matlab griddatan函数,matlab之griddata函数

griddata函数 ————- %使用griddata插值 A=[1.486,3.059,0.1;2.121,4.041,0.1;2.570,3.959,0.1;3.439,4.396,0.1;4.505,3.012,0.1;3.402,1.604,0.1;2.570,2.065,0.1;2.150,1.970,0.1;1.794,3.059,0.2;2.121,3.615,0.2;2.570,3.473,0.2;3.421,4.160,0.2;4.271,3.036,0.2;3.411,1.876,0.2;2.561,2.562,0.2;2.179,2.420,0.2;2.757,3.024,0.3;3.439,3.970,0.3;4.084,3.036,0.3;3.402,2.077,0.3;2.879,3.036,0.4;3.421,3.793,0.4;3.953,3.036,0.4;3.402,2.219,0.4;3.000,3.047,0.5;3.430,3.639,0.5;3.822,3.012,0.5;3.411,2.385,0.5;3.103,3.012,0.6;3.430,3.462,0.6;3.710,3.036,0.6;3.402,2.562,0.6;3.224,3.047,0.7;3.411,3.260,0.7;3.542,3.024,0.7;3.393,2.763,0.7]; x=A(:,1); y=A(:,2); z=A(:,3); scatter(x,y,5,z)%散点图 figure [X,Y,Z]=griddata(x,y,z,linspace(1.486,4.271)’,linspace(1.604,4.276),’v4′);%插值 pcolor(X,Y,Z); shading interp%伪彩色图 figure, contourf(X,Y,Z) %等高线图 figure, surf(X,Y,Z)%三维曲面 ————– x = rand(1,12); y = rand(1,12); z = rand(1,12); % now use some random z axis data xi = linspace(min(x),max(x),30);        % x interpolation points yi = linspace(min(y),max(y),30);        % x interpolation points [Xi,Yi] = meshgrid(xi,yi);              % create grid of x and y Zi = griddata(x,y,z,Xi,Yi);             % grid the data at Xi,Yi points % Zi = griddata(x,y,z,Xi,Yi, ‘linear’)          % same as above(default) % Zi = griddata(x,y,z,Xi,Yi, ‘cubic’)           % triangle based cubic interpolation % Zi = griddata(x,y,z,Xi,Yi, ‘nearest’) % triangle based nearest neighbor % Zi = griddata(x,y,z,Xi,Yi, ‘invdist’)         % inverse distance method mesh(Xi,Yi,Zi) hold on plot3(x,y,z, ‘ko’)      % show original data as well hold off title(‘Figure 18.10: Griddata Example’)