def drawMassRes(data,mc,output,rapidity,ptda,ptmc,trackType,funct,mcIsData,dataIsMC,year):
style = setTDRStyle()
print (data)
pt_e = [0 for x in range(len(data))]
pt_x = [0 for x in range(len(data))]
for i,pt in enumerate(pt_x):
pt_x[i] = ptbins[i]+(ptbins[i+1]-ptbins[i])/2.
pt_e[i] = (ptbins[i+1]-ptbins[i])/2.
if dataIsMC:
(da_mean,da_meane,da_sig,da_sige, da_nChi2) = doFit(data,output,rapidity,"MC2",trackType,funct)
else:
(da_mean,da_meane,da_sig,da_sige, da_nChi2) = doFit(data,output,rapidity,"DATA",trackType,funct)
if mcIsData:
(mc_mean,mc_meane,mc_sig,mc_sige, mc_nChi2) = doFit(mc ,output,rapidity,"DATA2",trackType,funct)
else:
(mc_mean,mc_meane,mc_sig,mc_sige, mc_nChi2) = doFit(mc ,output,rapidity,"MC",trackType,funct)
result = {}
result["data_mean"] = da_mean
result["data_meane"] = da_meane
result["data_sig"] = da_sig
result["data_sige"] = da_sige
result["mc_mean"] = mc_mean
result["mc_meane"] = mc_meane
result["mc_sig"] = mc_sig
result["mc_sige"] = mc_sige
result["ptda"] = ptda
result["ptmc"] = ptmc
result["da_nChi2"] = da_nChi2
result["mc_nChi2"] = mc_nChi2
pklFile = open(output+"/MassResolutionVsPt_%s_%s.pkl" %(trackType,rapidity),"wb")
pickle.dump(result,pklFile)
pklFile.close()
c2 = TCanvas("c2","c2",800,800)
c2.cd()
# Upper plot will be in pad1
pad1 = TPad("pad1", "pad1", 0.01, 0.01, 0.99, 0.99)
# ~ pad1.SetGrid() # Vertical grid
pad1.SetTopMargin(0.05)
pad1.SetLeftMargin(0.13)
pad1.SetRightMargin(0.045)
pad1.SetBottomMargin(0.3)
pad1.Draw() # Draw the upper pad: pad1
pad1.cd() # pad1 becomes the current pad
pad1.SetTicks()
res_data = TGraphAsymmErrors()
res_data.SetName("res_data")
res_mc = TGraphAsymmErrors()
res_mc .SetName("res_mc")
ratio = TGraphErrors()
ratio .SetName("ratio")
#~ print len(pt_x)
for i,pt in enumerate(pt_x):
res_data.SetPoint(i,ptda[i],da_sig[i])
res_data.SetPointError(i,ptda[i]-ptbins[i],ptbins[i+1]-ptda[i],da_sige[i],da_sige[i])
res_mc .SetPoint(i,ptmc[i],mc_sig[i])
res_mc .SetPointError(i,ptmc[i]-ptbins[i],ptbins[i+1]-ptmc[i],mc_sige[i],mc_sige[i])
if mc_sig[i] > 0:
ratio .SetPoint(i,pt,da_sig[i]/mc_sig[i])
ratio .SetPointError(i,pt_e[i],(da_sig[i]/mc_sig[i])*math.sqrt((da_sige[i]/da_sig[i])**2+(mc_sige[i]/mc_sig[i])**2))
res_data.SetMarkerStyle(22)
res_data.SetMarkerSize(2)
res_data.SetMarkerColor(kBlack)
res_data.SetLineColor(kBlack)
res_data.SetLineWidth(2)
res_data.SetFillColor(0)
res_data.SetTitle("Dimuon mass resolution vs pT for %s tracks"%trackType)
res_data.GetYaxis().SetTitle("Mass resolution at Z peak (GeV)")
# ~ res_data.GetXaxis().SetTitle("p_{T} (#mu^{#pm}) [GeV]")
res_data.GetYaxis().SetTitleFont(42)
res_data.GetYaxis().SetTitleSize(0.05)
res_data.GetYaxis().SetTitleOffset(1.35)
res_data.GetYaxis().SetLabelFont(42)
res_data.GetYaxis().SetLabelSize(0.038)
res_data.GetYaxis().SetRangeUser(0.,6.)
res_data.GetXaxis().SetTitleSize(0.0)
res_data.GetXaxis().SetLabelSize(0.0)
if trackType == "Outer":
res_data.GetYaxis().SetRangeUser(1.,20.)
res_data.GetXaxis().SetRangeUser(ptbins[0],ptbins[len(ptda)])
res_data.Draw("AP E0")
res_mc.SetMarkerStyle(22)
res_mc.SetMarkerSize(2)
res_mc.SetMarkerColor(kRed)
res_mc.SetLineColor(kRed)
res_mc.SetLineWidth(2)
res_mc.SetFillColor(0)
res_mc.SetTitle("Dimuon mass resolution vs pT for %s tracks"%trackType)
res_mc.GetYaxis().SetTitle("Mass resolution at Z peak (GeV)")
res_mc.GetXaxis().SetTitle("p_{T} (#mu^{#pm}) (GeV)")
res_mc.GetYaxis().SetTitleOffset(1.5)
res_mc.Draw("P E0 SAME")
if rapidity == "BB": leg = TLegend(0.5,0.65,0.95,0.90,"Both muons |#eta| < 1.2","brNDC")
elif rapidity == "BE": leg = TLegend(0.5,0.65,0.95,0.9,"At least one muon |#eta| > 1.2","brNDC")
else: leg = TLegend(0.2,0.65,0.9,0.9,"At least one muon |#eta| > 1.6","brNDC")
if mcIsData:
leg.AddEntry(res_data,"DATA 2017")
leg.AddEntry(res_mc,"DATA 2016")
elif dataIsMC:
leg.AddEntry(res_data,"MC 2017")
leg.AddEntry(res_mc,"MC 2016")
else:
leg.AddEntry(res_data,"Data","p")
leg.AddEntry(res_mc,"Simulation")
leg.SetTextFont(62)
leg.SetFillColor(10)
leg.SetFillStyle(0)
leg.SetLineColor(10)
leg.SetShadowColor(0)
leg.SetBorderSize(0)
leg.SetMargin(0.15)
leg.Draw("SAME")
latex = TLatex()
# ~ latex.SetTextFont(42)
# ~ latex.SetTextAlign(31)
# ~ latex.SetTextSize(0.04)
# ~ latex.SetNDC(True)
# ~ latexCMS = TLatex()
# ~ latexCMS.SetTextFont(62)
# ~ latexCMS.SetTextSize(0.04)
# ~ latexCMS.SetNDC(True)
latexCMSExtra = TLatex()
latexCMSExtra.SetTextFont(52)
latexCMSExtra.SetTextSize(0.03/0.7)
latexCMSExtra.SetNDC(True)
# ~ if '2016' in year:
# ~ latex.DrawLatex(0.95, 0.96, "2016, 36.3 fb^{-1} (13 TeV)")
# ~ elif '2017' in year:
# ~ latex.DrawLatex(0.95, 0.96, "2017, 42.1 fb^{-1} (13 TeV)")
# ~ else:
# ~ latex.DrawLatex(0.95, 0.96, "2018, 61.3 fb^{-1} (13 TeV)")
if '2016' in year:
latex.DrawLatexNDC(0.50, 0.96, "#scale[0.8]{#font[42]{ 2016, 36.3 fb^{-1} (13 TeV)}}")
elif '2017' in year:
latex.DrawLatexNDC(0.50, 0.96, "#scale[0.8]{#font[42]{ 2017, 42.1 fb^{-1} (13 TeV)}}")
else:
latex.DrawLatexNDC(0.50, 0.96, "#scale[0.8]{#font[42]{ 2018, 61.3 fb^{-1} (13 TeV)}}")
# ~ cmsExtra = "Preliminary"
# ~ latexCMS.DrawLatex(0.15,0.96,"CMS")
latex.DrawLatexNDC(0.13, 0.96, "#font[62]{CMS}")
# ~ yLabelPos = 0.84
# ~ latexCMSExtra.DrawLatex(0.78,yLabelPos,"%s"%(cmsExtra))
c2.cd() # Go back to the main canvas before defining pad2
pad2 = TPad("pad2", "pad2",0.01, 0.01, 0.99, 0.29)
pad2.SetTopMargin(0)
pad2.SetTopMargin(0.05)
pad2.SetLeftMargin(0.13)
pad2.SetRightMargin(0.045)
pad2.SetBottomMargin(0.4)
# ~ pad2.SetGrid()
pad2.Draw()
pad2.cd()
pad2.SetTicks()
ratio.SetMarkerColor(kBlue-4)
ratio.SetFillColor(kBlue-4 )
ratio.SetTitle("")
ratio.GetYaxis().SetTitle("Data/MC")
ratio.GetXaxis().SetNoExponent(0)
ratio.GetXaxis().SetTitleFont(42)
ratio.GetXaxis().SetTitleOffset(0.85)
ratio.GetXaxis().SetTitleSize(0.2)
ratio.GetXaxis().SetLabelColor(1)
ratio.GetXaxis().SetLabelOffset(0.01)
ratio.GetXaxis().SetLabelFont(42)
ratio.GetXaxis().SetLabelSize(0.17)
if mcIsData:
ratio.GetYaxis().SetTitle("Data 2017 / Data 2016")
elif dataIsMC:
ratio.GetYaxis().SetTitle("MC 2017 / MC 2016")
ratio.GetXaxis().SetTitle("p_{T} (\mu^{\pm}) (GeV)")
ratio.GetYaxis().SetRangeUser(0.5,1.5)
ratio.GetXaxis().SetRangeUser(ptbins[0],ptbins[len(ptda)])
ratio.GetYaxis().SetTitleOffset(0.55)
ratio.GetYaxis().SetTitleSize(0.12)
ratio.GetYaxis().SetTitleFont(42)
ratio.GetYaxis().SetLabelSize(0.14)
ratio.GetYaxis().SetLabelOffset(0.007)
ratio.GetYaxis().SetLabelFont(42)
ratio.GetYaxis().SetNdivisions(505)
ratio.Draw("A P E2")
pad2.Update()
line = TLine(ptbins[0],1,ptbins[len(ptda)],1)
line.SetLineColor(kBlue+1)
line.SetLineWidth(2)
line.Draw()
pad1.RedrawAxis()
saveas = "/MassResolutionVsPt_%s_%s" %(trackType,rapidity)
c2.SaveAs(output+saveas+".png")
c2.SaveAs(output+saveas+".pdf")
c2.SaveAs(output+saveas+".root")
c2.SaveAs(output+saveas+".C")
def cutvariation_makeplots(self, plotname, min_cv_cut, max_cv_cut):
self.loadstyle2()
leg = TLegend(.15, .65, .85, .85)
leg.SetBorderSize(0)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextFont(42)
leg.SetTextSize(0.024)
leg.SetNColumns(4)
colours = [kBlack, kRed, kGreen+2, kBlue, kOrange+2, kViolet-1, \
kAzure+1, kOrange-7, kViolet+2, kYellow-3]
ntrials = 2 * self.p_ncutvar + 1
for imult in range(len(self.lvar2_binmin)):
canv = TCanvas("%s%d" % (plotname, imult), '', 400, 400)
diffratio = 2 * self.p_maxperccutvar
if plotname == "histoSigmaCorr":
diffratio = 0.2
canv.cd(1).DrawFrame(0, 1 - diffratio, 25, 1 + diffratio, \
"%s %.2f < %s < %.2f;#it{p}_{T} (GeV/#it{c});Ratio %s" % \
(self.typean, self.lvar2_binmin[imult], self.v_var2_binning, \
self.lvar2_binmax[imult], plotname))
fileoutcrossmultref = self.make_file_path(self.d_results_cv, self.cross_filename, \
"root", None, [self.typean, "cutvar", \
str(self.p_ncutvar), \
"mult", str(imult)])
f_fileoutcrossmultref = TFile.Open(fileoutcrossmultref)
href = f_fileoutcrossmultref.Get(plotname)
imk = 0
icol = 0
legname = "looser"
hcutvar = []
markers = [20, 21, 22, 23]
for icv in range(ntrials):
if icv == self.p_ncutvar:
markers = [markers[i] + 4 for i in range(len(markers))]
imk = 0
legname = "tighter"
continue
if icol == len(colours) - 1:
imk = imk + 1
fileoutcrossmult = self.make_file_path(self.d_results_cv, self.cross_filename, \
"root", None, [self.typean, "cutvar", str(icv), \
"mult", str(imult)])
f_fileoutcrossmult = TFile.Open(fileoutcrossmult)
hcutvar.append(f_fileoutcrossmult.Get(plotname))
hcutvar[icol].SetDirectory(0)
hcutvar[icol].SetLineColor(colours[icol % len(colours)])
hcutvar[icol].SetMarkerColor(colours[icol % len(colours)])
hcutvar[icol].SetMarkerStyle(markers[imk])
hcutvar[icol].SetMarkerSize(0.8)
hcutvar[icol].Divide(href)
hcutvar[icol].Draw("same")
if imult == 0:
leg.AddEntry(hcutvar[icol], "Set %d (%s)" % (icv, legname), "LEP")
icol = icol + 1
f_fileoutcrossmult.Close()
leg.Draw()
canv.SaveAs("%s/Cutvar_%s_mult%d.eps" % (self.d_results_cv, plotname, imult))
f_fileoutcrossmultref.Close()
if plotname == "histoSigmaCorr":
if self.p_nptfinbins < 9:
nx = 4
ny = 2
canvy = 533
elif self.p_nptfinbins < 13:
nx = 4
ny = 3
canvy = 800
else:
nx = 5
ny = 4
canvy = 1200
canv = [TCanvas("canvas_corryieldvspt%d_%d" % (icv, imult), "Data", \
1000, canvy) for imult in range(len(self.lvar2_binmin))]
arrhistos = [None for ipt in range(self.p_nptfinbins)]
for imult in range(len(self.lvar2_binmin)):
for ipt in range(self.p_nptfinbins):
arrhistos[ipt] = TH1F("hcorryieldvscut%d%d" % (imult, ipt), \
"%d < #it{p}_{T} < %d;cut set;Corr. Yield" % \
(self.lpt_finbinmin[ipt], self.lpt_finbinmax[ipt]),
ntrials, -0.5, ntrials - 0.5)
arrhistos[ipt].SetDirectory(0)
for icv in range(ntrials):
fileoutcrossmult = self.make_file_path(self.d_results_cv, \
self.cross_filename, "root", None, \
[self.typean, "cutvar", str(icv), \
"mult", str(imult)])
f_fileoutcrossmult = TFile.Open(fileoutcrossmult)
hcutvar2 = f_fileoutcrossmult.Get(plotname)
for ipt in range(self.p_nptfinbins):
arrhistos[ipt].SetBinContent(icv + 1, hcutvar2.GetBinContent(ipt + 1))
arrhistos[ipt].SetBinError(icv + 1, hcutvar2.GetBinError(ipt + 1))
f_fileoutcrossmult.Close()
canv[imult].Divide(nx, ny)
for ipt in range(self.p_nptfinbins):
canv[imult].cd(ipt + 1)
arrhistos[ipt].SetLineColor(colours[ipt])
arrhistos[ipt].SetMarkerColor(colours[ipt])
arrhistos[ipt].Draw("ep")
canv[imult].SaveAs("%s/Cutvar_CorrYieldvsSet_mult%d.eps" % (self.d_results_cv, imult))
if min_cv_cut is not None and max_cv_cut is not None:
probcuts = [None for ipt in range(self.p_nptfinbins)]
probarr = [None for icv in range(2 + 2 * ntrials)]
for ipt in range(self.p_nptfinbins):
bin_id = self.bin_matching[ipt]
stepsmin = (self.lpt_probcutfin[bin_id] - min_cv_cut[ipt]) / self.p_ncutvar
stepsmax = (max_cv_cut[ipt] - self.lpt_probcutfin[bin_id]) / self.p_ncutvar
probarr[0] = 0
icvmax = 1
for icv in range(ntrials):
if icv < self.p_ncutvar:
probarr[2 * icv + 1] = min_cv_cut[ipt] + (icv - 0.1) * stepsmin
probarr[2 * icv + 2] = min_cv_cut[ipt] + (icv + 0.1) * stepsmin
elif icv == self.p_ncutvar:
probarr[2 * icv + 1] = self.lpt_probcutfin[bin_id] - 0.1 * stepsmax
probarr[2 * icv + 2] = self.lpt_probcutfin[bin_id] + 0.1 * stepsmax
else:
probarr[2 * icv + 1] = self.lpt_probcutfin[bin_id] + (icvmax - 0.1) * stepsmax
probarr[2 * icv + 2] = self.lpt_probcutfin[bin_id] + (icvmax + 0.1) * stepsmax
icvmax = icvmax + 1
probarr[-1] = 1
probcuts[ipt] = probarr[:]
canv2 = [TCanvas("canvas_corryieldvsprob%d_%d" % (icv, imult), "Data", \
1000, canvy) for imult in range(len(self.lvar2_binmin))]
arrhistos2 = [None for ipt in range(self.p_nptfinbins)]
for imult in range(len(self.lvar2_binmin)):
for ipt in range(self.p_nptfinbins):
arrhistos2[ipt] = TH1F("hcorryieldvsprob%d%d" % (imult, ipt), \
"%d < #it{p}_{T} < %d;Probability;Corr. Yield" % \
(self.lpt_finbinmin[ipt], self.lpt_finbinmax[ipt]), \
len(probcuts[ipt]) - 1, array('f', probcuts[ipt]))
arrhistos2[ipt].SetDirectory(0)
icvmax = 1
for icv in range(ntrials):
fileoutcrossmult = self.make_file_path(self.d_results_cv, \
self.cross_filename, "root", None, \
[self.typean, "cutvar", str(icv), \
"mult", str(imult)])
f_fileoutcrossmult = TFile.Open(fileoutcrossmult)
hcutvar2 = f_fileoutcrossmult.Get(plotname)
for ipt in range(self.p_nptfinbins):
bin_id = self.bin_matching[ipt]
stepsmin = (self.lpt_probcutfin[bin_id] - min_cv_cut[ipt]) / self.p_ncutvar
stepsmax = (max_cv_cut[ipt] - self.lpt_probcutfin[bin_id]) / self.p_ncutvar
selml_cvval = 0
if icv < self.p_ncutvar:
selml_cvval = min_cv_cut[ipt] + icv * stepsmin
elif icv == self.p_ncutvar:
selml_cvval = self.lpt_probcutfin[bin_id]
else:
selml_cvval = self.lpt_probcutfin[bin_id] + icvmax * stepsmax
ibin = arrhistos2[ipt].FindBin(selml_cvval)
arrhistos2[ipt].SetBinContent(ibin, hcutvar2.GetBinContent(ipt + 1))
arrhistos2[ipt].SetBinError(ibin, hcutvar2.GetBinError(ipt + 1))
if icv > self.p_ncutvar:
icvmax = icvmax + 1
f_fileoutcrossmult.Close()
canv2[imult].Divide(nx, ny)
for ipt in range(self.p_nptfinbins):
canv2[imult].cd(ipt + 1)
arrhistos2[ipt].SetLineColor(colours[ipt])
arrhistos2[ipt].SetLineWidth(1)
arrhistos2[ipt].SetMarkerColor(colours[ipt])
arrhistos2[ipt].Draw("ep")
canv2[imult].SaveAs("%s/Cutvar_CorrYieldvsProb_mult%d.eps" % (self.d_results_cv, imult))
def efficiency_ctau(cutlist, labellist, setcut='', maxy=0.50):
basecut = ""
signame = ""
ncuts = len(cutlist)
file = {}
nevt = {}
tree = {}
effs = {}
for i, s in enumerate(sign_ctau):
print "c_tau sample: ", s
if 'BulkGravToZZToZhadZinv' in samples[s]['files'][0]:
signame = "XZZInv"
elif 'WprimeToWZToWhadZinv' in samples[s]['files'][0]:
signame = "XWZInv"
elif 'WplusH' in samples[s]['files'][0]:
signame = "WplusH"
elif 'WminusH' in samples[s]['files'][0]:
signame = "WminusH"
elif 'ZH' in samples[s]['files'][
0] and not 'ggZH' in samples[s]['files'][0]:
signame = "ZH"
elif 'ggZH' in samples[s]['files'][0]:
signame = "ggZH"
file[s] = TFile(NTUPLEDIR + samples[s]['files'][0] + ".root",
"READ") # Read TFile
##nevt[s] = (file[s].Get('counter/c_nEvents')).GetBinContent(1)
tree[s] = file[s].Get("trigger/tree") # Read TTree
nevt[s] = file[s].Get("trigger/tree").GetEntriesFast()
effs[s] = [0] * (ncuts + 1)
for j, c in enumerate(cutlist):
#print cutlist[j]
br = 1.
n = tree[s].GetEntries(cutlist[j])
d = nevt[
s] #d = sample[samples[s]['files'][0]]['nevents']#tree[s].GetEntries(basecut)
effs[s][j] = float(n) / (d * br)
line_ctau = []
outFile = TFile("Efficiency/Eff_spline.root", "UPDATE")
outFile.cd()
#flagLP = True
for j, c in enumerate(cutlist):
line_ctau.append(TGraph(ncuts))
line_ctau[j].SetTitle(";c#tau_{#pi} (mm);Efficiency")
print labellist[j]
for i, s in enumerate(sign_ctau):
#mass = int( ''.join(x for x in s if x.isdigit()) )
ctau = float(samples[s]['ctau'])
print i, ctau, effs[s][j]
line_ctau[j].SetPoint(i, ctau, effs[s][j])
line_ctau[j].SetMarkerStyle(20)
line_ctau[j].SetMarkerColor(colors[j])
line_ctau[j].SetLineColor(colors[j])
line_ctau[j].SetLineWidth(3)
line_ctau[j].GetXaxis().SetTitleOffset(
line_ctau[j].GetXaxis().GetTitleOffset() * 1.2)
line_ctau[j].GetYaxis().SetTitleOffset(
line_ctau[j].GetYaxis().GetTitleOffset() * 1.2)
line_ctau[j].Print()
outFile.Close()
leg = TLegend(0.2, 0.6, 0.9, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
for i, c in enumerate(cutlist):
leg.AddEntry(line_ctau[i], labellist[i], "lp")
if 'BulkGravToZZToZlepZhad' in samples[s]['files'][0]:
leg.AddEntry(line_ctau[0], 'G_{Bulk} #rightarrow Z_{had}Z_{lep}', "")
elif 'BulkGravToZZToZlepZinv' in samples[s]['files'][0]:
leg.AddEntry(line_ctau[0], 'G_{Bulk} #rightarrow Z_{lep}Z_{inv}', "")
elif 'WprimeToWZToWhadZlep' in samples[s]['files'][0]:
leg.AddEntry(line_ctau[0], 'W\' #rightarrow W_{had}Z_{lep}', "")
elif 'BulkGravToZZToZhadZinv' in samples[s]['files'][0]:
#signame += "XZZInv"
leg.AddEntry(line_ctau[0], 'G_{Bulk} #rightarrow Z_{had}Z_{inv}', "")
elif 'WprimeToWZToWhadZinv' in samples[s]['files'][0]:
#signame += "XWZInv"
leg.AddEntry(line_ctau[0], 'W\' #rightarrow W_{had}Z_{inv}', "")
elif 'WplusH' in samples[s]['files'][0]:
#signame += "XWZInv"
leg.AddEntry(line_ctau[0],
'W^{+} H #rightarrow #pi #pi #rightarrow 4b', "")
elif 'WminusH' in samples[s]['files'][0]:
leg.AddEntry(line_ctau[0],
'W^{-} H #rightarrow #pi #pi #rightarrow 4b', "")
elif 'ZH' in samples[s]['files'][
0] and not 'ggZH' in samples[s]['files'][0]:
leg.AddEntry(line_ctau[0], 'Z H #rightarrow #pi #pi #rightarrow 4b',
"")
elif 'ggZH' in samples[s]['files'][0]:
leg.AddEntry(line_ctau[0], 'gg Z H #rightarrow #pi #pi #rightarrow 4b',
"")
c1 = TCanvas("c1", "Signals", 800, 600)
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetTicks(1, 1)
line_ctau[0].GetXaxis().SetTitle("c#tau_{#pi} (mm)")
line_ctau[0].GetYaxis().SetTitle("Efficiency")
line_ctau[0].GetYaxis().SetRangeUser(0., maxy)
for i, s in enumerate(cutlist):
line_ctau[i].Draw("APL" if i == 0 else "SAME, PL")
leg.Draw()
#drawCMS(-1, "Simulation")
name = ""
if '#mu#mu' in labellist[0]: name = "Muon"
elif 'ee' in labellist[0]: name = "Ele"
else: name = "Met"
c1.Print("Efficiency/Efficiency_vs_ctau_" + signame + setcut + ".png")
c1.Print("Efficiency/Efficiency_vs_ctau_" + signame + setcut + ".pdf")
c1.Print("Efficiency/Efficiency_vs_ctau_" + signame + setcut + ".C")
line_ctau = []
def binFile(rerror, filename, xtitle, backgrounds):
file = TFile(filename)
keys = file.GetListOfKeys()
h_bkg = {}
h_data = {}
# load all the background and data histograms
for key in keys:
key = key.GetName()
#print key
info = hinfo(key)
if not info.systematic:
if info.process in backgrounds:
if info.channel in h_bkg:
#print info.channel
h_bkg[info.channel] = merge(h_bkg[info.channel], file.Get(key).Clone())
else:
h_bkg[info.channel] = file.Get(key).Clone()
elif info.process == 'data' or info.process == 'DATA':
#print info.process
#print info.channel
if info.channel in h_data:
h_data[info.channel] = merge(h_data[info.channel], file.Get(key).Clone())
else:
h_data[info.channel] = file.Get(key).Clone()
canvas = TCanvas()
# canvas.SetLogy()
keys = file.GetListOfKeys()
output = TFile(filename.split('.')[0]+'_rebinned.root', 'RECREATE')
# print all the histograms for all the channels
for key in h_bkg:
print h_bkg[key]
binning = array.array('d', computeBinning(h_bkg[key], rerror))
h_bkg[key] = h_bkg[key].Rebin(len(binning)-1, h_bkg[key].GetName(), binning)
h_data[key] = h_data[key].Rebin(len(binning)-1, h_data[key].GetName(), binning)
h_bkg[key].SetLineColor(ROOT.kGray+1)
h_bkg[key].SetFillColor(ROOT.kGray+1)
h_data[key].SetLineColor(ROOT.kBlack)
h_data[key].SetMarkerStyle(20)
pad = canvas.cd(1)
pad.SetLeftMargin(0.15)
pad.SetBottomMargin(0.15)
maxs = [h_data[key].GetMaximum(), h_bkg[key].GetMaximum()]
min = h_bkg[key].GetBinContent(h_bkg[key].GetMinimumBin())
h_data[key].GetYaxis().SetRangeUser(0.5*min,max(maxs)*1.8)
h_data[key].GetYaxis().SetLabelSize(0.05)
h_data[key].GetYaxis().SetTitleSize(0.05)
h_data[key].GetYaxis().SetTitle('event yield')
h_data[key].GetXaxis().SetLabelSize(0.05)
h_data[key].GetXaxis().SetTitleSize(0.05)
h_data[key].GetXaxis().SetTitle(xtitle)
h_data[key].Draw('e')
h_bkg[key].Draw('samehist')
h_data[key].Draw('samee')
legend = TLegend(.67, .78, .89, .88)
legend.SetMargin(0.12);
legend.SetTextSize(0.03);
legend.SetFillColor(10);
legend.SetBorderSize(0);
legend.AddEntry(h_bkg[key], "Background", "f")
legend.AddEntry(h_data[key], "CMS Data 2016", "lp")
legend.Draw()
labelcms = TLegend(.15, .91, 1, .96)
labelcms.SetTextSize(0.04)
labelcms.SetMargin(0.12);
labelcms.SetFillColor(10);
labelcms.SetBorderSize(0);
labelcms.SetHeader('CMS Preliminary #sqrt{s} = 13 TeV')
labelcms.Draw()
labellumi = TLegend(.73, .91, 1, .96)
labellumi.SetTextSize(0.04)
labellumi.SetMargin(0.12);
labellumi.SetFillColor(10);
labellumi.SetBorderSize(0);
# labellumi.SetHeader('L = 3.99 fb^{-1}')
# labellumi.SetHeader('L = 12.9 fb^{-1}')
labellumi.SetHeader('L = 35.9 fb^{-1}')
labellumi.Draw()
labellumi2 = TLegend(.67, .70, .89, .75)
labellumi2.SetTextSize(0.03)
labellumi2.SetMargin(0.12);
labellumi2.SetFillColor(10);
labellumi2.SetBorderSize(0);
labellumi2.SetHeader(key)
labellumi2.Draw()
canvas.SaveAs('h_'+filename.split('.')[0]+'_'+key+'.pdf')
for hkey in keys:
hkey = hkey.GetName()
info = hinfo(hkey)
if info.channel == key:
histogram = file.Get(hkey).Clone()
# #if info.systematic == 'pdf':
# # continue
# if (info.systematic == 'scale' or info.systematic == 'matching') and 'light' in info.process and '1btag' in info.channel:
# print "Excluding : ", info.systematic, info.process, info.channel
# continue
# # Hack to fix the data lowercase names
# if 'data' in info.process:
# histogram.SetName(histogram.GetName().replace('data','DATA'))
# if 'ttbar' in info.process and info.systematic == 'scale':
# orig = histogram.GetName()
# histogram.SetName(histogram.GetName().replace('scale','scale_ttbar'))
# print "Renaming: %s to %s" % (orig, histogram.GetName())
# if ('wlight' in info.process or 'zlight' in info.process) and info.systematic == 'scale':
# orig = histogram.GetName()
# histogram.SetName(histogram.GetName().replace('scale','scale_vjets'))
# print "Renaming: %s to %s" % (orig, histogram.GetName())
# if ('wlight' in info.process or 'zlight' in info.process) and info.systematic == 'matching':
# orig = histogram.GetName()
# histogram.SetName(histogram.GetName().replace('matching','matching_vjets'))
# print "Renaming: %s to %s" % (orig, histogram.GetName())
# if info.process.startswith('zp') or info.process.startswith('rsg'):
# print "Scaling signal sample %s by x0.1" % histogram.GetName()
# histogram.Scale(0.1)
# histogram = histogram.Rebin(len(binning)-1, histogram.GetName(), binning)
output.cd()
histogram.Write()
def DrawRatioPlot(name, sample, xaxis_title, yaxis_title, c, data, MC, MCUp,
MCDn, logscale):
c.Divide(0, 2, 0, 0)
pad1 = c.cd(1)
pad1.SetBottomMargin(0.02)
pad1.SetTopMargin(0.18)
pad1.SetLeftMargin(0.10)
if logscale:
pad1.SetLogy()
max = 0.
for bin in range(MC.GetSize() - 2):
if (MC.GetBinContent(bin + 1) > max):
max = MC.GetBinContent(bin + 1)
MC.SetMaximum(1.4 * max)
MC.SetFillColor(kOrange + 1)
MC.GetYaxis().SetTitle(yaxis_title)
MC.Draw("HIST")
MC.GetXaxis().SetLabelSize(0)
MC.GetYaxis().SetTitleSize(0.07)
MC.GetYaxis().SetLabelSize(0.07)
MC.GetYaxis().SetTitleOffset(0.7)
data.SetLineColor(ROOT.kBlack)
data.SetMarkerStyle(20)
data.SetMarkerSize(0.6)
data.Draw("p E1 X0 SAME")
pad2 = c.cd(2)
pad2.SetBottomMargin(0.20)
pad2.SetTopMargin(0.02)
pad2.SetLeftMargin(0.10)
Ratio = TH1F("Ratio", "Ratio",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_up = TH1F("sigma_up", "sigma_up",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_dn = TH1F("sigma_dn", "sigma_dn",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
for bin in range(MC.GetSize() - 2):
if (MC.GetBinContent(bin + 1) == 0):
Ratio.SetBinContent(bin + 1, 0)
Ratio.SetBinError(bin + 1, 0)
sigma_up.SetBinContent(bin + 1, 0)
sigma_dn.SetBinContent(bin + 1, 0)
else:
Ratio.SetBinContent(
bin + 1,
float(data.GetBinContent(bin + 1)) / MC.GetBinContent(bin + 1))
Ratio.SetBinError(bin + 1, 1. / (data.GetBinContent(bin + 1))**0.5)
sigma_up.SetBinContent(
bin + 1,
float(MCUp.GetBinContent(bin + 1)) / MC.GetBinContent(bin + 1))
sigma_dn.SetBinContent(
bin + 1,
float(MCDn.GetBinContent(bin + 1)) / MC.GetBinContent(bin + 1))
for bin in range(Ratio.GetSize() - 2):
temp = Ratio.GetBinContent(bin + 1)
Ratio.SetBinContent(bin + 1, temp - 1)
for bin in range(sigma_up.GetSize() - 2):
temp = sigma_up.GetBinContent(bin + 1)
sigma_up.SetBinContent(bin + 1, temp - 1)
for bin in range(sigma_dn.GetSize() - 2):
temp = sigma_dn.GetBinContent(bin + 1)
sigma_dn.SetBinContent(bin + 1, temp - 1)
sigma_up.SetFillColor(ROOT.kGray)
sigma_up.SetLineColor(ROOT.kGray)
sigma_up.SetMaximum(2.0)
sigma_up.SetMinimum(-2.0)
sigma_up.GetXaxis().SetTitle(xaxis_title)
sigma_up.GetYaxis().SetTitle("(Data/MC)-1")
sigma_up.GetYaxis().SetTitleOffset(0.6)
sigma_up.Draw("HIST")
sigma_up.GetXaxis().SetLabelSize(0.07)
sigma_up.GetXaxis().SetTitleSize(0.07)
sigma_up.GetYaxis().SetLabelSize(0.07)
sigma_up.GetYaxis().SetTitleSize(0.07)
sigma_dn.SetFillColor(ROOT.kGray)
sigma_dn.SetLineColor(ROOT.kGray)
sigma_dn.Draw("HIST SAME")
gPad.RedrawAxis()
Ratio.SetMarkerStyle(20)
Ratio.SetMarkerSize(0.6)
Ratio.Draw("p E1 X0 SAME")
pad1.cd()
leg = TLegend(.75, .45, .95, .75)
leg.SetBorderSize(0)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextFont(42)
leg.SetTextSize(0.07)
leg.AddEntry(MC, sample, "f")
leg.AddEntry(data, "Data", "p")
leg.AddEntry(sigma_up, "JEC Uncertainty", "f")
leg.Draw()
#draw CMS and lumi text
CMS_lumi.writeExtraText = True
CMS_lumi.extraText = "Preliminary"
CMS_lumi.lumi_sqrtS = lumiText + " (13 TeV)"
CMS_lumi.cmsTextSize = 0.6
CMS_lumi.lumiTextSize = 0.46
CMS_lumi.extraOverCmsTextSize = 0.75
CMS_lumi.relPosX = 0.12
CMS_lumi.CMS_lumi(pad1, 0, 0)
c.Update()
c.SaveAs(name + ".pdf")
c.SaveAs(name + ".png")
c.Clear()
last_evt = tree.evt
last_bdtvalue = mvaValue
last_genMatch = tree.bWj1Wj2_isGenMatched
print "No. of signal ", hist_signal.Integral(
), " No. of bkg ", hist_bkg.Integral()
hist_signal.SetLineColor(2)
hist_bkg.SetLineColor(4)
hist_signal.SetFillColor(2)
hist_bkg.SetFillColor(4)
hist_signal.SetFillStyle(3004)
hist_bkg.SetFillStyle(3005)
leg = TLegend(0.2, 0.65, 0.5, 0.9)
leg.SetBorderSize(0)
leg.SetFillColor(10)
leg.SetLineColor(0)
leg.SetFillStyle(0)
leg.SetTextSize(0.04)
leg.SetTextFont(42)
leg.AddEntry(hist_signal, "signal", "l")
leg.AddEntry(hist_bkg, "background", "l")
histogram_base.Draw("hist")
hist_signal.DrawNormalized("histsame")
hist_bkg.DrawNormalized("histsame")
leg.Draw()
c1.SaveAs("plots/bdt_score_evtLevel_hadTopTagger_" + process + "_opt1.png")
c1.SaveAs("plots/bdt_score_evtLevel_hadTopTagger_" + process + "_opt1.C")
hs.GetXaxis().SetTitle("Mass [GeV/c^{2}]")
hs.GetXaxis().SetLabelFont(43)
hs.GetXaxis().SetLabelSize(15)
hs.GetYaxis().SetTitleSize(20)
hs.GetYaxis().SetTitleFont(43)
hs.GetYaxis().SetTitleOffset(1.8)
hs.GetYaxis().SetLabelFont(43)
hs.GetYaxis().SetLabelSize(15)
hs.GetYaxis().SetTitle("Events")
# legend
legend = TLegend(0.74, 0.68, 0.94, 0.87)
legend.AddEntry(ZMass_DATA[i], "Data", "p")
legend.AddEntry(ZMass_MC_DY[i], "Drell-Yan", "f")
legend.AddEntry(ZMass_MC_TTJets[i], "t#bar{t}", "f")
legend.SetFillColor(kWhite)
legend.SetLineColor(kBlack)
legend.SetTextFont(43)
legend.SetTextSize(20)
legend.Draw()
# box with fit results
pv = TPaveText(0.64, 0.35, 0.94, 0.65, "brNDC")
pv.AddText("DATA:")
pv.AddText("Z DCBmean = " + str(round(massFitDATA_dict[key], 2)) + " GeV")
pv.AddText("Z DCBwidth = " + str(round(widthFitDATA_dict[key], 2)) +
" GeV")
pv.AddText("MC:")
pv.AddText("Z DCBmean = " + str(round(massFitMC_dict[key], 2)) + " GeV")
pv.AddText("Z DCBwidth = " + str(round(widthFitMC_dict[key], 2)) + " GeV")
pv.SetFillColor(kWhite)
def do_plot(categories):
#this sets how tall the canvas is
N_cats = len(plot_categories.keys())
#sets the number of divisions on the plot
N_cuts = max(len(cuts) for cuts in plot_categories.itervalues())
canv = TCanvas('theorycomp', '', 500, 500 + 200 * N_cats)
main_pad_ylow = 1 - (500.0 / (500 + 200 * N_cats))
rat_pad_height = main_pad_ylow / N_cats
canv.cd()
max_cs = get_max_cs(categories)
min_cs = get_min_cs(categories)
scaffold = TH1F('scaf', '', N_cuts, 0, N_cuts)
scaffold.GetYaxis().SetLimits(10 * max_cs, min_cs / 10.0)
scaffold.GetYaxis().SetRangeUser(min_cs / 10.0, 10 * max_cs)
scaffold.SetNdivisions(100 * N_cuts)
scaffold.SetStats(False)
scaffold.GetYaxis().SetTitle(y_axis_title)
unshitify(scaffold)
main_pad = TPad('main_plot', '', 0, main_pad_ylow, 1, 1, 0, 0, 0)
main_pad.SetLogy()
main_pad.Draw()
main_pad.cd()
scaffold.Draw()
cut_labels = TLatex()
cut_labels.SetNDC()
plot_start = 0.15
column_width = (0.945 - plot_start) / N_cuts
cut_labels.SetTextSize(0.045)
cut_labels.SetTextFont(62)
# cut_labels.DrawLatex(0.18,0.96,
# "CMS Preliminary %i, #sqrt{s} = %s"%(year,sqrts))
# cut_labels.DrawLatex(0.77,0.96,luminosity)
cut_labels.DrawLatex(0.17, 0.96, "CMS Preliminary, L = 5.0 fb^{-1}")
cut_labels.DrawLatex(0.76, 0.96, "#sqrt{s} = 7 TeV")
labels_drawn = 0
ratio_pads = []
cat_meass = []
cat_preds = []
cat_meas_rats = []
cat_pred_rats = []
legend = TLegend(0.45, 0.7, 0.93, 0.93)
legend.SetTextFont(62)
legend.SetTextSize(0.038)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.SetBorderSize(1)
for j, (cat, cuts) in enumerate(plot_categories.iteritems()):
canv.cd()
ratio_pads.append(
TPad(cat, '', 0, rat_pad_height * j + 0.075, 1,
rat_pad_height * (j + 1) + rat_pad_height / 4, 0, 0, 0))
ratio_pads[-1].Draw()
ratio_pads[-1].cd()
rat_scaf = scaffold.DrawCopy()
rat_scaf.GetYaxis().SetLimits(-1, 3)
rat_scaf.GetYaxis().SetRangeUser(0, 2)
rat_scaf.GetYaxis().SetNdivisions(105)
rat_scaf.GetYaxis().SetTitle("#frac{Data}{%s}" % (prediction_name))
rat_scaf.GetYaxis().SetTitleOffset(0.6)
rat_scaf.GetYaxis().SetTitleSize(0.10)
main_pad.cd()
print j, cat
cat_meass.append([])
cat_preds.append(TGraphErrors())
cat_meas_rats.append([])
cat_pred_rats.append(TGraphErrors())
cat_preds[-1].SetName("%s (%s)" % (prediction_name, cat))
added_channels = False
for i, cut in enumerate(cuts):
cut_name = cut.keys()[0]
if len(cuts) == N_cuts and labels_drawn != len(cuts):
canv.cd()
cut_labels.DrawLatex(plot_start + column_width * (i + 0.35),
0.05, cut_name)
labels_drawn += 1
main_pad.cd()
print '\t%i : %s' % (i, cut_name)
realkeys = list(cut[cut_name].keys())
realkeys.remove('PREDICTION')
nchan = len(realkeys)
divsize = 1.0 / (nchan)
if not added_channels:
cat_meass[-1] += [TGraphErrors() for l in range(nchan)]
cat_meas_rats[-1] += [TGraphErrors() for l in range(nchan)]
print cat_meass[-1]
added_channels = True
#graph entries for prediction
pcs, perr = cut[cut_name]['PREDICTION']
cat_preds[-1].SetPoint(i, i + 0.5, pcs)
cat_preds[-1].SetPointError(i, 0.5, perr)
cat_pred_rats[-1].SetPoint(i, i + 0.5, 1.0)
cat_pred_rats[-1].SetPointError(i, 0.5, perr / pcs)
#graph entries for data measurement
for k, key in enumerate(realkeys):
gidx = i
print cut[cut_name][key]
cs, stat, syst = cut[cut_name][key]
err = hypot(stat, syst)
cat_meass[-1][k].SetName("%s (%s)" % (key, cat))
cat_meass[-1][k].SetPoint(gidx, i + (k + 0.5) * divsize, cs)
cat_meass[-1][k].SetPointError(gidx, 0, err)
cat_meas_rats[-1][k].SetPoint(gidx, i + (k + 0.5) * divsize,
cs / pcs)
cat_meas_rats[-1][k].SetPointError(gidx, 0, err / pcs)
# err/pcs)
main_pad.cd()
cat_preds[-1].SetFillColor(fill_colors[j] - 3)
cat_preds[-1].Draw('2')
legend.AddEntry(cat_preds[-1], cat_preds[-1].GetName(), 'f')
for i, gr in enumerate(cat_meass[-1]):
gr.SetMarkerStyle(20 + 4 * j + i)
gr.Draw('pe1')
legend.AddEntry(gr, gr.GetName(), 'pl')
ratio_pads[-1].cd()
cat_pred_rats[-1].SetFillColor(fill_colors[j] - 3)
cat_pred_rats[-1].Draw('2')
for i, gr in enumerate(cat_meas_rats[-1]):
gr.SetMarkerStyle(20 + 4 * j + i)
gr.Draw('pe1')
main_pad.cd()
legend.Draw()
canv.cd()
cut_labels.DrawLatex(0.80, 0.015, 'E_{T}^{#gamma} (GeV)')
canv.Print('vgamma_theory_comparison_Wg_app_corr_dict.pdf')
canv.Print('vgamma_theory_comparison_Wg_app_corr_dict.eps')
canv.Print('vgamma_theory_comparison_Wg_app_corr_dict.png')
canv.Print('vgamma_theory_comparison_Wg_app_corr_dict.gif')
canv.Print('vgamma_theory_comparison_Wg_app_corr_dict.root')
def limits():
# put real limits here: lepton+jets, dilepton, combined
obs = np.array([0.0, 0.0, 0.0])
expect = np.array([1, 1, 1])
upper1sig = np.array([1.179, 0.827, 0.736])
lower1sig = np.array([0.824, 0.578, 0.501])
upper2sig = np.array([2.661, 1.892, 1.662])
lower2sig = np.array([1.344, 0.9303, 0.806])
channel = np.array([1.5, 0.5, -0.5])
ey = np.array([0.495, 0.495, 0.495])
zero = np.zeros(nchannels)
xmin = 0.9
xmax = 20
c, h = draw_canvas_histo(
xmin, xmax,
"95% CL limit on #mu = #sigma/#sigma_{SM} at m_{H} = 125 GeV")
c.SetLogx()
gexpect1sig = TGraphAsymmErrors(nchannels, expect, channel, lower1sig,
upper1sig, ey, ey)
gexpect1sig.SetFillColor(kGreen)
gexpect1sig.SetLineWidth(2)
gexpect1sig.SetLineStyle(2)
gexpect2sig = TGraphAsymmErrors(nchannels, expect, channel, lower2sig,
upper2sig, ey, ey)
gexpect2sig.SetFillColor(kYellow)
gexpect2sig.SetLineWidth(2)
gexpect2sig.SetLineStyle(2)
gexpect2sig.Draw("2")
gexpect1sig.Draw("2")
gobs = TGraphErrors(nchannels, obs, channel, zero, ey)
gobs.SetMarkerStyle(21)
gobs.SetMarkerSize(1.5)
gobs.SetLineWidth(2)
#gobs.Draw("pz")
# dashed line at median expected limits
l = TLine()
l.SetLineStyle(2)
l.SetLineWidth(2)
for bin in range(nchannels):
l.DrawLine(expect[bin], channel[bin] - ey[bin], expect[bin],
channel[bin] + ey[bin])
# line to separate individual and combined limits
l.SetLineStyle(1)
l.SetLineWidth(1)
l.DrawLine(xmin, 0, xmax, 0)
# legend
leg = TLegend(0.67, 0.75, 0.95, 0.9)
leg.SetFillColor(4000)
leg.AddEntry(gexpect1sig, "Expected #pm1#sigma", "FL")
leg.AddEntry(gexpect2sig, "Expected #pm2#sigma", "FL")
#leg.AddEntry( gobs, "Observed", "pl" )
leg.Draw()
#draw_disclaimer()
c.RedrawAxis()
c.Modified()
c.Update()
c.SaveAs("limits.pdf")
def DrawRatioPlot(name, xaxis_title, yaxis_title, c, data, MC, MC_NvtxWEIGHT,
MC_jesUp, MC_jesDn, MC_jerUp, MC_jerDn, MC_puUp, MC_puDn,
MC_metUp, MC_metDn):
c.Divide(0, 2, 0, 0)
pad1 = c.cd(1)
pad1.SetBottomMargin(0.02)
pad1.SetTopMargin(0.18)
pad1.SetLeftMargin(0.10)
max = 0.
for bin in range(MC.GetSize() - 2):
if (MC.GetBinContent(bin + 1) > max):
max = MC.GetBinContent(bin + 1)
MC.SetMaximum(1.4 * max)
#norm = data.Integral()/MC.Integral() # Normalize MC to data
#MC.Scale(norm)
MC.SetFillColor(kOrange + 1)
MC.GetYaxis().SetTitle(yaxis_title)
MC.Draw("HIST")
MC.GetXaxis().SetLabelSize(0)
MC.GetYaxis().SetTitleSize(0.07)
MC.GetYaxis().SetLabelSize(0.07)
MC.GetYaxis().SetTitleOffset(0.7)
data.SetLineColor(ROOT.kBlack)
data.SetMarkerStyle(20)
data.SetMarkerSize(0.6)
data.Draw("p E1 X0 SAME")
MC_NvtxWEIGHT.SetMarkerStyle(23)
MC_NvtxWEIGHT.SetMarkerColor(kAzure)
MC_NvtxWEIGHT.SetMarkerSize(0.9)
MC_NvtxWEIGHT.Draw("p E1 X0 SAME")
pad2 = c.cd(2)
pad2.SetBottomMargin(0.20)
pad2.SetTopMargin(0.02)
pad2.SetLeftMargin(0.10)
Ratio = TH1F("Ratio", "Ratio",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
Ratio_W = TH1F("Ratio_W", "Ratio_W",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_up = TH1F("sigma_up", "sigma_up",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_dn = TH1F("sigma_dn", "sigma_dn",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_jesUp = TH1F("sigma_jesUp", "sigma_jesUp",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_jesDn = TH1F("sigma_jesDn", "sigma_jesDn",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_jerUp = TH1F("sigma_jerUp", "sigma_jerUp",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_jerDn = TH1F("sigma_jerDn", "sigma_jerDn",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_puUp = TH1F("sigma_puUp", "sigma_puUp",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_puDn = TH1F("sigma_puDn", "sigma_puDn",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_metUp = TH1F("sigma_metUp", "sigma_metUp",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
sigma_metDn = TH1F("sigma_metDn", "sigma_metDn",
MC.GetSize() - 2,
MC.GetXaxis().GetXmin(),
MC.GetXaxis().GetXmax())
up_list = []
dn_list = []
for bin in range(MC.GetSize() - 2):
temp_list = []
if (MC.GetBinContent(bin + 1) == 0):
Ratio.SetBinContent(bin + 1, 0)
Ratio.SetBinError(bin + 1, 0)
Ratio_W.SetBinContent(bin + 1, 0)
Ratio_W.SetBinError(bin + 1, 0)
sigma_jesUp.SetBinContent(bin + 1, 0)
sigma_jesDn.SetBinContent(bin + 1, 0)
sigma_jerUp.SetBinContent(bin + 1, 0)
sigma_jerDn.SetBinContent(bin + 1, 0)
sigma_puUp.SetBinContent(bin + 1, 0)
sigma_puDn.SetBinContent(bin + 1, 0)
sigma_metUp.SetBinContent(bin + 1, 0)
sigma_metDn.SetBinContent(bin + 1, 0)
else:
Ratio.SetBinContent(
bin + 1,
float(data.GetBinContent(bin + 1)) / MC.GetBinContent(bin + 1))
Ratio.SetBinError(bin + 1, 1. / (data.GetBinContent(bin + 1))**0.5)
Ratio_W.SetBinContent(
bin + 1,
float(data.GetBinContent(bin + 1)) /
MC_NvtxWEIGHT.GetBinContent(bin + 1))
Ratio_W.SetBinError(bin + 1,
1. / (data.GetBinContent(bin + 1))**0.5)
sigma_jesUp.SetBinContent(
bin + 1,
float(MC_jesUp.GetBinContent(bin + 1)) /
MC.GetBinContent(bin + 1))
sigma_jesDn.SetBinContent(
bin + 1,
float(MC_jesDn.GetBinContent(bin + 1)) /
MC.GetBinContent(bin + 1))
sigma_jerUp.SetBinContent(
bin + 1,
float(MC_jerUp.GetBinContent(bin + 1)) /
MC.GetBinContent(bin + 1))
sigma_jerDn.SetBinContent(
bin + 1,
float(MC_jerDn.GetBinContent(bin + 1)) /
MC.GetBinContent(bin + 1))
sigma_puUp.SetBinContent(
bin + 1,
float(MC_puUp.GetBinContent(bin + 1)) /
MC.GetBinContent(bin + 1))
sigma_puDn.SetBinContent(
bin + 1,
float(MC_puDn.GetBinContent(bin + 1)) /
MC.GetBinContent(bin + 1))
sigma_metUp.SetBinContent(
bin + 1,
float(MC_metUp.GetBinContent(bin + 1)) /
MC.GetBinContent(bin + 1))
sigma_metDn.SetBinContent(
bin + 1,
float(MC_metDn.GetBinContent(bin + 1)) /
MC.GetBinContent(bin + 1))
print '############### bin ' + str(bin + 1) + ' ##############'
print str(sigma_jesUp.GetBinContent(bin + 1))
print str(sigma_jerUp.GetBinContent(bin + 1))
print str(sigma_puUp.GetBinContent(bin + 1))
print str(sigma_metUp.GetBinContent(bin + 1))
print str(sigma_jesDn.GetBinContent(bin + 1))
print str(sigma_jerDn.GetBinContent(bin + 1))
print str(sigma_puDn.GetBinContent(bin + 1))
print str(sigma_metDn.GetBinContent(bin + 1))
temp_jesUp = sigma_jesUp.GetBinContent(bin + 1)
temp_jerUp = sigma_jerUp.GetBinContent(bin + 1)
temp_puUp = sigma_puUp.GetBinContent(bin + 1)
temp_metUp = sigma_metUp.GetBinContent(bin + 1)
temp_jesDn = sigma_jesDn.GetBinContent(bin + 1)
temp_jerDn = sigma_jerDn.GetBinContent(bin + 1)
temp_puDn = sigma_puDn.GetBinContent(bin + 1)
temp_metDn = sigma_metDn.GetBinContent(bin + 1)
temp_list.append(temp_jesUp)
temp_list.append(temp_jerUp)
temp_list.append(temp_puUp)
temp_list.append(temp_metUp)
temp_list.append(temp_jesDn)
temp_list.append(temp_jerDn)
temp_list.append(temp_puDn)
temp_list.append(temp_metDn)
print 'temp_list size is: ' + str(len(temp_list))
tempUp_list = []
tempDn_list = []
for i in range(len(temp_list)):
print 'Elemento', i, 'is ', temp_list[i]
if (temp_list[i] > 1.):
tempUp_list.append(temp_list[i])
elif (temp_list[i] < 1.):
tempDn_list.append(temp_list[i])
print 'tempUp_list size is: ' + str(len(tempUp_list))
print 'tempDn_list size is: ' + str(len(tempDn_list))
temp_up = 0.
for i in tempUp_list:
temp_up += (i - 1)**2
#print 'Elemento up iesimo della lista: ', str(i), '; temp_up al giro i = ', temp_up
up = math.sqrt(temp_up)
up_list.append(up)
temp_dn = 0.
for i in tempDn_list:
temp_dn += (i - 1)**2
#print 'Elemento dn iesimo della lista: ', str(i), '; temp_dn al giro i = ', temp_dn
dn = math.sqrt(temp_dn)
dn_list.append(dn)
print 'UP LIST SIZE is: ' + str(len(up_list))
print 'DN LIST SIZE is: ' + str(len(dn_list))
for bin in range(len(up_list)):
sigma_up.SetBinContent(bin + 1, up_list[bin])
print 'bin = ', bin, ' up = ', str(sigma_up.GetBinContent(bin + 1))
for bin in range(len(dn_list)):
sigma_dn.SetBinContent(bin + 1, -dn_list[bin])
print 'bin = ', bin, ' down = ', str(sigma_dn.GetBinContent(bin + 1))
print "Saving ratios for each systematic contribution bin per bin into root file ..."
outFile_DATA = TFile.Open("RatioHisto_PROVA_" + period + ".root",
"RECREATE")
outFile_DATA.cd()
sigma_jesUp.Write()
sigma_jesDn.Write()
sigma_jerUp.Write()
sigma_jerDn.Write()
sigma_puUp.Write()
sigma_puDn.Write()
sigma_metUp.Write()
sigma_metDn.Write()
outFile_DATA.Close()
for bin in range(Ratio.GetSize() - 2):
temp = Ratio.GetBinContent(bin + 1)
Ratio.SetBinContent(bin + 1, temp - 1)
for bin in range(Ratio_W.GetSize() - 2):
temp = Ratio_W.GetBinContent(bin + 1)
Ratio_W.SetBinContent(bin + 1, temp - 1)
sigma_up.SetFillColor(ROOT.kGray)
sigma_up.SetLineColor(ROOT.kGray)
sigma_up.SetMaximum(2.0)
sigma_up.SetMinimum(-2.0)
sigma_up.GetXaxis().SetTitle(xaxis_title)
sigma_up.GetYaxis().SetTitle("(Data/MC)-1")
sigma_up.GetYaxis().SetTitleOffset(0.6)
sigma_up.Draw("HIST")
sigma_up.GetXaxis().SetLabelSize(0.07)
sigma_up.GetXaxis().SetTitleSize(0.07)
sigma_up.GetYaxis().SetLabelSize(0.07)
sigma_up.GetYaxis().SetTitleSize(0.07)
sigma_dn.SetFillColor(ROOT.kGray)
sigma_dn.SetLineColor(ROOT.kGray)
sigma_dn.Draw("HIST SAME")
gPad.RedrawAxis()
Ratio.SetMarkerStyle(20)
Ratio.SetMarkerColor(kOrange + 1)
Ratio.SetMarkerSize(0.6)
Ratio.Draw("p E1 X0 SAME")
Ratio_W.SetMarkerStyle(23)
Ratio_W.SetMarkerSize(0.9)
Ratio_W.SetMarkerColor(kAzure)
Ratio_W.Draw("p E1 X0 SAME")
pad1.cd()
leg = TLegend(.75, .3, .85, .75)
leg.SetBorderSize(0)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextFont(42)
leg.SetTextSize(0.07)
#leg.AddEntry(MC, "Z/t#bar{t} + jets", "f" )
leg.AddEntry(MC, "DY + t#bar{t} MC", "f")
leg.AddEntry(data, "Data", "p")
leg.AddEntry(MC_NvtxWEIGHT, "MC reweighted", "p")
leg.AddEntry(sigma_up, "Tot uncertainty", "f")
leg.Draw()
#draw CMS and lumi text
CMS_lumi.writeExtraText = True
CMS_lumi.extraText = "Preliminary"
CMS_lumi.lumi_sqrtS = lumiText + " (13 TeV)"
CMS_lumi.cmsTextSize = 0.6
CMS_lumi.lumiTextSize = 0.46
CMS_lumi.extraOverCmsTextSize = 0.75
CMS_lumi.relPosX = 0.12
CMS_lumi.CMS_lumi(pad1, 0, 0)
c.Update()
c.SaveAs(name + ".pdf")
c.SaveAs(name + ".png")
c.Clear()
leg = TLegend(.55,.2,.93,.4)
try: leg.AddEntry(hists['Low'], 'Low', "l")
except: pass
try: leg.AddEntry(hists['Med'], 'Med', "l")
except: pass
try: leg.AddEntry(hists['800'], '800', "l")
except: pass
try: leg.AddEntry(hists['1000'], '1000', "l")
except: pass
try: leg.AddEntry(hists['2000'], '2000', "l")
except: pass
try: leg.AddEntry(hists['3000'], '3000', "l")
except: pass
leg.SetShadowColor(0)
leg.SetFillColor(0)
leg.SetLineColor(0)
leg.SetNColumns(2)
leg.Draw()
canv.SaveAs('ROCscan19vars.png')
canvrel = TCanvas("canvrel","relROCs", 1000, 800)
#canvrel.SetLogy()
canvrel.SetBottomMargin(0.15)
canvrel.SetRightMargin(0.06)
relhists['Low'].GetXaxis().LabelsOption("u")
relhists['Low'].GetXaxis().SetLabelSize(0.05)
relhists['Low'].GetYaxis().SetRangeUser(0.7,1.0)
relhists['Low'].GetXaxis().SetTitle('N_{variables}')
relhists['Low'].GetYaxis().SetTitle('relative ROC-integral')
def kkmass1(opts, args, figname):
parsed = parse_args(args)
datatype = parsed[0]
tag = parsed[1]
modes = parsed[2]
label = parsed[3]
mode = modes[0]
modekey = tools.get_modekey(mode)
binbase = parse_opts_set(opts.set, 'binbase')
binwidth = parse_opts_set(opts.set, 'binwidth')
numbins = parse_opts_set(opts.set, 'numbins')
nohtml = parse_opts_set(opts.set, 'nohtml')
if nohtml == None:
outputhtml = True
else:
outputhtml = False
dbfile = os.path.join(attr.figpath, label, figname + '.db')
alldata, fullrangemc = get_fullrange_data_mc(opts, dbfile, label, 'kkmass',
modekey, tag, binbase,
binwidth, numbins)
fullrangedata = {}
init_dict(fullrangedata)
evtpath = os.path.join(attr.datpath, 'sel', label, 'kkmass')
evtfilemc = tools.set_file('evt',
'signal',
modekey,
tag,
prefix='',
forceCombine=1,
extbase=evtpath)
for i in xrange(numbins):
lowmass = binbase + i * binwidth
highmass = binbase + (i + 1) * binwidth
prefix = 'dir_%s/kkmass/%s_%s' % (label, lowmass, highmass)
datafile = tools.set_file('txt',
'data',
modekey,
tag,
prefix=prefix,
extbase=attr.fitpath)
if os.access(datafile, os.F_OK):
parsefile(datafile, lowmass, highmass, fullrangedata)
else:
sys.stdout.write('Skipped %s %s ...\n' % (lowmass, highmass))
scaledict(fullrangedata, 1 / sumdict(fullrangedata))
scaledict(fullrangemc, 1 / sumdict(fullrangemc))
gdata = TGraphAsymmErrors(len(fullrangedata['x']),
getarray('x', fullrangedata),
getarray('y', fullrangedata),
getarray('exl', fullrangedata),
getarray('exh', fullrangedata),
getarray('eyl', fullrangedata),
getarray('eyh', fullrangedata))
gmc = TGraph(len(fullrangemc['x']), getarray('x', fullrangemc),
getarray('y', fullrangemc))
c1 = TCanvas('c', 'kk', 600, 600)
c1.SetLeftMargin(0.15)
c1.SetRightMargin(0.05)
c1.SetBottomMargin(0.15)
c1.SetTopMargin(0.05)
#hr = c1.DrawFrame(0.5, 0.0, 1.5, 0.04)
hr = c1.DrawFrame(0.9, 0.0, 1.8, 0.1)
hr.GetYaxis().SetTitle('Yield (arbitrary units)')
hr.GetXaxis().SetTitle('M(K^{-} #pi^{+}) (GeV/#font[72]{c}^{2})')
hr.GetYaxis().SetTitleOffset(1.4)
hr.GetYaxis().SetTitleSize(0.05)
hr.GetXaxis().SetTitleOffset(1.3)
hr.GetXaxis().SetTitleSize(0.05)
gmc.SetMarkerStyle(21)
gmc.SetMarkerColor(kRed)
gmc.SetMarkerSize(0.6)
gmc.SetLineColor(kRed)
gmc.Draw("PL")
gdata.SetMarkerStyle(20)
gdata.SetMarkerColor(kBlue)
gdata.SetMarkerSize(0.6)
gdata.Draw("P")
leg = TLegend(0.6, 0.75, 0.92, 0.92)
leg.AddEntry(gdata, 'Data', 'pl')
leg.AddEntry(gmc, 'MC', 'pl')
leg.SetFillColor(0)
leg.SetBorderSize(0)
leg.Draw()
canvas_output(c1, figname, label, opts.test, outputhtml=outputhtml)
def plot_BDT(channel, var, massPoint, bin, low, high, ylabel, xlabel, save, nBtags = -1, setLog = False, finalcuts = False):
if (channel == 'electron'):
cut = 'jet_0_pt_WprimeCalc >= 120 && jet_1_pt_WprimeCalc >= 40 && elec_1_pt_WprimeCalc > 32 && abs(elec_1_eta_WprimeCalc) < 2.5 && elec_1_RelIso_WprimeCalc < 0.1 && corr_met_WprimeCalc > 20'
#cut = 'jet_0_pt_WprimeCalc >= 100 && jet_1_pt_WprimeCalc >= 40 && elec_1_pt_WprimeCalc > 30 && abs(elec_1_eta_WprimeCalc) < 2.5 && elec_1_RelIso_WprimeCalc < 0.1 && corr_met_WprimeCalc > 35'
if (channel == 'muon'):
cut = 'jet_0_pt_WprimeCalc >= 120 && jet_1_pt_WprimeCalc >= 40 && muon_1_pt_WprimeCalc > 26 && abs(muon_1_eta_WprimeCalc) < 2.1 && muon_1_RelIso_WprimeCalc < 0.12 && corr_met_WprimeCalc > 20'
#cut = 'jet_0_pt_WprimeCalc >= 100 && jet_1_pt_WprimeCalc >= 40 && muon_1_pt_WprimeCalc > 26 && abs(muon_1_eta_WprimeCalc) < 2.1 && muon_1_RelIso_WprimeCalc < 0.12 && corr_met_WprimeCalc > 35'
if finalcuts: cut+= '&& BestTop_LjetsTopoCalcNew > 130 && BestTop_LjetsTopoCalcNew < 210 && BestTop_Pt_LjetsTopoCalcNew > 75 && Jet1Jet2_Pt_LjetsTopoCalcNew > 100'
njets = ""
if nBtags == -1:
cutbtag = ''
save = save
if nBtags == 0:
cutbtag = ' && ( (jet_0_tag_WprimeCalc + jet_1_tag_WprimeCalc + jet_2_tag_WprimeCalc + jet_3_tag_WprimeCalc + jet_4_tag_WprimeCalc + jet_5_tag_WprimeCalc + jet_6_tag_WprimeCalc + jet_7_tag_WprimeCalc + jet_8_tag_WprimeCalc + jet_9_tag_WprimeCalc) == 0 )'
save = save + '_0bTags'
njets = "N_{b tags} = 0"
if nBtags == 1:
cutbtag = ' && ( (jet_0_tag_WprimeCalc + jet_1_tag_WprimeCalc) == 1)'
save = save + '_1bTags'
njets = "N_{b tags} = 1"
if nBtags == 2:
cutbtag = ' && ( (jet_0_tag_WprimeCalc + jet_1_tag_WprimeCalc) >= 1 ) '
save = save + '_GE1bTags'
njets = "N_{b tags} #geq 1"
cutwbb = ' && n_Bjets_WprimeCalc > 0' # Wb(b)
cutwcc = ' && n_Bjets_WprimeCalc==0 && n_Cjets_WprimeCalc>0' # Wc(c)
cutwjj = ' && n_Bjets_WprimeCalc==0 && n_Cjets_WprimeCalc==0' # W+light
#cutwbb = ' ' # Wb(b)
#cutwcc = ' ' # Wc(c)
#cutwjj = ' ' # W+light
SFWjmu = 1.08*0.85
SFWcmu = 1.06*0.92*1.66
SFWbmu = 1.06*0.92*1.21
#SFWjmu = 1.0
#SFWcmu = 1.0
#SFWbmu = 1.0
WjjHist = TH1D('WjjHist', 'WjjHist', bin,low,high)
WccHist = TH1D('WccHist', 'WccHist', bin,low,high)
WbbHist = TH1D('WbbHist', 'WbbHist', bin,low,high)
Variables = {}
efficiency = {}
BkgList = []
for Type in Samples:
#print channel,' ',Type
if (Type.endswith('_el') and channel == 'muon'): continue
if (Type.endswith('_mu') and channel == 'electron'): continue
if (not Type.startswith(massPoint) ): continue
Variables[Type] = TH1D(Type+var+channel, Type+var+channel, bin, low, high)
histName = Type+var+channel
if (channel == 'electron'): chan = '_el'
if (channel == 'muon'): chan = '_mu'
if (channel == 'electron'): weight = 'weight_PU_ABC_PileUpCalc*weight_ElectronEff_WprimeCalc'
if (channel == 'muon'): weight = 'weight_PU_ABC_PileUpCalc*weight_MuonEff_WprimeCalc'
if ((not Type == massPoint+massPoint+chan) and (not Type == massPoint+'_data'+chan)): BkgList.extend([Type])
if Type.endswith('wjets'+chan):
#print 'Filling ',Type
Trees[Type].Draw(var + " >> " + histName, "("+weight+")*(" + str(SFWjmu) + ")*(" + cut + cutbtag + cutwjj + ")", 'goff')
Trees[Type].Draw(var + " >> " + "WbbHist","("+weight+")*(" + str(SFWbmu) + ")*(" + cut + cutbtag + cutwbb + ")", 'goff')
Trees[Type].Draw(var + " >> " + "WccHist","("+weight+")*(" + str(SFWcmu) + ")*(" + cut + cutbtag + cutwcc + ")", 'goff')
#print 'Raw Wjj ',Variables['WJets'].Integral()
Variables[Type].Add(WbbHist)
#print 'Raw Wjj + Wbb ',Variables['WJets'].Integral()
Variables[Type].Add(WccHist)
#print 'Raw Wjj + Wbb + Wcc',Variables['WJets'].Integral()
elif Type.endswith('data'+chan):
#print 'Filling ',Type
Trees[Type].Draw(var + " >> " + histName, "(" + cut + cutbtag + ")", 'goff')
else:
#print 'Filling ',Type
Trees[Type].Draw(var + " >> " + histName, "("+weight+")*(" + cut + cutbtag + ")", 'goff')
if (not Type.endswith('data'+chan)):
SF = 1.0
if (channel == 'electron'):
lumi = lumi_el
if (channel == 'muon'):
lumi = lumi_mu
if (Type.startswith(massPoint+'_'+massPoint)): SF *= 20
#print 'EVENTS Before Scaling FOR ',Type,' = ',Variables[Type].Integral()
#print 'Pre Events before scaling for ',Type,' = ',VariablesPre[Type].Integral()
#print str(SF),' ',str(lumi),' ',str(xsec_norm[Type]),' ',str(Nevents[Type])
if Variables[Type].Integral() != 0:
#print Type,' Lumi scaling: ',str(SF*lumi*xsec[Type]/Nevents[Type])
Variables[Type].Scale ( SF*Yield[Type]/Variables[Type].Integral() )
efficiency[Type] = Variables[Type].Integral()/Nevents[Type]
else:
efficiency[Type] = 0
Variables[massPoint+'_ttbar'+chan].SetFillColor(ROOT.kRed-7)
Variables[massPoint+'_s'+chan].SetFillColor(ROOT.kRed-7)
Variables[massPoint+'_bs'+chan].SetFillColor(ROOT.kRed-7)
Variables[massPoint+'_t'+chan].SetFillColor(ROOT.kRed-7)
Variables[massPoint+'_bt'+chan].SetFillColor(ROOT.kRed-7)
Variables[massPoint+'_tw'+chan].SetFillColor(ROOT.kRed-7)
Variables[massPoint+'_btw'+chan].SetFillColor(ROOT.kRed-7)
Variables[massPoint+'_s'+chan].SetLineColor(ROOT.kRed-7)
Variables[massPoint+'_bs'+chan].SetLineColor(ROOT.kRed-7)
Variables[massPoint+'_t'+chan].SetLineColor(ROOT.kRed-7)
Variables[massPoint+'_bt'+chan].SetLineColor(ROOT.kRed-7)
Variables[massPoint+'_tw'+chan].SetLineColor(ROOT.kRed-7)
Variables[massPoint+'_btw'+chan].SetLineColor(ROOT.kRed-7)
Variables[massPoint+'_wjets'+chan].SetFillColor(ROOT.kGreen-3)
Variables[massPoint+'_zjets'+chan].SetFillColor(ROOT.kGreen-3)
Variables[massPoint+'_ww'+chan].SetFillColor(ROOT.kGreen-3)
Variables[massPoint+'_zjets'+chan].SetLineColor(ROOT.kGreen-3)
Variables[massPoint+'_ww'+chan].SetLineColor(ROOT.kGreen-3)
Variables[massPoint+'_ttbar'+chan].SetLineWidth(2)
Variables[massPoint+'_wjets'+chan].SetLineWidth(2)
Variables[massPoint+'_'+massPoint+chan].SetLineColor(1)
Variables[massPoint+'_'+massPoint+chan].SetLineWidth(2)
Variables[massPoint+'_'+massPoint+chan].SetLineStyle(6)
stack = THStack('a', 'a')
added = TH1D('a', 'a',bin,low,high)
topadded = TH1D('topadded', 'topadded', bin, low, high)
wjetsadded = TH1D('wjetsadded', 'wjetsadded',bin,low,high)
wjetsadded = Variables[massPoint+'_wjets'+chan].Clone()
wjetsadded.Add(Variables[massPoint+'_ww'+chan])
wjetsadded.Add(Variables[massPoint+'_zjets'+chan])
topadded = Variables[massPoint+'_ttbar'+chan].Clone()
topadded.Add(Variables[massPoint+'_s'+chan])
topadded.Add(Variables[massPoint+'_bs'+chan])
topadded.Add(Variables[massPoint+'_t'+chan])
topadded.Add(Variables[massPoint+'_bt'+chan])
topadded.Add(Variables[massPoint+'_tw'+chan])
topadded.Add(Variables[massPoint+'_btw'+chan])
wjetsadded.SetFillColor(ROOT.kGreen-3)
wjetsadded.SetLineColor(1)
wjetsadded.SetLineWidth(2)
topadded.SetFillColor(ROOT.kRed-7)
topadded.SetLineColor(1)
topadded.SetLineWidth(2)
stack.Add(wjetsadded)
stack.Add(topadded)
added.Add(wjetsadded)
added.Add(topadded)
print 'Data: ',Variables[massPoint+'_data'+chan].Integral(),' Background: ',added.Integral(),' Data/Background: ',Variables[massPoint+'_data'+chan].Integral()/added.Integral()
lumi_error = 0.022
ttbar_error = 0.15
wjets_error = 0.20
other_error = 0.20
uncert_list = []
lumiband = added.Clone();
for hbin in range(0,lumiband.GetNbinsX()+1):
uncert_lumi = 0
uncert_xsec = 0
uncert_stat = 0
for i in BkgList:
error = 0
if i in xsec.keys():
if (i.startswith(massPoint+'_t') or i.startswith(massPoint+'_b') or i.startswith(massPoint+'_s')):
error = ttbar_error
uncert_lumi += (efficiency[i]*xsec[i])**2
uncert_xsec += (Variables[i].GetBinContent(hbin+1)*error)**2
uncert_stat += Variables[i].GetBinError(hbin+1)**2
elif (i.startswith(massPoint+'_w') or i.startswith(massPoint+'_z')):
error = wjets_error
uncert_lumi += (efficiency[i]*xsec[i])**2
uncert_xsec += (Variables[i].GetBinContent(hbin+1)*error)**2
uncert_stat += Variables[i].GetBinError(hbin+1)**2
#print 'uncert_lumi: ',uncert_lumi,' uncert_xsec ',uncert_xsec,' uncert_stat ',uncert_stat
uncert = sqrt( (lumi_error**2)*uncert_lumi + uncert_xsec + uncert_stat )
if lumiband.GetBinContent(hbin+1) != 0:
dummy = 1.0
#print lumiband.GetBinContent(hbin+1),'+/-',uncert,'(',100*uncert/lumiband.GetBinContent(hbin+1),'%)'
lumiband.SetBinError(hbin+1,uncert);
added.SetBinError(hbin+1,uncert);
uncert_list . append(uncert)
#gStyle.SetHatchesSpacing(2.0);
gStyle.SetHatchesLineWidth(1);
lumiband.SetFillStyle(3344);
#lumiband.SetFillStyle(3001);
lumiband.SetFillColor(1);
legend = TLegend(.60,.70,.90,.90)
legend . AddEntry( Variables[massPoint+'_data'+chan], 'Data' , "lp")
legend . AddEntry( Variables[massPoint+'_ttbar'+chan], "t#bar{t} + Single-Top", "f")
legend . AddEntry( Variables[massPoint+'_wjets'+chan], "W#rightarrowl#nu + Z/#gamma*#rightarrowl^{+}l^{-} + VV" , "f")
if (massPoint.endswith('R')): coupling = 'R'
if (massPoint.endswith('L')): coupling = 'L'
if (massPoint.endswith('RL')): coupling = 'RL'
massval = copy.copy(massPoint)
massval = massval.lstrip('wp')
massval = massval.rstrip('00'+coupling)
if (len(massval)==1): massval = '0'+massval
massval = massval[0]+'.'+massval[1]
legend . AddEntry( Variables[massPoint+'_'+massPoint+chan], "W'_{"+coupling+"} x 20, m="+massval+" TeV", "l")
legend . AddEntry( lumiband , "Uncertainty" , "f")
c4 = TCanvas("c4","c4", 1000, 800)
c4.SetBottomMargin(0.3)
c4.SetRightMargin(0.06)
stack.SetMaximum( 2*stack.GetMaximum() )
if setLog:
c4.SetLogy()
stack.SetMaximum( stack.GetMaximum() + 30*stack.GetMaximum() )
stack.SetMinimum(0.1 )
#stack.SetMarkerSize(0)
stack.Draw("")
stack.GetYaxis().CenterTitle()
stack.GetYaxis().SetTitle(ylabel)
stack.GetXaxis().SetLabelSize(0)
#stack.GetXaxis().SetTitle(xlabel)
lumiband.Draw("samee2")
#lumiband.Draw("esame")
legend.SetShadowColor(0);
legend.SetFillColor(0);
legend.SetLineColor(0);
legend.Draw("same")
if (Variables[massPoint+'_data'+chan].GetBinContent(bin+1)>0):
print "Overflow for ",massPoint+"_"+channel," data "
Variables[massPoint+'_data'+chan].SetMarkerStyle(20)
Variables[massPoint+'_data'+chan].Draw('SAMES:E1')
Variables[massPoint+'_'+massPoint+chan].Draw("same")
latex2 = TLatex()
latex2.SetNDC()
latex2.SetTextSize(0.04)
latex2.SetTextAlign(31) # align right
if channel == ('electron'):
latex2.DrawLatex(0.87, 0.95, "CMS Preliminary, "+lumiPlot_el+" fb^{-1} at #sqrt{s} = 8 TeV");
if channel == ('muon'):
latex2.DrawLatex(0.87, 0.95, "CMS Preliminary, "+lumiPlot_mu+" fb^{-1} at #sqrt{s} = 8 TeV");
latex3 = TLatex()
latex3.SetNDC()
latex3.SetTextSize(0.04)
latex3.SetTextAlign(31) # align right
if (channel == 'electron'):
latex3.DrawLatex(0.47, 0.85, "e+jets " + njets);
if (channel == 'muon'):
latex3.DrawLatex(0.47, 0.85, "#mu+jets " + njets);
latex4 = TLatex()
latex4.SetNDC()
latex4.SetTextSize(0.03)
latex4.SetTextAlign(22) # align right
latex4.SetTextAngle(90) # align right
#latex4.DrawLatex(0.905, 0.56, "overflow")
Pull = TH1D('Pull', 'Pull', bin, low, high)
Pull = Variables[massPoint+'_data'+chan].Clone();
Pull.Add(added,-1)
Pull.Divide(added)
for i in range(bin):
i += 1
#print i+1,' ',added.GetBinContent(i+1)
if (Pull.GetBinContent(i+1) != 0 and Pull.GetBinError(i+1) != 0):
Pull.SetBinContent(i+1,Pull.GetBinContent(i+1)/Pull.GetBinError(i+1))
else: Pull.SetBinContent(i+1,0)
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)
Pull.SetMarkerStyle(20)
Pull.SetMaximum(3.0 )
Pull.SetMinimum(-3.0)
Pull.SetFillColor(2)
Pull.GetXaxis().SetTitle(xlabel)
Pull.GetYaxis().SetTitleSize(0.04)
Pull.GetYaxis().SetTitle('#sigma(Data-MC)')
Pull.SetMarkerSize(0.7)
Pull.GetYaxis().SetNdivisions(5);
Pull.Draw("HIST")
c4.SaveAs('Wprime_Plots/StackedHisto_' + save + '.pdf')
del c4
#del pad
del stack
del Variables
def DrawOverlap(fileVec, histVec, titleVec,legendtext,pngname,logstatus=[0,0],xRange=[-99999,99999,1]):
gStyle.SetOptTitle(0)
gStyle.SetOptStat(0)
gStyle.SetTitleOffset(1.1,"Y");
gStyle.SetTitleOffset(0.9,"X");
gStyle.SetLineWidth(3)
gStyle.SetFrameLineWidth(3);
i=0
histList_=[]
histList=[]
histList1=[]
maximum=[]
## Legend
leg = TLegend(0.4, 0.70, 0.939, 0.89)#,NULL,"brNDC");
leg.SetBorderSize(0)
leg.SetNColumns(2)
leg.SetLineColor(1)
leg.SetLineStyle(1)
leg.SetLineWidth(1)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextFont(22)
leg.SetTextSize(0.045)
c = TCanvas("c1", "c1",0,0,500,500)
#c.SetBottomMargin(0.15)
#c.SetLeftMargin(0.15)
#c.SetLogy(0)
#c.SetLogx(0)
c1_2 = TPad("c1_2","newpad",0.04,0.05,1,0.994)
c1_2.Draw()
print ("you have provided "+str(len(fileVec))+" files and "+str(len(histVec))+" histograms to make a overlapping plot" )
print "opening rootfiles"
c.cd()
#c1_2.SetBottomMargin(0.13)
c1_2.SetLogy(logstatus[1])
c1_2.SetLogx(logstatus[0])
c1_2.cd()
ii=0
inputfile={}
print str(fileVec[(len(fileVec)-1)])
for ifile_ in range(len(fileVec)):
print ("opening file "+fileVec[ifile_])
inputfile[ifile_] = TFile( fileVec[ifile_] )
print "fetching histograms"
for ihisto_ in range(len(histVec)):
print ("printing histo "+str(histVec[ihisto_]))
histo = inputfile[ifile_].Get(histVec[ihisto_])
#status_ = type(histo) is TGraphAsymmErrors
histList.append(histo)
# for ratio plot as they should nt be normalize
histList1.append(histo)
print histList[ii].Integral()
#histList[ii].Rebin(xRange[2])
type_obj = type(histList[ii])
if (type_obj is TH1D) or (type_obj is TH1F) or (type_obj is TH1) or (type_obj is TH1I) :
histList[ii].Rebin(1)
histList[ii].Scale(1.0/histList[ii].Integral())
maximum.append(histList[ii].GetMaximum())
maximum.sort()
ii=ii+1
print histList
for ih in range(len(histList)):
tt = type(histList[ih])
# print "graph_status =" ,(tt is TGraphAsymmErrors)
# print "hist status =", (tt is TH1D) or (tt is TH1F)
if ih == 0 :
if (tt is TGraphAsymmErrors) | (tt is TGraph) :
histList[ih].Draw("A3P")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I) :
histList[ih].Draw("HIST")
if ih > 0 :
#histList[ih].SetLineWidth(2)
if (tt is TGraphAsymmErrors) | (tt is TGraph) :
histList[ih].Draw("3P same")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I) :
histList[ih].Draw("HISTsame")
if (tt is TGraphAsymmErrors) | (tt is TGraph) :
# histList[ih].SetMaximum(0.06)
# histList[ih].SetMinimum(0.02)
histList[ih].SetMaximum(maximum+0.3)
histList[ih].SetMinimum(0)
histList[ih].SetMarkerColor(colors[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetLineWidth(3)
histList[ih].SetLineStyle(linestyle[ih])
histList[ih].SetMarkerStyle(markerStyle[ih])
histList[ih].SetMarkerSize(1)
leg.AddEntry(histList[ih],legendtext[ih],"P")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I) :
histList[ih].SetLineStyle(linestyle[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetLineWidth(3)
histList[ih].SetMaximum(maximum[0]+0.3)
histList[ih].SetMinimum(0)
leg.AddEntry(histList[ih],legendtext[ih],"L")
histList[ih].GetYaxis().SetTitle(titleVec[1])
histList[ih].GetYaxis().SetTitleSize(0.045)
histList[ih].GetYaxis().SetTitleOffset(1.1000998)
histList[ih].GetYaxis().SetTitleFont(22)
histList[ih].GetYaxis().SetLabelFont(22)
histList[ih].GetYaxis().SetLabelSize(.045)
histList[ih].GetXaxis().SetRangeUser(xRange[0],xRange[1])
histList[ih].GetXaxis().SetLabelSize(0.0000);
histList[ih].GetXaxis().SetTitle(titleVec[0])
histList[ih].GetXaxis().SetLabelSize(0.052)
histList[ih].GetXaxis().SetTitleSize(0.052)
histList[ih].GetXaxis().SetTitleOffset(1.04)
histList[ih].GetXaxis().SetTitleFont(22)
histList[ih].GetXaxis().SetTickLength(0.07)
histList[ih].GetXaxis().SetLabelFont(22)
histList[ih].GetYaxis().SetLabelFont(22)
# histList[ih].GetXaxis().SetNdivisions(508)
#
i=i+1
pt = TPaveText(0.0877181,0.9,0.9580537,0.96,"brNDC")
pt.SetBorderSize(0)
pt.SetTextAlign(12)
pt.SetFillStyle(0)
pt.SetTextFont(22)
pt.SetTextSize(0.046)
text = pt.AddText(0.05,0.5,"CMS Preliminary")
#text = pt.AddText(0.5,0.5,"12.9 fb^{-1} (13 TeV)")
# text = pt.AddText(0.8,0.5," (13 TeV)")
# text = pt.AddText(0.65,0.5," AK8")
pt.Draw()
# t2a = TPaveText(0.0877181,0.81,0.9580537,0.89,"brNDC")
# t2a.SetBorderSize(0)
# t2a.SetFillStyle(0)
# t2a.SetTextSize(0.040)
# t2a.SetTextAlign(12)
# t2a.SetTextFont(62)
# histolabel1= str(fileVec[(len(fileVec)-1)])
# text1 = t2a.AddText(0.06,0.5,"CMS Internal")
# t2a.Draw()
leg.Draw()
#
# c.cd()
outputdirname = 'HGCAL/'
histname=outputdirname+pngname
c.SaveAs(histname+'.png')
c.SaveAs(histname+'.pdf')
hist_itpulses.Draw("SAME")
hist_ittrigger.SetTitle("Triggers, IceTop and uncleaned InIce pulses")
hist_ittrigger.GetYaxis().SetTitle("For pulses: number of pulses")
hist_ittrigger.GetXaxis().SetTitle("time [ns]")
hist_ittrigger.GetYaxis().SetRangeUser(
0,
np.amax([hist_itpulses.GetMaximum(),
hist_iipulses.GetMaximum()]) + 10)
leg = TLegend(0.7, 0.7, 0.89, 0.89)
leg.AddEntry(hist_ittrigger, "IT SMT", "l")
leg.AddEntry(hist_iitrigger, "II SMT", "l")
leg.AddEntry(hist_itpulses, "IT pulses", "l")
leg.AddEntry(hist_iipulses, "II pulses", "l")
leg.SetFillColor(10)
leg.SetBorderSize(0)
leg.Draw("SAME")
canv.cd(2)
gStyle.SetOptStat(0)
hist_iitrigger.Draw("")
hist_ittrigger.Draw("SAME")
hist_itpulses.Draw("SAME")
hist_coincpulses.Draw("SAME")
hist_iitrigger.SetTitle("Triggers, IceTop and selected InIce pulses")
hist_iitrigger.GetYaxis().SetTitle("For pulses: number of pulses")
hist_iitrigger.GetXaxis().SetTitle("time [ns]")
hist_iitrigger.GetYaxis().SetRangeUser(
0,
h2.SetLineColor(2)
h2.SetFillColor(2)
h2.SetLineStyle(9)
h2.SetMarkerColor(2)
h2.SetBarWidth(0.4)
h2.SetBarOffset(0.55)
h2.Draw("bar2same")
#h2.Draw("TEXT45same");
# Draw the legend
infoBox = TLegend(0.65, 0.82, 0.95, 0.92, "")
if (tree["Entries"] > 0.):
infoBox.AddEntry(
h1, "New LA: %.5s kB/entry" %
str(float(tree["Size"]) / float(1024 * tree["Entries"])), "F")
infoBox.AddEntry(
h2, "original: %.5s kB/entry" %
str(float(tree2["Size"]) / float(1024 * tree2["Entries"])), "F")
infoBox.SetShadowColor(0)
# 0 = transparent
infoBox.SetFillColor(0)
infoBox.Draw("same")
#h1.Draw("BAR")
c1.Update()
c1.SaveAs(tree["Name"].replace('/', '_') + ".png")
c1.SaveAs(tree["Name"].replace('/', '_') + ".pdf")
#pprint(data)
def setPUHistogram(l):
# https://raw.githubusercontent.com/cms-sw/cmssw/CMSSW_7_4_X/SimGeneral/MixingModule/python/mix_2015_25ns_Startup_PoissonOOTPU_cfi.py
probValue = [
4.8551E-07, 1.74806E-06, 3.30868E-06, 1.62972E-05, 4.95667E-05,
0.000606966, 0.003307249, 0.010340741, 0.022852296, 0.041948781,
0.058609363, 0.067475755, 0.072817826, 0.075931405, 0.076782504,
0.076202319, 0.074502547, 0.072355135, 0.069642102, 0.064920999,
0.05725576, 0.047289348, 0.036528446, 0.026376131, 0.017806872,
0.011249422, 0.006643385, 0.003662904, 0.001899681, 0.00095614,
0.00050028, 0.000297353, 0.000208717, 0.000165856, 0.000139974,
0.000120481, 0.000103826, 8.88868E-05, 7.53323E-05, 6.30863E-05,
5.21356E-05, 4.24754E-05, 3.40876E-05, 2.69282E-05, 2.09267E-05,
1.5989E-05, 4.8551E-06, 2.42755E-06, 4.8551E-07, 2.42755E-07,
1.21378E-07, 4.8551E-08
]
mc = TH1F("mc", "nPV distribution", 50, 0, 50)
mc.Sumw2()
for i in range(50):
mc.SetBinContent(i + 1, probValue[i])
mc.SetLineWidth(3)
mc.SetLineColor(1)
mc.SetLineStyle(2)
mc.Scale(1. / mc.Integral())
puFile = TFile("./PU/PU_%s_69000.root" % l, "READ")
data = puFile.Get("pileup")
data.SetLineWidth(3)
data.SetLineColor(1)
data.Scale(1. / data.Integral())
puUpFile = TFile("./PU/PU_%s_72450.root" % l, "READ")
dataUp = puUpFile.Get("pileup")
dataUp.SetLineWidth(3)
dataUp.SetLineColor(634)
dataUp.Scale(1. / dataUp.Integral())
puDownFile = TFile("./PU/PU_%s_65550.root" % l, "READ")
dataDown = puDownFile.Get("pileup")
dataDown.SetLineWidth(3)
dataDown.SetLineColor(598)
dataDown.Scale(1. / dataDown.Integral())
ratio = data.Clone("ratio")
ratioUp = dataUp.Clone("ratioUp")
ratioDown = dataDown.Clone("ratioDown")
ratio.Divide(mc)
ratioUp.Divide(mc)
ratioDown.Divide(mc)
outFile = TFile("./PU/PU_%s.root" % l, "RECREATE")
outFile.cd()
mc.Write()
data.Write()
dataUp.Write()
dataDown.Write()
ratio.Write()
ratioUp.Write()
ratioDown.Write()
outFile.Close()
print "Histograms written to ./PU/PU_%s.root file" % l
leg = TLegend(0.65, 0.7, 0.98, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("pile-up reweighting")
leg.AddEntry(dataUp, "Up", "pl")
leg.AddEntry(data, "Central", "pl")
leg.AddEntry(dataDown, "Down", "pl")
leg.AddEntry(mc, "MC 25ns", "pl")
c1 = TCanvas("c1", "PileUp reweighting", 800, 800)
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetTicks(1, 1)
dataDown.SetTitle(";number of true interactions")
dataDown.GetXaxis().SetRangeUser(0., 30)
dataDown.Draw("HIST")
dataUp.Draw("SAME, HIST")
data.Draw("SAME, HIST")
mc.Draw("SAME, L")
leg.Draw()
c1.Print("PU/PU_%s.pdf" % l)
c1.Print("PU/PU_%s.png" % l)
class DataMCPlot(object):
'''Handles a Data vs MC plot.
Features a list of histograms (some of them being stacked),
and several Drawing functions.
'''
_f_keeper = {}
_t_keeper = {}
def __init__(self, name):
self.histosDict = {}
self.histos = []
self.supportHist = None
self.name = name
self.stack = None
self.legendOn = True
self.legend = None
self.legendBorders = 0.20, 0.46, 0.44, 0.89
self.legendPos = 'left'
# self.lastDraw = None
# self.lastDrawArgs = None
self.nostack = None
self.blindminx = None
self.blindmaxx = None
self.groups = {}
self.axisWasSet = False
self.histPref = histPref
def __contains__(self, name):
return name in self.histosDict
def __getitem__(self, name):
return self.histosDict[name]
def readTree(self, file_name, tree_name='tree', verbose=False):
'''Cache files/trees'''
if file_name in self.__class__._t_keeper:
ttree = self.__class__._t_keeper[file_name]
if verbose:
print 'got cached tree', ttree
else:
tfile = self.__class__._f_keeper[file_name] = TFile.Open(file_name)
ttree = self.__class__._t_keeper[file_name] = tfile.Get(tree_name)
if verbose:
print 'read tree', ttree, 'from file', file_name
gROOT.cd()
return ttree
def Blind(self, minx, maxx, blindStack):
self.blindminx = minx
self.blindmaxx = maxx
if self.stack and blindStack:
self.stack.Blind(minx, maxx)
if self.nostack:
for hist in self.nostack:
hist.Blind(minx, maxx)
def AddHistogram(self, name, histo, layer=0, legendLine=None, stack=True):
'''Add a ROOT histogram, with a given name.
Histograms will be drawn by increasing layer.'''
tmp = Histogram(name, histo, layer, legendLine, stack=stack)
self.histos.append(tmp)
self.histosDict[name] = tmp
return tmp
def Group(self, groupName, namesToGroup, layer=None, style=None):
'''Group all histos with names in namesToGroup into a single
histo with name groupName. All histogram properties are taken
from the first histogram in namesToGroup.
See UnGroup as well
'''
groupHist = None
realNames = []
actualNamesInGroup = []
for name in namesToGroup:
hist = self.histosDict.get(name, None)
if hist is None:
print 'warning, no histo with name', name
continue
if groupHist is None:
groupHist = hist.Clone(groupName)
self.histos.append(groupHist)
self.histosDict[groupName] = groupHist
else:
groupHist.Add(hist)
actualNamesInGroup.append(name)
realNames.append(hist.realName)
hist.on = False
if groupHist:
self.groups[groupName] = actualNamesInGroup
groupHist.realName = ','.join(realNames)
if style is not None:
groupHist.SetStyle(style)
self._ApplyPrefs()
def UnGroup(self, groupName):
'''Ungroup groupName, recover the histograms in the group'''
group = self.groups.get(groupName, None)
if group is None:
print groupName, 'is not a group in this plot.'
return
for name in group:
self.histosDict[name].on = True
self.histosDict[groupName].on = False
def Replace(self, name, pyhist):
'''Not very elegant... should have a clone function in Histogram...'''
oldh = self.histosDict.get(name, None)
if oldh is None:
print 'histogram', name, 'does not exist, cannot replace it.'
return
pythist = copy.deepcopy(pyhist)
pythist.layer = oldh.layer
pythist.stack = oldh.stack
pythist.name = oldh.name
pythist.legendLine = oldh.legendLine
pythist.SetStyle(oldh.style)
pythist.weighted.SetFillStyle(oldh.weighted.GetFillStyle())
index = self.histos.index(oldh)
self.histosDict[name] = pythist
self.histos[index] = pythist
def _SortedHistograms(self, reverse=False):
'''Returns the histogram dictionary, sorted by increasing layer,
excluding histograms which are not "on".
This function is used in all the Draw functions.'''
byLayer = sorted(self.histos, key=attrgetter('layer'))
byLayerOn = [hist for hist in byLayer if (hist.on is True)]
if reverse:
byLayerOn.reverse()
return byLayerOn
def Hist(self, histName):
'''Returns a histogram.
Print the DataMCPlot object to see which histograms are available.'''
return self.histosDict[histName]
def DrawNormalized(self, opt=''):
'''All histograms are drawn as PDFs, even the stacked ones'''
same = ''
for hist in self._SortedHistograms():
hist.obj.DrawNormalized(same + opt)
if same == '':
same = 'same'
self.DrawLegend()
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawNormalized'
# self.lastDrawArgs = [ opt ]
def Draw(self, opt=''):
'''All histograms are drawn.'''
same = ''
self.supportHist = None
for hist in self._SortedHistograms():
if self.supportHist is None:
self.supportHist = hist
hist.Draw(same + opt)
if same == '':
same = 'same'
yaxis = self.supportHist.GetYaxis()
yaxis.SetRangeUser(0.01, ymax(self._SortedHistograms()))
self.DrawLegend()
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'Draw'
# self.lastDrawArgs = [ opt ]
def CreateLegend(self, ratio=False, print_norm=False):
if self.legend is None:
self.legend = TLegend(*self.legendBorders)
self.legend.SetFillColor(0)
self.legend.SetFillStyle(0)
self.legend.SetLineColor(0)
else:
self.legend.Clear()
hists = self._SortedHistograms(reverse=True)
if ratio:
hists = hists[:-1] # removing the last histo.
for index, hist in enumerate(hists):
if print_norm:
if not hist.legendLine:
hist.legendLine = hist.name
hist.legendLine += ' ({norm:.1f})'.format(norm=hist.Yield())
hist.AddEntry(self.legend)
def DrawLegend(self, ratio=False, print_norm=False):
'''Draw the legend.'''
if self.legendOn:
self.CreateLegend(ratio=ratio, print_norm=print_norm)
self.legend.Draw('same')
def DrawRatio(self, opt=''):
'''Draw ratios : h_i / h_0.
h_0 is the histogram with the smallest layer, and h_i, i>0 are the other histograms.
if the DataMCPlot object contains N histograms, N-1 ratio plots will be drawn.
To take another histogram as the denominator, change the layer of this histogram by doing:
dataMCPlot.Hist("histName").layer = -99 '''
same = ''
denom = None
self.ratios = []
for hist in self._SortedHistograms():
if denom == None:
denom = hist
continue
ratio = copy.deepcopy(hist)
ratio.obj.Divide(denom.obj)
ratio.obj.Draw(same)
self.ratios.append(ratio)
if same == '':
same = 'same'
self.DrawLegend(ratio=True)
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawRatio'
# self.lastDrawArgs = [ opt ]
def DrawDataOverMCMinus1(self, ymin=-0.5, ymax=0.5):
stackedHists = []
dataHist = None
for hist in self._SortedHistograms():
if hist.stack is False:
dataHist = hist
continue
stackedHists.append(hist)
self._BuildStack(stackedHists, ytitle='Data/MC')
mcHist = self.stack.totalHist
self.dataOverMCHist = copy.deepcopy(dataHist)
# self.dataOverMCHist.Add(mcHist, -1)
self.dataOverMCHist.Divide(mcHist)
self.dataOverMCHist.Draw()
yaxis = self.dataOverMCHist.GetYaxis()
yaxis.SetRangeUser(ymin + 1., ymax + 1.)
yaxis.SetTitle('Data/MC')
yaxis.SetNdivisions(5)
fraclines = 0.2
if ymax <= 0.2 or ymin >= -0.2:
fraclines = 0.1
self.DrawRatioLines(self.dataOverMCHist, fraclines, 1.)
if TPad.Pad():
TPad.Pad().Update()
def DrawRatioStack(self, opt='',
xmin=None, xmax=None, ymin=None, ymax=None):
'''Draw ratios.
The stack is considered as a single histogram.'''
denom = None
# import pdb; pdb.set_trace()
histForRatios = []
denom = None
for hist in self._SortedHistograms():
if hist.stack is False:
# if several unstacked histograms, the highest layer is used
denom = hist
continue
histForRatios.append(hist)
self._BuildStack(histForRatios, ytitle='MC/Data')
self.stack.Divide(denom.obj)
if self.blindminx and self.blindmaxx:
self.stack.Blind(self.blindminx, self.blindmaxx)
self.stack.Draw(opt,
xmin=xmin, xmax=xmax,
ymin=ymin, ymax=ymax)
self.ratios = []
for hist in self.nostack:
if hist is denom:
continue
ratio = copy.deepcopy(hist)
ratio.obj.Divide(denom.obj)
ratio.obj.Draw('same')
self.ratios.append(ratio)
self.DrawLegend(ratio=True)
self.DrawRatioLines(denom, 0.2, 1)
if TPad.Pad():
TPad.Pad().Update()
def DrawNormalizedRatioStack(self, opt='',
xmin=None, xmax=None,
ymin=None, ymax=None):
'''Draw ratios.
The stack is considered as a single histogram.
All histograms are normalized before computing the ratio'''
denom = None
histForRatios = []
for hist in self._SortedHistograms():
# taking the first histogram (lowest layer)
# as the denominator histogram.
if denom == None:
denom = copy.deepcopy(hist)
continue
# other histograms will be divided by the denominator
histForRatios.append(hist)
self._BuildStack(histForRatios, ytitle='MC p.d.f. / Data p.d.f.')
self.stack.Normalize()
denom.Normalize()
self.stack.Divide(denom.weighted)
self.stack.Draw(opt,
xmin=xmin, xmax=xmax,
ymin=ymin, ymax=ymax)
self.ratios = []
for hist in self.nostack:
# print 'nostack ', hist
ratio = copy.deepcopy(hist)
ratio.Normalize()
ratio.obj.Divide(denom.weighted)
ratio.obj.Draw('same')
self.ratios.append(ratio)
self.DrawLegend(ratio=True)
self.DrawRatioLines(denom, 0.2, 1)
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawNormalizedRatioStack'
# self.lastDrawArgs = [ opt ]
def DrawRatioLines(self, hist, frac=0.2, y0=1.):
'''Draw a line at y = 1, at 1+frac, and at 1-frac.
hist is used to get the x axis range.'''
xmin = hist.obj.GetXaxis().GetXmin()
xmax = hist.obj.GetXaxis().GetXmax()
line = TLine()
line.DrawLine(xmin, y0, xmax, y0)
line.SetLineStyle(2)
line.DrawLine(xmin, y0+frac, xmax, y0+frac)
line.DrawLine(xmin, y0-frac, xmax, y0-frac)
def GetStack(self):
'''Returns stack; builds stack if not there yet'''
if not self.stack:
self._BuildStack(self._SortedHistograms(), ytitle='Events')
return self.stack
def DrawStack(self, opt='',
xmin=None, xmax=None, ymin=None, ymax=None, print_norm=False,
scale_signal=''):
'''Draw all histograms, some of them in a stack.
if Histogram.stack is True, the histogram is put in the stack.
scale_signal: mc_int -> scale to stack integral'''
self._BuildStack(self._SortedHistograms(), ytitle='Events')
same = 'same'
if len(self.nostack) == 0:
same = ''
self.supportHist = None
for hist in self.nostack:
if hist.style.drawAsData:
hist.Draw('SAME' if self.supportHist else '')
else:
if scale_signal == 'mc_int':
hist.Scale(hist.Yield(weighted=True)/self.stack.integral)
hist.Draw('SAME HIST' if self.supportHist else 'HIST')
if not self.supportHist:
self.supportHist = hist
self.stack.Draw(opt+same,
xmin=xmin, xmax=xmax,
ymin=ymin, ymax=ymax)
if self.supportHist is None:
self.supportHist = self.stack.totalHist
if not self.axisWasSet:
mxsup = self.supportHist.weighted.GetBinContent(
self.supportHist.weighted.GetMaximumBin()
)
mxstack = self.stack.totalHist.weighted.GetBinContent(
self.stack.totalHist.weighted.GetMaximumBin()
)
mx = max(mxsup, mxstack)
if ymin is None:
ymin = 0.01
if ymax is None:
ymax = mx*1.3
self.supportHist.GetYaxis().SetRangeUser(ymin, ymax)
self.axisWasSet = True
for hist in self.nostack:
if self.blindminx and hist.style.drawAsData:
hist.Blind(self.blindminx, self.blindmaxx)
if hist.style.drawAsData:
hist.Draw('SAME')
else:
hist.Draw('SAME HIST')
if self.supportHist.weighted.GetMaximumBin() < self.supportHist.weighted.GetNbinsX()/2:
self.legendBorders = 0.62, 0.46, 0.88, 0.89
self.legendPos = 'right'
self.DrawLegend(print_norm=print_norm)
if TPad.Pad():
TPad.Pad().Update()
def DrawNormalizedStack(self, opt='',
xmin=None, xmax=None, ymin=0.001, ymax=None):
'''Draw all histograms, some of them in a stack.
if Histogram.stack is True, the histogram is put in the stack.
all histograms out of the stack, and the stack itself, are shown as PDFs.'''
self._BuildStack(self._SortedHistograms(), ytitle='p.d.f.')
self.stack.DrawNormalized(opt,
xmin=xmin, xmax=xmax,
ymin=ymin, ymax=ymax)
for hist in self.nostack:
hist.obj.DrawNormalized('same')
self.DrawLegend()
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawNormalizedStack'
# self.lastDrawArgs = [ opt ]
def Rebin(self, factor):
'''Rebin, and redraw.'''
# the dispatching technique is not too pretty,
# but keeping a self.lastDraw function initialized to one of the Draw functions
# when calling it creates a problem in deepcopy.
for hist in self.histos:
hist.Rebin(factor)
self.axisWasSet = False
def NormalizeToBinWidth(self):
'''Normalize each Histograms bin to the bin width.'''
for hist in self.histos:
hist.NormalizeToBinWidth()
def WriteDataCard(self, filename=None, verbose=True,
mode='RECREATE', dir=None):
'''Export current plot to datacard'''
if not filename:
filename = self.name+'.root'
outf = TFile(filename, mode)
if dir and outf.Get(dir):
print 'Directory', dir, 'already present in output file, recreate'
outf = TFile(filename, 'RECREATE')
if dir:
outf_dir = outf.mkdir(dir)
outf_dir.cd()
for hist in self._SortedHistograms():
'Writing', hist, 'as', hist.name
hist.weighted.Write(hist.name)
outf.Write()
def _BuildStack(self, hists, ytitle=None):
'''build a stack from a list of Histograms.
The histograms for which Histogram.stack is False are put in self.nostack'''
self.stack = None
self.stack = Stack(self.name+'_stack', ytitle=ytitle)
self.nostack = []
for hist in hists:
if hist.stack:
self.stack.Add(hist)
else:
self.nostack.append(hist)
def _GetHistPref(self, name):
'''Return the preference dictionary for a given component'''
thePref = None
for prefpat, pref in self.histPref.iteritems():
if fnmatch.fnmatch(name, prefpat):
if thePref is not None:
print 'several matching preferences for', name
thePref = pref
if thePref is None:
print 'cannot find preference for hist', name
thePref = {'style': Style(), 'layer': 999}
return thePref
def _ApplyPrefs(self):
for hist in self.histos:
pref = self._GetHistPref(hist.name)
hist.layer = pref['layer']
hist.SetStyle(pref['style'])
hist.legendLine = pref['legend']
def __str__(self):
if self.stack is None:
self._BuildStack(self._SortedHistograms(), ytitle='Events')
tmp = [' '.join(['DataMCPlot: ', self.name])]
tmp.append('Histograms:')
for hist in self._SortedHistograms(reverse=True):
tmp.append(' '.join(['\t', str(hist)]))
tmp.append('Stack yield = {integ:7.1f}'.format(integ=self.stack.integral))
return '\n'.join(tmp)
#h1.Sumw2()
#h1.Scale(1/h1.Integral())
#h2.Sumw2()
#h2.Scale(1/h2.Integral())
h3.Sumw2()
h3.Scale(1/h3.Integral())
a = TH1F ("a","a",10,0,100)
a.Fill(1)
c = TCanvas("c1", "c1",0,0,500,500)
gStyle.SetOptStat(0)
leg = TLegend(0.1,0.7,0.3,0.9)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextSize(0.04)
leg.SetBorderSize(0)
leg.SetTextFont(22)
leg.Draw()
h1.SetLineColor(1)
h1.SetLineWidth(2)
h1.SetLineStyle(1)
h2.SetLineColor(2)
h2.SetLineWidth(2)
h2.SetLineStyle(1)
h3.SetLineColor(3)
def compareMassRes(trackType):
file2016BB = open("default/MassResolutionVsMass_%s_BB.pkl" % trackType,
"rb")
file2016BE = open("default/MassResolutionVsMass_%s_BE.pkl" % trackType,
"rb")
file2017BB = open("cruijff/MassResolutionVsMass_%s_BB.pkl" % trackType,
"rb")
file2017BE = open("cruijff/MassResolutionVsMass_%s_BE.pkl" % trackType,
"rb")
fileCBB = open("crystal/MassResolutionVsMass_%s_BB.pkl" % trackType, "rb")
fileCBE = open("crystal/MassResolutionVsMass_%s_BE.pkl" % trackType, "rb")
results2016BB = pickle.load(file2016BB)
results2016BE = pickle.load(file2016BE)
results2017BB = pickle.load(file2017BB)
results2017BE = pickle.load(file2017BE)
resultsCBB = pickle.load(fileCBB)
resultsCBE = pickle.load(fileCBE)
graph2016BB = getGraph(results2016BB, "DCBBB")
graph2016BE = getGraph(results2016BE, "DCBBE")
graph2017BB = getGraph(results2017BB, "CruijffBB")
graph2017BE = getGraph(results2017BE, "CruijffBE")
graphCBB = getGraph(resultsCBB, "CBB")
graphCBE = getGraph(resultsCBE, "CBE")
ratioBB = getRatio(results2016BB, results2017BB, "ratioBB")
ratioBE = getRatio(results2016BE, results2017BE, "ratioBE")
ratioCBB = getRatio(results2016BB, resultsCBB, "ratioCBB")
ratioCBE = getRatio(results2016BE, resultsCBE, "ratioCBE")
canv = TCanvas("c1", "c1", 800, 1200)
plotPad = TPad("plotPad", "plotPad", 0, 0.3, 1, 1)
ratioPad = TPad("ratioPad", "ratioPad", 0, 0., 1, 0.3)
style = setTDRStyle()
gStyle.SetOptStat(0)
plotPad.UseCurrentStyle()
ratioPad.UseCurrentStyle()
plotPad.Draw()
ratioPad.Draw()
plotPad.cd()
plotPad.cd()
plotPad.SetGrid()
gStyle.SetTitleXOffset(1.45)
gStyle.SetTitleYOffset(1.55)
xMax = 0.08
if trackType == "Inner":
xMax = 0.2
if trackType == "Outer":
xMax = 0.4
plotPad.DrawFrame(0, 0, 6000, xMax, ";M [GeV]; mass resolution")
graph2016BB.Draw("samepe")
graph2017BB.Draw("samepe")
graphCBB.Draw("samepe")
graph2017BB.SetLineColor(kRed)
graph2017BB.SetMarkerColor(kRed)
graphCBB.SetLineColor(kBlue)
graphCBB.SetMarkerColor(kBlue)
latex = TLatex()
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(0.04)
latex.SetNDC(True)
latexCMS = TLatex()
latexCMS.SetTextFont(61)
latexCMS.SetTextSize(0.055)
latexCMS.SetNDC(True)
latexCMSExtra = TLatex()
latexCMSExtra.SetTextFont(52)
latexCMSExtra.SetTextSize(0.03)
latexCMSExtra.SetNDC(True)
latex.DrawLatex(0.95, 0.96, "(13 TeV)")
cmsExtra = "#splitline{Preliminary}{}"
latexCMS.DrawLatex(0.19, 0.88, "CMS")
if "Simulation" in cmsExtra:
yLabelPos = 0.81
else:
yLabelPos = 0.84
latexCMSExtra.DrawLatex(0.19, yLabelPos, "%s" % (cmsExtra))
leg = TLegend(0.52, 0.76, 0.95, 0.91, "%s BB" % trackType, "brNDC")
leg.SetFillColor(10)
leg.SetFillStyle(0)
leg.SetLineColor(10)
leg.SetShadowColor(0)
leg.SetBorderSize(1)
leg.AddEntry(graph2016BB, "Cruijff", "l")
leg.AddEntry(graph2017BB, "Double CB", "l")
leg.AddEntry(graphCBB, "Crystal Ball", "l")
leg.Draw()
plotPad.RedrawAxis()
ratioPad.cd()
ratioBB.SetLineColor(kRed)
ratioCBB.SetLineColor(kBlue)
ratioPad.DrawFrame(0, 0.5, 6000, 1.5, ";ratio")
ratioBB.Draw("samepe")
ratioCBB.Draw("samepe")
canv.Print("massResolutionCompareFunc_%s_BB.pdf" % trackType)
canv = TCanvas("c1", "c1", 800, 1200)
plotPad = TPad("plotPad", "plotPad", 0, 0.3, 1, 1)
ratioPad = TPad("ratioPad", "ratioPad", 0, 0., 1, 0.3)
style = setTDRStyle()
gStyle.SetOptStat(0)
plotPad.UseCurrentStyle()
ratioPad.UseCurrentStyle()
plotPad.Draw()
ratioPad.Draw()
plotPad.cd()
plotPad.cd()
plotPad.SetGrid()
gStyle.SetTitleXOffset(1.45)
gStyle.SetTitleYOffset(1.55)
xMax = 0.08
if trackType == "Inner":
xMax = 0.2
if trackType == "Outer":
xMax = 0.4
plotPad.DrawFrame(0, 0, 6000, xMax, ";M [GeV]; mass resolution")
graph2016BE.Draw("samepe")
graph2017BE.Draw("samepe")
graphCBE.Draw("samepe")
graph2017BE.SetLineColor(kRed)
graph2017BE.SetMarkerColor(kRed)
graphCBE.SetLineColor(kBlue)
graphCBE.SetMarkerColor(kBlue)
latex = TLatex()
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(0.04)
latex.SetNDC(True)
latexCMS = TLatex()
latexCMS.SetTextFont(61)
latexCMS.SetTextSize(0.055)
latexCMS.SetNDC(True)
latexCMSExtra = TLatex()
latexCMSExtra.SetTextFont(52)
latexCMSExtra.SetTextSize(0.03)
latexCMSExtra.SetNDC(True)
latex.DrawLatex(0.95, 0.96, "(13 TeV)")
cmsExtra = "#splitline{Preliminary}{}"
latexCMS.DrawLatex(0.19, 0.88, "CMS")
if "Simulation" in cmsExtra:
yLabelPos = 0.81
else:
yLabelPos = 0.84
latexCMSExtra.DrawLatex(0.19, yLabelPos, "%s" % (cmsExtra))
leg = TLegend(0.52, 0.76, 0.95, 0.91, "%s BE" % trackType, "brNDC")
leg.SetFillColor(10)
leg.SetFillStyle(0)
leg.SetLineColor(10)
leg.SetShadowColor(0)
leg.SetBorderSize(1)
leg.AddEntry(graph2016BE, "Cruijff", "l")
leg.AddEntry(graph2017BE, "Double CB", "l")
leg.AddEntry(graphCBE, "Crystal Ball", "l")
leg.Draw()
plotPad.RedrawAxis()
ratioPad.cd()
ratioBE.SetLineColor(kRed)
ratioCBE.SetLineColor(kBlue)
ratioPad.DrawFrame(0, 0.5, 6000, 1.5, ";;ratio")
ratioBE.Draw("samepe")
ratioCBE.Draw("samepe")
canv.Print("massResolutionCompareFunc_%s_BE.pdf" % trackType)
gr.GetYaxis().CenterTitle()
#gr.GetXaxis().SetTitleOffset(1)
#gr.GetYaxis().SetTitleOffset(1)
#================================
gr.GetYaxis().SetTitleSize(0.04)
gr.GetXaxis().SetTitleSize(0.04)
gr.GetXaxis().SetLabelSize(0.04)
gr.GetYaxis().SetLabelSize(0.04)
gr.GetXaxis().SetLabelFont(22)
gr.GetYaxis().SetLabelFont(22)
gr.GetXaxis().SetTitleColor(1)
gr.GetYaxis().SetTitleColor(1)
leg1=TLegend(0.5,0.7,0.7,0.9)
leg1.SetFillColor(0)
leg1.SetFillStyle(0)
leg1.SetTextSize(0.05)
leg1.SetBorderSize(0)
leg1.SetTextFont(22)
leg1.AddEntry("","GeIA group","")
leg1.AddEntry("","#propto e^{-#frac{E}{2k_{B}T}}","")
line_120K =TLine(1000/120-2,0,1000/120+2,Desity_of_impurities(120,math.pow(10,10))[0])
line_77K =TLine(1000/77-2,0,1000/77+2,Desity_of_impurities(77,math.pow(10,10))[0])
line_4K =TLine(1000/4-2,0,1000/4+2,Desity_of_impurities(4,math.pow(10,10))[0])
line_120K.SetLineWidth(2)
line_77K.SetLineWidth(2)
line_4K.SetLineWidth(2)
def dijet(category):
channel = 'bb'
stype = channel
isSB = True # relict from using Alberto's more complex script
isData = not ISMC
nTupleDir = NTUPLEDIR
samples = data if isData else back
pd = []
if options.test:
if ISMC and YEAR == '2016':
pd.append("MC_QCD_" + YEAR)
nTupleDir = NTUPLEDIR.replace("weighted/", "test_for_fit/")
else:
print "No test sample for real data was implemented. Select '-M' if you want to test on a small MC QCD sample."
sys.exit()
else:
for sample_name in samples:
if YEAR == 'run2':
pd += sample[sample_name]['files']
else:
pd += [x for x in sample[sample_name]['files'] if YEAR in x]
print "datasets:", pd
if not os.path.exists(PLOTDIR): os.makedirs(PLOTDIR)
if BIAS: print "Running in BIAS mode"
order = 0
RSS = {}
X_mass = RooRealVar("jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV")
j1_pt = RooRealVar("jpt_1", "jet1 pt", 0., 13000., "GeV")
jbtag_WP_1 = RooRealVar("jbtag_WP_1", "", -1., 4.)
jbtag_WP_2 = RooRealVar("jbtag_WP_2", "", -1., 4.)
fatjetmass_1 = RooRealVar("fatjetmass_1", "", -1., 2500.)
fatjetmass_2 = RooRealVar("fatjetmass_2", "", -1., 2500.)
jnmuons_1 = RooRealVar("jnmuons_1", "j1 n_{#mu}", -1., 8.)
jnmuons_2 = RooRealVar("jnmuons_2", "j2 n_{#mu}", -1., 8.)
jmuonpt_1 = RooRealVar("jmuonpt_1", "j1 muon pt", 0., 13000.)
jmuonpt_2 = RooRealVar("jmuonpt_2", "j2 muon pt", 0., 13000.)
jid_1 = RooRealVar("jid_1", "j1 ID", -1., 8.)
jid_2 = RooRealVar("jid_2", "j2 ID", -1., 8.)
nmuons = RooRealVar("nmuons", "n_{#mu}", -1., 10.)
nelectrons = RooRealVar("nelectrons", "n_{e}", -1., 10.)
jj_deltaEta = RooRealVar("jj_deltaEta_widejet", "", 0., 5.)
HLT_AK8PFJet500 = RooRealVar("HLT_AK8PFJet500", "", -1., 1.)
HLT_PFJet500 = RooRealVar("HLT_PFJet500", "", -1., 1.)
HLT_CaloJet500_NoJetID = RooRealVar("HLT_CaloJet500_NoJetID", "", -1., 1.)
HLT_PFHT900 = RooRealVar("HLT_PFHT900", "", -1., 1.)
HLT_AK8PFJet550 = RooRealVar("HLT_AK8PFJet550", "", -1., 1.)
HLT_PFJet550 = RooRealVar("HLT_PFJet550", "", -1., 1.)
HLT_CaloJet550_NoJetID = RooRealVar("HLT_CaloJet550_NoJetID", "", -1., 1.)
HLT_PFHT1050 = RooRealVar("HLT_PFHT1050", "", -1., 1.)
HLT_DoublePFJets100_CaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets100_CaloBTagDeepCSV_p71", "", -1., 1.)
HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1.)
HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1.)
HLT_DoublePFJets200_CaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets200_CaloBTagDeepCSV_p71", "", -1., 1.)
HLT_DoublePFJets350_CaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets350_CaloBTagDeepCSV_p71", "", -1., 1.)
HLT_DoublePFJets40_CaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets40_CaloBTagDeepCSV_p71", "", -1., 1.)
weight = RooRealVar("eventWeightLumi", "", -1.e9, 1.e9)
variables = RooArgSet(X_mass)
variables.add(
RooArgSet(jbtag_WP_1, jbtag_WP_2, fatjetmass_1, fatjetmass_2,
jnmuons_1, jnmuons_2, nmuons, nelectrons, weight))
variables.add(
RooArgSet(j1_pt, jj_deltaEta, jid_1, jid_2, jmuonpt_1, jmuonpt_2))
variables.add(
RooArgSet(HLT_AK8PFJet500, HLT_PFJet500, HLT_CaloJet500_NoJetID,
HLT_PFHT900, HLT_AK8PFJet550, HLT_PFJet550,
HLT_CaloJet550_NoJetID, HLT_PFHT1050))
variables.add(
RooArgSet(HLT_DoublePFJets100_CaloBTagDeepCSV_p71,
HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71,
HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71,
HLT_DoublePFJets200_CaloBTagDeepCSV_p71,
HLT_DoublePFJets350_CaloBTagDeepCSV_p71,
HLT_DoublePFJets40_CaloBTagDeepCSV_p71))
if VARBINS:
binsXmass = RooBinning(len(abins) - 1, abins)
X_mass.setBinning(RooBinning(len(abins_narrow) - 1, abins_narrow))
#binsXmass = RooBinning(len(abins)-1, abins)
#X_mass.setBinning(binsXmass)
plot_binning = RooBinning(
int((X_mass.getMax() - X_mass.getMin()) / 100), X_mass.getMin(),
X_mass.getMax())
else:
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin()) / 10))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin()) / 100),
X_mass.getMin(), X_mass.getMax())
plot_binning = binsXmass
if BTAGGING == 'semimedium':
baseCut = aliasSM[category]
else:
baseCut = alias[category].format(WP=working_points[BTAGGING])
if ADDSELECTION: baseCut += SELECTIONS[options.selection]
print stype, "|", baseCut
print " - Reading from Tree"
treeBkg = TChain("tree")
if options.unskimmed or options.test:
for i, ss in enumerate(pd):
j = 0
while True:
if os.path.exists(nTupleDir + ss + "/" + ss +
"_flatTuple_{}.root".format(j)):
treeBkg.Add(nTupleDir + ss + "/" + ss +
"_flatTuple_{}.root".format(j))
j += 1
else:
print "found {} files for sample:".format(j), ss
break
else:
for ss in pd:
if os.path.exists(nTupleDir + ss + ".root"):
treeBkg.Add(nTupleDir + ss + ".root")
else:
print "found no file for sample:", ss
#setData = RooDataSet("setData", "Data" if isData else "Data (QCD MC)", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeBkg))
if isData or options.test:
setData = RooDataSet("setData", "Data", variables, RooFit.Cut(baseCut),
RooFit.Import(treeBkg))
else:
setData = RooDataSet("setData", "Data (QCD+TTbar MC)", variables,
RooFit.Cut(baseCut), RooFit.WeightVar(weight),
RooFit.Import(treeBkg))
nevents = setData.sumEntries()
dataMin, dataMax = array('d', [0.]), array('d', [0.])
setData.getRange(X_mass, dataMin, dataMax)
xmin, xmax = dataMin[0], dataMax[0]
lastBin = X_mass.getMax()
if VARBINS:
for b in narrow_bins: # switched to narrow bins here
if b > xmax:
lastBin = b
break
print "Imported", (
"data" if isData else "MC"
), "RooDataSet with", nevents, "events between [%.1f, %.1f]" % (xmin, xmax)
#xmax = xmax+binsXmass.averageBinWidth() # start form next bin
# 1 parameter
print "fitting 1 parameter model"
p1_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p1_1", "p1", 7.0, 0.,
2000.)
modelBkg1 = RooGenericPdf("Bkg1", "Bkg. fit (2 par.)",
"1./pow(@0/13000, @1)", RooArgList(X_mass, p1_1))
normzBkg1 = RooRealVar(
modelBkg1.GetName() + "_norm", "Number of background events", nevents,
0., 5. * nevents) #range dependent of actual number of events!
modelExt1 = RooExtendPdf(modelBkg1.GetName() + "_ext",
modelBkg1.GetTitle(), modelBkg1, normzBkg1)
fitRes1 = modelExt1.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes1.Print()
RSS[1] = drawFit("Bkg1", category, X_mass, modelBkg1, setData, binsXmass,
[fitRes1], normzBkg1.getVal())
# 2 parameters
print "fitting 2 parameter model"
p2_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p2_1", "p1", 0., -100.,
1000.)
p2_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p2_2", "p2",
p1_1.getVal(), -100., 600.)
modelBkg2 = RooGenericPdf("Bkg2", "Bkg. fit (3 par.)",
"pow(1-@0/13000, @1) / pow(@0/13000, @2)",
RooArgList(X_mass, p2_1, p2_2))
normzBkg2 = RooRealVar(modelBkg2.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt2 = RooExtendPdf(modelBkg2.GetName() + "_ext",
modelBkg2.GetTitle(), modelBkg2, normzBkg2)
fitRes2 = modelExt2.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes2.Print()
RSS[2] = drawFit("Bkg2", category, X_mass, modelBkg2, setData, binsXmass,
[fitRes2], normzBkg2.getVal())
# 3 parameters
print "fitting 3 parameter model"
p3_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_1", "p1",
p2_1.getVal(), -2000., 2000.)
p3_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_2", "p2",
p2_2.getVal(), -400., 2000.)
p3_3 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_3", "p3", -2.5,
-500., 500.)
modelBkg3 = RooGenericPdf(
"Bkg3", "Bkg. fit (4 par.)",
"pow(1-@0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000))",
RooArgList(X_mass, p3_1, p3_2, p3_3))
normzBkg3 = RooRealVar(modelBkg3.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt3 = RooExtendPdf(modelBkg3.GetName() + "_ext",
modelBkg3.GetTitle(), modelBkg3, normzBkg3)
fitRes3 = modelExt3.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes3.Print()
RSS[3] = drawFit("Bkg3", category, X_mass, modelBkg3, setData, binsXmass,
[fitRes3], normzBkg3.getVal())
# 4 parameters
print "fitting 4 parameter model"
p4_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_1", "p1",
p3_1.getVal(), -2000., 2000.)
p4_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_2", "p2",
p3_2.getVal(), -2000., 2000.)
p4_3 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_3", "p3",
p3_3.getVal(), -50., 50.)
p4_4 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_4", "p4", 0.1, -50.,
50.)
modelBkg4 = RooGenericPdf(
"Bkg4", "Bkg. fit (5 par.)",
"pow(1 - @0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000)+@4*pow(log(@0/13000), 2))",
RooArgList(X_mass, p4_1, p4_2, p4_3, p4_4))
normzBkg4 = RooRealVar(modelBkg4.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt4 = RooExtendPdf(modelBkg4.GetName() + "_ext",
modelBkg4.GetTitle(), modelBkg4, normzBkg4)
fitRes4 = modelExt4.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes4.Print()
RSS[4] = drawFit("Bkg4", category, X_mass, modelBkg4, setData, binsXmass,
[fitRes4], normzBkg4.getVal())
# Normalization parameters are should be set constant, but shape ones should not
# if BIAS:
# p1_1.setConstant(True)
# p2_1.setConstant(True)
# p2_2.setConstant(True)
# p3_1.setConstant(True)
# p3_2.setConstant(True)
# p3_3.setConstant(True)
# p4_1.setConstant(True)
# p4_2.setConstant(True)
# p4_3.setConstant(True)
# p4_4.setConstant(True)
normzBkg1.setConstant(True)
normzBkg2.setConstant(True)
normzBkg3.setConstant(True)
normzBkg4.setConstant(True)
#*******************************************************#
# #
# Fisher #
# #
#*******************************************************#
# Fisher test
with open(PLOTDIR + "/Fisher_" + category + ".tex", 'w') as fout:
fout.write(r"\begin{tabular}{c|c|c|c|c}")
fout.write("\n")
fout.write(r"function & $\chi^2$ & RSS & ndof & F-test \\")
fout.write("\n")
fout.write("\hline")
fout.write("\n")
CL_high = False
for o1 in range(1, 5):
o2 = min(o1 + 1, 5)
if o2 > len(RSS):
fout.write("%d par & %.2f & %.2f & %d & " %
(o1 + 1, RSS[o1]["chi2"], RSS[o1]["rss"],
RSS[o1]["nbins"] - RSS[o1]["npar"]))
fout.write(r"\\")
fout.write("\n")
continue #order==0 and
CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1 + 1., o2 + 1.,
RSS[o1]["nbins"])
if CL > 0.10: # The function with less parameters is enough
if not CL_high:
order = o1
fout.write("\\rowcolor{MarkerColor}\n")
CL_high = True
else:
#fout.write( "%d par are needed " % (o2+1))
if not CL_high:
order = o2
fout.write("%d par & %.2f & %.2f & %d & " %
(o1 + 1, RSS[o1]["chi2"], RSS[o1]["rss"],
RSS[o1]["nbins"] - RSS[o1]["npar"]))
fout.write("CL=%.3f " % (CL))
fout.write(r"\\")
fout.write("\n")
fout.write("\hline")
fout.write("\n")
fout.write(r"\end{tabular}")
print "saved F-test table as", PLOTDIR + "/Fisher_" + category + ".tex"
#print "-"*25
#print "function & $\\chi^2$ & RSS & ndof & F-test & result \\\\"
#print "\\multicolumn{6}{c}{", "Zprime_to_bb", "} \\\\"
#print "\\hline"
#CL_high = False
#for o1 in range(1, 5):
# o2 = min(o1 + 1, 5)
# print "%d par & %.2f & %.2f & %d & " % (o1+1, RSS[o1]["chi2"], RSS[o1]["rss"], RSS[o1]["nbins"]-RSS[o1]["npar"]),
# if o2 > len(RSS):
# print "\\\\"
# continue #order==0 and
# CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1+1., o2+1., RSS[o1]["nbins"])
# print "%d par vs %d par CL=%f & " % (o1+1, o2+1, CL),
# if CL > 0.10: # The function with less parameters is enough
# if not CL_high:
# order = o1
# print "%d par are sufficient" % (o1+1),
# CL_high=True
# else:
# print "%d par are needed" % (o2+1),
# if not CL_high:
# order = o2
# print "\\\\"
#print "\\hline"
#print "-"*25
#print "@ Order is", order, "("+category+")"
#order = min(3, order)
#order = 2
if order == 1:
modelBkg = modelBkg1 #.Clone("Bkg")
modelAlt = modelBkg2 #.Clone("BkgAlt")
normzBkg = normzBkg1 #.Clone("Bkg_norm")
fitRes = fitRes1
elif order == 2:
modelBkg = modelBkg2 #.Clone("Bkg")
modelAlt = modelBkg3 #.Clone("BkgAlt")
normzBkg = normzBkg2 #.Clone("Bkg_norm")
fitRes = fitRes2
elif order == 3:
modelBkg = modelBkg3 #.Clone("Bkg")
modelAlt = modelBkg4 #.Clone("BkgAlt")
normzBkg = normzBkg3 #.Clone("Bkg_norm")
fitRes = fitRes3
elif order == 4:
modelBkg = modelBkg4 #.Clone("Bkg")
modelAlt = modelBkg3 #.Clone("BkgAlt")
normzBkg = normzBkg4 #.Clone("Bkg_norm")
fitRes = fitRes4
else:
print "Functions with", order + 1, "or more parameters are needed to fit the background"
exit()
modelBkg.SetName("Bkg_" + YEAR + "_" + category)
modelAlt.SetName("Alt_" + YEAR + "_" + category)
normzBkg.SetName("Bkg_" + YEAR + "_" + category + "_norm")
print "-" * 25
# Generate pseudo data
setToys = RooDataSet()
setToys.SetName("data_toys")
setToys.SetTitle("Data (toys)")
if not isData:
print " - Generating", nevents, "events for toy data"
setToys = modelBkg.generate(RooArgSet(X_mass), nevents)
#setToys = modelAlt.generate(RooArgSet(X_mass), nevents)
print "toy data generated"
if VERBOSE: raw_input("Press Enter to continue...")
#*******************************************************#
# #
# Plot #
# #
#*******************************************************#
print "starting to plot"
c = TCanvas("c_" + category, category, 800, 800)
c.Divide(1, 2)
setTopPad(c.GetPad(1), RATIO)
setBotPad(c.GetPad(2), RATIO)
c.cd(1)
frame = X_mass.frame()
setPadStyle(frame, 1.25, True)
if VARBINS: frame.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin)
signal = getSignal(
category, stype,
2000) #replacing Alberto's getSignal by own dummy function
graphData = setData.plotOn(frame, RooFit.Binning(plot_binning),
RooFit.Scaling(False), RooFit.Invisible())
modelBkg.plotOn(frame, RooFit.VisualizeError(fitRes, 1, False),
RooFit.LineColor(602), RooFit.FillColor(590),
RooFit.FillStyle(1001), RooFit.DrawOption("FL"),
RooFit.Name("1sigma"))
modelBkg.plotOn(frame, RooFit.LineColor(602), RooFit.FillColor(590),
RooFit.FillStyle(1001), RooFit.DrawOption("L"),
RooFit.Name(modelBkg.GetName()))
modelAlt.plotOn(frame, RooFit.LineStyle(7), RooFit.LineColor(613),
RooFit.FillColor(609), RooFit.FillStyle(1001),
RooFit.DrawOption("L"), RooFit.Name(modelAlt.GetName()))
if not isSB and signal[0] is not None: # FIXME remove /(2./3.)
signal[0].plotOn(
frame,
RooFit.Normalization(signal[1] * signal[2], RooAbsReal.NumEvent),
RooFit.LineStyle(3), RooFit.LineWidth(6), RooFit.LineColor(629),
RooFit.DrawOption("L"), RooFit.Name("Signal"))
graphData = setData.plotOn(
frame, RooFit.Binning(plot_binning), RooFit.Scaling(False),
RooFit.XErrorSize(0 if not VARBINS else 1),
RooFit.DataError(RooAbsData.Poisson if isData else RooAbsData.SumW2),
RooFit.DrawOption("PE0"), RooFit.Name(setData.GetName()))
fixData(graphData.getHist(), True, True, not isData)
pulls = frame.pullHist(setData.GetName(), modelBkg.GetName(), True)
chi = frame.chiSquare(setData.GetName(), modelBkg.GetName(), True)
#setToys.plotOn(frame, RooFit.DataError(RooAbsData.Poisson), RooFit.DrawOption("PE0"), RooFit.MarkerColor(2))
frame.GetYaxis().SetTitle("Events / ( 100 GeV )")
frame.GetYaxis().SetTitleOffset(1.05)
frame.Draw()
#print "frame drawn"
# Get Chi2
# chi2[1] = frame.chiSquare(modelBkg1.GetName(), setData.GetName())
# chi2[2] = frame.chiSquare(modelBkg2.GetName(), setData.GetName())
# chi2[3] = frame.chiSquare(modelBkg3.GetName(), setData.GetName())
# chi2[4] = frame.chiSquare(modelBkg4.GetName(), setData.GetName())
frame.SetMaximum(frame.GetMaximum() * 10)
frame.SetMinimum(max(frame.GetMinimum(), 1.e-1))
c.GetPad(1).SetLogy()
drawAnalysis(category)
drawRegion(category, True)
#drawCMS(LUMI, "Simulation Preliminary")
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawCMS(LUMI, "", suppressCMS=True)
leg = TLegend(0.575, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.AddEntry(setData.GetName(),
setData.GetTitle() + " (%d events)" % nevents, "PEL")
leg.AddEntry(modelBkg.GetName(), modelBkg.GetTitle(),
"FL") #.SetTextColor(629)
leg.AddEntry(modelAlt.GetName(), modelAlt.GetTitle(), "L")
if not isSB and signal[0] is not None:
leg.AddEntry("Signal", signal[0].GetTitle(), "L")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextFont(42)
if not isSB:
latex.DrawLatex(leg.GetX1() * 1.16,
leg.GetY1() - 0.04, "HVT model B (g_{V}=3)")
# latex.DrawLatex(0.67, leg.GetY1()-0.045, "#sigma_{X} = 1.0 pb")
c.cd(2)
frame_res = X_mass.frame()
setPadStyle(frame_res, 1.25)
frame_res.addPlotable(pulls, "P")
setBotStyle(frame_res, RATIO, False)
if VARBINS: frame_res.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin)
frame_res.GetYaxis().SetRangeUser(-5, 5)
frame_res.GetYaxis().SetTitle("pulls(#sigma)")
frame_res.GetYaxis().SetTitleOffset(0.3)
frame_res.Draw()
fixData(pulls, False, True, False)
drawChi2(RSS[order]["chi2"], RSS[order]["nbins"] - (order + 1), True)
line = drawLine(X_mass.getMin(), 0, lastBin, 0)
if VARBINS:
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".pdf")
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".png")
else:
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".pdf")
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
if BIAS:
gSystem.Load("libHiggsAnalysisCombinedLimit.so")
from ROOT import RooMultiPdf
cat = RooCategory("pdf_index", "Index of Pdf which is active")
pdfs = RooArgList(modelBkg, modelAlt)
roomultipdf = RooMultiPdf("roomultipdf", "All Pdfs", cat, pdfs)
normulti = RooRealVar("roomultipdf_norm",
"Number of background events", nevents, 0., 1.e6)
normzBkg.setConstant(
False
) ## newly put here to ensure it's freely floating in the combine fit
# create workspace
w = RooWorkspace("Zprime_" + YEAR, "workspace")
# Dataset
if isData: getattr(w, "import")(setData, RooFit.Rename("data_obs"))
else: getattr(w, "import")(setToys, RooFit.Rename("data_obs"))
#getattr(w, "import")(setData, RooFit.Rename("data_obs"))
if BIAS:
getattr(w, "import")(cat, RooFit.Rename(cat.GetName()))
getattr(w, "import")(normulti, RooFit.Rename(normulti.GetName()))
getattr(w, "import")(roomultipdf, RooFit.Rename(roomultipdf.GetName()))
getattr(w, "import")(modelBkg, RooFit.Rename(modelBkg.GetName()))
getattr(w, "import")(modelAlt, RooFit.Rename(modelAlt.GetName()))
getattr(w, "import")(normzBkg, RooFit.Rename(normzBkg.GetName()))
w.writeToFile(
WORKDIR + "%s_%s%s.root" %
(DATA_TYPE + "_" + YEAR, category, "_test" if options.test else ""),
True)
print "Workspace", WORKDIR + "%s_%s%s.root" % (
DATA_TYPE + "_" + YEAR, category,
"_test" if options.test else ""), "saved successfully"
if VERBOSE: raw_input("Press Enter to continue...")
theory_curve_1110_2.Draw('l')
#theory_curve_1110_3=theory_curve_1110.Clone()
#theory_curve_1110_4=theory_curve_1110_2.Clone()
#theory_curve_1110_3.Draw('c')
#theory_curve_1110_4.Draw('c')
legend = TLegend(0.335, 0.55, 0.9, 0.9)
legend.SetTextSize(0.030)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetLineColor(1)
legend.SetLineStyle(1)
legend.SetLineWidth(1)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetHeader(
'#bf{#it{#Beta}}(T#rightarrow bW, tH, tZ) = 50%, 25%, 25%')
if triplet[0] == 0.0:
legend.SetHeader(
'#bf{#it{#Beta}}(T#rightarrow tH, tZ) = 50%, 50%')
legend.AddEntry(obslim, 'Observed', 'l')
legend.AddEntry(explim, 'Expected', 'l')
legend.AddEntry(exp1sigma, '#pm 1 std. deviation', 'f')
legend.AddEntry(exp2sigma, '#pm 2 std. deviation', 'f')
#legend.AddEntry(theory_curve,"SSM Z'#rightarrow Tt, B(Z'#rightarrow Tt) = 70%",'l')
#legend.AddEntry(theory_curve_1110,"G*#rightarrow Tt (arXiv:1110.6058)",'l')
if not triplet[0] == 0.0:
legend.AddEntry(
theory_curve_1110,
if len(hnames) < 2: continue
if hnames[1] == printHistName or 'DNN' in hnames[1]:
print hnames[2] + "_" + hnames[3]
string_fname += "%s \n" % hnames
string_nevt += "%s \n" % hnames
##if hnames[1] == printHistName :
#print hnames[1], " ", hnames[2], " ", hnames[3]
hs = THStack()
#l = TLegend(0.30, 0.99 - 0.8 * N_bkgsamples / 20., 0.89, 0.85)
l = TLegend(0.15, 0.71, 0.89, 0.87)
l.SetNColumns(4)
l.SetTextSize(0.05)
l.SetLineColor(0)
l.SetFillColor(0)
if mode == 0 or mode == 1:
h_data = datasamples[datasamples.keys()[mode]]["file"].Get(
datasamples[datasamples.keys()[mode]]["hname"][i])
elif mode == 2:
h_data = datasamples[datasamples.keys()[0]]["file"].Get(
datasamples[datasamples.keys()[0]]["hname"][i])
h_data.Add(datasamples[datasamples.keys()[1]]["file"].Get(
datasamples[datasamples.keys()[1]]["hname"][i]))
nbins = h_data.GetNbinsX()
h_data.AddBinContent(nbins, h_data.GetBinContent(nbins + 1)) #overflow
h_data.Rebin(rebin)
h_sub = h_data.Clone("h_sub")
def systematics(sys, isShape=False, antiCorr=False):
treeRead = True
file = {}
hist = {}
tree = {}
histUp = {}
histDown = {}
up = {}
down = {}
gUp = TGraph()
gDown = TGraph()
gUp.SetLineWidth(3)
gDown.SetLineWidth(3)
gUp.SetLineColor(632)
gDown.SetLineColor(602)
BTagAK4deepup = False
BTagAK4deepdown = False
BTagAK4deep = False
BTagAK8deepup = False
BTagAK8deepdown = False
BTagAK8deep = False
# g.SetMarkerStyle(20)
# g.SetMarkerColor(418)
# g.SetMarkerSize(1.25)
var = sys
cut = 'nnbbSR'
if var == 'BTagAK4Weight_deep_up':
var = 'X_mass'
BTagAK4deepup = True
elif var == 'BTagAK4Weight_deep_down':
var = 'X_mass'
BTagAK4deepdown = True
elif var == 'BTagAK4Weight_deep':
var = 'X_mass'
BTagAK4deep = True
elif var == 'BTagAK8Weight_deep_up':
var = 'X_mass'
BTagAK8deepup = True
elif var == 'BTagAK8Weight_deep_down':
var = 'X_mass'
BTagAK8deepdown = True
elif var == 'BTagAK8Weight_deep':
var = 'X_mass'
BTagAK8deep = True
upAvg, downAvg, upMin, downMin, upMax, downMax = 0., 0., 2., 2., 0., 0.
for k in sorted(selection.keys(), key=len, reverse=True):
if k in cut:
cut = cut.replace(k, selection[k])
for i, s in enumerate(sign):
if '_MZ' in s: m = int((s.split('_MZ')[1]).split('_MA')[0])
elif '_M' in s: m = int(s.split('_M')[1])
else: m = 0
if treeRead: # Project from tree
tree[s] = TChain("tree")
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
tree[s].Add(NTUPLEDIR + ss + ".root")
if variable[var]['nbins'] > 0:
min_value = variable[var]['min']
max_value = variable[var]['max']
title = variable[var]['title']
if 'isZtoNN' in cut:
if var == 'MET':
min_value = 200
max_value = 2000
elif var == 'DPhi':
title = "#Delta #varphi (AK8 jet-#slash{E}_{T})"
elif var == 'VH_deltaR':
title = "#Delta R (#slash{E}_{T}, AK8 jet)"
hist[s] = TH1F(
s, ";" + title + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], min_value, max_value)
else:
hist[s] = TH1F(s, ";" + variable[var]['title'],
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
cutstring = "(eventWeightLumi)" + ("*(" + cut + ")")
if var == 'LeptonWeightUp':
cutstring = "(eventWeightLumi * LeptonWeightUp/LeptonWeight)" + (
"*(" + cut + ")")
elif var == 'LeptonWeightDown':
cutstring = "(eventWeightLumi * LeptonWeightDown/LeptonWeight)" + (
"*(" + cut + ")")
elif var == 'TriggerWeightUp':
cutstring = "(eventWeightLumi * TriggerWeightUp/TriggerWeight)" + (
"*(" + cut + ")")
elif var == 'TriggerWeightDown':
cutstring = "(eventWeightLumi * TriggerWeightDown/TriggerWeight)" + (
"*(" + cut + ")")
#division by BTagAk4Weight_deep is because the weighted samples are used
elif BTagAK4deepup:
cutstring = "(eventWeightLumi * BTagAK4Weight_deep_up/BTagAK4Weight_deep)" + (
"*(" + cut + ")")
elif BTagAK4deepdown:
cutstring = "(eventWeightLumi * BTagAK4Weight_deep_down/BTagAK4Weight_deep)" + (
"*(" + cut + ")")
elif BTagAK8deep:
cutstring = "(eventWeightLumi * BTagAK8Weight_deep)" + (
"*(" + cut + ")")
elif BTagAK8deepup:
cutstring = "(eventWeightLumi * BTagAK8Weight_deep_up)" + (
"*(" + cut + ")")
elif BTagAK8deepdown:
cutstring = "(eventWeightLumi * BTagAK8Weight_deep_down)" + (
"*(" + cut + ")")
tree[s].Project(s, var, cutstring)
if not tree[s].GetTree() == None:
hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
"""
for j, ss in enumerate(sample[s]['files']):
file[ss] = TFile(NTUPLEDIR + ss + ".root", "READ")
tmp = file[ss].Get("Sys/"+sys)
if tmp == None: continue
if not s in hist.keys(): hist[s] = tmp
else:
if antiCorr:
for x in range(1, hist[s].GetNbinsX()+1): hist[s].SetBinContent(x, hist[s].GetBinContent(x)+tmp.GetBinContent(tmp.GetNbinsX()+1-x))
else: hist[s].Add( tmp )
if isShape:
tmp = file[ss].Get("Sys/"+sys+"_up")
print "Sys/"+sys+"_up"
if tmp == None: continue
if not s in histUp.keys(): histUp[s] = tmp
else: histUp[s].Add( tmp )
#
tmp = file[ss].Get("Sys/"+sys+"_down")
if tmp == None: continue
if not s in histDown.keys(): histDown[s] = tmp
else: histDown[s].Add( tmp )
if 'accept' in sys:
norm = None
for j, ss in enumerate(sample[s]['files']):
tmp = file[ss].Get("Sys/PDF_scale")
if tmp == None: continue
if norm == None: norm = tmp
else: norm.Add( tmp )
hist[s].Divide(norm)
"""
if (isShape):
shape = TF1("shape", "gaus", 0, 5000)
shapeUp = TF1("shapeUp", "gaus", 0, 5000)
shapeDown = TF1("shapeDown", "gaus", 0, 5000)
hist[s].Fit(shape, "Q0", "")
histUp[s].Fit(shapeUp, "Q0", "")
histDown[s].Fit(shapeDown, "Q0", "")
if 'scale' in sys or 'unc' in sys:
up[s] = histUp[s].GetMean() / hist[s].GetMean()
down[s] = histDown[s].GetMean() / hist[s].GetMean()
# up[s] = shapeUp.GetParameter(1)/shape.GetParameter(1)
# down[s] = shapeDown.GetParameter(1)/shape.GetParameter(1)
elif 'res' in sys:
up[s] = histUp[s].GetRMS() / hist[s].GetRMS()
down[s] = histDown[s].GetRMS() / hist[s].GetRMS()
# up[s] = shapeUp.GetParameter(2)/shape.GetParameter(2)
# down[s] = shapeDown.GetParameter(2)/shape.GetParameter(2)
else:
up[s] = hist[s].GetBinContent(
hist[s].FindBin(+1)) / hist[s].GetBinContent(
hist[s].FindBin(0))
down[s] = hist[s].GetBinContent(
hist[s].FindBin(-1)) / hist[s].GetBinContent(
hist[s].FindBin(0))
gUp.SetPoint(i, m, up[s])
gDown.SetPoint(i, m, down[s])
#if mass < 1000: continue
upAvg += up[s]
downAvg += down[s]
if abs(up[s]) > upMax: upMax = abs(up[s])
if abs(up[s]) < upMin: upMin = abs(up[s])
if abs(down[s]) > downMax: downMax = abs(down[s])
if abs(down[s]) < downMin: downMin = abs(down[s])
upAvg /= len(sign)
downAvg /= len(sign)
print " ---", sys, "--- | up: %.3f, down: %.3f, average: %.3f" % (
upAvg, downAvg, abs(upAvg - 1 + 1. - downAvg) /
2.), "|", "^{%.1f-%.1f}_{%.1f-%.1f}" % (100. * (1. - upMin), 100. *
(1. - upMax), 100. *
(downMin - 1.), 100. *
(downMax - 1.))
c1 = TCanvas("c1", "Signals", 800, 600)
c1.cd()
c1.GetPad(0).SetTicky(2)
gUp.Draw("AL")
gDown.Draw("SAME, L")
gUp.GetYaxis().SetRangeUser(0.65, 1.35)
gUp.GetXaxis().SetTitle("m_{X} (GeV)")
gUp.GetYaxis().SetTitle("Uncertainty")
leg = TLegend(0.5, 0.90 - 0.20, 0.9, 0.90)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader(sys.replace('_', ' '))
leg.AddEntry(gUp, "+1 s. d. (%.1f%%)" % (100. * (upAvg - 1.)), "l")
leg.AddEntry(gDown, " -1 s. d. (%.1f%%)" % (100. * (1. - downAvg)), "l")
leg.Draw()
drawCMS(-1, "Simulation", False)
c1.Update()
filename = sys
if sys.startswith('W_mass') or sys.startswith('Z_mass'):
filename += "_" + sign[0][:3]
# c1.Print("plots/Systematics/"+filename+".png")
# c1.Print("plots/Systematics/"+filename+".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if 'doubleB' in sys or 'subjet' in sys or 'mass' in sys:
print sys + " = {",
for m in [
800, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800,
2000, 2500, 3000, 3500, 4000, 4500
]:
print "%d : [%.3f, %.3f], " % (m, gUp.Eval(m), gDown.Eval(m)),
print "}"
if 'extr' in sys or 'tagging' in sys:
print sys + " = {",
for m in [800, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500]:
print "%d : [%.3f, %.3f], " % (m, gUp.Eval(m), gDown.Eval(m)),
print "}"
if 'QCD_scale' in sys or 'PDF_scale' in sys:
print sys + " = {",
for m in range(800, 4500 + 1, 10):
print "%d : [%.3f, %.3f], " % (m, gUp.Eval(m), gDown.Eval(m)),
print "}"
files_rate_L1[a] = TFile(outpath+"Graph_HV_scan_rate_SL1_L1_"+run_interval+"_"+a+".root", 'READ')
graphs_rate_L1[a] = TGraphAsymmErrors(files_rate_L1[a].Get("HV_scan_rate_SL1_L1_"+run_interval))
files_rate_L4[a] = TFile(outpath+"Graph_HV_scan_rate_SL1_L4_"+run_interval+"_"+a+".root", 'READ')
graphs_rate_L4[a] = TGraphAsymmErrors(files_rate_L4[a].Get("HV_scan_rate_SL1_L4_"+run_interval))
##Efficiency SL1_L1
can_HV_scan_SL1_L1 = TCanvas("can_compare_HV_scan_SL1_L1","can_compare_HV_scan_SL1_L1", 1000, 800)
can_HV_scan_SL1_L1.SetGrid()
can_HV_scan_SL1_L1.cd()
leg_L1 = TLegend(0.2, 0.7, 0.4, 0.9)
leg_L1.SetBorderSize(0)
leg_L1.SetFillStyle(0)
leg_L1.SetFillColor(0)
x_min = 3190
x_max = 3610
imin = 100
for i, j in enumerate(graphs_L1):
if "L1" in j:
imin = min(imin,i)
graphs_L1[j].SetMarkerSize(1.)
graphs_L1[j].SetMarkerStyle(21)
graphs_L1[j].SetMarkerColor(colors[i])
graphs_L1[j].SetFillColor(colors[i])
graphs_L1[j].SetLineColor(colors[i])
graphs_L1[j].SetLineWidth(3)
leg_L1.AddEntry(graphs_L1[j],j,'PL')
graphs_L1[j].GetXaxis().SetLimits(x_min,x_max)
graphs_L1[j].GetXaxis().SetTitle("HV [V]")
class TriggerRate(object):
"""
Class that will plot one or more trigger rate histograms into the TPad provided.
Author Mark Grimes ([email protected])
Date 02/Sep/2013
"""
instantiationCount = 0 # Need this to get around root's stupid requirement for globally unique names
def __init__(self, pad):
self.instantiationCount = TriggerRate.instantiationCount
TriggerRate.instantiationCount += 1
self.histograms = []
self.verticalLines = []
# Create a legend. This might not actually be plotted if drawLegend is changed to False
self.legend = TLegend(0.59, 0.72 * 0.9, 0.99, 0.95 * 0.9)
self.legend.SetTextSize(0.04)
self.legend.SetFillColor(0)
self.drawLegend = True
self.pad = pad
#self.pad.cd()
self.pad.SetGrid()
self.pad.SetLogy()
self.maxiumumBinHeight = 0
self.minimumBinHeight = 999999999
self.axisLabelSize = None
self.axisTitleSize = None
self.axisTitleOffset = None
def add(self, histogram, legendTitle=None):
availableColours = [1, 2, 4, 8, 9, 11, 41, 44, 46]
if legendTitle == None: legendTitle = histogram.GetTitle()
self.histograms.append(histogram.Clone())
# Make sure the copy has a globally unique name
self.histograms[-1].SetName(self.histograms[-1].GetName() + "_hist" +
str(len(self.histograms)) + "inst" +
str(self.instantiationCount))
self.histograms[-1].SetDirectory(
0) # Make sure the copy isn't saved to disk
self.histograms[-1].SetLineColor(
availableColours[(len(self.histograms) - 1) %
len(availableColours)])
self.histograms[-1].SetMarkerColor(
availableColours[(len(self.histograms) - 1) %
len(availableColours)])
self.histograms[-1].SetStats(0)
if self.axisLabelSize == None:
self.axisLabelSize = self.histograms[-1].GetXaxis().GetLabelSize()
if self.axisTitleSize == None:
self.axisTitleSize = self.histograms[-1].GetXaxis().GetTitleSize()
if self.axisTitleOffset == None:
self.axisTitleOffset = self.histograms[-1].GetYaxis(
).GetTitleOffset()
self.histograms[-1].GetYaxis().SetTitle("Rate/kHz")
self.histograms[-1].GetXaxis().SetTitle("Offline threshold/GeV")
self.legend.AddEntry(self.histograms[-1], legendTitle)
# See if the histogram maximum is larger than the current maximum
# I can have a look at histogram.GetMaximumBin() but that doesn't include
# the error, which I want.
for bin in range(1, self.histograms[-1].GetXaxis().GetNbins() + 1):
#bin=self.histograms[-1].GetMaximumBin()
try:
max = self.histograms[-1].GetBinContent(
bin) + self.histograms[-1].GetBinErrorUp(bin)
except:
max = self.histograms[-1].GetBinContent(
bin) + self.histograms[-1].GetBinError(bin)
if max > self.maxiumumBinHeight: self.maxiumumBinHeight = max
# Minimum is not actaully used to change the plot, but I need to know it to draw any vertical lines
try:
min = self.histograms[-1].GetBinContent(
bin) - self.histograms[-1].GetBinErrorDown(bin)
except:
min = self.histograms[-1].GetBinContent(
bin) - self.histograms[-1].GetBinError(bin)
if min < self.minimumBinHeight: self.minimumBinHeight = min
def addVerticalLine(self, xPosition, colour=1, thickness=3):
self.verticalLines.append(
TLine(xPosition, 0.00001, xPosition, self.maxiumumBinHeight))
self.verticalLines[-1].SetLineColor(colour)
self.verticalLines[-1].SetLineWidth(thickness)
def draw(self):
self.pad.cd()
for index in range(0, len(self.histograms)):
#self.histograms[index].SetMinimum( self.minimumBinHeight*0.9 )
self.histograms[index].SetMaximum(self.maxiumumBinHeight * 1.2)
self.histograms[index].GetYaxis().SetTitleSize(self.axisTitleSize)
self.histograms[index].GetYaxis().SetTitleOffset(
self.axisTitleOffset)
self.histograms[index].GetXaxis().SetTitleSize(self.axisTitleSize)
if index == 0: self.histograms[index].Draw()
else: self.histograms[index].Draw("same")
for line in self.verticalLines:
line.SetY1(self.minimumBinHeight * 0.9)
line.SetY2(self.maxiumumBinHeight * 1.2)
line.Draw()
if self.drawLegend: self.legend.Draw()
self.pad.Update()
def getGraph(self):
from array import array
from ROOT import TMultiGraph, TLegend, TGraphAsymmErrors
n = len(self.__x)
if n != len(self.__y) or n != len(self.__yErrLow) or n != len(
self.__yErrHigh):
raise StandardError, "The length of the x(%s), y(%s) and y error(%s,%s) lists does not match" % (
len(self.__x), len(self.__y), len(
self.__yErrLow), len(self.__yErrHigh))
result = TMultiGraph()
legendPosition = [
float(i) for i in self.__getStyleOption("legendPosition").split()
]
legend = TLegend(*legendPosition)
legend.SetFillColor(0)
result.SetTitle("%s;%s;%s" %
(self.__title, self.__xTitle, self.__yTitle))
#(refArrays, refLabel) = self.__getRefernceGraphArrays()
#refGraph = TGraphAsymmErrors(*refArrays)
#refGraph.SetLineWidth(2)
#refGraph.SetLineColor(int(self.__config.get("reference","lineColor")))
#refGraph.SetFillColor(int(self.__config.get("reference","fillColor")))
#result.Add(refGraph,"L3")
#legend.AddEntry(refGraph,self.__config.get("reference","name"))
xErr = array("d", [0 for i in range(n)])
print "__x = ", self.__x
print "__y = ", self.__y
lst = []
for inc in range(0, n):
d = {}
d['run'] = self.__runs[inc]
d['x'] = self.__x[inc]
d['y'] = self.__y[inc]
d['yErr'] = self.__yErrLow[inc]
d['yTitle'] = self.__yTitle
if self.__config.has_option(self.__section,
"yMin") and self.__config.has_option(
self.__section, "yMax"):
d['ymin'] = float(self.__config.get(self.__section, "yMin"))
d['ymax'] = float(self.__config.get(self.__section, "yMax"))
else:
d['ymin'] = 0
d['ymax'] = 0
lst.append(d)
obj = {}
obj[self.__title] = lst
#finalObj[self.__title]=lst
#finalList.append(finalObj)
# save_path = './JSON/'
#completeName = os.path.join(save_path, self.__title+".json")
if not os.path.exists("JSON"):
os.makedirs("JSON")
with open("./JSON/" + self.__title + ".json", 'w') as outfile:
json.dump(obj, outfile, indent=4)
print json.dumps(obj, indent=2)
graph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr,
self.__yErrLow, self.__yErrHigh)
graph.SetLineWidth(2)
graph.SetFillColor(0)
graph.SetLineColor(int(self.__getStyleOption("lineColor")))
graph.SetMarkerColor(int(self.__getStyleOption("markerColor")))
graph.SetMarkerStyle(int(self.__getStyleOption("markerStyle")))
graph.SetMarkerSize(float(self.__getStyleOption("markerSize")))
sysGraph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr,
self.__ySysErrLow, self.__ySysErrHigh)
sysGraph.SetLineWidth(1)
sysGraph.SetFillColor(0)
sysGraph.SetLineColor(int(self.__getStyleOption("lineColor")))
sysGraph.SetMarkerColor(int(self.__getStyleOption("markerColor")))
sysGraph.SetMarkerStyle(int(self.__getStyleOption("markerStyle")))
sysGraph.SetMarkerSize(float(self.__getStyleOption("markerSize")))
#TOMAS removed sys error from the plot
#result.Add(sysGraph,"[]")
result.Add(graph, "P")
# result.SetName("MultiPlots")
# result.SetTitle("%s;%s;%s"%(self.__title,self.__xTitle,self.__yTitle))
result.SetName("MG_%s" % (self.__title))
legend.AddEntry(graph, self.__getStyleOption("name"))
#for (x,y,yErr) in zip(self.__x, self.__y, zip(self.__yErrLow,self.__yErrHigh)):
# self.__addAnnotaion("hallo",x,y,yErr)
return (result, legend)
def createPlots_(plot, geometry):
"""Cumulative material budget from simulation.
Internal function that will produce a cumulative profile of the
material budget inferred from the simulation starting from the
single detectors that compose the tracker. It will iterate over
all existing detectors contained in the DETECTORS
dictionary. The function will automatically skip non-existent
detectors.
"""
IBs = ["InnerServices", "Phase2PixelBarrel", "TIB", "TIDF", "TIDB"]
theDirname = "Figures"
if plot not in plots.keys():
print("Error: chosen plot name not known %s" % plot)
return
hist_X0_detectors = OrderedDict()
hist_X0_IB = None
hist_X0_elements = OrderedDict()
for subDetector, color in DETECTORS.items():
h = get1DHisto_(subDetector, plots[plot].plotNumber, geometry)
if not h:
print('Warning: Skipping %s' % subDetector)
continue
hist_X0_detectors[subDetector] = h
# Merge together the "inner barrel detectors".
if subDetector in IBs:
hist_X0_IB = assignOrAddIfExists_(hist_X0_IB,
hist_X0_detectors[subDetector])
# category profiles
for label, [num, color, leg] in hist_label_to_num.items():
if label is 'SUM': continue
hist_label = get1DHisto_(subDetector, num + plots[plot].plotNumber,
geometry)
hist_X0_elements[label] = assignOrAddIfExists_(
hist_X0_elements.setdefault(label, None),
hist_label,
)
hist_X0_elements[label].SetFillColor(color)
cumulative_matbdg = TH1D("CumulativeSimulMatBdg", "CumulativeSimulMatBdg",
hist_X0_IB.GetNbinsX(),
hist_X0_IB.GetXaxis().GetXmin(),
hist_X0_IB.GetXaxis().GetXmax())
cumulative_matbdg.SetDirectory(0)
# colors
for det, color in DETECTORS.items():
setColorIfExists_(hist_X0_detectors, det, color)
# First Plot: BeamPipe + Pixel + TIB/TID + TOB + TEC + Outside
# stack
stackTitle_SubDetectors = "Tracker Material Budget;%s;%s" % (
plots[plot].abscissa, plots[plot].ordinate)
stack_X0_SubDetectors = THStack("stack_X0", stackTitle_SubDetectors)
for det, histo in hist_X0_detectors.items():
stack_X0_SubDetectors.Add(histo)
cumulative_matbdg.Add(histo, 1)
# canvas
can_SubDetectors = TCanvas("can_SubDetectors", "can_SubDetectors", 800,
800)
can_SubDetectors.Range(0, 0, 25, 25)
can_SubDetectors.SetFillColor(kWhite)
# Draw
stack_X0_SubDetectors.SetMinimum(plots[plot].ymin)
stack_X0_SubDetectors.SetMaximum(plots[plot].ymax)
stack_X0_SubDetectors.Draw("HIST")
stack_X0_SubDetectors.GetXaxis().SetLimits(plots[plot].xmin,
plots[plot].xmax)
# Legenda
theLegend_SubDetectors = TLegend(0.180, 0.8, 0.98, 0.92)
theLegend_SubDetectors.SetNColumns(3)
theLegend_SubDetectors.SetFillColor(0)
theLegend_SubDetectors.SetFillStyle(0)
theLegend_SubDetectors.SetBorderSize(0)
for det, histo in hist_X0_detectors.items():
theLegend_SubDetectors.AddEntry(histo, det, "f")
theLegend_SubDetectors.Draw()
# text
text_SubDetectors = TPaveText(0.180, 0.727, 0.402, 0.787, "NDC")
text_SubDetectors.SetFillColor(0)
text_SubDetectors.SetBorderSize(0)
text_SubDetectors.AddText("CMS Simulation")
text_SubDetectors.SetTextAlign(11)
text_SubDetectors.Draw()
# Store
can_SubDetectors.Update()
if not checkFile_(theDirname):
os.mkdir(theDirname)
can_SubDetectors.SaveAs("%s/Tracker_SubDetectors_%s.pdf" %
(theDirname, plot))
can_SubDetectors.SaveAs("%s/Tracker_SubDetectors_%s.root" %
(theDirname, plot))
# Second Plot: BeamPipe + SEN + ELE + CAB + COL + SUP + OTH/AIR +
# Outside stack
stackTitle_Materials = "Tracker Material Budget;%s;%s" % (
plots[plot].abscissa, plots[plot].ordinate)
stack_X0_Materials = THStack("stack_X0", stackTitle_Materials)
stack_X0_Materials.Add(hist_X0_detectors["BeamPipe"])
for label, [num, color, leg] in hist_label_to_num.items():
if label is 'SUM':
continue
stack_X0_Materials.Add(hist_X0_elements[label])
# canvas
can_Materials = TCanvas("can_Materials", "can_Materials", 800, 800)
can_Materials.Range(0, 0, 25, 25)
can_Materials.SetFillColor(kWhite)
# Draw
stack_X0_Materials.SetMinimum(plots[plot].ymin)
stack_X0_Materials.SetMaximum(plots[plot].ymax)
stack_X0_Materials.Draw("HIST")
stack_X0_Materials.GetXaxis().SetLimits(plots[plot].xmin, plots[plot].xmax)
# Legenda
theLegend_Materials = TLegend(0.180, 0.8, 0.95, 0.92)
theLegend_Materials.SetNColumns(3)
theLegend_Materials.SetFillColor(0)
theLegend_Materials.SetBorderSize(0)
theLegend_Materials.AddEntry(hist_X0_detectors["BeamPipe"], "Beam Pipe",
"f")
for label, [num, color, leg] in hist_label_to_num.items():
if label is 'SUM':
continue
theLegend_Materials.AddEntry(hist_X0_elements[label], leg, "f")
theLegend_Materials.Draw()
# text
text_Materials = TPaveText(0.180, 0.727, 0.402, 0.787, "NDC")
text_Materials.SetFillColor(0)
text_Materials.SetBorderSize(0)
text_Materials.AddText("CMS Simulation")
text_Materials.SetTextAlign(11)
text_Materials.Draw()
# Store
can_Materials.Update()
can_Materials.SaveAs("%s/Tracker_Materials_%s.pdf" % (theDirname, plot))
can_Materials.SaveAs("%s/Tracker_Materials_%s.root" % (theDirname, plot))
return cumulative_matbdg
if "PV" in Group:
X_axis="NPU"
print "X_axis= ",X_axis
for obj in (1,2):
if (obj==1):
Prefix+="_LCT"
if (obj==2):
Prefix=Prefix.replace("LCT","SEG")
c1=TCanvas("c1","c1",600,600)
c2=TCanvas("c2","c2",600,600)
leg=TLegend(0.45, 0.25, 0.95, 0.36)
leg.SetNColumns(2)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetShadowColor(0)
leg.SetBorderSize(0)
leg.SetTextFont(132)
leg.SetTextSize(0.04)
leg_ME1=TLegend(0.45, 0.25, 0.95, 0.36)
leg_ME1.SetNColumns(2)
leg_ME1.SetFillColor(0)
leg_ME1.SetFillStyle(0)
leg_ME1.SetShadowColor(0)
leg_ME1.SetBorderSize(0)
leg_ME1.SetTextFont(132)
leg_ME1.SetTextSize(0.04)
