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Chapter Contents
Chapter Introduction
NAG Toolbox

# NAG Toolbox: nag_sum_fft_real_sine_simple (c06ra)

## Purpose

nag_sum_fft_real_sine_simple (c06ra) computes the discrete Fourier sine transforms of m$m$ sequences of real data values.

## Syntax

[x, ifail] = c06ra(m, n, x)
[x, ifail] = nag_sum_fft_real_sine_simple(m, n, x)

## Description

Given m$m$ sequences of n1 $n-1$ real data values xjp ${x}_{\mathit{j}}^{\mathit{p}}$, for j = 1,2,,n1$\mathit{j}=1,2,\dots ,n-1$ and p = 1,2,,m$\mathit{p}=1,2,\dots ,m$, nag_sum_fft_real_sine_simple (c06ra) simultaneously calculates the Fourier sine transforms of all the sequences defined by
 n − 1 x̂kp = sqrt(2/n) ∑ xjp × sin(jkπ/n),  k = 1,2, … ,n − 1​ and ​p = 1,2, … ,m. j = 1
$x^ kp = 2n ∑ j=1 n-1 xjp × sin( jk πn ) , k= 1, 2, …, n-1 ​ and ​ p= 1, 2, …, m .$
(Note the scale factor sqrt(2/n) $\sqrt{\frac{2}{n}}$ in this definition.)
Since the Fourier sine transform defined above is its own inverse, two consecutive calls of this function will restore the original data.
The transform calculated by this function can be used to solve Poisson's equation when the solution is specified at both left and right boundaries (see Swarztrauber (1977)).
The function uses a variant of the fast Fourier transform (FFT) algorithm (see Brigham (1974)) known as the Stockham self-sorting algorithm, described in Temperton (1983), together with pre- and post-processing stages described in Swarztrauber (1982). Special coding is provided for the factors 2$2$, 3$3$, 4$4$ and 5$5$.

## References

Brigham E O (1974) The Fast Fourier Transform Prentice–Hall
Swarztrauber P N (1977) The methods of cyclic reduction, Fourier analysis and the FACR algorithm for the discrete solution of Poisson's equation on a rectangle SIAM Rev. 19(3) 490–501
Swarztrauber P N (1982) Vectorizing the FFT's Parallel Computation (ed G Rodrique) 51–83 Academic Press
Temperton C (1983) Fast mixed-radix real Fourier transforms J. Comput. Phys. 52 340–350

## Parameters

### Compulsory Input Parameters

1:     m – int64int32nag_int scalar
m$m$, the number of sequences to be transformed.
Constraint: m1${\mathbf{m}}\ge 1$.
2:     n – int64int32nag_int scalar
One more than the number of real values in each sequence, i.e., the number of values in each sequence is n1$n-1$.
Constraint: n1${\mathbf{n}}\ge 1$.
3:     x( m × (n + 2) ${\mathbf{m}}×\left({\mathbf{n}}+2\right)$) – double array
the data must be stored in x as if in a two-dimensional array of dimension (1 : m,1 : n + 2)$\left(1:{\mathbf{m}},1:{\mathbf{n}}+2\right)$; each of the m$m$ sequences is stored in a row of the array. In other words, if the n1$n-1$ data values of the p$\mathit{p}$th sequence to be transformed are denoted by xjp${x}_{\mathit{j}}^{\mathit{p}}$, for j = 1,2,,n1$\mathit{j}=1,2,\dots ,n-1$ and p = 1,2,,m$\mathit{p}=1,2,\dots ,m$, then the first m(n1)$m\left(n-1\right)$ elements of the array x must contain the values
 x11 , x12 , … , x1m , x21 , x22 , … , x2m , … , xn − 11 , xn − 12 , … , xn − 1m . $x11 , x12 ,…, x1m , x21 , x22 ,…, x2m ,…, x n-1 1 , x n-1 2 ,…, x n-1 m .$
The n$n$th to (n + 2)$\left(n+2\right)$th elements of each row xnp ,, xn + 2p${x}_{n}^{\mathit{p}},\dots ,{x}_{n+2}^{\mathit{p}}$, for p = 1,2,,m$\mathit{p}=1,2,\dots ,m$, are required as workspace. These 3m$3m$ elements may contain arbitrary values as they are set to zero by the function.

None.

work

### Output Parameters

1:     x( m × (n + 2) ${\mathbf{m}}×\left({\mathbf{n}}+2\right)$) – double array
the m$m$ Fourier sine transforms stored as if in a two-dimensional array of dimension (1 : m,1 : n + 2)$\left(1:{\mathbf{m}},1:{\mathbf{n}}+2\right)$. Each of the m$m$ transforms is stored in a row of the array, overwriting the corresponding original sequence. If the (n1)$\left(n-1\right)$ components of the p$p$th Fourier sine transform are denoted by kp${\stackrel{^}{x}}_{\mathit{k}}^{\mathit{p}}$, for k = 1,2,,n1$\mathit{k}=1,2,\dots ,n-1$ and p = 1,2,,m$\mathit{p}=1,2,\dots ,m$, then the m(n + 2)$m\left(n+2\right)$ elements of the array x contain the values
 x̂11 , x̂12 , … , x̂1m , x̂21 , x̂22 , … , x̂2m , … , x̂n − 11 , x̂n − 12 , … , x̂n − 1m , 0 , 0 , … , 0  (3m times) .
2:     ifail – int64int32nag_int scalar
${\mathrm{ifail}}={\mathbf{0}}$ unless the function detects an error (see [Error Indicators and Warnings]).

## Error Indicators and Warnings

Errors or warnings detected by the function:
ifail = 1${\mathbf{ifail}}=1$
 On entry, m < 1${\mathbf{m}}<1$.
ifail = 2${\mathbf{ifail}}=2$
 On entry, n < 1${\mathbf{n}}<1$.
ifail = 3${\mathbf{ifail}}=3$
An unexpected error has occurred in an internal call. Check all function calls and array dimensions. Seek expert help.

## Accuracy

Some indication of accuracy can be obtained by performing a subsequent inverse transform and comparing the results with the original sequence (in exact arithmetic they would be identical).

The time taken by nag_sum_fft_real_sine_simple (c06ra) is approximately proportional to nm log(n)$nm\mathrm{log}\left(n\right)$, but also depends on the factors of n$n$. nag_sum_fft_real_sine_simple (c06ra) is fastest if the only prime factors of n$n$ are 2$2$, 3$3$ and 5$5$, and is particularly slow if n$n$ is a large prime, or has large prime factors.

## Example

```function nag_sum_fft_real_sine_simple_example
m = int64(3);
n = int64(6);
x = [0.6772;
0.2983;
0.0644;
0.1138;
0.1181;
0.6037;
0.6751;
0.7255;
0.643;
0.6362;
0.8638;
0.0428;
0.1424;
0.8723;
0.4815;
0;
0;
0;
0;
0;
0;
0;
0;
0];
[xOut, ifail] = nag_sum_fft_real_sine_simple(m, n, x)
```
```

xOut =

1.0014
1.2477
0.8521
0.0062
-0.6599
0.0719
0.0834
0.2570
-0.0561
0.5286
0.0859
-0.4890
0.2514
0.2658
0.2056
0
0
0
0
0
0
0
0
0

ifail =

0

```
```function c06ra_example
m = int64(3);
n = int64(6);
x = [0.6772;
0.2983;
0.0644;
0.1138;
0.1181;
0.6037;
0.6751;
0.7255;
0.643;
0.6362;
0.8638;
0.0428;
0.1424;
0.8723;
0.4815;
0;
0;
0;
0;
0;
0;
0;
0;
0];
[xOut, ifail] = c06ra(m, n, x)
```
```

xOut =

1.0014
1.2477
0.8521
0.0062
-0.6599
0.0719
0.0834
0.2570
-0.0561
0.5286
0.0859
-0.4890
0.2514
0.2658
0.2056
0
0
0
0
0
0
0
0
0

ifail =

0

```