## Plot Likelihood contours vs scale and resolution
canvases.next("NLL_Contours")
phoRes.setRange(
max(min(phoRes.getVal(), phoEResMC) - 6 * phoRes.getError(), phoRes.getMin()),
min(max(phoRes.getVal(), phoEResMC) + 6 * phoRes.getError(), phoRes.getMax()),
)
phoScale.setRange(
max(min(phoScale.getVal(), 100 * phoEScaleMC) - 10 * phoRes.getError(), phoRes.getMin()),
min(max(phoScale.getVal(), 100 * phoEScaleMC) + 10 * phoRes.getError(), phoRes.getMax()),
)
m = RooMinuit(nll)
m.migrad()
m.minos()
plot = m.contour(phoScale, phoRes, 1, 2, 3)
plot.SetTitle("NLL Countours")
plot.Draw()
mcTruth = ROOT.TMarker(100 * phoEScaleMC, phoEResMC, 2)
mcTruth.DrawClone()
mcTruth.Draw()
## canvases.next('nominal')
## mmgFrame = mmgMass.frame(Range(80,100))
## mmgData.plotOn(mmgFrame)
## #phoScale.setVal(0)
## model.plotOn(mmgFrame)
## # theory.plotOn(mmgFrame, LineStyle(kDashed), LineColor(kRed))
## theoryXphoSmear.plotOn(mmgFrame, LineStyle(kDashed), LineColor(kRed))
## theoryXphoSmear.paramOn(mmgFrame)
## ## Shift the photon smearing to the Z mass and scale t
def rooFit601():
print ">>> setup pdf and likelihood..."
x = RooRealVar("x", "x", -20, 20)
mean = RooRealVar("mean", "mean of g1 and g2", 0)
sigma1 = RooRealVar("sigma1", "width of g1", 3)
sigma2 = RooRealVar("sigma2", "width of g2", 4, 3.0,
6.0) # intentional strong correlations
gauss1 = RooGaussian("gauss1", "gauss1", x, mean, sigma1)
gauss2 = RooGaussian("gauss2", "gauss2", x, mean, sigma2)
frac = RooRealVar("frac", "frac", 0.5, 0.0, 1.0)
model = RooAddPdf("model", "model", RooArgList(gauss1, gauss2),
RooArgList(frac))
print ">>> generate to data..."
data = model.generate(RooArgSet(x), 1000) # RooDataSet
print ">>> construct unbinned likelihood of model wrt data..."
nll = model.createNLL(data) # RooAbsReal
print ">>> interactive minimization and error analysis with MINUIT interface object..."
minuit = RooMinuit(nll)
print ">>> set avtive verbosity for logging of MINUIT parameter space stepping..."
minuit.setVerbose(kTRUE)
print ">>> call MIGRAD to minimize the likelihood..."
minuit.migrad()
print "\n>>> parameter values and error estimates that are back propagated from MINUIT:"
model.getParameters(RooArgSet(x)).Print("s")
print "\n>>> disable verbose logging..."
minuit.setVerbose(kFALSE)
print ">>> run HESSE to calculate errors from d2L/dp2..."
minuit.hesse()
print ">>> value of and error on sigma2 (back propagated from MINUIT):"
sigma2.Print()
print "\n>>> run MINOS on sigma2 parameter only..."
minuit.minos(RooArgSet(sigma2))
print "\n>>> value of and error on sigma2 (back propagated from MINUIT after running MINOS):"
sigma2.Print()
print "\n>>> saving results, contour plots..."
# Save a snapshot of the fit result. This object contains the initial
# fit parameters, the final fit parameters, the complete correlation
# matrix, the EDM, the minimized FCN , the last MINUIT status code and
# the number of times the RooFit function object has indicated evaluation
# problems (e.g. zero probabilities during likelihood evaluation)
result = minuit.save() # RooFitResult
# Make contour plot of mx vs sx at 1,2,3 sigma
frame1 = minuit.contour(frac, sigma2, 1, 2, 3) # RooPlot
frame1.SetTitle("RooMinuit contour plot")
# Print the fit result snapshot
result.Print("v")
print "\n>>> change value of \"mean\" parameter..."
mean.setVal(0.3)
# Rerun MIGRAD,HESSE
print ">>> rerun MIGRAD, HESSE..."
minuit.migrad()
minuit.hesse()
print ">>> value on and error of frac:"
frac.Print()
print "\n>>> fix value of \"sigma\" parameter (setConstant)..."
sigma2.setConstant(kTRUE)
print ">>> rerun MIGRAD, HESSE..."
minuit.migrad()
minuit.hesse()
frac.Print()
