k-Wave Toolbox |

# gradientFD

Calculate the gradient using a finite-difference method

## Syntax

    fx = gradientFD(f, dx)
    fx = gradientFD(f, dx, [], deriv_order)
    fx = gradientFD(f, dx, [], deriv_order, accuracy_order)
    fn = gradientFD(f, dn, dim)
    fn = gradientFD(f, dn, dim, deriv_order, accuracy_order)
    [fx, fy] = gradientFD(f, dn)
    [fx, fy] = gradientFD(f, dn, [], deriv_order, accuracy_order)
    [fx, fy, fz, ...] = gradientFD(f, dn)
    [fx, fy, fz, ...] = gradientFD(f, dn, [], deriv_order, accuracy_order)

## Description

`gradientFD` calculates the gradient of an n-dimensional input matrix using the finite-difference method. For one-dimensional inputs, the gradient is always computed along the non-singleton dimension. For higher dimensional inputs, the gradient for singleton dimensions is returned as 0. For elements in the centre of the grid, the gradient is computed using centered finite-differences. For elements on the edge of the grid, the gradient is computed using forward or backward finite-differences. The order of accuracy of the finite-difference approximation is controlled by `accuracy_order` (default = 2). The calculations are done using sparse multiplication, so the input matrix is always cast to double precision.

A 1D example:

    % compute gradient of a 2 period sinusoid
    x = pi/20:pi/20:4*pi;
    y = sin(x);
    dydx_4th_ord = gradientFD(y, pi/20, [], 1, 4);

    % plot gradient and error compared to analytical solution
    subplot(2, 1, 1), plot(x, cos(x), 'k-', x, dydx_4th_ord, 'bx');
    axis tight;
    title('dy/dx');
    subplot(2, 1, 2), plot(x, cos(x) - dydx_4th_ord, 'k-');
    axis tight;
    title('Relative Error');

## Inputs

`f` | matrix or vector to find the gradient of
`dn` | array of values for the grid point spacing in each dimension. If a value for `dim` is given, `dn` is the spacing in dimension `dim`.

## Optional Inputs

`dim` | optional input to specify a single dimension over which to compute the gradient for n-dimension input functions
`deriv_order` | order of the derivative to compute, e.g., use 1 to compute df/dx, 2 to compute df^2/dx^2, etc. (default = 1)
`accuracy_order` | order of accuracy for the finite difference coefficients. Because centered differences are used, this must be set to an integer multiple of 2 (default = 2)

## Outputs

`fx, fy, ...` | gradient in the each dimension, where x corresponds to dim = 1, y corresponds to dim = 2 etc.

## See Also

`getFDMatrix`, `gradient`, `gradientSpect`

© 2009-2014 Bradley Treeby and Ben Cox.