246 lines
6.7 KiB
Java
246 lines
6.7 KiB
Java
package Maths;
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import java.util.ArrayList;
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import java.util.Collections;
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/**
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* Class for calculating the Fast Fourier Transform (FFT) of a discrete signal using the
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* Cooley-Tukey algorithm.
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*
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* @author Ioannis Karavitsis
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* @version 1.0
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*/
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public class FFT {
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/**
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* This class represents a complex number and has methods for basic operations.
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*
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* <p>More info: https://introcs.cs.princeton.edu/java/32class/Complex.java.html
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*/
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static class Complex {
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private double real, img;
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/** Default Constructor. Creates the complex number 0. */
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public Complex() {
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real = 0;
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img = 0;
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}
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/**
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* Constructor. Creates a complex number.
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*
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* @param r The real part of the number.
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* @param i The imaginary part of the number.
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*/
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public Complex(double r, double i) {
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real = r;
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img = i;
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}
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/**
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* Returns the real part of the complex number.
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*
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* @return The real part of the complex number.
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*/
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public double getReal() {
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return real;
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}
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/**
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* Returns the imaginary part of the complex number.
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*
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* @return The imaginary part of the complex number.
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*/
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public double getImaginary() {
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return img;
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}
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/**
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* Adds this complex number to another.
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*
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* @param z The number to be added.
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* @return The sum.
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*/
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public Complex add(Complex z) {
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Complex temp = new Complex();
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temp.real = this.real + z.real;
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temp.img = this.img + z.img;
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return temp;
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}
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/**
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* Subtracts a number from this complex number.
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*
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* @param z The number to be subtracted.
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* @return The difference.
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*/
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public Complex subtract(Complex z) {
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Complex temp = new Complex();
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temp.real = this.real - z.real;
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temp.img = this.img - z.img;
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return temp;
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}
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/**
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* Multiplies this complex number by another.
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*
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* @param z The number to be multiplied.
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* @return The product.
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*/
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public Complex multiply(Complex z) {
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Complex temp = new Complex();
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temp.real = this.real * z.real - this.img * z.img;
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temp.img = this.real * z.img + this.img * z.real;
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return temp;
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}
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/**
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* Multiplies this complex number by a scalar.
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*
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* @param n The real number to be multiplied.
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* @return The product.
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*/
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public Complex multiply(double n) {
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Complex temp = new Complex();
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temp.real = this.real * n;
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temp.img = this.img * n;
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return temp;
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}
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/**
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* Finds the conjugate of this complex number.
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*
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* @return The conjugate.
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*/
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public Complex conjugate() {
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Complex temp = new Complex();
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temp.real = this.real;
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temp.img = -this.img;
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return temp;
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}
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/**
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* Finds the magnitude of the complex number.
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*
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* @return The magnitude.
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*/
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public double abs() {
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return Math.hypot(this.real, this.img);
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}
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/**
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* Divides this complex number by another.
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*
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* @param z The divisor.
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* @return The quotient.
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*/
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public Complex divide(Complex z) {
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Complex temp = new Complex();
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temp.real = (this.real * z.real + this.img * z.img) / (z.abs() * z.abs());
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temp.img = (this.img * z.real - this.real * z.img) / (z.abs() * z.abs());
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return temp;
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}
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/**
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* Divides this complex number by a scalar.
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*
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* @param n The divisor which is a real number.
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* @return The quotient.
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*/
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public Complex divide(double n) {
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Complex temp = new Complex();
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temp.real = this.real / n;
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temp.img = this.img / n;
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return temp;
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}
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}
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/**
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* Iterative In-Place Radix-2 Cooley-Tukey Fast Fourier Transform Algorithm with Bit-Reversal. The
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* size of the input signal must be a power of 2. If it isn't then it is padded with zeros and the
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* output FFT will be bigger than the input signal.
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*
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* <p>More info: https://www.algorithm-archive.org/contents/cooley_tukey/cooley_tukey.html
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* https://www.geeksforgeeks.org/iterative-fast-fourier-transformation-polynomial-multiplication/
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* https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm
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* https://cp-algorithms.com/algebra/fft.html
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*
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* @param x The discrete signal which is then converted to the FFT or the IFFT of signal x.
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* @param inverse True if you want to find the inverse FFT.
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*/
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public static void fft(ArrayList<Complex> x, boolean inverse) {
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/* Pad the signal with zeros if necessary */
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paddingPowerOfTwo(x);
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int N = x.size();
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/* Find the log2(N) */
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int log2N = 0;
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while ((1 << log2N) < N) log2N++;
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/* Swap the values of the signal with bit-reversal method */
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int reverse;
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for (int i = 0; i < N; i++) {
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reverse = reverseBits(i, log2N);
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if (i < reverse) Collections.swap(x, i, reverse);
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}
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int direction = inverse ? -1 : 1;
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/* Main loop of the algorithm */
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for (int len = 2; len <= N; len *= 2) {
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double angle = -2 * Math.PI / len * direction;
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Complex wlen = new Complex(Math.cos(angle), Math.sin(angle));
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for (int i = 0; i < N; i += len) {
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Complex w = new Complex(1, 0);
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for (int j = 0; j < len / 2; j++) {
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Complex u = x.get(i + j);
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Complex v = w.multiply(x.get(i + j + len / 2));
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x.set(i + j, u.add(v));
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x.set(i + j + len / 2, u.subtract(v));
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w = w.multiply(wlen);
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}
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}
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}
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/* Divide by N if we want the inverse FFT */
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if (inverse) {
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for (int i = 0; i < x.size(); i++) {
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Complex z = x.get(i);
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x.set(i, z.divide(N));
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}
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}
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}
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/**
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* This function reverses the bits of a number. It is used in Cooley-Tukey FFT algorithm.
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*
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* <p>E.g. num = 13 = 00001101 in binary log2N = 8 Then reversed = 176 = 10110000 in binary
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*
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* <p>More info: https://cp-algorithms.com/algebra/fft.html
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* https://www.geeksforgeeks.org/write-an-efficient-c-program-to-reverse-bits-of-a-number/
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*
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* @param num The integer you want to reverse its bits.
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* @param log2N The number of bits you want to reverse.
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* @return The reversed number
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*/
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private static int reverseBits(int num, int log2N) {
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int reversed = 0;
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for (int i = 0; i < log2N; i++) {
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if ((num & (1 << i)) != 0) reversed |= 1 << (log2N - 1 - i);
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}
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return reversed;
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}
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/**
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* This method pads an ArrayList with zeros in order to have a size equal to the next power of two
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* of the previous size.
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*
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* @param x The ArrayList to be padded.
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*/
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private static void paddingPowerOfTwo(ArrayList<Complex> x) {
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int n = 1;
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int oldSize = x.size();
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while (n < oldSize) n *= 2;
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for (int i = 0; i < n - oldSize; i++) x.add(new Complex());
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}
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}
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