def plotFit():
axPeak = 2.464
# load workspace
f = TFile("./data/splitWorkspace.root")
fitWorkspace = f.Get("fitWorkspace")
fData = fitWorkspace.allData().front()
fitResult = fitWorkspace.allGenericObjects().front()
nPars = fitResult.floatParsFinal().getSize()
fEnergy = fitWorkspace.var("energy_keV")
modelPDF = fitWorkspace.pdf("model")
# fitWorkspace.Print()
pdfNames = ["ext-axion"]
# -- create spectrum rooplot --
nCol = float(gStyle.GetNumberOfColors())
binSize = 0.04
nBins = int((eHi - eLo) / binSize + 0.5)
fSpec = fEnergy.frame(RF.Range(eLo, eHi), RF.Bins(nBins))
fData.plotOn(fSpec)
modelPDF.plotOn(fSpec, RF.LineColor(ROOT.kRed), RF.Name("FullModel"))
chiSquare = fSpec.chiSquare(nPars)
modelPDF.plotOn(fSpec, RF.Components(pdfNames[0]),
RF.LineColor(ROOT.kBlue), RF.Name(pdfNames[0]))
# -- make a fake Gaussian --
# gaus = ROOT.TF1("g","gaus",-3, eHi)
# gaus.SetParameters(10., axPeak, getSigma(axPeak))
# gaus.SetParameter(0,10.)
# gaus.SetParameter(1, axPeak)
# gaus.SetParameter(2, getSigma(axPeak))
# erf
# rrvGaus = ROOT.RooRealVar("ax","ax",-3.,3.)
# rarGaus = RF.bindFunction("gaus", ROOT.TMath.Erf, rrvGaus)
# rarGaus.Print()
# frame2 = rrvGaus.frame(RF.Title("mygaus"))
# rarGaus.plotOn(frame2, RF.LineColor(ROOT.kGreen), RF.LineStyle(ROOT.kDashed), RF.Name("axGaus"))
# c0 = TCanvas("c0","",800,600)
# frame2.Draw()
# c0.Print("./plots/testGaus.pdf")
# -- print fit results --
print "-- SHIFTFIT RESULTS -- "
print "%-10s = %.3f" % ("chiSq", chiSquare)
fitValsFinal = getRooArgDict(fitResult.floatParsFinal())
bkgVal = 0.
for name in sorted(fitValsFinal):
fitVal = fitValsFinal[name]
if "amp-" in name:
error = fitWorkspace.var(name).getError()
print "%-10s = best %-7.3f error %.3f (w/o profile)" % (
name, fitVal, error)
elif "mu-" in name:
# compare the energy offset
pkName = name[3:]
pct = 100 * (1 - fitVal / axPeak)
print "%-10s : fit %-6.3f lit %-6.3f (%.3f%%)" % (name, fitVal,
axPeak, pct)
elif "sig-" in name:
# compare the sigma difference
pkName = name[4:]
pct = 100 * (1 - fitVal / getSigma(axPeak))
print "%-10s : fit %-6.3f func %-6.3f (%.3f%%)" % (
name, fitVal, getSigma(axPeak), pct)
else:
print "%s = %.4f (%.4f)" % (name, fitVal, fitVal / nBins)
bkgVal = fitVal / nBins
continue
# -- make spectrum plot --
c = TCanvas("c", "Bob Ross's Canvas", 1400, 1000)
c.SetRightMargin(0.2)
fSpec.SetTitle(" ")
fSpec.Draw()
ymax = fSpec.GetMaximum()
l1 = ROOT.TLine(axPeak, 0., axPeak, ymax)
l1.SetLineColor(ROOT.kBlue)
l1.SetLineWidth(2)
l1.Draw("same")
leg = TLegend(0.83, 0.6, 0.97, 0.9)
leg.AddEntry(fSpec.findObject("FullModel"),
"model #chi^{2}=%.3f" % chiSquare, "l")
leg.AddEntry(fSpec.findObject("FullModel"), "cts/bin=%.2f" % bkgVal, "")
leg.AddEntry(fSpec.findObject(pdfNames[0]), "axion gaussian", "l")
leg.AddEntry(l1, "axion-%.2f" % axPeak, "l")
leg.Draw("same")
c.Print("./plots/shiftFit.pdf")
# -- get FC Limit --
# Calculate the confidence interval for the axion peak, which is too low to use profile likelihood.
# TFeldmanCousins version:
# https://root.cern.ch/root/html/tutorials/math/FeldmanCousins.C.html
# RooStats version:
# https://root.cern.ch/root/html/tutorials/roostats/StandardFeldmanCousinsDemo.C.html
# FC Paper: https://arxiv.org/pdf/physics/9711021.pdf
f = TFeldmanCousins(0.95)
N_obs = 1.
N_bkg = bkgVal / 3.
ul = f.CalculateUpperLimit(N_obs, N_bkg)
ll = f.GetLowerLimit()
print "For %.2f events observed, and %.2f background events," % (N_obs,
N_bkg)
print "F-C method gives UL: %.2f and LL %.2f (90%% CL)" % (ul, ll)
