I am having trouble explaining/understanding the following phenomenon:
To test fftw3 i am using the 2d poisson test case:
laplacian(f(x,y)) = - g(x,y) with periodic boundary conditions.
After applying the fourier transform to the equation we obtain : F(kx,ky) = G(kx,ky) /(kx² + ky²) (1)
if i take g(x,y) = sin (x) + sin(y) , (x,y) \in [0,2 \pi] i have immediately f(x,y) = g(x,y)
which is what i am trying to obtain with the fft :
i compute G from g with a forward Fourier transform
From this i can compute the Fourier transform of f with (1).
Finally, i compute f with the backward Fourier transform (without forgetting to normalize by 1/(nx*ny)).
In practice, the results are pretty bad?
(For instance, the amplitude for N = 256 is twice the amplitude obtained with N = 512)
Even worse, if i try g(x,y) = sin(x)*sin(y) , the curve has not even the same form of the solution.
(note that i must change the equation; i divide by two the laplacian in this case : (1) becomes F(kx,ky) = 2*G(kx,ky)/(kx²+ky²)
Here is the code:
/*
* fftw test -- double precision
*/
#include <iostream>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <fftw3.h>
using namespace std;
int main()
{
int N = 128;
int i, j ;
double pi = 3.14159265359;
double *X, *Y ;
X = (double*) malloc(N*sizeof(double));
Y = (double*) malloc(N*sizeof(double));
fftw_complex *out1, *in2, *out2, *in1;
fftw_plan p1, p2;
double L = 2.*pi;
double dx = L/((N - 1)*1.0);
in1 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
out2 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
out1 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
in2 = (fftw_complex*) fftw_malloc(sizeof(fftw_complex)*(N*N) );
p1 = fftw_plan_dft_2d(N, N, in1, out1, FFTW_FORWARD,FFTW_MEASURE );
p2 = fftw_plan_dft_2d(N, N, in2, out2, FFTW_BACKWARD,FFTW_MEASURE);
for(i = 0; i < N; i++){
X[i] = -pi + (i*1.0)*2.*pi/((N - 1)*1.0) ;
for(j = 0; j < N; j++){
Y[j] = -pi + (j*1.0)*2.*pi/((N - 1)*1.0) ;
in1[i*N + j][0] = sin(X[i]) + sin(Y[j]) ; // row major ordering
//in1[i*N + j][0] = sin(X[i]) * sin(Y[j]) ; // 2nd test case
in1[i*N + j][1] = 0 ;
}
}
fftw_execute(p1); // FFT forward
for ( i = 0; i < N; i++){ // f = g / ( kx² + ky² )
for( j = 0; j < N; j++){
in2[i*N + j][0] = out1[i*N + j][0]/ (i*i+j*j+1e-16);
in2[i*N + j][1] = out1[i*N + j][1]/ (i*i+j*j+1e-16);
//in2[i*N + j][0] = 2*out1[i*N + j][0]/ (i*i+j*j+1e-16); // 2nd test case
//in2[i*N + j][1] = 2*out1[i*N + j][1]/ (i*i+j*j+1e-16);
}
}
fftw_execute(p2); //FFT backward
// checking the results computed
double erl1 = 0.;
for ( i = 0; i < N; i++) {
for( j = 0; j < N; j++){
erl1 += fabs( in1[i*N + j][0] - out2[i*N + j][0]/N/N )*dx*dx;
cout<< i <<" "<< j<<" "<< sin(X[i])+sin(Y[j])<<" "<< out2[i*N+j][0]/N/N <<" "<< endl; // > output
}
}
cout<< erl1 << endl ; // L1 error
fftw_destroy_plan(p1);
fftw_destroy_plan(p2);
fftw_free(out1);
fftw_free(out2);
fftw_free(in1);
fftw_free(in2);
return 0;
}
I can't find any (more) mistakes in my code (i installed the fftw3 library last week) and i don't see a problem with the maths either but i don't think it's the fft's fault. Hence my predicament. I am all out of ideas and all out of google as well.
Any help solving this puzzle would be greatly appreciated.
note :
compiling : g++ test.cpp -lfftw3 -lm
executing : ./a.out output
and i use gnuplot in order to plot the curves :
(in gnuplot ) splot "output" u 1:2:4 ( for the computed solution )
