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RealFFT: Real-to-complex FFT and complex-to-real iFFT based on RustFFT

This library is a wrapper for RustFFT that enables performing FFT of real-valued data. The API is designed to be as similar as possible to RustFFT.

Using this library instead of RustFFT directly avoids the need of converting real-valued data to complex before performing a FFT. If the length is even, it also enables faster computations by using a complex FFT of half the length. It then packs a 2N long real vector into an N long complex vector, which is transformed using a standard FFT. The FFT result is then post-processed to give only the first half of the complex spectrum, as an N+1 long complex vector.

The iFFT goes through the same steps backwards, to transform an N+1 long complex spectrum to a 2N long real result.

The speed increase compared to just converting the input to a 2N long complex vector and using a 2N long FFT depends on the length of the input data. The largest improvements are for long FFTs and for lengths over around 1000 elements there is an improvement of about a factor 2. The difference shrinks for shorter lengths, and around 30 elements there is no longer any difference.

Why use real-to-complex FFT?

Using a complex-to-complex FFT

A simple way to get the FFT of a real valued vector is to convert it to complex, and using a complex-to-complex FFT.

Let’s assume x is a 6 element long real vector:

x = [x0r, x1r, x2r, x3r, x4r, x5r]

We now convert x to complex by adding an imaginary part with value zero. Using the notation (xNr, xNi) for the complex value xN, this becomes:

x_c = [(x0r, 0), (x1r, 0), (x2r, 0), (x3r, 0), (x4r, 0, (x5r, 0)]

Performing a normal complex FFT, the result of FFT(x_c) is:

FFT(x_c) = [(X0r, X0i), (X1r, X1i), (X2r, X2i), (X3r, X3i), (X4r, X4i), (X5r, X5i)]

But because our x_c is real-valued (all imaginary parts are zero), some of this becomes redundant:

FFT(x_c) = [(X0r, 0), (X1r, X1i), (X2r, X2i), (X3r, 0), (X2r, -X2i), (X1r, -X1i)]

The last two values are the complex conjugates of X1 and X2. Additionally, X0i and X3i are zero. As we can see, the output contains 6 independent values, and the rest is redundant. But it still takes time for the FFT to calculate the redundant values. Converting the input data to complex also takes a little bit of time.

If the length of x instead had been 7, result would have been:

FFT(x_c) = [(X0r, 0), (X1r, X1i), (X2r, X2i), (X3r, X3i), (X3r, -X3i), (X2r, -X2i), (X1r, -X1i)]

The result is similar, but this time there is no zero at X3i. Also in this case we got the same number of independent values as we started with.


Using a real-to-complex FFT removes the need for converting the input data to complex. It also avoids calculating the redundant output values.

The result for 6 elements is:

RealFFT(x) = [(X0r, 0), (X1r, X1i), (X2r, X2i), (X3r, 0)]

The result for 7 elements is:

RealFFT(x) = [(X0r, 0), (X1r, X1i), (X2r, X2i), (X3r, X3i)]

This is the data layout output by the real-to-complex FFT, and the one expected as input to the complex-to-real iFFT.


RealFFT matches the behaviour of RustFFT and does not normalize the output of either FFT of iFFT. To get normalized results, each element must be scaled by 1/sqrt(length). If the processing involves both an FFT and an iFFT step, it is advisable to merge the two normalization steps to a single, by scaling by 1/length.


The full documentation can be generated by rustdoc. To generate and view it run:

cargo doc --open


To run a set of benchmarks comparing real-to-complex FFT with standard complex-to-complex, type:

cargo bench

The results are printed while running, and are compiled into an html report containing much more details. To view, open target/criterion/report/index.html in a browser.


Transform a vector, and then inverse transform the result.

use realfft::RealFftPlanner;
use rustfft::num_complex::Complex;
use rustfft::num_traits::Zero;

let length = 256;

// make a planner
let mut real_planner = RealFftPlanner::<f64>::new();

// create a FFT
let r2c = real_planner.plan_fft_forward(length);
// make input and output vectors
let mut indata = r2c.make_input_vec();
let mut spectrum = r2c.make_output_vec();

// Are they the length we expect?
assert_eq!(indata.len(), length);
assert_eq!(spectrum.len(), length/2+1);

// Forward transform the input data
r2c.process(&mut indata, &mut spectrum).unwrap();

// create an iFFT and an output vector
let c2r = real_planner.plan_fft_inverse(length);
let mut outdata = c2r.make_output_vec();
assert_eq!(outdata.len(), length);

c2r.process(&mut spectrum, &mut outdata).unwrap();


  • 3.0.0: Improved error reporting.
  • 2.0.1: Minor bugfix.
  • 2.0.0: Update RustFFT to 6.0.0 and num-complex to 0.4.0.
  • 1.1.0: Add missing Sync+Send.
  • 1.0.0: First version with new api.


The realfft crate requires rustc version 1.37 or newer.


pub use rustfft::num_complex;
pub use rustfft::num_traits;


A planner is used to create FFTs. It caches results internally, so when making more than one FFT it is advisable to reuse the same planner.


Custom error returned by FFTs


An FFT that takes a complex-valued input vector of length N+1 and transforms it to a complex spectrum of length 2*N.

Generic floating point number, implemented for f32 and f64

An FFT that takes a real-valued input vector of length 2*N and transforms it to a complex spectrum of length N+1.