``````package Maths;

import java.util.ArrayList;
import java.util.Collections;

/**
* Class for calculating the Fast Fourier Transform (FFT) of a discrete signal using the
* Cooley-Tukey algorithm.
*
* @author Ioannis Karavitsis
* @version 1.0
*/
public class FFT {
/**
* This class represents a complex number and has methods for basic operations.
*
*/
static class Complex {
private double real, img;

/** Default Constructor. Creates the complex number 0. */
public Complex() {
real = 0;
img = 0;
}

/**
* Constructor. Creates a complex number.
*
* @param r The real part of the number.
* @param i The imaginary part of the number.
*/
public Complex(double r, double i) {
real = r;
img = i;
}

/**
* Returns the real part of the complex number.
*
* @return The real part of the complex number.
*/
public double getReal() {
return real;
}

/**
* Returns the imaginary part of the complex number.
*
* @return The imaginary part of the complex number.
*/
public double getImaginary() {
return img;
}

/**
* Adds this complex number to another.
*
* @param z The number to be added.
* @return The sum.
*/
Complex temp = new Complex();
temp.real = this.real + z.real;
temp.img = this.img + z.img;
return temp;
}

/**
* Subtracts a number from this complex number.
*
* @param z The number to be subtracted.
* @return The difference.
*/
public Complex subtract(Complex z) {
Complex temp = new Complex();
temp.real = this.real - z.real;
temp.img = this.img - z.img;
return temp;
}

/**
* Multiplies this complex number by another.
*
* @param z The number to be multiplied.
* @return The product.
*/
public Complex multiply(Complex z) {
Complex temp = new Complex();
temp.real = this.real * z.real - this.img * z.img;
temp.img = this.real * z.img + this.img * z.real;
return temp;
}

/**
* Multiplies this complex number by a scalar.
*
* @param n The real number to be multiplied.
* @return The product.
*/
public Complex multiply(double n) {
Complex temp = new Complex();
temp.real = this.real * n;
temp.img = this.img * n;
return temp;
}

/**
* Finds the conjugate of this complex number.
*
* @return The conjugate.
*/
public Complex conjugate() {
Complex temp = new Complex();
temp.real = this.real;
temp.img = -this.img;
return temp;
}

/**
* Finds the magnitude of the complex number.
*
* @return The magnitude.
*/
public double abs() {
return Math.hypot(this.real, this.img);
}

/**
* Divides this complex number by another.
*
* @param z The divisor.
* @return The quotient.
*/
public Complex divide(Complex z) {
Complex temp = new Complex();
temp.real = (this.real * z.real + this.img * z.img) / (z.abs() * z.abs());
temp.img = (this.img * z.real - this.real * z.img) / (z.abs() * z.abs());
return temp;
}

/**
* Divides this complex number by a scalar.
*
* @param n The divisor which is a real number.
* @return The quotient.
*/
public Complex divide(double n) {
Complex temp = new Complex();
temp.real = this.real / n;
temp.img = this.img / n;
return temp;
}
}

/**
* Iterative In-Place Radix-2 Cooley-Tukey Fast Fourier Transform Algorithm with Bit-Reversal. The
* size of the input signal must be a power of 2. If it isn't then it is padded with zeros and the
* output FFT will be bigger than the input signal.
*
* https://www.geeksforgeeks.org/iterative-fast-fourier-transformation-polynomial-multiplication/
* https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm
* https://cp-algorithms.com/algebra/fft.html
*
* @param x The discrete signal which is then converted to the FFT or the IFFT of signal x.
* @param inverse True if you want to find the inverse FFT.
*/
public static void fft(ArrayList<Complex> x, boolean inverse) {
/* Pad the signal with zeros if necessary */
int N = x.size();

/* Find the log2(N) */
int log2N = 0;
while ((1 << log2N) < N) log2N++;

/* Swap the values of the signal with bit-reversal method */
int reverse;
for (int i = 0; i < N; i++) {
reverse = reverseBits(i, log2N);
if (i < reverse) Collections.swap(x, i, reverse);
}

int direction = inverse ? -1 : 1;

/* Main loop of the algorithm */
for (int len = 2; len <= N; len *= 2) {
double angle = -2 * Math.PI / len * direction;
Complex wlen = new Complex(Math.cos(angle), Math.sin(angle));
for (int i = 0; i < N; i += len) {
Complex w = new Complex(1, 0);
for (int j = 0; j < len / 2; j++) {
Complex u = x.get(i + j);
Complex v = w.multiply(x.get(i + j + len / 2));
x.set(i + j + len / 2, u.subtract(v));
w = w.multiply(wlen);
}
}
}

/* Divide by N if we want the inverse FFT */
if (inverse) {
for (int i = 0; i < x.size(); i++) {
Complex z = x.get(i);
x.set(i, z.divide(N));
}
}
}

/**
* This function reverses the bits of a number. It is used in Cooley-Tukey FFT algorithm.
*
* <p>E.g. num = 13 = 00001101 in binary log2N = 8 Then reversed = 176 = 10110000 in binary
*
* https://www.geeksforgeeks.org/write-an-efficient-c-program-to-reverse-bits-of-a-number/
*
* @param num The integer you want to reverse its bits.
* @param log2N The number of bits you want to reverse.
* @return The reversed number
*/
private static int reverseBits(int num, int log2N) {
int reversed = 0;
for (int i = 0; i < log2N; i++) {
if ((num & (1 << i)) != 0) reversed |= 1 << (log2N - 1 - i);
}
return reversed;
}

/**
* This method pads an ArrayList with zeros in order to have a size equal to the next power of two
* of the previous size.
*
* @param x The ArrayList to be padded.
*/
private static void paddingPowerOfTwo(ArrayList<Complex> x) {
int n = 1;
int oldSize = x.size();
while (n < oldSize) n *= 2;
for (int i = 0; i < n - oldSize; i++) x.add(new Complex());
}
}
``````

#### FFT

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