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NAG Toolbox: nag_sum_fft_real_qtrsine_simple (c06rc)

 Contents

    1  Purpose
    2  Syntax
    7  Accuracy
    9  Example

Purpose

nag_sum_fft_real_qtrsine_simple (c06rc) computes the discrete quarter-wave Fourier sine transforms of m sequences of real data values.

Syntax

[x, ifail] = c06rc(direct, m, n, x)
[x, ifail] = nag_sum_fft_real_qtrsine_simple(direct, m, n, x)

Description

Given m sequences of n real data values xjp , for j=1,2,,n and p=1,2,,m, nag_sum_fft_real_qtrsine_simple (c06rc) simultaneously calculates the quarter-wave Fourier sine transforms of all the sequences defined by
x^kp = 1n j=1 n-1 xjp × sin j 2k-1 π2n + 12 -1 k-1 xnp ,   if ​ direct='F' ,  
or its inverse
xkp = 2n j=1 n x^ j p × sin 2j- 1 k π2n ,   if ​ direct='B' ,  
where k=1,2,,n and p=1,2,,m.
(Note the scale factor 1n  in this definition.)
A call of nag_sum_fft_real_qtrsine_simple (c06rc) with direct='F' followed by a call with direct='B' 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 the left boundary, and the derivative of the solution is specified at the right boundary (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, 3, 4 and 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:     direct – string (length ≥ 1)
If the forward transform as defined in Description is to be computed, then direct must be set equal to 'F'.
If the backward transform is to be computed then direct must be set equal to 'B'.
Constraint: direct='F' or 'B'.
2:     m int64int32nag_int scalar
m, the number of sequences to be transformed.
Constraint: m1.
3:     n int64int32nag_int scalar
n, the number of real values in each sequence.
Constraint: n1.
4:     x m×n+2 – double array
the data must be stored in x as if in a two-dimensional array of dimension 1:m,1:n+2; each of the m sequences is stored in a row of the array. In other words, if the data values of the pth sequence to be transformed are denoted by xjp, for j=1,2,,n and p=1,2,,m, then the first mn elements of the array x must contain the values
x11 , x12 ,, x1m , x21 , x22 ,, x2m ,, xn1 , xn2 ,, xnm .  
The n+1th and n+2th elements of each row x n+1 p , x n+2 p , for p=1,2,,m, are required as workspace. These 2m elements may contain arbitrary values as they are set to zero by the function.

Optional Input Parameters

None.

Output Parameters

1:     x m×n+2 – double array
the m quarter-wave sine transforms stored as if in a two-dimensional array of dimension 1:m,1:n+2. Each of the m transforms is stored in a row of the array, overwriting the corresponding original sequence. If the n components of the pth quarter-wave sine transform are denoted by x^kp, for k=1,2,,n and p=1,2,,m, then the mn+2 elements of the array x contain the values
x^11 , x^12 ,, x^1m , x^21 , x^22 ,, x^2m ,, x^n1 , x^n2 ,, x^nm , 0 , 0 ,, 0  2m times .  
2:     ifail int64int32nag_int scalar
ifail=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
On entry,m<1.
   ifail=2
On entry,n<1.
   ifail=3
On entry,direct'F' or 'B'.
   ifail=4
An unexpected error has occurred in an internal call. Check all function calls and array dimensions. Seek expert help.
   ifail=-99
An unexpected error has been triggered by this routine. Please contact NAG.
   ifail=-399
Your licence key may have expired or may not have been installed correctly.
   ifail=-999
Dynamic memory allocation failed.

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).

Further Comments

The time taken by nag_sum_fft_real_qtrsine_simple (c06rc) is approximately proportional to nm logn, but also depends on the factors of n. nag_sum_fft_real_qtrsine_simple (c06rc) is fastest if the only prime factors of n are 2, 3 and 5, and is particularly slow if n is a large prime, or has large prime factors.

Example

This example reads in sequences of real data values and prints their quarter-wave sine transforms as computed by nag_sum_fft_real_qtrsine_simple (c06rc) with direct='F'. It then calls the function again with direct='B' and prints the results which may be compared with the original data.
function c06rc_example


fprintf('c06rc example results\n\n');

% Discrete quarter-wave sine transform of 3 sequences of length 6
direct = 'Forward';
m = int64(3);
n = int64(6);
x = zeros(m,(n+2));
x(1:m,1:n) = [ 0.3854  0.6772  0.1138  0.6751  0.6362  0.1424;
               0.5417  0.2983  0.1181  0.7255  0.8638  0.8723; 
               0.9172  0.0644  0.6037  0.6430  0.0428  0.4815];

[xt, ifail] = c06rc(direct, m, n, x);
disp('X under discrete quarter-wave sine transform:');
disp(reshape(xt(1:m*n),m,n));

% Reconstruct using same transform
direct = 'Backward';
[xr, ifail] = c06rc(direct, m, n, xt);
disp('X reconstructed by inverse quarter-wave sine transform:');
y = reshape(xr(1:m*n),m,n);
disp(y);


c06rc example results

X under discrete quarter-wave sine transform:
    0.7304    0.2078    0.1150    0.2577   -0.2869   -0.0815
    0.9274   -0.1152    0.2532    0.2883   -0.0026   -0.0635
    0.6268    0.3547    0.0760    0.3078    0.4987   -0.0507

X reconstructed by inverse quarter-wave sine transform:
    0.3854    0.6772    0.1138    0.6751    0.6362    0.1424
    0.5417    0.2983    0.1181    0.7255    0.8638    0.8723
    0.9172    0.0644    0.6037    0.6430    0.0428    0.4815


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