ROOT   Reference Guide
rf211_paramconv.py File Reference

## Namespaces

namespace  rf211_paramconv

## Detailed Description

'ADDITION AND CONVOLUTION' RooFit tutorial macro #211 Working a with a p.d.f.

with a convolution operator in terms of a parameter

(require ROOT to be compiled with –enable-fftw3)

import ROOT
# Set up component pdfs
# ---------------------------------------
# Gaussian g(x ; mean,sigma)
x = ROOT.RooRealVar("x", "x", -10, 10)
mean = ROOT.RooRealVar("mean", "mean", -3, 3)
sigma = ROOT.RooRealVar("sigma", "sigma", 0.5, 0.1, 10)
modelx = ROOT.RooGaussian("gx", "gx", x, mean, sigma)
# Block function in mean
a = ROOT.RooRealVar("a", "a", 2, 1, 10)
model_mean = ROOT.RooGenericPdf("model_mean", "abs(mean)<a", [mean, a])
# Convolution in mean model = g(x,mean,sigma) (x) block(mean)
x.setBins(1000, "cache")
mean.setBins(50, "cache")
model = ROOT.RooFFTConvPdf("model", "model", mean, modelx, model_mean)
# Configure convolution to construct a 2-D cache in (x,mean)
# rather than a 1-d cache in mean that needs to be recalculated
# for each value of x
model.setCacheObservables({x})
model.setBufferFraction(1.0)
# Integrate model over projModel = Int model dmean
projModel = model.createProjection({mean})
# Generate 1000 toy events
d = projModel.generateBinned({x}, 1000)
# Fit p.d.f. to toy data
projModel.fitTo(d, Verbose=True)
# Plot data and fitted p.d.f.
frame = x.frame(Bins=25)
d.plotOn(frame)
projModel.plotOn(frame)
# Make 2d histogram of model(x;mean)
hh = model.createHistogram(
"hh",
x,
Binning=50,
YVar=dict(var=mean, Binning=50),
ConditionalObservables={mean},
)
hh.SetTitle("histogram of model(x|mean)")
hh.SetLineColor(ROOT.kBlue)
# Draw frame on canvas
c = ROOT.TCanvas("rf211_paramconv", "rf211_paramconv", 800, 400)
c.Divide(2)
c.cd(1)