def makePlot(data):
c=r.TCanvas();c.SetLogx();c.SetLogy()
r.gPad.SetTopMargin(0.06)
r.gPad.SetBottomMargin(0.15)
r.gPad.SetLeftMargin(0.2)
mg=r.TMultiGraph()
gs=r.TGraphErrors()
obs=[r.TGraph() for i in range(len(data['graphs']))]
exp=[r.TGraph() for i in range(len(data['graphs']))]
stylize(gs,obs,exp)
leg=r.TLegend(0.23,0.6 - (0.13 if H in [350,700] else 0),0.55,0.82 - (0.13 if H in [350,700] else 0))
leg2=r.TLegend(0.58,0.7 - (0.13 if H in [350,700] else 0),0.9,0.82 - (0.13 if H in [350,700] else 0))
leg.SetFillColor(0);leg2.SetFillColor(0)
leg.SetHeader("95% CL limits:")
leg2.SetTextFont(62)
gempty=r.TGraph()
gempty.SetMarkerColor(0)
leg2.AddEntry(gempty,'m_{#tilde{q}} = '+str(data['H'])+' GeV','P')
for i,graph in enumerate(data['graphs']):
points = graph['points']
n=len(points)
for j,(ctau,lim) in enumerate(points):
gs.SetPoint(gs.GetN(),ctau,data['sigma'])
gs.SetPointError(gs.GetN()-1,0,data['sigmaErr'])
obs[i].SetPoint(j,ctau,lim['obs'])
print data['H'], graph['X'], ctau, lim['obs']*1000
exp[i].SetPoint(j,ctau,lim['1ps'])
exp[i].SetPoint(2*n-1-j,ctau,lim['1ms'])
leg.AddEntry(obs[i],'m_{#tilde{#chi}^{0}_{1}}=%s GeV' %graph['X'],'PL')
for i in range(len(data['graphs'])): mg.Add(exp[i],'F')
for i in range(len(data['graphs'])): mg.Add(obs[i],'LP')
leg.AddEntry(exp[0],'Exp. limits (\\pm 1\\sigma)','F')
leg.AddEntry(gs,'#sigma_{#tilde{q}#tilde{q}*+#tilde{q}#tilde{q}} \\pm 1\\sigma_{theory}','LF')
mg.Add(gs,'LE3')
cmsStamp(lumi=18510,coords=(0.5,0.87),preliminary=False)
mg.Draw('A')
mg.SetMaximum((8 if H in [350,700] else 40)*max(data['sigma'],max([r.TMath.MaxElement(e.GetN(),e.GetY()) for e in exp])))
gmin,gmax = r.TMath.MinElement(gs.GetN(),gs.GetX()),r.TMath.MaxElement(gs.GetN(),gs.GetX())
mg.GetXaxis().SetRangeUser(0.95*gmin,2*gmax)
mg.GetXaxis().SetMoreLogLabels()
mg.GetYaxis().SetTitle("#sigma(#tilde{q}#tilde{q}*+#tilde{q}#tilde{q}) \
B^{2}(#tilde{#chi}^{0}_{1} #rightarrow u#bar{d}#mu) [pb]")
mg.GetXaxis().SetTitle('#tilde{#chi}^{0}_{1} c#tau [cm]')
mg.GetXaxis().SetTitleOffset(1.4)
mg.GetYaxis().SetTitleOffset(1.8)
leg.Draw('same')
leg2.Draw('same')
name=str(data['H'])
c.SaveAs(name+'.eps')
os.system('epstopdf '+name+'.eps && rm '+name+'.eps && mv '+name+'.pdf '+plotDir)
def limitPlot(MH = None,MX = None,list = None,observed=False):
c=r.TCanvas()
n=len(list)
g=[r.TGraph(n),r.TGraph(n),r.TGraph(2*n),r.TGraph(2*n)]
mg=r.TMultiGraph()
for i,obj in enumerate(list):
if observed : g[0].SetPoint(i,obj['ctau'],obj['obs'])
g[1].SetPoint(i,obj['ctau'],obj['exp'])
g[2].SetPoint(i,obj['ctau'],obj['1ps'])
g[2].SetPoint(2*n-1-i,obj['ctau'],obj['1ms'])
g[3].SetPoint(i,obj['ctau'],obj['2ms'])
g[3].SetPoint(2*n-1-i,obj['ctau'],obj['2ps'])
stylize(g)
mg.Add(g[3],'F')
mg.Add(g[2],'F')
mg.Add(g[1],'L')
mg.SetMaximum(50*r.TMath.MaxElement(g[3].GetN(),g[3].GetY()))
if observed : mg.Add(g[0],'PL')
c.SetLogy()
c.SetLogx()
mg.Draw('A')
mg.GetXaxis().SetTitle('X^{0} c#tau [cm]')
ctaus=sorted([obj['ctau'] for obj in list])
mg.GetXaxis().SetRangeUser(ctaus[0]*0.95,ctaus[-1]*2)
if option=='ea':
#mg.GetYaxis().SetTitle('#sigma #times BR #times Acceptance [pb] (95% CL)')
mg.GetYaxis().SetTitle('#sigma B^{2} #times Acceptance [pb] (95% CL)')
else:
#mg.GetYaxis().SetTitle('#sigma #times BR [pb] (95% CL)')
mg.GetYaxis().SetTitle('#sigma(H^{0} #rightarrow X^{0}X^{0}) B^{2}(X^{0} #rightarrow q#bar{q}) [pb]')
leg=r.TLegend(0.23,0.55,0.60,0.79)
leg.SetHeader("95% CL limits:")
leg.SetFillColor(0)
leg2=r.TLegend(0.55,0.65,0.85,0.79)
leg2.SetFillColor(0)
gempty=r.TGraph()
gempty.SetMarkerColor(0)
#leg.AddEntry(gempty,'95% CL limits','P')
leg2.AddEntry(gempty,'m_{H^{0}} = '+str(MH)+' GeV','P')
leg2.AddEntry(gempty,'m_{X^{0}} = '+str(MX)+' GeV','P')
if observed : leg.AddEntry(g[0],'Observed','L')
leg.AddEntry(g[1],'Expected','L')
leg.AddEntry(g[2],'Expected \\pm 1\\sigma','F')
leg.AddEntry(g[3],'Expected \\pm 2\\sigma','F')
leg.Draw('same')
leg2.Draw('same')
cmsStamp(lumi=18510,coords=(0.5,0.87),preliminary=False)
name=str(MH)+'_'+str(MX)+option
os.chdir(plotDir)
c.SaveAs(name+'.eps')
os.system('epstopdf '+name+'.eps')
os.system('rm '+name+'.eps')
os.chdir('../../')
def makePlot(data):
c = r.TCanvas()
c.SetLogx()
c.SetLogy()
r.gPad.SetTopMargin(0.06)
r.gPad.SetBottomMargin(0.15)
r.gPad.SetLeftMargin(0.2)
mg = r.TMultiGraph()
obs = [r.TGraph() for i in range(len(data["graphs"]))]
exp = [r.TGraph() for i in range(len(data["graphs"]))]
stylize(obs, exp)
leg = r.TLegend(0.23, 0.62 - (0.13 if H in [350, 700] else 0), 0.55, 0.82 - (0.13 if H in [350, 700] else 0))
leg2 = r.TLegend(0.58, 0.7 - (0.13 if H in [350, 700] else 0), 0.9, 0.82 - (0.13 if H in [350, 700] else 0))
leg.SetFillColor(0)
leg2.SetFillColor(0)
leg.SetHeader("95% CL limits:")
leg2.SetTextFont(62)
gempty = r.TGraph()
gempty.SetMarkerColor(0)
leg2.AddEntry(gempty, "m_{H^{0}} = " + str(data["H"]) + " GeV", "P")
for i, graph in enumerate(data["graphs"]):
points = graph["points"]
n = len(points)
for j, (ctau, lim) in enumerate(points):
obs[i].SetPoint(j, ctau, lim["obs"])
exp[i].SetPoint(j, ctau, lim["1ps"])
exp[i].SetPoint(2 * n - 1 - j, ctau, lim["1ms"])
leg.AddEntry(obs[i], "m_{X^{0}}=%s GeV" % graph["X"], "PL")
for i in range(len(data["graphs"])):
mg.Add(exp[i], "F")
for i in range(len(data["graphs"])):
mg.Add(obs[i], "PL")
leg.AddEntry(exp[0], "Exp. limits (\\pm 1\\sigma)", "F")
cmsStamp(lumi=18510, coords=(0.5, 0.87), preliminary=False)
mg.Draw("A")
mg.SetMaximum(40 * max([r.TMath.MaxElement(e.GetN(), e.GetY()) for e in exp]))
mg.GetYaxis().SetTitle("#sigma(H^{0} #rightarrow X^{0}X^{0}) B^{2}(X^{0} #rightarrow q#bar{q}) [pb]")
mg.GetXaxis().SetTitle("X^{0} c#tau [cm]")
mg.GetXaxis().SetTitleOffset(1.4)
mg.GetYaxis().SetTitleOffset(1.8)
leg.Draw("same")
leg2.Draw("same")
name = str(data["H"])
c.SaveAs(name + ".eps")
os.system("epstopdf " + name + ".eps && rm " + name + ".eps && mv " + name + ".pdf " + plotDir)
def limitPlot(list = None):
c=r.TCanvas()
n=len(list)
g=[r.TGraph(n),r.TGraph(n),r.TGraph(2*n),r.TGraph(2*n)]
mg=r.TMultiGraph()
den=list[0]['exp']
for i,obj in enumerate(list):
g[1].SetPoint(i,obj['relErr'],obj['exp']/den)
g[2].SetPoint(i,obj['relErr'],obj['1ps']/den)
g[2].SetPoint(2*n-1-i,obj['relErr'],obj['1ms']/den)
g[3].SetPoint(i,obj['relErr'],obj['2ms']/den)
g[3].SetPoint(2*n-1-i,obj['relErr'],obj['2ps']/den)
stylize(g)
func=r.TF1('func','1+[0]*x + [1]*x*x',0,90)
func.SetLineWidth(2)
func.SetMarkerSize(0)
g[1].Fit('func')
#mg.Add(g[3],'F')
mg.Add(g[2],'F')
mg.Add(g[1],'L')
mg.SetMaximum(1.2*r.TMath.MaxElement(g[2].GetN(),g[2].GetY()))
#c.SetLogy()
#c.SetLogx()
mg.Draw('A')
mg.GetXaxis().SetTitle('Systematic uncertainty [%]')
ctaus=sorted([obj['relErr'] for obj in list])
mg.GetXaxis().SetRangeUser(ctaus[0]*0.95,ctaus[-1]*2)
mg.GetYaxis().SetTitle('Limit / Limit (no uncertainty)')
leg=r.TLegend(0.2,0.61,0.7,0.8)
leg.SetFillColor(0)
gempty=r.TGraph()
gempty.SetMarkerColor(0)
gempty.SetFillColor(0)
gempty.SetLineColor(0)
leg.AddEntry(gempty,'signal efficiency = '+str(dict['eff']*100)+'%')
leg.AddEntry(g[1],'Exp. Limit','L')
leg.AddEntry(g[2],'Exp. \\pm 1\\sigma','F')
#leg.AddEntry(g[3],'Exp. \\pm 2\\sigma','F')
leg.AddEntry(func,'1 + '+str(rnd(func.GetParameter(0),2))+' x + '+str(rnd(func.GetParameter(1),2))+' x^{2}')
leg.Draw('same')
cmsStamp(lumi=None,coords=(0.45,0.89))
name=str(100*eff)
os.chdir(plotDir)
c.SaveAs(name+'percent.eps')
os.system('epstopdf '+name+'percent.eps')
os.system('rm '+name+'percent.eps')
os.chdir('../../')
def limitPlot(MH = None,MX = None,list = None,observed=False):
if MX==148: MX=150
elif MX==494: MX=500
print MX
c=r.TCanvas()
n=len(list)
g=[r.TGraph(n),r.TGraph(n),r.TGraph(2*n),r.TGraph(2*n),r.TGraphErrors(n)]
mg=r.TMultiGraph()
for i,obj in enumerate(list):
if observed : g[0].SetPoint(i,obj['ctau'],obj['obs'])
g[1].SetPoint(i,obj['ctau'],obj['exp'])
g[2].SetPoint(i,obj['ctau'],obj['1ps'])
g[2].SetPoint(2*n-1-i,obj['ctau'],obj['1ms'])
g[3].SetPoint(i,obj['ctau'],obj['2ms'])
g[3].SetPoint(2*n-1-i,obj['ctau'],obj['2ps'])
g[4].SetPoint(i,obj['ctau'],obj['sigma'])
g[4].SetPointError(i,0,obj['sigmaErr']*obj['sigma'])
stylize(g)
mg.Add(g[3],'F')
mg.Add(g[2],'F')
mg.Add(g[1],'L')
mg.Add(g[4],'LE3')
maxg=r.TMath.MaxElement(g[3].GetN(),g[3].GetY())
ming=r.TMath.MinElement(g[3].GetN(),g[3].GetY())
mg.SetMaximum((6 if MH in [350,700] else 40)*max(maxg,obj['sigma']))
mg.SetMinimum(0.5*min(ming,obj['sigma']))
if observed : mg.Add(g[0],'PL')
c.SetLogy()
c.SetLogx()
mg.Draw('A')
mg.GetXaxis().SetTitle('#tilde{#chi}^{0}_{1} c#tau [cm]')
mg.GetXaxis().SetMoreLogLabels()
mg.GetXaxis().SetNdivisions(303)
ctaus=sorted([obj['ctau'] for obj in list])
mg.GetXaxis().SetRangeUser(ctaus[0]*0.95,ctaus[-1]*2)
if option=='ea':
#mg.GetYaxis().SetTitle('#sigma #times BR #times Acceptance [pb] (95% CL)')
mg.GetYaxis().SetTitle('#sigma #times Acceptance [pb] (95% CL)')
else:
#mg.GetYaxis().SetTitle('#sigma #times BR [pb] (95% CL)')
mg.GetYaxis().SetTitle("#sigma(#tilde{q}#tilde{q}*+#tilde{q}#tilde{q}) \
B^{2}(#tilde{#chi}^{0}_{1} #rightarrow u#bar{d}#mu) [pb]")
leg=r.TLegend(0.23,0.6 - (0.13 if MH in [350,700] else 0),0.55,0.82 - (0.13 if MH in [350,700] else 0))
leg.SetFillColor(0)
leg2=r.TLegend(0.58,0.6 - (0.13 if MH in [350,700] else 0),0.9,0.82 - (0.13 if MH in [350,700] else 0))
leg2.SetFillColor(0)
gempty=r.TGraph()
gempty.SetMarkerColor(0)
leg2.AddEntry(gempty,'m_{#tilde{q}} = '+str(MH)+' GeV','P')
leg2.AddEntry(gempty,'m_{#tilde{#chi}^{0}_{1}} = '+str(MX)+' GeV','P')
leg2.AddEntry(g[4],'#sigma_{#tilde{q}#tilde{q}*+#tilde{q}#tilde{q}} \\pm 1\\sigma_{theory}','LF')
if observed : leg.AddEntry(g[0],'Observed','PL')
leg.SetHeader("95% CL limits:")
leg.AddEntry(g[1],'Expected','L')
leg.AddEntry(g[2],'Expected \\pm 1\\sigma','F')
leg.AddEntry(g[3],'Expected \\pm 2\\sigma','F')
leg.Draw('same')
leg2.Draw('same')
cmsStamp(lumi=18510,coords=(0.5,0.87),preliminary=False)
name=str(MH)+'_'+str(MX)+option
os.chdir(plotDir)
c.SaveAs(name+'.eps')
os.system('epstopdf '+name+'.eps')
os.system('rm '+name+'.eps')
os.chdir('../../')
def plotABCDscan(analysis,org,plotter,n,blind=True,onlyB=False):
setupTdrStyle()
plotter.pdfFileName = plotter.pdfFileName.replace(analysis.name+'.pdf','Scans_'+analysis.name+'.pdf')
plotter.canvas.Clear()
plotter.printCanvas("[")
text1 = plotter.printTimeStamp()
plotter.flushPage()
# get all the counts
counts = [[0]*len(analysis.scan) for sample in org.samples]
for step in org.steps :
for plotName in sorted(step.keys()) :
if 'ABCDEFGHcounts' not in plotName: continue
i = eval(plotName[:plotName.find('ABCDEFGH')])
for j in range(len(org.samples)): counts[j][i] = getCounts(step[plotName][j])
# pick points to scan
scans=[]
for cut in analysis.scanPrompt: scans.append((cut,cut,None))
# constant names
cutNames = ['(NPrompt1,PromptFrac1)','(NPrompt2,PromptFrac2)','DiscVtx']
histNames = ['observed','FG/B','EG/C','DG/A','BE/A','CF/A','EF/D','BCD/A^{2}']
# plot scans
for scan in scans:
plotter.canvas=r.TCanvas("c","c",600,600)
plotter.canvas.Divide(1,2)
plotter.canvas.cd(1).SetPad(0.01,0.30+0.0,0.99,0.99)
plotter.canvas.cd(1).SetTopMargin(0.07)
plotter.canvas.cd(1).SetBottomMargin(0.)
plotter.canvas.cd(2).SetPad(0.01,0.01,0.99,0.30)
plotter.canvas.cd(2).SetTopMargin(0.)
plotter.canvas.cd(2).SetBottomMargin(0.35)
plotter.canvas.cd(1)
r.gPad.SetLogy()
#r.gPad.SetTicky(0)
title=''
#title='max Prompt Tracks = %s, max Prompt Energy Fraction = %s'%(scan[0][0],scan[0][1])
xtitle = 'Vertex/cluster discriminant'
ytitle = 'Number of events'
indices = [i for i,cuts in enumerate(analysis.scan) if len(listdiff(cuts,scan))<=1]
labels = [string(cuts[scan.index(None)]) for i,cuts in enumerate(analysis.scan) if i in indices]
# first make a plot of signal efficiency
sigSamples = [sample for sample in org.samples if 'H' in sample['name']]
sigeff = [r.TH1F('','',len(indices),0,1) for sample in sigSamples]
for j,sample in enumerate(sigSamples):
i = org.indexOfSampleWithName(sample['name'])
norm = sample['xs']*org.lumi
for k,idx in enumerate(indices):
sigeff[j].SetBinContent(k+1,100*counts[i][idx][0][0]/float(2*norm))
sigeff[j].SetBinError(k+1,100*counts[i][idx][0][1]/float(norm))
sigeff[j].GetXaxis().SetBinLabel(k+1,labels[k])
# Data/QCD plots with signal efficiency on the same plot
for j,sample in enumerate(org.samples):
if 'H' in sample['name'] : continue
histos = [r.TH1F(name,title,len(indices),0,1) for name in histNames]
histob = r.TH1F('predicted bkg.',title,len(indices),0,1)
histob0 = r.TH1F('predicted bkg.',title,len(indices),0,1)
histoz = r.TH1F('','',len(indices),0,1)
for h in histos: h.GetXaxis().SetNdivisions(len(indices)*100+10,False)
histoz.GetXaxis().SetNdivisions(len(indices)*100+10,False)
for k,idx in enumerate(indices):
b,berr = getBkg(counts[j][idx],None)
histob.SetBinContent(k+1,b)
histob.SetBinError(k+1,berr)
histob0.SetBinContent(k+1,b)
histob0.SetBinError(k+1,0.00001)
N=int(counts[j][idx][0][0])
if N==0:
histoz.SetBinContent(k+1,-1e5)
continue
p=pvalue(b,berr,N,1e4)
z=r.RooStats.PValueToSignificance(p)
histoz.SetBinContent(k+1,z)
histoz.SetBinError(k+1,0.)
if onlyB:
legend = r.TLegend(0.5, 0.65, 0.93, 0.83)
else:
legend = r.TLegend(0.7, 0.55, 0.93, 0.93)
for i in reversed(range(n)):
if blind and 'Data' in sample['name'] and i==0: continue
for k,idx in enumerate(indices):
histos[i].SetBinContent(k+1,counts[j][idx][i][0])
histos[i].SetBinError(k+1,counts[j][idx][i][1])
histos[i].GetXaxis().SetBinLabel(k+1,labels[k])
histoz.GetXaxis().SetBinLabel(k+1,labels[k])
histob.GetXaxis().SetBinLabel(k+1,'')
histob0.GetXaxis().SetBinLabel(k+1,'')
histos[i].GetXaxis().SetTitle(xtitle)
histos[i].GetYaxis().SetTitle(ytitle)
histos[i].SetStats(False)
histob.SetStats(False)
histos[i].GetYaxis().SetTitleOffset(1.25)
histos[i].GetYaxis().SetTitleSize(0.066)
histos[i].GetYaxis().SetLabelSize(0.062)
histoz.GetXaxis().SetTitle(xtitle)
histoz.SetTitle('')
histoz.SetStats(0)
histoz.SetMarkerStyle(8)
histob.SetMarkerSize(0)
histob0.SetMarkerSize(0)
histos[i].SetMarkerStyle(25 if i!=0 else 8)
if i==(n-1):histos[i].SetMarkerStyle(21)
histos[i].SetMarkerColor(i+1)
histos[i].SetFillColor(0)
histos[i].SetLabelSize(0.07)
if onlyB:
if i==0:
legend.AddEntry(histos[i],'Observed background')
else:
legend.AddEntry(histos[i],histNames[i])
option='EX0' if i==(n-1) else 'EX0same'
histos[i].Draw(option)
if onlyB:
legend.AddEntry(histob,'Predicted background')
histob.SetFillColor(r.kBlue)
histob0.SetFillColor(r.kWhite)
histob.SetFillStyle(3001)
histob0.SetLineWidth(3)
histob.SetLineWidth(3)
histob0.SetLineColor(r.kBlue)
histob.SetLineColor(r.kBlue)
histos[0].SetLineWidth(2)
histos[0].Draw('EX0')
histob.Draw("E2same")
histob0.Draw("Lsame")
histos[0].Draw('EX0same')
histos_tmp=tuple([histos[i] for i in range(n)])
plotter.setRanges(histos_tmp,*plotter.getExtremes(1,histos_tmp,[False]*n))
if onlyB:
cmsStamp(lumi=18500,coords=(0.71,0.84),preliminary=False)
#cmsStamp(lumi=None,coords=(0.78,0.88))
else:
cmsStamp(lumi=org.lumi,coords=(0.45,0.88),simulation=True)
#cmsStamp(lumi=None,coords=(0.45,0.85))
pad = r.TPad("pad2","",0,0,1,1)
pad.SetFillStyle(4000)
pad.SetFrameFillStyle(0)
pad.SetLogy(0)
pad.Draw()
pad.cd()
for i,sample in enumerate(sigSamples):
option='histY+' + ('same' if i>0 else '')
sigeff[i].SetLineWidth(1)
sigeff[i].SetStats(False)
sigeff[i].SetLabelSize(0.06)
sigeff[i].SetLineColor(i+2)
sigeff[i].SetLineStyle(2)
sigeff[i].GetYaxis().SetRangeUser(0,45)
sigeff[i].GetYaxis().SetTitle('efficiency [%]')
sigeff[i].Draw(option)
legend.AddEntry(sigeff[i],sample['name'].split('.')[0])
legend.SetFillColor(0)
legend.SetBorderSize(0)
legend.Draw("same")
plotter.canvas.cd(2)
r.gPad.SetGridy()
histoz.GetYaxis().SetNdivisions(505,True)
histoz.GetYaxis().SetTitleOffset(0.5)
histoz.GetYaxis().SetTitleSize(0.16)
histoz.GetXaxis().SetTitleOffset(1.0)
histoz.GetXaxis().SetTitleSize(0.16)
histoz.GetYaxis().SetLabelSize(0.14)
histoz.GetYaxis().SetLabelOffset(0.015)
histoz.GetYaxis().SetTickLength(1.3*histoz.GetYaxis().GetTickLength())
histoz.GetXaxis().SetTickLength(2.*histoz.GetXaxis().GetTickLength())
histoz.GetXaxis().SetLabelSize(0.19)
histoz.GetXaxis().SetLabelOffset(0.02)
histoz.GetYaxis().SetTitle('Significance ')
#histoz.GetYaxis().CenterTitle()
histoz.GetYaxis().SetRangeUser(-2.99,2.99)
histoz.Draw('P')
plotter.printCanvas()
#plotter.canvas.Clear()
plotter.printCanvas("]")
print plotter.pdfFileName, "has been written."
