doc/latest-stable/rf514__RooCustomizer_8py.html

import ROOT


E = ROOT.RooRealVar("Energy", "Energy", 0, 3000)


meanG = ROOT.RooRealVar("meanG", "meanG", 100.0, 0.0, 3000.0)

sigmaG = ROOT.RooRealVar("sigmaG", "sigmaG", 3.0)

gauss = ROOT.RooGaussian("gauss", "gauss", E, meanG, sigmaG)


pol1 = ROOT.RooRealVar("pol1", "Constant of the polynomial", 1, -10, 10)

linear = ROOT.RooPolynomial("linear", "linear", E, pol1)


yieldSig = ROOT.RooRealVar("yieldSig", "yieldSig", 1, 0, 1.0e4)

yieldBkg = ROOT.RooRealVar("yieldBkg", "yieldBkg", 1, 0, 1.0e4)


model = ROOT.RooAddPdf("model", "S + B model", [gauss, linear], [yieldSig, yieldBkg])


print("The proto model before customisation:\n")

model.Print("T")  # "T" prints the model as a tree


# Build the categories

sample = ROOT.RooCategory("sample", "sample", {"Sample1": 1, "Sample2": 2, "Sample3": 3})


# Start to customise the proto model that was defined above.

# ---------------------------------------------------------------------------


# We need two sets for bookkeeping of PDF nodes:

newLeaves = ROOT.RooArgSet()

allCustomiserNodes = ROOT.RooArgSet()


# 1. Each sample should have its own mean for the gaussian

# The customiser will make copies of `meanG` for each category.

# These will all appear in the set `newLeaves`, which will own the new nodes.

cust = ROOT.RooCustomizer(model, sample, newLeaves, allCustomiserNodes)

cust.splitArg(meanG, sample)


# 2. Each sample should have its own signal yield, but there is an extra complication:

# We need the yields 1 and 2 to be a function of the variable "mass".

# For this, we pre-define nodes with exactly the names that the customiser would have created automatically,

# that is, "<nodeName>_<categoryName>", and we register them in the set of customiser nodes.

# The customiser will pick them up instead of creating new ones.

# If we don't provide one (e.g. for "yieldSig_Sample3"), it will be created automatically by cloning `yieldSig`.

mass = ROOT.RooRealVar("M", "M", 1, 0, 12000)

yield1 = ROOT.RooFormulaVar("yieldSig_Sample1", "Signal yield in the first sample", "M/3.360779", mass)

yield2 = ROOT.RooFormulaVar("yieldSig_Sample2", "Signal yield in the second sample", "M/2", mass)

allCustomiserNodes.add(yield1)

allCustomiserNodes.add(yield2)


# Instruct the customiser to replace all yieldSig nodes for each sample:

cust.splitArg(yieldSig, sample)


# Now we can start building the PDFs for all categories:

pdf1 = cust.build("Sample1")

pdf2 = cust.build("Sample2")

pdf3 = cust.build("Sample3")


# And we inspect the two PDFs

print("\nPDF 1 with a yield depending on M:\n")

pdf1.Print("T")

print("\nPDF 2 with a yield depending on M:\n")

pdf2.Print("T")

print("\nPDF 3 with a free yield:\n")

pdf3.Print("T")


print("\nThe following leaves have been created automatically while customising:\n")

newLeaves.Print("V")


#  If we needed to set reasonable values for the means of the gaussians, this could be done as follows:

meanG1 = allCustomiserNodes["meanG_Sample1"]

meanG1.setVal(200)

meanG2 = allCustomiserNodes["meanG_Sample2"]

meanG2.setVal(300)


print(

    "\nThe following leaves have been used while customising\n\t(partial overlap with the set of automatically created leaves.\n\ta new customiser for a different PDF could reuse them if necessary.):"

)

allCustomiserNodes.Print("V")

TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358