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('../../')
option=sys.argv[3]
limDir=sys.argv[1]+'/limits'+option+'/'
files = [f for f in os.listdir(limDir)]
plotDir=sys.argv[1]+'/plots/'
setupTdrStyle()
CTAUS={(1500,500):17.3,(1000,150):5.9,(350,150):17.8, \
(700,150):8.1, (700,500):27.9, (1000,500):22.7, (1500,150):4.5}
MH = [1500, 1000, 700, 350]
THS = [0.00067, 0.0144, 0.139, 9.97]
THERRS = [0.38, 0.24, 0.2, 0.16]
MX = [150, 500]
for i,H in enumerate(MH):
TH=THS[i]
THERR=THERRS[i]
for X in MX:
if X>H : continue
print H,X
CTAU = CTAUS[(H,X)]
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."
