[(4800, 5400)],
[(4200, 4800)],
[(3600, 4200)],
[(3000, 3600)],
[(2400, 3000)],
]
for signal, signalMass, massbinsset in [
("CIplusLL", "12000", massbinssets2),
("cs_ct14nlo_", "13000", massbinssets1),
("AntiCIplusLL", "12000", massbinssets2),
("DMAxial_Dijet_LO_Mphi_1_1p0_1p0_Mar5_gdmv_0_gdma_1p0_gv_0_ga_1", "6000",
""),
]:
for massbins in massbinsset:
limits = {}
name = "pvalue_LHCa" + signal + "_" + ("_".join([
s[0:4] for s in str(massbins).strip("[]").split("(")
])).strip("_") + "_exp_" + signalMass + "_2016"
print name
f1 = open(name + ".txt")
observed = 0
for l in f1.readlines():
if "CLb" in l:
pval = float(l.split("=")[1].split("+")[0].strip(" "))
print "pvalue", pval, "significance", RooStats.PValueToSignificance(
(1. - pval) / 2.)
if "Significance:" in l:
significance = float(l.strip().split(" ")[-1].strip(")"))
print "significance", significance
if opts.nullFit:
fr_null.Write()
#fitter.ws.Print()
output.Close()
if pars.btagSelection:
c1.SaveAs("DibosonBoostedBtaglnuJ_%s_%ijets_Stacked.png" % (mode, opts.Nj))
c2.SaveAs("DibosonBoostedBtaglnuJ_%s_%ijets_Subtracted.png" %
(mode, opts.Nj))
cp1.SaveAs("DibosonBoostedBtaglnuJ_%s_%ijets_Pull.png" % (mode, opts.Nj))
else:
c1.SaveAs("DibosonBoostedlnuJ_%s_%ijets_Stacked.png" % (mode, opts.Nj))
c2.SaveAs("DibosonBoostedlnuJ_%s_%ijets_Subtracted.png" % (mode, opts.Nj))
cp1.SaveAs("DibosonBoostedlnuJ_%s_%ijets_Pull.png" % (mode, opts.Nj))
print 'Time elapsed: %.1f sec' % timer.RealTime()
print 'CPU time used: %.1f sec' % timer.CpuTime()
print '%i degrees of freedom' % ndf
print 'chi2: %.2f / %i = %.2f' % (chi2_1, ndf, (chi2 / ndf))
print 'chi2 probability: %.4g' % (TMath.Prob(chi2, ndf))
if opts.nullFit:
likelihoodRatio = 2. * fr_null.minNll() - 2. * fr.minNll()
print '2*nll_null - 2*nll: %.4f - %.4f = %.4f' % (
2. * fr_null.minNll(), 2. * fr.minNll(), likelihoodRatio)
pval = TMath.Prob(likelihoodRatio, 1)
print 'p-value: %.4g' % pval
print 'Gaussian significance: %.3g' % RooStats.PValueToSignificance(pval)
xpoints1=array.array('d')
ypoints1=array.array('d')
xerrors1=array.array('d')
yerrors1=array.array('d')
for i in range(2,20):
x=i*0.25
ys=[]
for toy in range(jobs*toysPerJob):
ymax=0
for mass,significances in fWW:
#if mass<1200: continue
ymax=max(ymax,significances[toy])
ys+=[ymax]
y=RooStats.PValueToSignificance(float(len([s for s in ys if s>=x]))/len(ys))
if y>10 or y<-10: continue
xpoints1.append(x)
ypoints1.append(y)
xerrors1.append(0)
if len([s for s in ys if s>=x])>0:
yerror=2*(y-RooStats.PValueToSignificance(float(sqrt(len([s for s in ys if s>=x]))+len([s for s in ys if s>=x]))/len(ys)))
else:
yerror=y
yerrors1.append(yerror)
print x,y,yerror
canvas = TCanvas("","",0,0,200,200)
legend=TLegend(0.3,0.7,0.95,0.90,"")
observedZValue = [] tmpIntegratedLumi = 0.0 for ibin in xrange( h_den.GetNbinsX() ): if tmpIntegratedLumi/1000.>22.1: break tmpIntegratedLumi += h_den.GetBinContent(ibin) nobs = h_num["SRG3b"].Integral(0,ibin) nexp = 2.8*tmpIntegratedLumi/(22.1 * 1000.) nexpsig = 10*tmpIntegratedLumi/(22.1 * 1000.) integratedLumi.append(tmpIntegratedLumi/1000.) tmpPvalue = RooStats.NumberCountingUtils.BinomialObsP(nexpsig,nexp,0.27)/2. tmpZvalue = RooStats.PValueToSignificance( tmpPvalue ) if tmpPvalue else 0 expectedZValue.append( tmpZvalue ) tmpPvalue = RooStats.NumberCountingUtils.BinomialObsP(nexpsig,nexp*(1+0.27),0.27)/2. tmpZvalue = RooStats.PValueToSignificance( tmpPvalue ) if tmpPvalue else 0 expectedUpZValue.append( tmpZvalue ) tmpPvalue = RooStats.NumberCountingUtils.BinomialObsP(nexpsig,nexp*(1-0.27),0.27)/2. tmpZvalue = RooStats.PValueToSignificance( tmpPvalue ) if tmpPvalue else 0 expectedDownZValue.append( tmpZvalue ) tmpPvalue = RooStats.NumberCountingUtils.BinomialObsP(nobs,nexp,0.27)/2. tmpZvalue = RooStats.PValueToSignificance( tmpPvalue ) if tmpPvalue else 0 observedZValue.append( tmpZvalue )
