Free small FFT in multiple languages
Introduction
The fast Fourier transform (FFT) is a versatile tool for digital signal processing (DSP) algorithms and applications. On this page, I provide a free implementation of the FFT in multiple languages, small enough that you can even paste it directly into your application (you don’t need to treat this code as an external library).
Also included is a fast circular convolution function based on the FFT. Note that the FFT, with a bit of pre and postprocessing, can quickly calculate the discrete cosine transform (DCT), which is used in many multimedia compression algorithms.
My implementation has a reasonable amount of optimization (such as building trigonometric tables), but is not intended to squeeze every last drop of performance. Instead, the implementation is optimized for clarity and conciseness. You’re welcome to add more optimizations on your own if you wish.
Math documentation
Note: In the text below, all input and output vectors are complex, and \(n\) is the length of each vector.
The forward FFT is based on this formula: \( \text{Out}(k) = \displaystyle\sum_{t=0}^{n1} \, \text{In}(t) \, e^{2 π i t k / n} \).
The inverse FFT is based on this formula: \( \text{Out}(k) = \displaystyle\sum_{t=0}^{n1} \, \text{In}(t) \, e^{2 π i t k / n} \).
This FFT does not perform any scaling. So for a vector of length \(n\), after performing a transform and an inverse transform on it, the result will be the original vector multiplied by \(n\) (plus approximation errors).
The circular convolution is based on this formula: \( \text{Out}(j) = \displaystyle\sum_{k=0}^{n1} \, \text{X}((j  k) \text{ mod } n) \, \text{Y}(k) \).
In other words, \(\text{Out}(j)\) is the sum of all the products \(\text{X}(a) \text{Y}(b)\) where the indices satisfy \(a + b \equiv j \text{ mod } n\).
Source code and API
The test code are runnable main programs that check the FFT and fast convolution for correctness against a naive algorithm. They also demonstrate how this FFT code is called. You do not need to include the test code into your own program. Also, feel free to strip down the set of FFT functions to the bare minimum that you need.
The radix2 FFT code is essential since every function depends on it. The Bluestein FFT code can be omitted if you are not doing FFTs or convolutions of nonpowerof2 sizes. The convolution code can be omitted if you are not computing convolutions and you are not using the Bluestein FFT.
If you are running many FFTs on the same array length, please consider modifying the code to compute the cosine and sine tables once and reuse them. This is especially important when benchmarking the speed of the algorithm.
License summary (MIT License): Please keep the copyright notice and URL intact. You may use, edit, and publish the code for any purpose including commercial use. No warranty is provided by Nayuki. The official license text is included in the source code.
 Java

Download: Fft.java, FftTest.java
 Forward FFT (wrapper) (inplace):
void transform(double[] real, double[] imag)
 Inverse FFT (wrapper) (inplace):
void inverseTransform(double[] real, double[] imag)
 Forward FFT (radix2) (inplace):
void transformRadix2(double[] real, double[] imag)
 Forward FFT (Bluestein) (inplace):
void transformBluestein(double[] real, double[] imag)
 Circular convolution (real):
void convolve(double[] x, double[] y, double[] out)
 Circular convolution (complex):
void convolve(double[] xreal, double[] ximag, double[] yreal, double[] yimag, double[] outreal, double[] outimag)
When calling any of the above functions, all the argument arrays must have the same length.
 Forward FFT (wrapper) (inplace):
 JavaScript

Download: fft.js, ffttest.html
 Forward FFT (wrapper) (inplace):
transform(real, imag)
 Inverse FFT (wrapper) (inplace):
inverseTransform(real, imag)
 Forward FFT (radix2) (inplace):
transformRadix2(real, imag)
 Forward FFT (Bluestein) (inplace):
transformBluestein(real, imag)
 Circular convolution (real):
convolveReal(x, y, out)
 Circular convolution (complex):
convolveComplex(xreal, ximag, yreal, yimag, outreal, outimag)
For each function above, the API is that every parameter is an array of floatingpoint numbers and the return value is nothing. When calling any of the above functions, all the argument arrays must have the same length. The API is almost the same as the Java version’s.
 Forward FFT (wrapper) (inplace):
 Python

Download: fft.py, ffttest.py
 Forward FFT (wrapper):
transform(list of numeric, False)
returning a list of complex  Inverse FFT (wrapper):
transform(list of numeric, True)
returning a list of complex  Forward FFT (radix2):
transform_radix2(list of numeric, False)
returning a list of complex  Inverse FFT (radix2):
transform_radix2(list of numeric, True)
returning a list of complex  Forward FFT (Bluestein):
transform_bluestein(list of numeric, False)
returning a list of complex  Inverse FFT (Bluestein):
transform_bluestein(list of numeric, True)
returning a list of complex  Circular convolution (real):
convolve(list of int/long/float, list of int/long/float, True)
returning a list of float  Circular convolution (complex):
convolve(list of numeric, list of numeric, False)
returning a list of complex
The numeric types are int, long, float, and complex. When calling any of the above functions, the returned list has the same length as the argument list(s). When calling
convolve()
, the two argument lists must have the same length. Note that this Python implementation returns the result instead of storing it back into the argument list(s); this is unlike the implementations for the other languages.  Forward FFT (wrapper):
 C#

Download: Fft.cs, FftTest.cs
 Forward FFT (wrapper) (inplace):
void Transform(Complex[] vector, false)
 Inverse FFT (wrapper) (inplace):
void Transform(Complex[] vector, true)
 Forward FFT (radix2) (inplace):
void TransformRadix2(Complex[] vector, false)
 Inverse FFT (radix2) (inplace):
void TransformRadix2(Complex[] vector, true)
 Forward FFT (Bluestein) (inplace):
void TransformBluestein(Complex[] vector, false)
 Inverse FFT (Bluestein) (inplace):
void TransformBluestein(Complex[] vector, true)
 Circular convolution (complex):
void Convolve(Complex[] xvector, Complex[] yvector, Complex[] outvector)
The convolution function requires all argument arrays to have the same length. Because C# supports complex numbers natively, the codebase is a blend of the Java and Python versions.
 Forward FFT (wrapper) (inplace):
 C

Download: fft.c, fft.h, ffttest.c
 Forward FFT (wrapper) (inplace):
bool transform(double real[], double imag[], size_t n)
 Inverse FFT (wrapper) (inplace):
bool inverse_transform(double real[], double imag[], size_t n)
 Forward FFT (radix2) (inplace):
bool transform_radix2(double real[], double imag[], size_t n)
 Forward FFT (Bluestein) (inplace):
bool transform_bluestein(double real[], double imag[], size_t n)
 Circular convolution (real):
bool convolve_real(const double x[], const double y[], double out[], size_t n)
 Circular convolution (complex):
bool convolve_complex(const double xreal[], const double ximag[], const double yreal[], const double yimag[], double outreal[], double outimag[], size_t n)
When calling any of the above functions, all the argument arrays must have the same length. Each function returns true to indicate success or false to indicate failure.
 Forward FFT (wrapper) (inplace):
 C++

Download: fft.cpp, fft.hpp, ffttest.cpp
 Forward FFT (wrapper) (inplace):
void transform(vector<double> &real, vector<double> &imag)
 Inverse FFT (wrapper) (inplace):
void inverseTransform(vector<double> &real, vector<double> &imag)
 Forward FFT (radix2) (inplace):
void transformRadix2(vector<double> &real, vector<double> &imag)
 Forward FFT (Bluestein) (inplace):
void transformBluestein(vector<double> &real, vector<double> &imag)
 Circular convolution (real):
void convolve(const vector<double> &x, const vector<double> &y, vector<double> &out)
 Circular convolution (complex):
void convolve(const vector<double> &xreal, const vector<double> &ximag, const vector<double> &yreal, const vector<double> &yimag, vector<double> &outreal, vector<double> &outimag)
When calling any of the above functions, all the argument vectors must have the same length.
 Forward FFT (wrapper) (inplace):
More info
 Wikipedia: Fast Fourier transform, Convolution theorem
 Wikipedia: CooleyTukey FFT algorithm, Bluestein’s FFT algorithm
 Wikipedia: FFTW (a highquality opensource library in C)