dataSample.buildHisto([ndataC], "C", "cuts", 0.5)
dataSample.buildHisto([ndataD], "D", "cuts", 0.5)
backgroundSample = Sample("NonQCDBackground", kBlack)
backgroundSample.buildHisto([nbkgA], "A", "cuts", 0.5)
backgroundSample.buildHisto([nbkgB], "B", "cuts", 0.5)
backgroundSample.buildHisto([nbkgC], "C", "cuts", 0.5)
backgroundSample.buildHisto([nbkgD], "D", "cuts", 0.5)
ana.addSamples([dataSample, backgroundSample])
#make dummy samples
bkgSampleA = Sample("dummy_BkgA", kBlue)
bkgSampleA.buildHisto([1], "A", "cuts", 0.5)
#mu_A become the estimated Bkg events in SR
bkgSampleA.addNormFactor("mu_A", 1.0, 100, 0, False)
bkgSampleB = Sample("dummy_BkgB", kBlue)
bkgSampleB.buildHisto([1], "B", "cuts", 0.5)
bkgSampleB.addNormFactor("mu_A", 1.0, 100, 0, False)
bkgSampleB.addNormFactor("eff_B", EffB, 2. * EffB, 0, False)
bkgSampleC = Sample("dummy_BkgC", kBlue)
bkgSampleC.buildHisto([1], "C", "cuts", 0.5)
bkgSampleC.addNormFactor("mu_A", 1.0, 100, 0, False)
bkgSampleC.addNormFactor("eff_C", EffC, 2. * EffC, 0, False)
#in region D eff_B* eff_C become the eff_D, because we assume we can use ABCD method
bkgSampleD = Sample("dummy_BkgD", kBlue)
bkgSampleD.buildHisto([1], "D", "cuts", 0.5)
bkgSampleD.addNormFactor("mu_A", 1.0, 100, 0, False)
