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Reference Guide
fitConvolution.C File Reference

Detailed Description

View in nbviewer Open in SWAN Tutorial for convolution of two functions

FCN=298.12 FROM MIGRAD STATUS=CONVERGED 457 CALLS 458 TOTAL
EDM=1.08093e-08 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 p0 7.32859e+00 3.70795e-02 1.23437e-05 -3.46193e-02
2 p1 7.33040e-02 2.44083e-03 3.62176e-06 -7.16223e-02
3 p2 -2.26420e+00 4.91803e-02 5.24021e-05 -1.27917e-02
4 p3 1.12811e+00 6.28810e-02 1.94847e-05 -2.72591e-02
#include <TCanvas.h>
#include <TRandom.h>
#include <TF1Convolution.h>
#include <TF1.h>
#include <TH1F.h>
{
// Construction of histogram to fit.
TH1F *h_ExpGauss = new TH1F("h_ExpGauss", "Exponential convoluted by Gaussian", 100, 0., 5.);
for (int i = 0; i < 1e6; i++) {
// Gives a alpha of -0.3 in the exp.
double x = gRandom->Exp(1. / 0.3);
x += gRandom->Gaus(0., 3.);
// Probability density function of the addition of two variables is the
// convolution of two density functions.
h_ExpGauss->Fill(x);
}
TF1Convolution *f_conv = new TF1Convolution("expo", "gaus", -1, 6, true);
f_conv->SetRange(-1., 6.);
f_conv->SetNofPointsFFT(1000);
TF1 *f = new TF1("f", *f_conv, 0., 5., f_conv->GetNpar());
f->SetParameters(1., -0.3, 0., 1.);
// Fit.
new TCanvas("c", "c", 800, 1000);
h_ExpGauss->Fit("f");
h_ExpGauss->Draw();
}
#define f(i)
Definition: RSha256.hxx:104
R__EXTERN TRandom * gRandom
Definition: TRandom.h:62
The Canvas class.
Definition: TCanvas.h:23
Class wrapping convolution of two functions.
Int_t GetNpar() const
void SetRange(Double_t a, Double_t b) override
Set the actual range used for the convolution.
void SetNofPointsFFT(Int_t n)
Set the number of points used for the FFT convolution.
1-Dim function class
Definition: TF1.h:213
1-D histogram with a float per channel (see TH1 documentation)}
Definition: TH1.h:574
virtual TFitResultPtr Fit(const char *formula, Option_t *option="", Option_t *goption="", Double_t xmin=0, Double_t xmax=0)
Fit histogram with function fname.
Definition: TH1.cxx:3894
virtual Int_t Fill(Double_t x)
Increment bin with abscissa X by 1.
Definition: TH1.cxx:3338
void Draw(Option_t *option="") override
Draw this histogram with options.
Definition: TH1.cxx:3060
virtual Double_t Gaus(Double_t mean=0, Double_t sigma=1)
Samples a random number from the standard Normal (Gaussian) Distribution with the given mean and sigm...
Definition: TRandom.cxx:274
virtual Double_t Exp(Double_t tau)
Returns an exponential deviate.
Definition: TRandom.cxx:251
Double_t x[n]
Definition: legend1.C:17
Author
Aurelie Flandi

Definition in file fitConvolution.C.