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## [aefc8d]: inst / example_optiminterp.m Maximize Restore History

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67``` ```%% Copyright (C) 2008 Alexander Barth %% %% This program is free software; you can redistribute it and/or modify it under %% the terms of the GNU General Public License as published by the Free Software %% Foundation; either version 3 of the License, or (at your option) any later %% version. %% %% This program is distributed in the hope that it will be useful, but WITHOUT %% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or %% FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more %% details. %% %% You should have received a copy of the GNU General Public License along with %% this program; if not, see . % Example program of the optimal interpolation toolbox % the grid onto which the observations are interpolated [xi,yi] = ndgrid(linspace(0,1,100)); % background estimate or first guess xb = 10 + xi; % number of observations to interpolate on = 200; % create randomly located observations within % the square [0 1] x [0 1] x = rand(1,on); y = rand(1,on); % the underlying function to interpolate yo = 10 + x + sin(6*x) .* cos(6*y); % the error variance of the observations divided by the error % variance of the background field var = 0.1 * ones(on,1); % the correlation length in x and y direction lenx = 0.1; leny = 0.1; % number of influential observations m = 30; % subtract the first guess from the observations % (DON'T FORGET THIS - THIS IS VERY IMPORTANT) Hxb = interp2(xi(:,1),yi(1,:),xb',x,y); f = yo - Hxb; % run the optimal interpolation % fi is the interpolated field and vari is its error variance [fi,vari] = optiminterp2(x,y,f,var,lenx,leny,m,xi,yi); % Add the first guess back xa = fi + xb; ```