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