def plot(NObserved, results, NControl, R, bkgPredict):
r.gROOT.SetStyle("Plain")
c = r.TCanvas("test")
c.SetGrid()
nbins = len(NObserved)
obs = r.TH1D("obs", "obs", nbins, 0.5, nbins+0.5)
pred = r.TH1D("pred", "pred", nbins, 0.5, nbins+0.5)
pred2 = r.TH1D("pred2", "pred2", nbins, 0.5, nbins+0.5)
for idx, b in enumerate(NObserved):
obs.SetBinContent(idx+1, b)
if bkgPredict: pred2.SetBinContent(idx+1, R["nominal"][idx]*bkgPredict[0][idx])
pred.SetBinContent(idx+1, results[idx].predicted())
perr = math.sqrt(results[idx].predicted())
serr = R["MCStats"][idx]*NControl[idx]
pred.SetBinError(idx+1,
math.sqrt(perr**2 + serr**2))
obs.GetXaxis().SetBinLabel(idx+1, utils.formatBin(idx, False))
pred.SetLineColor(r.kRed)
pred2.SetLineColor(r.kGreen);
obs.GetYaxis().SetRangeUser(0, 1.2*max(obs.GetMaximum(), pred.GetMaximum()))
obs.SetStats(r.kFALSE)
obs.Draw("hist")
if bkgPredict: pred2.Draw("hist e same")
pred.Draw("hist e same")
leg = makeLegend()
leg.AddEntry(pred, "Predicted", "L")
if bkgPredict: leg.AddEntry(pred2, "Predicted (QCD Fit)", "L")
leg.AddEntry(obs, "Observed", "L")
leg.Draw()
c.SaveAs("limit/%s_pred_obs.pdf" % channel.name)
# Load MC pseudo-data in bins
data = utils.getZeroMC(channel, ctrl_channel)
# This also calculates statistical uncertainties
results = utils.makePredictions(data)
# Get the scaled/smeared systematic variations
systs = utils.getLiterallyAllSystematicsBkg(channel, ctrl_channel)
# Loop through systematics and calculate systematic uncertainty
for name, scaled in systs:
utils.addSystematic(name, data, results, scaled)
print name
# LaTeX table
l = Table()
cols = [("value", "Bin", "l")] + [("bin%d" % i, utils.formatBin(i), "c") for i in range(len(bins))]
l.defineColumns(cols)
def fields(f):
return dict([("bin%d" % idx, f(idx)) for idx in range(len(bins))])
l.addRowFields(value = "Events Predicted", **fields(lambda idx : "%.1f" % results[idx].predicted()))
l.addRowFields(value = "Events Observed", **fields(lambda idx : "%.1f" % results[idx].observed()))
l.addRowFields(value = "$N_{\\textrm{signal}}/N_{\\textrm{control}}$", **fields(lambda idx : "%.2f" % data[idx].R()))
l.addHLine()
l.addRowFields(value = "Statistical Uncertainty (\%)",
**fields(lambda idx : "%.2f" % (results[idx].statErrorValue(0)*100/results[idx].predicted())))
l.addHLine()
l.addRowFields(value = "Systematic Uncertainty (\%)", **fields(
lambda idx : "%.2f" % (math.sqrt(results[idx].totalSystError()**2 +
