def getData(workspace,
mean1 = 1., sigma1 = 0.01,
mean2 = 1., sigma2 = 0.01, nevents = 5000):
## Make a local workspace to use as a factory.
w = RooWorkspace('w', 'w')
## Define the PDF's for m, f1 and f2 in m*sqrt(f1*f2)
mPdf = w.factory("BreitWigner::mPdf(m[50,130], MZ[91.12], GammaZ[2.5])")
f1Pdf = w.factory("Gaussian::f1Pdf(f1[0.5, 2], mean1[%f], sigma1[%f])" %
(mean1, sigma1))
f2Pdf = w.factory("Gaussian::f2Pdf(f2[0.5, 2], mean2[%f], sigma2[%f])" %
(mean2, sigma2))
## Import the PDF's in the given workspace.
workspace.Import(mPdf, RenameAllVariables("True"))
workspace.Import(f1Pdf, RenameAllVariables("True"))
workspace.Import(f2Pdf, RenameAllVariables("True"))
## Generate samples of M, F1 and F2 with a 10% margin for boundaries.
moreEvents = int(2*nevents)
mData = mPdf.generate(RooArgSet(w.var("m")), moreEvents, NumCPU(3))
f1Data = f1Pdf.generate(RooArgSet(w.var("f1")), moreEvents, NumCPU(3))
f2Data = f2Pdf.generate(RooArgSet(w.var("f2")), moreEvents, NumCPU(3))
## Create the new data with toy reco mass
data = RooDataSet('data', 'toy reco Z->ll mass data',
RooArgSet(w.factory('mass[40,140]')))
entry = data.get()
## Loop over the generated values and fill the new reco mass data.
for i in range(moreEvents):
## Do we have enough entries already?
if data.sumEntries() >= nevents:
break
## Get the values of the random variables m, f1, f2.
m = mData.get(i).first().getVal()
f1 = f1Data.get(i).first().getVal()
f2 = f2Data.get(i).first().getVal()
## Here comes the formula!!
mass = m * sqrt(f1*f2)
## Is the reco mass within its range?
if 60. < mass and mass < 120.:
## Add the reco mass to the data
entry.first().setVal(mass)
data.addFast(entry)
## End of loop over the generated values
workspace.Import(data)
return data
