This is the first of a series of posts about implementing the Fast Fourier Transform in Haskell (and trying to make it go *fast*). This part is going to be pretty pedestrian, but we need to lay some groundwork before the interesting stuff.

Fourier transforms turn up everywhere. They’re important in pure mathematics, but they’re also used in pretty much any application that deals with time series: filtering, signal analysis, numerical solution of differential equations and many others. While the classical theory of Fourier analysis is based on functions, in applications we most often deal with discrete series of values sampled from a function. The *discrete Fourier transform* is how we do Fourier analysis in this setting.

In this series of posts, we’re not going to look very much at applications (well, maybe we’ll have one little exception a bit later) and we’re not going to talk a lot about the theory behind the Fourier transform. Instead, after briefly introducing the discrete Fourier transform, we’re going to use the *fast Fourier transform* algorithm and some variations to explore some aspects of Haskell programming. In particular, we’ll look at how Haskell deals with complex numbers, vectors, profiling and benchmarking, meta-programming and finally, as our *pièce de résistance*, we’ll use all of this to build a Haskell fast Fourier transform package that does compile-time empirical optimisation for arbitrary-sized transforms.

In practice, if you want to do Fourier transforms of discrete data, you use something called FFTW (“the Fastest Fourier Transform in the West”). Our code has no chance of competing with FFTW, which is incredibly clever^{1}, but we can demonstrate some of the methods that they use and learn some techniques along the way that are applicable to more complicated problems. (There are Haskell bindings for FFTW – the `vector-fftw`

package is a good choice.)

According to the Fourier theorem, any piecewise-continuous complex-valued function of a real variable, $h(t)$, periodic on the interval $[0, T]$, can be written as a Fourier series

$h(t) = \sum_{n=-\infty}^{\infty} a_n e^{2\pi i n t / T}$

where the orthogonality of the functions $\exp (2\pi i n t / T), n \in \mathbb{Z}$ on the interval $[0, T]$ allows us to calculate the complex-valued Fourier coefficients $a_n$ as

$a_n = \frac{1}{T} \int_0^T h(t) e^{-2\pi i n t / T} \, dt.$

In data analysis applications, we most commonly don’t have continuous functions, but discretely *sampled* data. Suppose that we have $N$ data values from a function $h(t)$ sampled at a regular interval $\Delta$:

$h_n = h(n \Delta) \qquad n = 0, 1, 2, \dots, N-1.$

The discrete Fourier transform of this set of data values is defined as

$H_n = \sum_{k=0}^{N-1} h_k e^{2\pi i k n/N} \qquad n = 0, 1, 2, \dots, N-1. \qquad (*)$

There are a few things to note here. First, like the classical Fourier transform, the discrete Fourier transform is a *linear* operation. That will prove to be important! Second, we have $N$ input values and $N$ output values, which is as many independent values as we can generate from $N$ inputs with a linear operation.

If we think of our input data as sampling a continuous function $h(t)$ at $N$ discrete times, $n\Delta$ with $n = 0, 1, 2, \dots, N-1$, then we can think of the discrete Fourier transform as sampling the “full” Fourier transform at a finite set of frequencies

$f_n = n/(N\Delta) \qquad n = -N/2, \dots, N/2.$

Because we our sampling at an interval $\Delta$, we cannot represent any functions containing frequency components above the *Nyquist frequency*

$f_c = \frac{1}{2\Delta},$

which is just the frequency at the limits of the frequency range given above. (There are $N+1$ values listed in the range above because the Fourier components for $\pm f_c$ are identical.)

From the $H_n$, we can recover the original samples $h_k$ using the inverse discrete Fourier transform

$h_k = \frac{1}{N} \sum_{n=0}^{N-1} H_n e^{-2\pi i k n/N} \qquad n = 0, 1, 2,$

which is identical to the forward transform except for the sign in the complex exponential and the factor of $1/N$. We’ll get a better understanding of this in the next article when we think about the DFT as a matrix multiplying our data vector.

The transform in $(*)$ is easy to implement directly in Haskell. Here’s a very naïve and inefficient implementation (we’re going to be spending a lot of time thinking about optimisation and efficiency later, so let’s not worry about efficiency right now):

```
import Prelude hiding (length, sum, map, zipWith, (++))
import Data.Complex
import Data.Vector
i :: Complex Double
i = 0 :+ 1
omega :: Int -> Complex Double
omega n = cis (2 * pi / fromIntegral n)
dft, idft :: Vector (Complex Double) -> Vector (Complex Double)
dft = dft' 1 1
idft v = dft' (-1) (1.0 / (fromIntegral $ length v)) v
dft' :: Int -> Double -> Vector (Complex Double) -> Vector (Complex Double)
dft' sign scale h = generate bigN (((scale :+ 0) *) . doone)
where bigN = length h
w = omega bigN
doone n = sum $
zipWith (*) h $ generate bigN (\k -> w^^(sign*n*k))
defuzz :: Vector (Complex Double) -> Vector (Complex Double)
defuzz = map (\(r :+ i) -> df r :+ df i)
where df x = if abs x < 1.0E-6 then 0 else x
```

We’re going to be manipulating complex numbers throughout this set of articles, so we import the `Data.Complex`

module, part of the Haskell 2010 standard, to make this easy. The `Complex`

data type is parameterised over the type of the real and imaginary parts of the number and has a constructor `:+`

to create a complex number from its real and imaginary parts. `Data.Complex`

provides instances of all the expected numeric classes so that you can do arithmetic with complex values just as for other numeric values. Definitions are also provided for transcendental functions ($\sin$, $\cos$, $\log$, etc.), and the standard explicitly mandates definitions of these so you can see how they deal with branch cuts and other issues. Finally, `Data.Complex`

provides some convenience functions for working with complex numbers, in particular for converting between Cartesian and polar representations. It also provides the `cis`

function: $\mathtt{cis}\, \theta = \cos \theta + i \sin \theta$.

We’re also going to be dealing with vectors of numbers a lot. For that, we use the `vector`

package, which provides arrays indexed by `Int`

s with $O(1)$ indexing and powerful fusion rules for loop optimisation. It also has vector equivalents of a lot of the standard Prelude list functions (`length`

, `take`

, `drop`

, `map`

, `zipWith`

, etc.). To avoid name clashes, we’ll mostly hide the Prelude versions of these functions, although you can also use a qualified import of `Data.Vector`

if you prefer.

Take a look at the type of `dft`

in the listing above. It both takes and returns a `Vector (Complex Double)`

. Throughout this series of posts, we’re only going to be dealing with complex-to-complex DFTs and we’re going to do everything in double precision. In production-quality library code, we would parameterise over the floating point type that we use, and we would also implement the full range of real-to-complex and real-to-real versions of the DFT. The data management issues involved in that aren’t too interesting for us here though, so we’ll stick with complex-to-complex transforms.

The algorithm used above is basically just a direct transcription of the sum in $(*)$, using

`Data.Vector.generate :: Int -> (Int -> a) -> Vector a`

to calculate vector entries for each integer vector index. It’s very inefficient, not only because it does an $O(N^2)$ matrix multiplication (which the FFT aims to fix!), but also because of repeated computation of the complex exponential factors. Anyway, efficiency doesn’t matter here: we just want a simple and clear implementation to compare our more complicated FFT implementations to later on. We use the `dft'`

function as a helper to allow us to implement the forward (`dft`

) and inverse (`idft`

) transforms using the same code.

We can do a couple of quick tests to see if it works. In GHCi (the `defuzz`

function just sets any small components of numbers to zero for display):

```
> :load DFT.hs
[1 of 1] Compiling DFT ( DFT.hs, interpreted )
Ok, modules loaded: DFT.
> dft $ fromList [1,0,0,0,0,0,0,0]
fromList [1.0 :+ 0.0,1.0 :+ 0.0,1.0 :+ 0.0,1.0 :+ 0.0,
1.0 :+ 0.0,1.0 :+ 0.0,1.0 :+ 0.0,1.0 :+ 0.0]
> defuzz $ idft $ dft $ fromList [1,0,0,0]
fromList [1.0 :+ 0.0,0.0 :+ 0.0,0.0 :+ 0.0,0.0 :+ 0.0]
> defuzz $ dft $ generate 8 (\i ->
sin (2 * pi * fromIntegral i / 8))
fromList [0.0 :+ 0.0,0.0 :+ 4.0,0.0 :+ 0.0,0.0 :+ 0.0,
0.0 :+ 0.0,0.0 :+ 0.0,0.0 :+ 0.0,0.0 :+ (-3.9999999999999987)]
```

First, we look at the DFT of a single “spike”, which corresponds (more or less) to a Dirac $\delta$-function in classical Fourier analysis. The Fourier transform of a $\delta$-function is a constant, and we see that the result of the DFT is constant for our spike, as we would hope. Applying the inverse DFT to the transform gets us back our original data (although as a vector of `Complex Double`

). Next, we look at a sine curve. Recall that

$\sin (n\theta) = \frac{e^{in\theta} - e^{-in\theta}}{2i} = -\frac{i}{2} e^{in\theta} + \frac{i}{2} e^{-in\theta}$

so, for a complex Fourier transform, we would expect to see a negative imaginary component of magnitude $\frac12$ at a positive frequency $n$ and a positive imaginary component of magnitude $\frac12$ at negative frequency $n$. Here, $n = 1$ (we’re fitting the biggest whole sine curve into our data array that we can). Because of the data layout conventionally used in the DFT, that means that our positive frequency component is at index 1 of the output array, and the negative frequency component is at index 7. The magnitudes of the Fourier components are scaled with respect to those of classical Fourier analysis by a factor of $N$, the size of the input array, so we expect our entries to have magnitude 4. This is indeed what we see, except that the signs appear to be wrong – but remember that the sign in the complex exponential in the inverse DFT is the opposite of the forward transform, and it’s the inverse transform that we’d be plugging these values into. So all is well, and the code above appears to work.

So, that wasn’t very exciting, but it sets the scene for what comes next, which is more fun. In the next article, we’ll examine how the fast Fourier transform algorithm (specifically, the *Cooley-Tukey decimation-in-time* algorithm) allows us to replace the $O(N^2)$ DFT calculation with one that scales as $O(N \log N)$ for vectors whose lengths are powers of two. We’ll use a clever matrix decomposition to represent the key step in the FFT algorithm, and we’ll write some “toy algebra” code that will enable us to experiment, check some assumptions and generally get a better idea of just what’s going on.

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