Hi,
I thought I posted it in the beginning, but it seems it didn’t work 
It should be attached to this post, and if not, here is the code anyway:
[code]#!/usr/bin/env python2
import ROOT
ROOT.gROOT.SetBatch()
get shape (used to fit), a simple landau
hShape = ROOT.TH1F(“landau”, “”, 120, 60, 120)
for i in range(10000):
hShape.Fill(ROOT.gRandom.Landau(91, 2.5))
get data, laundau convolutet with a gausian
hData = ROOT.TH1F(“landauGaus”, “”, 120, 60, 120)
for i in range(1000000):
hData.Fill(ROOT.gRandom.Landau(91, 2.5) + ROOT.gRandom.Gaus(0, 3))
x = ROOT.RooRealVar(“x”, “x”, 60, 120)
x.setBins(10000, “cache”)
taken from Problem with a Breit-Wigner convolved with a Crystal-Ball
x.setMin(“cache”, 50.5)
x.setMax(“cache”, 130.5)
data is breit-wigner convoluted with a gaussian, taken from histogram
dhData = ROOT.RooDataHist(“dhSig”, “”, ROOT.RooArgList(x), ROOT.RooFit.Import(hData))
take the background shape from a histogram (this time breit-wigner only)
dhShape = ROOT.RooDataHist(“dhShape”, “”, ROOT.RooArgList(x), ROOT.RooFit.Import(hShape))
pdfShape = ROOT.RooHistPdf(“pdfShape”, “”, ROOT.RooArgSet(x), dhShape, 0)
define gausian to smear background shape
mean = ROOT.RooRealVar(“mean”, “mean”, 0, -5, 5)
width = ROOT.RooRealVar(“width”, “width”, 2, 0, 10)
smearGaus = ROOT.RooGaussian(“smearGaus”, “”, x, mean, width)
smear our background histogram with an gaussian
smearedShape = ROOT.RooFFTConvPdf(“smearedShape”,"", x, pdfShape, smearGaus)
fit the result
smearedShape.fitTo(dhData)
and draw all
frame = x.frame(ROOT.RooFit.Title(" "))
dhData.plotOn(frame)
smearedShape.plotOn(frame)
c = ROOT.TCanvas()
frame.Draw()
ROOT.gPad.SaveAs(“fitExample.pdf”)
ROOT.gPad.SaveAs(“fitExample.png”)
[/code]
fitExample.py (1.54 KB)