Fast Fourier transform
You are encouraged to solve this task according to the task description, using any language you may know.
The purpose of this task is to calculate the FFT (Fast Fourier Transform) of an input sequence. The most general case allows for complex numbers at the input and results in a sequence of equal length, again of complex numbers. If you need to restrict yourself to real numbers the output should be the magnitude (i.e. sqrt(re²+im²)) of the complex result. The classic version is the recursive Cooley–Tukey FFT. Wikipedia has pseudocode for that. Further optimizations are possible but not required.
Ada
The FFT function is defined as a generic function, instantiated upon a user instance of Ada.Numerics.Generic_Complex_Arrays.
<lang Ada> with Ada.Numerics.Generic_Complex_Arrays;
generic
with package Complex_Arrays is
new Ada.Numerics.Generic_Complex_Arrays (<>);
use Complex_Arrays;
function Generic_FFT (X : Complex_Vector) return Complex_Vector;
</lang>
<lang Ada> with Ada.Numerics; with Ada.Numerics.Generic_Complex_Elementary_Functions;
function Generic_FFT (X : Complex_Vector) return Complex_Vector is
package Complex_Elementary_Functions is
new Ada.Numerics.Generic_Complex_Elementary_Functions
(Complex_Arrays.Complex_Types);
use Ada.Numerics;
use Complex_Elementary_Functions;
use Complex_Arrays.Complex_Types;
function FFT (X : Complex_Vector; N, S : Positive)
return Complex_Vector is
begin
if N = 1 then
return (1..1 => X (X'First));
else
declare
F : constant Complex := exp (Pi * j / Real_Arrays.Real (N/2));
Even : Complex_Vector := FFT (X, N/2, 2*S);
Odd : Complex_Vector := FFT (X (X'First + S..X'Last), N/2, 2*S);
begin
for K in 0..N/2 - 1 loop
declare
T : constant Complex := Odd (Odd'First + K) / F ** K;
begin
Odd (Odd'First + K) := Even (Even'First + K) - T;
Even (Even'First + K) := Even (Even'First + K) + T;
end;
end loop;
return Even & Odd;
end;
end if;
end FFT;
begin
return FFT (X, X'Length, 1);
end Generic_FFT; </lang>
Example:
<lang Ada> with Ada.Numerics.Complex_Arrays; use Ada.Numerics.Complex_Arrays; with Ada.Complex_Text_IO; use Ada.Complex_Text_IO; with Ada.Text_IO; use Ada.Text_IO;
with Ada.Numerics.Complex_Elementary_Functions; with Generic_FFT;
procedure Example is
function FFT is new Generic_FFT (Ada.Numerics.Complex_Arrays); X : Complex_Vector := (1..4 => (1.0, 0.0), 5..8 => (0.0, 0.0)); Y : Complex_Vector := FFT (X);
begin
Put_Line (" X FFT X ");
for I in Y'Range loop
Put (X (I - Y'First + X'First), Aft => 3, Exp => 0);
Put (" ");
Put (Y (I), Aft => 3, Exp => 0);
New_Line;
end loop;
end; </lang>
Output:
X FFT X ( 1.000, 0.000) ( 4.000, 0.000) ( 1.000, 0.000) ( 1.000,-2.414) ( 1.000, 0.000) ( 0.000, 0.000) ( 1.000, 0.000) ( 1.000,-0.414) ( 0.000, 0.000) ( 0.000, 0.000) ( 0.000, 0.000) ( 1.000, 0.414) ( 0.000, 0.000) ( 0.000, 0.000) ( 0.000, 0.000) ( 1.000, 2.414)
ALGOL 68
Note: This specimen retains the original Python coding style.
File: Template.Fast_Fourier_transform.a68<lang algol68>PRIO DICE = 9; # ideally = 11 #
OP DICE = ([]SCALAR in, INT step)[]SCALAR: (
### Dice the array, extract array values a "step" apart ###
IF step = 1 THEN
in
ELSE
INT upb out := 0;
[(UPB in-LWB in)%step+1]SCALAR out;
FOR index FROM LWB in BY step TO UPB in DO
out[upb out+:=1] := in[index] OD;
out[@LWB in]
FI
);
PROC fft = ([]SCALAR in t)[]SCALAR: (
### The Cooley-Tukey FFT algorithm ###
IF LWB in t >= UPB in t THEN
in t[@0]
ELSE
[]SCALAR t = in t[@0];
INT n = UPB t + 1, half n = n % 2;
[LWB t:UPB t]SCALAR coef;
[]SCALAR even = fft(t DICE 2),
odd = fft(t[1:]DICE 2);
COMPL i = 0 I 1;
REAL w = 2*pi / n;
FOR k FROM LWB t TO half n-1 DO
COMPL cis t = scalar exp(0 I (-w * k))*odd[k];
coef[k] := even[k] + cis t;
coef[k + half n] := even[k] - cis t
OD;
coef
FI
);</lang>File: test.Fast_Fourier_transform.a68<lang algol68>#!/usr/local/bin/a68g --script #
- -*- coding: utf-8 -*- #
MODE SCALAR = COMPL; PROC (COMPL)COMPL scalar exp = complex exp; PR READ "Template.Fast_Fourier_transform.a68" PR
FORMAT real fmt := $g(0,3)$; FORMAT real array fmt := $f(real fmt)", "$; FORMAT compl fmt := $f(real fmt)"⊥"f(real fmt)$; FORMAT compl array fmt := $f(compl fmt)", "$;
test:(
[]COMPL
tooth wave ft = fft((1, 1, 1, 1, 0, 0, 0, 0)),
one and a quarter wave ft = fft((0, 0.924, 0.707,-0.383,-1,-0.383, 0.707, 0.924,
0,-0.924,-0.707, 0.383, 1, 0.383,-0.707,-0.924));
printf((
$"Tooth wave: "$,compl array fmt, tooth wave ft, $l$,
$"1¼ cycle wave: "$, compl array fmt, one and a quarter wave ft, $l$
))
)</lang>Output:
Tooth wave: 4.000⊥.000, 1.000⊥-2.414, .000⊥.000, 1.000⊥-.414, .000⊥.000, 1.000⊥.414, .000⊥.000, 1.000⊥2.414, 1¼ cycle wave: .000⊥.000, .000⊥.001, .000⊥.000, .000⊥-8.001, .000⊥.000, -.000⊥-.001, .000⊥.000, .000⊥.001, .000⊥.000, .000⊥-.001, .000⊥.000, -.000⊥.001, .000⊥.000, -.000⊥8.001, .000⊥.000, -.000⊥-.001,
BBC BASIC
<lang bbcbasic> @% = &60A
DIM Complex{r#, i#}
DIM in{(7)} = Complex{}, out{(7)} = Complex{}
DATA 1, 1, 1, 1, 0, 0, 0, 0
PRINT "Input (real, imag):"
FOR I% = 0 TO 7
READ in{(I%)}.r#
PRINT in{(I%)}.r# "," in{(I%)}.i#
NEXT
PROCfft(out{()}, in{()}, 0, 1, DIM(in{()},1)+1)
PRINT "Output (real, imag):"
FOR I% = 0 TO 7
PRINT out{(I%)}.r# "," out{(I%)}.i#
NEXT
END
DEF PROCfft(b{()}, o{()}, B%, S%, N%)
LOCAL I%, t{} : DIM t{} = Complex{}
IF S% < N% THEN
PROCfft(o{()}, b{()}, B%, S%*2, N%)
PROCfft(o{()}, b{()}, B%+S%, S%*2, N%)
FOR I% = 0 TO N%-1 STEP 2*S%
t.r# = COS(-PI*I%/N%)
t.i# = SIN(-PI*I%/N%)
PROCcmul(t{}, o{(B%+I%+S%)})
b{(B%+I% DIV 2)}.r# = o{(B%+I%)}.r# + t.r#
b{(B%+I% DIV 2)}.i# = o{(B%+I%)}.i# + t.i#
b{(B%+(I%+N%) DIV 2)}.r# = o{(B%+I%)}.r# - t.r#
b{(B%+(I%+N%) DIV 2)}.i# = o{(B%+I%)}.i# - t.i#
NEXT
ENDIF
ENDPROC
DEF PROCcmul(c{},d{})
LOCAL r#, i#
r# = c.r#*d.r# - c.i#*d.i#
i# = c.r#*d.i# + c.i#*d.r#
c.r# = r#
c.i# = i#
ENDPROC
</lang> Output:
Input (real, imag):
1, 0
1, 0
1, 0
1, 0
0, 0
0, 0
0, 0
0, 0
Output (real, imag):
4, 0
1, -2.41421
0, 0
1, -0.414214
0, 0
1, 0.414214
0, 0
1, 2.41421
C
Inplace FFT with O(n) memory usage. Note: array size is assumed to be power of 2 and not checked by code; you can just pad it with 0 otherwise. Also, complex is C99 standard.<lang C>#include <stdio.h>
- include <math.h>
- include <complex.h>
double PI; typedef double complex cplx;
void _fft(cplx buf[], cplx out[], int n, int step) { if (step < n) { _fft(out, buf, n, step * 2); _fft(out + step, buf + step, n, step * 2);
for (int i = 0; i < n; i += 2 * step) { cplx t = cexp(-I * PI * i / n) * out[i + step]; buf[i / 2] = out[i] + t; buf[(i + n)/2] = out[i] - t; } } }
void fft(cplx buf[], int n) { cplx out[n]; for (int i = 0; i < n; i++) out[i] = buf[i];
_fft(buf, out, n, 1); }
int main() { PI = atan2(1, 1) * 4; cplx buf[] = {1, 1, 1, 1, 0, 0, 0, 0};
void show(const char * s) { printf("%s", s); for (int i = 0; i < 8; i++) if (!cimag(buf[i])) printf("%g ", creal(buf[i])); else printf("(%g, %g) ", creal(buf[i]), cimag(buf[i])); }
show("Data: "); fft(buf, 8); show("\nFFT : ");
return 0; }</lang>Output<lang>Data: 1 1 1 1 0 0 0 0 FFT : 4 (1, -2.41421) 0 (1, -0.414214) 0 (1, 0.414214) 0 (1, 2.41421)</lang>
C++
<lang cpp>#include <complex>
- include <iostream>
- include <valarray>
const double PI = 3.141592653589793238460;
typedef std::complex<double> Complex; typedef std::valarray<Complex> CArray;
// Cooley–Tukey FFT (in-place) void fft(CArray& x) {
const size_t N = x.size(); if (N <= 1) return;
// divide CArray even = x[std::slice(0, N/2, 2)]; CArray odd = x[std::slice(1, N/2, 2)];
// conquer fft(even); fft(odd);
// combine
for (size_t k = 0; k < N/2; ++k)
{
Complex t = std::polar(1.0, -2 * PI * k / N) * odd[k];
x[k ] = even[k] + t;
x[k+N/2] = even[k] - t;
}
}
// inverse fft (in-place) void ifft(CArray& x) {
// conjugate the complex numbers x = x.apply(std::conj);
// forward fft fft( x );
// conjugate the complex numbers again x = x.apply(std::conj);
// scale the numbers x /= x.size();
}
int main() {
const Complex test[] = { 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0 };
CArray data(test, 8);
// forward fft fft(data);
std::cout << "fft" << std::endl;
for (int i = 0; i < 8; ++i)
{
std::cout << data[i] << std::endl;
}
// inverse fft ifft(data);
std::cout << std::endl << "ifft" << std::endl;
for (int i = 0; i < 8; ++i)
{
std::cout << data[i] << std::endl;
}
return 0;
}</lang> Output:
fft (4,0) (1,-2.41421) (0,0) (1,-0.414214) (0,0) (1,0.414214) (0,0) (1,2.41421) ifft (1,-0) (1,-5.55112e-017) (1,2.4895e-017) (1,-5.55112e-017) (5.55112e-017,0) (5.55112e-017,5.55112e-017) (0,-2.4895e-017) (5.55112e-017,5.55112e-017)
D
Standard Version
<lang d>import std.stdio, std.numeric;
void main() {
[1.0, 1, 1, 1, 0, 0, 0, 0].fft.writeln;
}</lang>
- Output:
[4+0i, 1-2.41421i, 0-0i, 1-0.414214i, 0+0i, 1+0.414214i, 0+0i, 1+2.41421i]
creals Version
Built-in complex numbers will be deprecated. <lang d>import std.stdio, std.algorithm, std.range, std.math;
const(creal)[] fft(in creal[] x) /*pure nothrow*/ {
immutable N = x.length; if (N <= 1) return x; const ev = x.stride(2).array.fft; const od = x[1 .. $].stride(2).array.fft; auto l = iota(N / 2).map!(k => ev[k] + expi(-2*PI * k/N) * od[k]); auto r = iota(N / 2).map!(k => ev[k] - expi(-2*PI * k/N) * od[k]); return l.chain(r).array;
}
void main() {
[1.0L+0i, 1, 1, 1, 0, 0, 0, 0].fft.writeln;
}</lang>
- Output:
[4+0i, 1+-2.41421i, 0+0i, 1+-0.414214i, 0+0i, 1+0.414214i, 0+0i, 1+2.41421i]
Phobos Complex Version
<lang d>import std.stdio, std.algorithm, std.range, std.math, std.complex;
auto fft(T)(in T[] x) /*pure nothrow*/ {
immutable N = x.length; if (N <= 1) return x; const ev = x.stride(2).array.fft; const od = x[1 .. $].stride(2).array.fft; alias E = std.complex.expi; auto l = iota(N / 2).map!(k => ev[k] + T(E(-2* PI * k/N)) * od[k]); auto r = iota(N / 2).map!(k => ev[k] - T(E(-2* PI * k/N)) * od[k]); return l.chain(r).array;
}
void main() {
[1.0, 1, 1, 1, 0, 0, 0, 0].map!complex.array.fft.writeln;
}</lang>
- Output:
[4+0i, 1-2.41421i, 0+0i, 1-0.414214i, 0+0i, 1+0.414214i, 0+0i, 1+2.41421i]
Factor
<lang Factor> IN: USE math.transforms.fft IN: { 1 1 1 1 0 0 0 0 } fft . {
C{ 4.0 0.0 }
C{ 1.0 -2.414213562373095 }
C{ 0.0 0.0 }
C{ 1.0 -0.4142135623730949 }
C{ 0.0 0.0 }
C{ 0.9999999999999999 0.4142135623730949 }
C{ 0.0 0.0 }
C{ 0.9999999999999997 2.414213562373095 }
} </lang>
Fortran
<lang Fortran> module fft_mod
implicit none integer, parameter :: dp=selected_real_kind(15,300) real(kind=dp), parameter :: pi=3.141592653589793238460_dp
contains
! In place Cooley-Tukey FFT recursive subroutine fft(x) complex(kind=dp), dimension(:), intent(inout) :: x complex(kind=dp) :: t integer :: N integer :: i complex(kind=dp), dimension(:), allocatable :: even, odd
N=size(x)
if(N .le. 1) return
allocate(odd((N+1)/2)) allocate(even(N/2))
! divide odd =x(1:N:2) even=x(2:N:2)
! conquer call fft(odd) call fft(even)
! combine
do i=1,N/2
t=exp(cmplx(0.0_dp,-2.0_dp*pi*real(i-1,dp)/real(N,dp),KIND=DP))*even(i)
x(i) = odd(i) + t
x(i+N/2) = odd(i) - t
end do
deallocate(odd) deallocate(even)
end subroutine fft
end module fft_mod
program test
use fft_mod implicit none complex(kind=dp), dimension(8) :: data = (/1.0, 1.0, 1.0, 1.0, 0.0,
0.0, 0.0, 0.0/)
integer :: i
call fft(data)
do i=1,8
write(*,'("(", F20.15, ",", F20.15, "i )")') data(i)
end do
end program test</lang>
Output:
( 4.000000000000000, 0.000000000000000i ) ( 1.000000000000000, -2.414213562373095i ) ( 0.000000000000000, 0.000000000000000i ) ( 1.000000000000000, -0.414213562373095i ) ( 0.000000000000000, 0.000000000000000i ) ( 1.000000000000000, 0.414213562373095i ) ( 0.000000000000000, 0.000000000000000i ) ( 1.000000000000000, 2.414213562373095i )
GAP
<lang gap># Here an implementation with no optimization (O(n^2)).
- In GAP, E(n) = exp(2*i*pi/n), a primitive root of the unity.
Fourier := function(a) local n, z; n := Size(a); z := E(n); return List([0 .. n - 1], k -> Sum([0 .. n - 1], j -> a[j + 1]*z^(-k*j))); end;
InverseFourier := function(a) local n, z; n := Size(a); z := E(n); return List([0 .. n - 1], k -> Sum([0 .. n - 1], j -> a[j + 1]*z^(k*j)))/n; end;
Fourier([1, 1, 1, 1, 0, 0, 0, 0]);
- [ 4, 1-E(8)-E(8)^2-E(8)^3, 0, 1-E(8)+E(8)^2-E(8)^3,
- 0, 1+E(8)-E(8)^2+E(8)^3, 0, 1+E(8)+E(8)^2+E(8)^3 ]
InverseFourier(last);
- [ 1, 1, 1, 1, 0, 0, 0, 0 ]</lang>
Go
<lang go>package main
import (
"fmt" "math" "math/cmplx"
)
func ditfft2(x []float64, y []complex128, n, s int) {
if n == 1 {
y[0] = complex(x[0], 0)
return
}
ditfft2(x, y, n/2, 2*s)
ditfft2(x[s:], y[n/2:], n/2, 2*s)
for k := 0; k < n/2; k++ {
tf := cmplx.Rect(1, -2*math.Pi*float64(k)/float64(n)) * y[k+n/2]
y[k], y[k+n/2] = y[k]+tf, y[k]-tf
}
}
func main() {
x := []float64{1, 1, 1, 1, 0, 0, 0, 0}
y := make([]complex128, len(x))
ditfft2(x, y, len(x), 1)
for _, c := range y {
fmt.Printf("%8.4f\n", c)
}
}</lang> Output:
( 4.0000 +0.0000i) ( 1.0000 -2.4142i) ( 0.0000 +0.0000i) ( 1.0000 -0.4142i) ( 0.0000 +0.0000i) ( 1.0000 +0.4142i) ( 0.0000 +0.0000i) ( 1.0000 +2.4142i)
Haskell
<lang haskell>import Data.Complex
-- Cooley-Tukey fft [] = [] fft [x] = [x] fft xs = zipWith (+) ys ts ++ zipWith (-) ys ts
where n = length xs
ys = fft evens
zs = fft odds
(evens, odds) = split xs
split [] = ([], [])
split [x] = ([x], [])
split (x:y:xs) = (x:xt, y:yt) where (xt, yt) = split xs
ts = zipWith (\z k -> exp' k n * z) zs [0..]
exp' k n = cis $ -2 * pi * (fromIntegral k) / (fromIntegral n)
main = mapM_ print $ fft [1,1,1,1,0,0,0,0]</lang>
And the output:
4.0 :+ 0.0 1.0 :+ (-2.414213562373095) 0.0 :+ 0.0 1.0 :+ (-0.4142135623730949) 0.0 :+ 0.0 0.9999999999999999 :+ 0.4142135623730949 0.0 :+ 0.0 0.9999999999999997 :+ 2.414213562373095
J
Based on j:Essays/FFT, with some simplifications -- sacrificing accuracy, optimizations and convenience which are not relevant to the task requirements, for clarity:
<lang j>cube =: ($~ q:@#) :. , rou =: ^@j.@o.@(% #)@i.@-: NB. roots of unity floop =: 4 : 'for_r. i.#$x do. (y=.{."1 y) ] x=.(+/x) ,&,:"r (-/x)*y end.' fft =: ] floop&.cube rou@#</lang>
Example (first row of result is sine, second row of result is fft of the first row, (**+)&.+. cleans an irrelevant least significant bit of precision from the result so that it displays nicely):
<lang j> (**+)&.+. (,: fft) 1 o. 2p1*3r16 * i.16 0 0.92388 0.707107 0.382683 1 0.382683 0.707107 0.92388 0 0.92388 0.707107 0.382683 1 0.382683 0.707107 0.92388 0 0 0 0j8 0 0 0 0 0 0 0 0 0 0j8 0 0</lang>
Here is a representation of an example which appears in some of the other implementations, here: <lang J> Re=: {.@+.@fft
Im=: {:@+.@fft
M=: 4#1 0
M
1 1 1 1 0 0 0 0
Re M
4 1 0 1 0 1 0 1
Im M
0 2.41421 0 0.414214 0 _0.414214 0 _2.41421</lang>
Note that Re and Im are not functions of 1 and 0 but are functions of the complete sequence.
Also note that J uses a different character for negative sign than for subtraction, to eliminate ambiguity (is this a single list of numbers or are lists being subtracted?).
JavaScript
Complex fourier transform & it's inverse reimplemented from the C++ & Python variants on this page.
<lang javascript> /* complex fast fourier transform and inverse from http://rosettacode.org/wiki/Fast_Fourier_transform#C.2B.2B
- /
function icfft(amplitudes) { var N = amplitudes.length; var iN = 1 / N;
//conjugate if imaginary part is not 0 for(var i = 0 ; i < N; ++i) if(amplitudes[i] instanceof Complex) amplitudes[i].im = -amplitudes[i].im;
//apply fourier transform amplitudes = cfft(amplitudes)
for(var i = 0 ; i < N; ++i) { //conjugate again amplitudes[i].im = -amplitudes[i].im; //scale amplitudes[i].re *= iN; amplitudes[i].im *= iN; } return amplitudes; }
function cfft(amplitudes) { var N = amplitudes.length; if( N <= 1 ) return amplitudes;
var hN = N / 2; var even = []; var odd = []; even.length = hN; odd.length = hN; for(var i = 0; i < hN; ++i) { even[i] = amplitudes[i*2]; odd[i] = amplitudes[i*2+1]; } even = cfft(even); odd = cfft(odd);
var a = -2*Math.PI; for(var k = 0; k < hN; ++k) { if(!(even[k] instanceof Complex)) even[k] = new Complex(even[k], 0); if(!(odd[k] instanceof Complex)) odd[k] = new Complex(odd[k], 0); var p = k/N; var t = new Complex(0, a * p); t.cexp(t).mul(odd[k], t); amplitudes[k] = even[k].add(t, odd[k]); amplitudes[k + hN] = even[k].sub(t, even[k]); } return amplitudes; }
//test code //console.log( cfft([1,1,1,1,0,0,0,0]) ); //console.log( icfft(cfft([1,1,1,1,0,0,0,0])) ); </lang> Very very basic Complex number that provides only the components required by the code above. <lang javascript> /* basic complex number arithmetic from http://rosettacode.org/wiki/Fast_Fourier_transform#Scala
- /
function Complex(re, im) { this.re = re; this.im = im || 0.0; } Complex.prototype.add = function(other, dst) { dst.re = this.re + other.re; dst.im = this.im + other.im; return dst; } Complex.prototype.sub = function(other, dst) { dst.re = this.re - other.re; dst.im = this.im - other.im; return dst; } Complex.prototype.mul = function(other, dst) { //cache re in case dst === this var r = this.re * other.re - this.im * other.im; dst.im = this.re * other.im + this.im * other.re; dst.re = r; return dst; } Complex.prototype.cexp = function(dst) { var er = Math.exp(this.re); dst.re = er * Math.cos(this.im); dst.im = er * Math.sin(this.im); return dst; } Complex.prototype.log = function() { /* although 'It's just a matter of separating out the real and imaginary parts of jw.' is not a helpful quote the actual formula I found here and the rest was just fiddling / testing and comparing with correct results. http://cboard.cprogramming.com/c-programming/89116-how-implement-complex-exponential-functions-c.html#post637921 */ if( !this.re ) console.log(this.im.toString()+'j'); else if( this.im < 0 ) console.log(this.re.toString()+this.im.toString()+'j'); else console.log(this.re.toString()+'+'+this.im.toString()+'j'); } </lang>
Julia
Julia has a built-in FFT function: <lang julia>fft([1,1,1,1,0,0,0,0])</lang> Output: <lang julia>8-element Array{Complex{Float64},1}:
4.0+0.0im
1.0-2.41421im
0.0+0.0im
1.0-0.414214im
0.0+0.0im
1.0+0.414214im
0.0+0.0im
1.0+2.41421im</lang>
Liberty BASIC
<lang lb>
P =8 S =int( log( P) /log( 2) +0.9999)
Pi =3.14159265 R1 =2^S
R =R1 -1 R2 =div( R1, 2) R4 =div( R1, 4) R3 =R4 +R2
Dim Re( R1), Im( R1), Co( R3)
for N =0 to P -1
read dummy: Re( N) =dummy
read dummy: Im( N) =dummy
next N
data 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
S2 =div( S, 2) S1 =S -S2 P1 =2^S1 P2 =2^S2
dim V( P1 -1) V( 0) =0 DV =1 DP =P1
for J =1 to S1
HA =div( DP, 2)
PT =P1 -HA
for I =HA to PT step DP
V( I) =V( I -HA) +DV
next I
DV =DV +DV
DP =HA
next J
K =2 *Pi /R1
for X =0 to R4
COX =cos( K *X)
Co( X) =COX
Co( R2 -X) =0 -COX
Co( R2 +X) =0 -COX
next X
print "FFT: bit reversal"
for I =0 to P1 -1
IP =I *P2
for J =0 to P2 -1
H =IP +J
G =V( J) *P2 +V( I)
if G >H then temp =Re( G): Re( G) =Re( H): Re( H) =temp
if G >H then temp =Im( G): Im( G) =Im( H): Im( H) =temp
next J
next I
T =1
for stage =0 to S -1
print " Stage:- "; stage
D =div( R2, T)
for Z =0 to T -1
L =D *Z
LS =L +R4
for I =0 to D -1
A =2 *I *T +Z
B =A +T
F1 =Re( A)
F2 =Im( A)
P1 =Co( L) *Re( B)
P2 =Co( LS) *Im( B)
P3 =Co( LS) *Re( B)
P4 =Co( L) *Im( B)
Re( A) =F1 +P1 -P2
Im( A) =F2 +P3 +P4
Re( B) =F1 -P1 +P2
Im( B) =F2 -P3 -P4
next I
next Z
T =T +T
next stage
print " M Re( M) Im( M)"
for M =0 to R
if abs( Re( M)) <10^-5 then Re( M) =0
if abs( Im( M)) <10^-5 then Im( M) =0
print " "; M, Re( M), Im( M)
next M
end
wait
function div( a, b)
div =int( a /b)
end function
end
</lang>
M Re( M) Im( M) 0 4 0 1 1.0 -2.41421356 2 0 0 3 1.0 -0.41421356 4 0 0 5 1.0 0.41421356 6 0 0 7 1.0 2.41421356
Maple
Maple has a built-in package DiscreteTransforms, and FourierTransform and InverseFourierTransform are in the commands available from that package. The FourierTransform command offers an FFT method by default.
<lang Maple> with( DiscreteTransforms ):
FourierTransform( <1,1,1,1,0,0,0,0>, normalization=none ); </lang>
[ 4. + 0. I ]
[ ]
[1. - 2.41421356237309 I ]
[ ]
[ 0. + 0. I ]
[ ]
[1. - 0.414213562373095 I]
[ ]
[ 0. + 0. I ]
[ ]
[1. + 0.414213562373095 I]
[ ]
[ 0. + 0. I ]
[ ]
[1. + 2.41421356237309 I ]
Optionally, the FFT may be performed inplace on a Vector of hardware double-precision complex floats. <lang Maple> v := Vector( [1,1,1,1,0,0,0,0], datatype=complex[8] ):
FourierTransform( v, normalization=none, inplace ):
v; </lang>
[ 4. + 0. I ]
[ ]
[1. - 2.41421356237309 I ]
[ ]
[ 0. + 0. I ]
[ ]
[1. - 0.414213562373095 I]
[ ]
[ 0. + 0. I ]
[ ]
[1. + 0.414213562373095 I]
[ ]
[ 0. + 0. I ]
[ ]
[1. + 2.41421356237309 I ]
<lang Maple> InverseFourierTransform( v, normalization=full, inplace ):
v; </lang>
[ 1. + 0. I ]
[ ]
[ 1. + 0. I ]
[ ]
[ 1. + 0. I ]
[ ]
[ 1. + 0. I ]
[ ]
[ 0. + 0. I ]
[ ]
[ 0. + 0. I ]
[ ]
[ -17 ]
[5.55111512312578 10 + 0. I]
[ ]
[ 0. + 0. I ]
Mathematica
Mathematica has a built-in FFT function which uses a proprietary algorithm developed at Wolfram Research. It also has an option to tune the algorithm for specific applications. The options shown below, while not default, produce output that is consistent with most other FFT routines.
<lang Mathematica> Fourier[{1,1,1,1,0,0,0,0}, FourierParameters->{1,-1}] </lang>
Output:
{4. + 0. I, 1. - 2.4142136 I, 0. + 0. I, 1. - 0.41421356 I, 0. + 0. I, 1. + 0.41421356 I, 0. + 0. I, 1. + 2.4142136 I}
MATLAB / Octave
Matlab/Octave have a builtin FFT function.
<lang MATLAB> fft([1,1,1,1,0,0,0,0]') </lang> Output:
ans = 4.00000 + 0.00000i 1.00000 - 2.41421i 0.00000 + 0.00000i 1.00000 - 0.41421i 0.00000 + 0.00000i 1.00000 + 0.41421i 0.00000 - 0.00000i 1.00000 + 2.41421i
Maxima
<lang maxima>load(fft)$ fft([1, 2, 3, 4]); [2.5, -0.5 * %i - 0.5, -0.5, 0.5 * %i - 0.5]</lang>
OCaml
This is a simple implementation of the Cooley-Tukey pseudo-code <lang OCaml>open Complex
let fac k n =
let m2pi = -4.0 *. acos 0.0 in polar 1.0 (m2pi*.(float k)/.(float n))
let merge l r n =
let f (k,t) x = (succ k, (mul (fac k n) x) :: t) in let z = List.rev (snd (List.fold_left f (0,[]) r)) in (List.map2 add l z) @ (List.map2 sub l z)
let fft lst =
let rec ditfft2 a n s =
if n = 1 then [List.nth lst a] else
let odd = ditfft2 a (n/2) (2*s) in
let even = ditfft2 (a+s) (n/2) (2*s) in
merge odd even n in
ditfft2 0 (List.length lst) 1;;
let show l =
let pr x = Printf.printf "(%f %f) " x.re x.im in (List.iter pr l; print_newline ()) in
let indata = [one;one;one;one;zero;zero;zero;zero] in show indata; show (fft indata)</lang>
Output:
(1.000000 0.000000) (1.000000 0.000000) (1.000000 0.000000) (1.000000 0.000000) (0.000000 0.000000) (0.000000 0.000000) (0.000000 0.000000) (0.000000 0.000000) (4.000000 0.000000) (1.000000 -2.414214) (0.000000 0.000000) (1.000000 -0.414214) (0.000000 0.000000) (1.000000 0.414214) (0.000000 0.000000) (1.000000 2.414214)
PARI/GP
Naive implementation, using the same testcase as Ada: <lang parigp>FFT(v)=my(t=-2*Pi*I/#v,tt);vector(#v,k,tt=t*(k-1);sum(n=0,#v-1,v[n+1]*exp(tt*n))); FFT([1,1,1,1,0,0,0,0])</lang> Output:
[4.0000000000000000000000000000000000000, 1.0000000000000000000000000000000000000 - 2.4142135623730950488016887242096980786*I, 0.E-37 + 0.E-38*I, 1.0000000000000000000000000000000000000 - 0.41421356237309504880168872420969807856*I, 0.E-38 + 0.E-37*I, 0.99999999999999999999999999999999999997 + 0.41421356237309504880168872420969807860*I, 4.701977403289150032 E-38 + 0.E-38*I, 0.99999999999999999999999999999999999991 + 2.4142135623730950488016887242096980785*I]
Perl
<lang Perl>use strict; use warnings; use Math::Complex;
sub fft {
return @_ if @_ == 1;
my @evn = fft(@_[grep { not $_ % 2 } 0 .. $#_ ]);
my @odd = fft(@_[grep { $_ % 2 } 1 .. $#_ ]);
my $twd = 2*i* pi / @_;
$odd[$_] *= exp( $_ * $twd ) for 0 .. $#odd;
return
(map { $evn[$_] + $odd[$_] } 0 .. $#evn ),
(map { $evn[$_] - $odd[$_] } 0 .. $#evn );
}
my @seq = 0 .. 15;
my $cycles = 3;
my @wave = map { sin( $_ * 2*pi/ @seq * $cycles ) } @seq;
print "wave: ", join " ", map { sprintf "%7.3f", $_ } @wave;
print "\n";
print "fft: ", join " ", map { sprintf "%7.3f", abs $_ } fft(@wave);</lang>
- Output:
wave: 0.000 0.924 0.707 -0.383 -1.000 -0.383 0.707 0.924 0.000 -0.924 -0.707 0.383 1.000 0.383 -0.707 -0.924 fft: 0.000 0.000 0.000 8.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 8.000 0.000 0.000
Perl 6
<lang perl6>sub fft {
return @_ if @_ == 1; my @evn = fft( @_[0, 2 ... *] ); my @odd = fft( @_[1, 3 ... *] ); @odd »*=» (1, * * exp( 2i * pi / @_ ) ... *); return @evn »+« @odd, @evn »-« @odd;
}
my @seq = ^16; my $cycles = 3; my @wave = map { sin( 2*pi * $_ / @seq * $cycles ) }, @seq; say "wave: ", @wave.fmt("%7.3f");
say "fft: ", fft(@wave)».abs.fmt("%7.3f");</lang>
- Output:
wave: 0.000 0.924 0.707 -0.383 -1.000 -0.383 0.707 0.924 0.000 -0.924 -0.707 0.383 1.000 0.383 -0.707 -0.924 fft: 0.000 0.000 0.000 8.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 8.000 0.000 0.000
PicoLisp
<lang PicoLisp># apt-get install libfftw3-dev
(scl 4)
(de FFTW_FORWARD . -1) (de FFTW_ESTIMATE . 64)
(de fft (Lst)
(let
(Len (length Lst)
In (native "libfftw3.so" "fftw_malloc" 'N (* Len 16))
Out (native "libfftw3.so" "fftw_malloc" 'N (* Len 16))
P (native "libfftw3.so" "fftw_plan_dft_1d" 'N
Len In Out FFTW_FORWARD FFTW_ESTIMATE ) )
(struct In NIL (cons 1.0 (apply append Lst)))
(native "libfftw3.so" "fftw_execute" NIL P)
(prog1 (struct Out (make (do Len (link (1.0 . 2)))))
(native "libfftw3.so" "fftw_destroy_plan" NIL P)
(native "libfftw3.so" "fftw_free" NIL Out)
(native "libfftw3.so" "fftw_free" NIL In) ) ) )</lang>
Test: <lang PicoLisp>(for R (fft '((1.0 0) (1.0 0) (1.0 0) (1.0 0) (0 0) (0 0) (0 0) (0 0)))
(tab (6 8)
(round (car R))
(round (cadr R)) ) )</lang>
Output:
4.000 0.000 1.000 -2.414 0.000 0.000 1.000 -0.414 0.000 0.000 1.000 0.414 0.000 0.000 1.000 2.414
PL/I
<lang PL/I>test: PROCEDURE OPTIONS (MAIN, REORDER); /* Derived from Fortran Rosetta Code */
/* In-place Cooley-Tukey FFT */
FFT: PROCEDURE (x) RECURSIVE;
DECLARE x(*) COMPLEX FLOAT (18); DECLARE t COMPLEX FLOAT (18); DECLARE ( N, Half_N ) FIXED BINARY (31); DECLARE ( i, j ) FIXED BINARY (31); DECLARE (even(*), odd(*)) CONTROLLED COMPLEX FLOAT (18); DECLARE pi FLOAT (18) STATIC INITIAL ( 3.14159265358979323E0);
N = HBOUND(x);
if N <= 1 THEN return;
allocate odd((N+1)/2), even(N/2);
/* divide */ do j = 1 to N by 2; odd((j+1)/2) = x(j); end; do j = 2 to N by 2; even(j/2) = x(j); end;
/* conquer */ call fft(odd); call fft(even);
/* combine */
half_N = N/2;
do i=1 TO half_N;
t = exp(COMPLEX(0, -2*pi*(i-1)/N))*even(i);
x(i) = odd(i) + t;
x(i+half_N) = odd(i) - t;
end;
FREE odd, even;
END fft;
DECLARE data(8) COMPLEX FLOAT (18) STATIC INITIAL (
1, 1, 1, 1, 0, 0, 0, 0);
DECLARE ( i ) FIXED BINARY (31);
call fft(data);
do i=1 TO 8;
PUT SKIP LIST ( fixed(data(i), 25, 12) );
end;
END test;</lang> output:
4.000000000000+0.000000000000I
1.000000000000-2.414213562373I
0.000000000000+0.000000000000I
1.000000000000-0.414213562373I
0.000000000000+0.000000000000I
0.999999999999+0.414213562373I
0.000000000000+0.000000000000I
0.999999999999+2.414213562373I
Prolog
Note: Similar algorithmically to the python example.
<lang prolog>:- dynamic twiddles/2. %_______________________________________________________________ % Arithemetic for complex numbers; only the needed rules add(cx(R1,I1),cx(R2,I2),cx(R,I)) :- R is R1+R2, I is I1+I2. sub(cx(R1,I1),cx(R2,I2),cx(R,I)) :- R is R1-R2, I is I1-I2. mul(cx(R1,I1),cx(R2,I2),cx(R,I)) :- R is R1*R2-I1*I2, I is R1*I2+R2*I1. polar_cx(Mag, Theta, cx(R, I)) :- % Euler R is Mag * cos(Theta), I is Mag * sin(Theta). %___________________________________________________ % FFT Implementation. Note: K rdiv N is a rational number, % making the lookup in dynamic database predicate twiddles/2 very % efficient. Also, polar_cx/2 gets called only when necessary- in % this case (N=8), exactly 3 times: (where Tf=1/4, 1/8, or 3/8). tw(0,cx(1,0)) :- !. % Calculate e^(-2*pi*k/N) tw(Tf, Cx) :- twiddles(Tf, Cx), !. % dynamic match? tw(Tf, Cx) :- polar_cx(1.0, -2*pi*Tf, Cx), assert(twiddles(Tf, Cx)).
fftVals(N, Even, Odd, V0, V1) :- % solves all V0,V1 for N,Even,Odd nth0(K,Even,E), nth0(K,Odd,O), Tf is K rdiv N, tw(Tf,Cx), mul(Cx,O,M), add(E,M,V0), sub(E,M,V1).
split([],[],[]). % split [[a0,b0],[a1,b1],...] into [a0,a1,...] and [b0,b1,...] split([[V0,V1]|T], [V0|T0], [V1|T1]) :- !, split(T, T0, T1).
fft([H], [H]). fft([H|T], List) :- length([H|T],N), findall(Ve, (nth0(I,[H|T],Ve),I mod 2 =:= 0), EL), !, fft(EL, Even), findall(Vo, (nth0(I,T,Vo),I mod 2 =:= 0),OL), !, fft(OL, Odd), findall([V0,V1],fftVals(N,Even,Odd,V0,V1),FFTVals), % calc FFT split(FFTVals,L0,L1), append(L0,L1,List). %___________________________________________________ test :- D=[cx(1,0),cx(1,0),cx(1,0),cx(1,0),cx(0,0),cx(0,0),cx(0,0),cx(0,0)], time(fft(D,DRes)), writef('fft=['), P is 10^3, !, (member(cx(Ri,Ii), DRes), R is integer(Ri*P)/P, I is integer(Ii*P)/P, write(R), (I>=0, write('+'),fail;write(I)), write('j, '), fail; write(']'), nl). </lang>
Output:
test. % 681 inferences, 0.000 CPU in 0.001 seconds (0% CPU, Infinite Lips) fft=[4+0j, 1-2.414j, 0+0j, 1-0.414j, 0+0j, 1+0.414j, 0+0j, 1+2.414j, ] true.
Python
<lang python>from cmath import exp, pi
def fft(x):
N = len(x)
if N <= 1: return x
even = fft(x[0::2])
odd = fft(x[1::2])
return [even[k] + exp(-2j*pi*k/N)*odd[k] for k in xrange(N/2)] + \
[even[k] - exp(-2j*pi*k/N)*odd[k] for k in xrange(N/2)]
print fft([1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0])</lang>
Output:
[(4+0j), (1-2.4142135623730949j), 0j, (1-0.41421356237309492j), 0j, (0.99999999999999989+0.41421356237309492j), 0j, (0.99999999999999967+2.4142135623730949j)]
Using module numpy
<lang python>>>> from numpy.fft import fft >>> from numpy import array >>> a = array([1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0]) >>> print( ' '.join("%5.3f" % abs(f) for f in fft(a)) ) 4.000 2.613 0.000 1.082 0.000 1.082 0.000 2.613<lang>
R
The function "fft" is readily available in R <lang R>fft(c(1,1,1,1,0,0,0,0))</lang> Output:
4+0.000000i 1-2.414214i 0+0.000000i 1-0.414214i 0+0.000000i 1+0.414214i 0+0.000000i 1+2.414214i
Racket
<lang racket>
- lang racket
(require math) (array-fft (array #[1. 1. 1. 1. 0. 0. 0. 0.])) </lang>
Output: <lang racket> (fcarray
#[4.0+0.0i 1.0-2.414213562373095i 0.0+0.0i 1.0-0.4142135623730949i 0.0+0.0i 0.9999999999999999+0.4142135623730949i 0.0+0.0i 0.9999999999999997+2.414213562373095i])
</lang>
REXX
This REXX program is modeled after the Run BASIC version and is a radix-2 DIC (decimation-in-time) form of the
Cooley-Turkey FFT algorithm, and as such, this simplified form assumes that the number of data points is equal to an exact power of two.
Note that the REXX language doesn't have any higher math functions, so the functions COS and R2R
(normalization of radians) have been included here, as well as the constant pi .
This program also adds zero values if the number of data points in the list doesn't exactly equal to a power of two.
This is called zero-padding.
<lang rexx>/*REXX pgm does a fast Fourier transform (FFT) on a set of complex nums.*/
numeric digits length( pi() ) - 1 /*limited by PI function result. */
arg data; if data= then data='1 1 1 1 0' /*no data? Then use default*/
data=translate(data, 'J', "I") /*allow use of i as well as j */
size=words(data); pad=left(,6) /*PAD: for indenting/padding SAYs*/
do sig=0 until 2**sig>=size ;end /* # args exactly a power of 2?*/ do j=size+1 to 2**sig;data=data 0;end /*add zeroes until a power of 2.*/
size=words(data); call hdr /*┌─────────────────────────────┐*/
/*│ Numbers in data can be in │*/
do j=0 for size /*│ 7 formats: real │*/
_=word(data,j+1) /*│ real,imag │*/
parse var _ #.1.j ',' #.2.j /*│ ,imag │*/
if right(#.1.j,1)=='J' then /*│ nnnJ │*/
parse var #.1.j #2.j "J" @.1.j /*│ nnnj │*/
do p=1 for 2 /*omitted?*/ /*│ nnnI │*/
#.p.j=word(#.p.j 0, 1) /*│ nnni │*/
end /*p*/ /*└─────────────────────────────┘*/
say pad " FFT in " center(j+1,7) pad nice(#.1.j) nice(#.2.j,'i')
end /*j*/
say; say; tran=2*pi()/2**sig; !.=0 hsig=2**sig%2; counterA=2**(sig-sig%2); pointer=counterA; doubler=1
do sig-sig%2; halfpointer=pointer%2
do i=halfpointer by pointer to counterA-halfpointer
_=i-halfpointer; !.i=!._+doubler
end /*i*/
doubler=doubler*2; pointer=halfpointer
end /*sig-sig%2*/
do j=0 to 2**sig%4; cmp.j=cos(j*tran); _m=hsig-j; cmp._m=-cmp.j
_p=hsig+j; cmp._p=-cmp.j
end /*j*/
counterB=2**(sig%2)
do i=0 for counterA; p=i*counterB
do j=0 for counterB; h=p+j; _=!.j*counterB+!.i; if _<=h then iterate
parse value #.1._ #.1.h #.2._ #.2.h with #.1.h #.1._ #.2.h #.2._
end /*j*/ /* [↓] switch two sets of values*/
end /*i*/
double=1; do sig ; w=2**sig%2%double
do k=0 for double ; lb=w*k ; lh=lb+2**sig%4
do j=0 for w ; a=j*double*2+k ; b=a+double
r=#.1.a; i=#.2.a ; c1=cmp.lb*#.1.b ; c4=cmp.lb*#.2.b
c2=cmp.lh*#.2.b ; c3=cmp.lh*#.1.b
#.1.a=r+c1-c2 ; #.2.a=i+c3+c4
#.1.b=r-c1+c2 ; #.2.b=i-c3-c4
end /*j*/
end /*k*/
double=double+double
end /*sig*/
call hdr
do i=0 for size
say pad " FFT out " center(i+1,7) pad nice(#.1.i) nice(#.2.i,'j')
end /*i*/
exit /*stick a fork in it, we're done.*/ /*──────────────────────────────────HDR subroutine──────────────────────*/ hdr: _='───data─── num real-part imaginary-part'; say pad _
say pad translate(_, " "copies('═',256), " "xrange()); return
/*──────────────────────────────────PI subroutine───────────────────────────────────*/ pi: return , /*add more digs if NUMERIC DIGITS > 85. */ 3.141592653589793238462643383279502884197169399375105820974944592307816406286208998628 /*──────────────────────────────────R2R subroutine──────────────────────*/ r2r: return arg(1) // (2*pi()) /*reduce radians to unit circle. */ /*──────────────────────────────────COS subroutine──────────────────────*/ cos: procedure; parse arg x; x=r2r(x); return .sincos(1,1,-1) .sincos: parse arg z,_,i; x=x*x; p=z
do k=2 by 2; _=-_*x/(k*(k+i)); z=z+_; if z=p then leave; p=z; end
return z
/*──────────────────────────────────NICE subroutine─────────────────────*/ nice: procedure; parse arg x,j /*makes complex nums look nicer. */ numeric digits digits()%10; nz='1e-'digits() /*show ≈10% of DIGITS.*/ if abs(x)<nz then x=0; x=x/1; if x=0 & j\== then return x=format(x,,digits()); if pos('.',x)\==0 then x=strip(x,'T',0) x=strip(x,,'.'); if x>=0 then x=' '||x; return left(x||j,digits()+4)</lang> output when using the default input of: 1 1 1 1 0
───data─── num real-part imaginary-part
══════════ ═══ ═════════ ══════════════
FFT in 1 1
FFT in 2 1
FFT in 3 1
FFT in 4 1
FFT in 5 0
FFT in 6 0
FFT in 7 0
FFT in 8 0
───data─── num real-part imaginary-part
══════════ ═══ ═════════ ══════════════
FFT out 1 4
FFT out 2 1 -2.4142136j
FFT out 3 0
FFT out 4 1 -0.41421356j
FFT out 5 0
FFT out 6 1 0.41421356j
FFT out 7 0
FFT out 8 1 2.4142136j
Ruby
<lang ruby>def fft(vec)
return vec if vec.size <= 1
evens_odds = vec.partition.with_index{|_,i| i.even?}
evens, odds = evens_odds.map{|even_odd| fft(even_odd)*2}
evens.zip(odds).map.with_index do |(even, odd),i|
even + odd * Math::E ** Complex(0, 2 * Math::PI * (-i)/ vec.size)
end
end
fft([1,1,1,1,0,0,0,0]).each{|c| p c} </lang>
- Output:
(4+0i) (1.0-2.414213562373095i) (-1.2246467991473532e-16-1.2246467991473532e-16i) (1.0-0.4142135623730949i) (0.0-2.4492935982947064e-16i) (0.9999999999999998+0.41421356237309515i) (1.2246467991473532e-16-1.224646799147353e-16i) (0.9999999999999993+2.414213562373095i)
Run BASIC
<lang runbasic>cnt = 8 sig = int(log(cnt) /log(2) +0.9999)
pi = 3.14159265 real1 = 2^sig
real = real1 -1 real2 = int(real1 / 2) real4 = int(real1 / 4) real3 = real4 +real2
dim rel(real1) dim img(real1) dim cmp(real3)
for i = 0 to cnt -1
read rel(i) read img(i)
next i
data 1,0, 1,0, 1,0, 1,0, 0,0, 0,0, 0,0, 0,0
sig2 = int(sig / 2) sig1 = sig -sig2 cnt1 = 2^sig1 cnt2 = 2^sig2
dim v(cnt1 -1) v(0) = 0 dv = 1 ptr = cnt1
for j = 1 to sig1
hlfPtr = int(ptr / 2)
pt = cnt1 - hlfPtr
for i = hlfPtr to pt step ptr
v(i) = v(i -hlfPtr) + dv
next i
dv = dv + dv
ptr = hlfPtr
next j
k = 2 *pi /real1
for x = 0 to real4
cmp(x) = cos(k *x) cmp(real2 - x) = 0 - cmp(x) cmp(real2 + x) = 0 - cmp(x)
next x
print "fft: bit reversal"
for i = 0 to cnt1 -1
ip = i *cnt2
for j = 0 to cnt2 -1
h = ip +j
g = v(j) *cnt2 +v(i)
if g >h then
temp = rel(g)
rel(g) = rel(h)
rel(h) = temp
temp = img(g)
img(g) = img(h)
img(h) = temp
end if
next j
next i
t = 1 for stage = 1 to sig
print " stage:- "; stage
d = int(real2 / t)
for ii = 0 to t -1
l = d *ii
ls = l +real4
for i = 0 to d -1
a = 2 *i *t +ii
b = a +t
f1 = rel(a)
f2 = img(a)
cnt1 = cmp(l) *rel(b)
cnt2 = cmp(ls) *img(b)
cnt3 = cmp(ls) *rel(b)
cnt4 = cmp(l) *img(b)
rel(a) = f1 + cnt1 - cnt2
img(a) = f2 + cnt3 + cnt4
rel(b) = f1 - cnt1 + cnt2
img(b) = f2 - cnt3 - cnt4
next i
next ii
t = t +t
next stage
print " Num real imag" for i = 0 to real
if abs(rel(i)) <10^-5 then rel(i) = 0
if abs(img(i)) <10^-5 then img(i) = 0
print " "; i;" ";using("##.#",rel(i));" ";img(i)
next i end</lang>
Num real imag 0 4.0 0 1 1.0 -2.41421356 2 0.0 0 3 1.0 -0.414213565 4 0.0 0 5 1.0 0.414213562 6 0.0 0 7 1.0 2.41421356
Scala
<lang Scala>object FFT extends App {
import scala.math._
case class Complex(re: Double, im: Double = 0.0) {
def +(x: Complex): Complex = Complex((this.re+x.re),(this.im+x.im))
def -(x: Complex): Complex = Complex((this.re-x.re),(this.im-x.im))
def *(x: Complex): Complex = Complex(this.re*x.re-this.im*x.im,this.re*x.im+this.im*x.re)
}
def fft(f: List[Complex]): List[Complex] = {
import Stream._
require((f.size==0)||(from(0) map {x=>pow(2,x).toInt}).takeWhile(_<2*f.size).toList.exists(_==f.size)==true,"list size "+f.size+" not allowed!")
f.size match {
case 0 => Nil
case 1 => f
case n => {
val cis: Double => Complex = phi => Complex(cos(phi),sin(phi))
val e = fft(f.zipWithIndex.filter(_._2%2==0).map(_._1))
val o = fft(f.zipWithIndex.filter(_._2%2!=0).map(_._1))
import scala.collection.mutable.ListBuffer
val lb = new ListBuffer[Pair[Int, Complex]]()
for (k <- 0 to n/2-1) {
lb += Pair(k,e(k)+o(k)*cis(-2*Pi*k/n))
lb += Pair(k+n/2,e(k)-o(k)*cis(-2*Pi*k/n))
}
lb.toList.sortWith((x,y)=>x._1<y._1).map(_._2)
}
}
}
fft(List(Complex(1),Complex(1),Complex(1),Complex(1),Complex(0),Complex(0),Complex(0),Complex(0))).foreach(println)
}</lang> Output:
Complex(4.0,0.0) Complex(1.0,-2.414213562373095) Complex(0.0,0.0) Complex(1.0,-0.4142135623730949) Complex(0.0,0.0) Complex(0.9999999999999999,0.4142135623730949) Complex(0.0,0.0) Complex(0.9999999999999997,2.414213562373095)
Scilab
Scilab has a builtin FFT function.
<lang Scilab>fft([1,1,1,1,0,0,0,0]')</lang>
Tcl
<lang tcl>package require math::constants package require math::fourier
math::constants::constants pi
- Helper functions
proc wave {samples cycles} {
global pi
set wave {}
set factor [expr {2*$pi * $cycles / $samples}]
for {set i 0} {$i < $samples} {incr i} {
lappend wave [expr {sin($factor * $i)}]
} return $wave
} proc printwave {waveName {format "%7.3f"}} {
upvar 1 $waveName wave
set out [format "%-6s" ${waveName}:]
foreach value $wave {
append out [format $format $value]
} puts $out
} proc waveMagnitude {wave} {
set out {}
foreach value $wave {
lassign $value re im lappend out [expr {hypot($re, $im)}]
} return $out
}
set wave [wave 16 3] printwave wave
- Uses FFT if input length is power of 2, and a less efficient algorithm otherwise
set fft [math::fourier::dft $wave]
- Convert to magnitudes for printing
set fft2 [waveMagnitude $fft] printwave fft2</lang> Output:
wave: 0.000 0.924 0.707 -0.383 -1.000 -0.383 0.707 0.924 0.000 -0.924 -0.707 0.383 1.000 0.383 -0.707 -0.924 fft2: 0.000 0.000 0.000 8.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 8.000 0.000 0.000
Ursala
The fftw library is callable from Ursala using the syntax ..u_fw_dft for a one dimensional forward discrete Fourier transform operating on a list of complex numbers. Ordinarily the results are scaled so that the forward and reverse transforms are inverses of each other, but additional scaling can be performed as shown below to conform to convention.
<lang ursala>#import nat
- import flo
f = <1+0j,1+0j,1+0j,1+0j,0+0j,0+0j,0+0j,0+0j> # complex sequence of 4 1's and 4 0's
g = c..mul^*D(sqrt+ float+ length,..u_fw_dft) f # its fft
- cast %jLW
t = (f,g)</lang> output:
(
<
1.000e+00+0.000e+00j,
1.000e+00+0.000e+00j,
1.000e+00+0.000e+00j,
1.000e+00+0.000e+00j,
0.000e+00+0.000e+00j,
0.000e+00+0.000e+00j,
0.000e+00+0.000e+00j,
0.000e+00+0.000e+00j>,
<
4.000e+00+0.000e+00j,
1.000e+00-2.414e+00j,
0.000e+00+0.000e+00j,
1.000e+00-4.142e-01j,
0.000e+00+0.000e+00j,
1.000e+00+4.142e-01j,
0.000e+00+0.000e+00j,
1.000e+00+2.414e+00j>)
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