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fitConvolution.py File Reference

Namespaces

namespace  fitConvolution
 

Detailed Description

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Tutorial for convolution of two functions

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Minimizer is Minuit2 / Migrad
Chi2 = 298.12
NDf = 96
Edm = 1.67196e-06
NCalls = 448
p0 = 7.32861 +/- 0.0370492
p1 = 0.0733018 +/- 0.00243973
p2 = -2.26418 +/- 0.0491372
p3 = 1.12808 +/- 0.0628185
import ROOT
# Construction of histogram to fit.
h_ExpGauss = ROOT.TH1F("h_ExpGauss", "Exponential convoluted by Gaussian", 100, 0.0, 5.0)
for i in range(1000000):
# Gives a alpha of -0.3 in the exp.
x = ROOT.gRandom.Exp(1.0 / 0.3)
x += ROOT.gRandom.Gaus(0.0, 3.0)
# Probability density function of the addition of two variables is the
# convolution of two density functions.
h_ExpGauss.Fill(x)
f_conv = ROOT.TF1Convolution("expo", "gaus", -1, 6, True)
f_conv.SetRange(-1.0, 6.0)
f_conv.SetNofPointsFFT(1000)
f = ROOT.TF1("f", f_conv, 0.0, 5.0, f_conv.GetNpar())
f.SetParameters(1.0, -0.3, 0.0, 1.0)
# Fit.
c1 = ROOT.TCanvas("c", "c", 800, 1000)
h_ExpGauss.Fit("f")
h_ExpGauss.Draw()
c1.SaveAs("fitConvolution.png")
Author
Jonas Rembser, Aurelie Flandi (C++ version)

Definition in file fitConvolution.py.