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/**
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* @author Orlando Selenu
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* Originally written in the Summer of 2008
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* Based on the algorithms originally published by E. Oran Brigham "The Fast Fourier Transform" 1973, in ALGOL60 and FORTRAN
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*/
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public class FFT {
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/**
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* The Fast Fourier Transform (generic version, with NO optimizations).
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*
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* @param inputReal
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* an array of length n, the real part
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* @param inputImag
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* an array of length n, the imaginary part
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* @param DIRECT
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* TRUE = direct transform, FALSE = inverse transform
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* @return a new array of length 2n
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*/
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public static double[] fft(final double[] inputReal, double[] inputImag,
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boolean DIRECT) {
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// - n is the dimension of the problem
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// - nu is its logarithm in base e
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int n = inputReal.length;
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// If n is a power of 2, then ld is an integer (_without_ decimals)
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double ld = Math.log(n) / Math.log(2.0);
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// Here I check if n is a power of 2. If exist decimals in ld, I quit
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// from the function returning null.
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// System.out.println(n);
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// System.out.println(((int) ld) - ld);
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if (((int) ld) - ld != 0) {
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System.out.println("The number of elements is not a power of 2.");
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return null;
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}
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// Declaration and initialization of the variables
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// ld should be an integer, actually, so I don't lose any information in
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// the cast
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int nu = (int) ld;
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int n2 = n / 2;
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int nu1 = nu - 1;
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double[] xReal = new double[n];
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double[] xImag = new double[n];
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double tReal, tImag, p, arg, c, s;
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// Here I check if I'm going to do the direct transform or the inverse
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// transform.
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double constant;
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if (DIRECT)
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constant = -2 * Math.PI;
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else
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constant = 2 * Math.PI;
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// I don't want to overwrite the input arrays, so here I copy them. This
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// choice adds \Theta(2n) to the complexity.
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for (int i = 0; i < n; i++) {
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xReal[i] = inputReal[i];
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xImag[i] = inputImag[i];
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}
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// First phase - calculation
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int k = 0;
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for (int l = 1; l <= nu; l++) {
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while (k < n) {
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for (int i = 1; i <= n2; i++) {
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p = bitreverseReference(k >> nu1, nu);
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// direct FFT or inverse FFT
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arg = constant * p / n;
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c = Math.cos(arg);
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s = Math.sin(arg);
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tReal = xReal[k + n2] * c + xImag[k + n2] * s;
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tImag = xImag[k + n2] * c - xReal[k + n2] * s;
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xReal[k + n2] = xReal[k] - tReal;
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xImag[k + n2] = xImag[k] - tImag;
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xReal[k] += tReal;
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xImag[k] += tImag;
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k++;
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}
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k += n2;
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}
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k = 0;
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nu1--;
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n2 /= 2;
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}
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// Second phase - recombination
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k = 0;
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int r;
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while (k < n) {
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r = bitreverseReference(k, nu);
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if (r > k) {
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tReal = xReal[k];
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tImag = xImag[k];
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xReal[k] = xReal[r];
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xImag[k] = xImag[r];
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xReal[r] = tReal;
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xImag[r] = tImag;
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}
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k++;
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}
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// Here I have to mix xReal and xImag to have an array (yes, it should
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// be possible to do this stuff in the earlier parts of the code, but
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// it's here to readability).
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double[] newArray = new double[xReal.length * 2];
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double radice = 1 / Math.sqrt(n);
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for (int i = 0; i < newArray.length; i += 2) {
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int i2 = i / 2;
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// I used Stephen Wolfram's Mathematica as a reference so I'm going
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// to normalize the output while I'm copying the elements.
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newArray[i] = xReal[i2] * radice;
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newArray[i + 1] = xImag[i2] * radice;
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}
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return newArray;
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}
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/**
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* The reference bit reverse function.
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*/
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private static int bitreverseReference(int j, int nu) {
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int j2;
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int j1 = j;
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int k = 0;
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for (int i = 1; i <= nu; i++) {
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j2 = j1 / 2;
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k = 2 * k + j1 - 2 * j2;
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j1 = j2;
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}
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return k;
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}
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}
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