griddata函数

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%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

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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’)