Organization and simultaneous fits: creating and writing a workspace
Author: Clemens Lange, Wouter Verkerke (C++ version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Wednesday, April 17, 2024 at 11:03 AM.
import ROOT
Declare observable x
x = ROOT.RooRealVar("x", "x", 0, 10)
Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
mean = ROOT.RooRealVar("mean", "mean of gaussians", 5, 0, 10)
sigma1 = ROOT.RooRealVar("sigma1", "width of gaussians", 0.5)
sigma2 = ROOT.RooRealVar("sigma2", "width of gaussians", 1)
sig1 = ROOT.RooGaussian("sig1", "Signal component 1", x, mean, sigma1)
sig2 = ROOT.RooGaussian("sig2", "Signal component 2", x, mean, sigma2)
[#0] WARNING:InputArguments -- The parameter 'sigma1' with range [-inf, inf] of the RooGaussian 'sig1' exceeds the safe range of (0, inf). Advise to limit its range. [#0] WARNING:InputArguments -- The parameter 'sigma2' with range [-inf, inf] of the RooGaussian 'sig2' exceeds the safe range of (0, inf). Advise to limit its range.
Build Chebychev polynomial pdf
a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0.0, 1.0)
a1 = ROOT.RooRealVar("a1", "a1", -0.2, 0.0, 1.0)
bkg = ROOT.RooChebychev("bkg", "Background", x, [a0, a1])
Sum the signal components into a composite signal pdf
sig1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0.0, 1.0)
sig = ROOT.RooAddPdf("sig", "Signal", [sig1, sig2], [sig1frac])
Sum the composite signal and background
bkgfrac = ROOT.RooRealVar("bkgfrac", "fraction of background", 0.5, 0.0, 1.0)
model = ROOT.RooAddPdf("model", "g1+g2+a", [bkg, sig], [bkgfrac])
Generate a data sample of 1000 events in x from model
data = model.generate({x}, 1000)
Create a empty workspace
w = ROOT.RooWorkspace("w", "workspace")
Import model and all its components into the workspace
w.Import(model)
False
[#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooAddPdf::model [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooChebychev::bkg [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::x [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::a0 [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::a1 [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::bkgfrac [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooAddPdf::sig [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooGaussian::sig1 [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::mean [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::sigma1 [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::sig1frac [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooGaussian::sig2 [#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing RooRealVar::sigma2
Import data into the workspace
w.Import(data)
False
[#1] INFO:ObjectHandling -- RooWorkspace::import(w) importing dataset modelData
Print workspace contents
w.Print()
RooWorkspace(w) workspace contents variables --------- (a0,a1,bkgfrac,mean,sig1frac,sigma1,sigma2,x) p.d.f.s ------- RooChebychev::bkg[ x=x coefList=(a0,a1) ] = 1 RooAddPdf::model[ bkgfrac * bkg + [%] * sig ] = 1/1 RooAddPdf::sig[ sig1frac * sig1 + [%] * sig2 ] = 1/1 RooGaussian::sig1[ x=x mean=mean sigma=sigma1 ] = 1 RooGaussian::sig2[ x=x mean=mean sigma=sigma2 ] = 1 datasets -------- RooDataSet::modelData(x)
Save the workspace into a ROOT file
w.writeToFile("rf502_workspace_py.root")
False