp.GetNbinsX(),"Gen boson p_{T} [GeV]","k-factor",0.0,2.0,5)
pull.SetLineColor(kMagenta+2)
pull.Draw("HIST0")
pull2 = plot_ratio(False,htemp2,hlo,htemp.GetNbinsX(),"Gen boson p_{T} [GeV]","k-factor",0.0,2.0,5)
pull2.SetLineColor(kMagenta-7)
pull2.Draw("HIST0SAME")
func = TF1("func","1",0,3000)
func.SetLineColor(kGray+2)
func.SetLineStyle(2)
func.Draw("same")
c_sr.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/v2/"+model+"_kfactor.pdf")
c_sr.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/v2/"+model+"_kfactor.png")
c_sr.SaveAs("/afs/cern.ch/user/z/zdemirag/www/zpt/panda/v2/"+model+"_kfactor.C")
def plot_stack(channel,
name,
var,
bin,
low,
high,
ylabel,
xlabel,
setLog=False):
folder = 'test'
yield_Zll = {}
yield_dic = {}
yield_Wln = {}
yield_signal = {}
stack = THStack('a', 'a')
added = TH1D('a', 'a', bin, low, high)
added.Sumw2()
f = {}
h1 = {}
Variables = {}
cut_standard = build_selection(channel, 200)
print "INFO Channel is: ", channel, " variable is: ", var, " Selection is: ", cut_standard, "\n"
print 'INFO time is:', datetime.datetime.fromtimestamp(time.time())
reordered_physics_processes = []
if channel == 'Zll':
reordered_physics_processes = reversed(ordered_physics_processes)
else:
reordered_physics_processes = ordered_physics_processes
for Type in reordered_physics_processes:
# Create the Histograms
histName = Type + '_' + name + '_' + channel
Variables[Type] = TH1F(histName, histName, bin, low, high)
Variables[Type].Sumw2()
print "\n"
# this right now breaks the tchain logic!
# if we have more than 1 file, this will break!!!!
print physics_processes[Type]['files'][0]
f[Type] = ROOT.TFile(physics_processes[Type]['files'][0], "read")
h1[Type] = f[Type].Get("htotal")
total = h1[Type].GetBinContent(1)
f[Type].Close()
input_tree = makeTrees(Type, "events", channel)
n_entries = input_tree.GetEntries()
#Incase you want to apply event by event re-weighting
w = 1.0
# this is the scale using the total number of effective events
scale = 1.0
scale = float(lumi) * physics_processes[Type]['xsec'] / total
#print "type: ", Type, "weight", w, "scale", scale, "lumi", lumi, physics_processes[Type]['xsec'], total
if Type.startswith('QCD') or Type.startswith('Zll') or \
Type.startswith('others') or Type.startswith('Wlv') or \
Type.startswith('Zvv'):
Variables[Type].SetFillColor(physics_processes[Type]['color'])
Variables[Type].SetLineColor(physics_processes[Type]['color'])
makeTrees(Type, 'events', channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(w), "goff")
Variables[Type].Scale(scale)
#print "Type: ", Type, "Total Events:", scale* Variables[Type].GetEntries() , "scale", scale, "raw events", Variables[Type].GetEntries()
stack.Add(Variables[Type], "hist")
added.Add(Variables[Type])
if Type.startswith('signal_higgs'):
Variables[Type].SetLineColor(1)
Variables[Type].SetLineWidth(3)
Variables[Type].SetLineStyle(8)
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName,
"(" + cut_standard + ")*mcWeight*" + str(w), "goff")
Variables[Type].Scale(scale)
if Type.startswith("data"):
Variables[Type].SetMarkerStyle(20)
makeTrees(Type, "events", channel).Draw(
var + " >> " + histName, "(" + cut_standard +
" && triggerFired[0]==1)*mcWeight*" + str(w), "goff")
yield_dic[Type] = round(Variables[Type].Integral(), 3)
dump_datacard(channel, yield_dic)
#added.Write()
print 'INFO - Drawing the Legend', datetime.datetime.fromtimestamp(
time.time())
legend = TLegend(.60, .60, .92, .92)
for process in ordered_physics_processes:
Variables[process].SetTitle(process)
#Variables[process].Write()
if process is not 'data' and process is not 'Zvv_ht200' and process is not 'Zvv_ht400' and process is not 'Zvv_ht600':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "f")
if process is 'data':
legend.AddEntry(Variables[process],
physics_processes[process]['label'], "p")
c4 = TCanvas("c4", "c4", 900, 1000)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMinimum(0.1)
if setLog:
c4.SetLogy()
stack.SetMaximum(stack.GetMaximum() + 1000 * stack.GetMaximum())
stack.Draw()
stack.GetYaxis().SetTitle(ylabel)
stack.GetYaxis().CenterTitle()
stack.GetXaxis().SetTitle(xlabel)
stack.GetXaxis().SetLabelSize(0)
stack.GetXaxis().SetTitle('')
Variables['data'].Draw("Esame")
Variables['signal_higgs'].Draw("same")
legend.SetShadowColor(0)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.Draw("same")
plot_cms(True, lumi)
Pad = TPad("pad", "pad", 0.0, 0.0, 1.0, 1.0)
Pad.SetTopMargin(0.7)
Pad.SetFillColor(0)
Pad.SetGridy(1)
Pad.SetFillStyle(0)
Pad.Draw()
Pad.cd(0)
Pad.SetRightMargin(0.06)
data = Variables['data'].Clone()
plot_ratio(False, data, added, bin, xlabel)
f1 = TF1("f1", "1", -5000, 5000)
f1.SetLineColor(4)
f1.SetLineStyle(2)
f1.SetLineWidth(2)
f1.Draw("same")
c4.SaveAs(folder + '/Histo_' + name + '_' + channel + '.pdf')
del Variables
del var
del f
del h1
c4.IsA().Destructor(c4)
stack.IsA().Destructor(stack)
