. The plus or minus sign in the argument of the exponential terms in the above definition determine the direction of the transform: a minus sign defines the forward direction and a plus sign defines the backward direction.

(Note the scale factor of

\frac{1}{\sqrt{n_{l}}}

in this definition.)

A call of nag_sum_fft_complex_multid_1d (c06pf) with

direct ='F'

followed by a call with

direct ='B'

will restore the original data.

The data values must be supplied in a one-dimensional complex array using column-major storage ordering of multidimensional data (i.e., with the first subscript

j_{1}

varying most rapidly).

This function calls nag_sum_fft_complex_1d_multi_row (c06pr) to perform one-dimensional discrete Fourier transforms. Hence, the function uses a variant of the fast Fourier transform (FFT) algorithm (see Brigham (1974)) known as the Stockham self-sorting algorithm, which is described in Temperton (1983).

References

Brigham E O (1974) The Fast Fourier Transform Prentice–Hall

Temperton C (1983) Self-sorting mixed-radix fast Fourier transforms J. Comput. Phys. 52 1–23

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: $l$ – int64int32nag_int scalar: $l$ , the index of the variable (or dimension) on which the discrete Fourier transform is to be performed.

Constraint: $1 \leq l \leq ndim$ .
3: $nd (ndim)$ – int64int32nag_int array: The elements of nd must contain the dimensions of the ndim variables; that is, $nd (i)$ must contain the dimension of the $i$ th variable.

Constraint: $nd (i) \geq 1$ , for $i = 1, 2, \dots, ndim$ .
4: $x (n)$ – complex array: The complex data values. Data values are stored in x using column-major ordering for storing multidimensional arrays; that is, $z_{j_{1} j_{2} \dots j_{m}}$ is stored in $x (1 + j_{1} + n_{1} j_{2} + n_{1} n_{2} j_{3} + \dots)$ .

Optional Input Parameters

1: $ndim$ – int64int32nag_int scalar: Default: the dimension of the array nd.
$m$ , the number of dimensions (or variables) in the multivariate data.

Constraint: $ndim \geq 1$ .
2: $n$ – int64int32nag_int scalar: Default: the dimension of the array x.
$n$ , the total number of data values.

Constraint: n must equal the product of the first ndim elements of the array nd.

Output Parameters

1: $x (n)$ – complex array: The corresponding elements of the computed transform.
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,

ndim < 1

$ifail = 2$

On entry,

l < 1

l > ndim

$ifail = 3$

On entry,

direct \neq'F'

'B'

$ifail = 4$

On entry,

at least one of the first ndim elements of nd is less than

1

$ifail = 5$

On entry,

n does not equal the product of the first ndim elements of nd.

$ifail = 6$

On entry,

lwork is too small. The minimum amount of workspace required is returned in

work (1)

$ifail = 8$: 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 is approximately proportional to

n \times \log n_{l}

, but also depends on the factorization of

n_{l}

. nag_sum_fft_complex_multid_1d (c06pf) is faster if the only prime factors of

n_{l}

are

2

3

5

; and fastest of all if

n_{l}

is a power of

2

Example

This example reads in a bivariate sequence of complex data values and prints the discrete Fourier transform of the second variable. It then performs an inverse transform and prints the sequence so obtained, which may be compared with the original data values.

Open in the MATLAB editor: c06pf_example

function c06pf_example


fprintf('c06pf example results\n\n');

% 2D Sequence
nd = [int64(3) 5];
x = [1.000+0.000i  0.999-0.040i  0.987-0.159i  0.936-0.352i  0.802-0.597i; 
     0.994-0.111i  0.989-0.151i  0.963-0.268i  0.891-0.454i  0.731-0.682i; 
     0.903-0.430i  0.885-0.466i  0.823-0.568i  0.694-0.720i  0.467-0.884i];

% Transform along second dimension
direct = 'F';
l = int64(2);
[xt, ifail] = c06pf(direct, l, nd, x);

% Restore x by inverse transform
direct = 'B';
[xr, ifail] = c06pf(direct, l, nd, xt);

disp('Original data:');
disp(x);
disp('Discrete Fourier Transform along second dimension:');
disp(reshape(xt,nd));
fprintf('Original sequence as restored by inverse transform:\n');
disp(reshape(xr,nd));

c06pf example results

Original data:
   1.0000 + 0.0000i   0.9990 - 0.0400i   0.9870 - 0.1590i   0.9360 - 0.3520i   0.8020 - 0.5970i
   0.9940 - 0.1110i   0.9890 - 0.1510i   0.9630 - 0.2680i   0.8910 - 0.4540i   0.7310 - 0.6820i
   0.9030 - 0.4300i   0.8850 - 0.4660i   0.8230 - 0.5680i   0.6940 - 0.7200i   0.4670 - 0.8840i

Discrete Fourier Transform along second dimension:
   2.1126 - 0.5134i   0.2880 - 0.0003i   0.1257 + 0.1298i  -0.0030 + 0.1899i  -0.2873 + 0.1940i
   2.0429 - 0.7451i   0.2862 - 0.0322i   0.1389 + 0.1148i   0.0180 + 0.1892i  -0.2633 + 0.2251i
   1.6869 - 1.3721i   0.2596 - 0.1246i   0.1695 + 0.0631i   0.0791 + 0.1731i  -0.1759 + 0.2988i

Original sequence as restored by inverse transform:
   1.0000 + 0.0000i   0.9990 - 0.0400i   0.9870 - 0.1590i   0.9360 - 0.3520i   0.8020 - 0.5970i
   0.9940 - 0.1110i   0.9890 - 0.1510i   0.9630 - 0.2680i   0.8910 - 0.4540i   0.7310 - 0.6820i
   0.9030 - 0.4300i   0.8850 - 0.4660i   0.8230 - 0.5680i   0.6940 - 0.7200i   0.4670 - 0.8840i

PDF version (NAG web site, 64-bit version, 64-bit version)

Chapter Contents

Chapter Introduction

NAG Toolbox