📄 centeredfirstsecond.m
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function deriv = centeredFirstSecond(grid, data, dim)
% centeredFirstSecond: second order centered difference approx of first deriv.
%
% deriv = centeredFirstSecond(grid, data, dim)
%
% Computes a second order centered difference approximation to the
% first derivative in dimension dim.
%
% Note that centered differences are not a good choice for first order
% terms in Hamilton-Jacobi equations (such as advection). Instead
% use an upwind scheme.
%
% parameters:
% grid Grid structure (see processGrid.m for details).
% data Data array.
% dim Which dimension to compute derivative on.
%
% deriv Centered approximation of first derivative (same size as data).
% Copyright 2004 Ian M. Mitchell (mitchell@cs.ubc.ca).
% This software is used, copied and distributed under the licensing
% agreement contained in the file LICENSE in the top directory of
% the distribution.
%
% Ian Mitchell, 02/02/03
%---------------------------------------------------------------------------
if((dim < 0) | (dim > grid.dim))
error('Illegal dim parameter');
end
dxInv = 1 ./ grid.dx(dim);
% How big is the stencil?
stencil = 1;
% Add ghost cells to every dimension.
gdata = feval(grid.bdry{dim}, data, dim, stencil, grid.bdryData{dim});
%---------------------------------------------------------------------------
% Create cell array with array indices.
sizeData = size(gdata);
indices1 = cell(grid.dim, 1);
for i = 1 : grid.dim
indices1{i} = 1:sizeData(i);
end
indices2 = indices1;
%---------------------------------------------------------------------------
% Take a difference in the appropriate dimension.
indices1{dim} = 1 : size(gdata, dim) - 2;
indices2{dim} = 3 : size(gdata, dim);
deriv = 0.5 * dxInv * (gdata(indices2{:}) - gdata(indices1{:}));
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