How to get contour plot of multiple Datas in the same plot?

Question

Vaswati Biswas 2022-11-26

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1864048-how-to-get-contour-plot-of-multiple-datas-in-the-same-plot

评论： Star Strider 2022-11-27

I have X axis values [0 10 20 30 40 50 55] and y axis values starting from 300 to 2000 at the interval of 2. I also have seven Z axis values matching the dimension of y axis. I want to have the contour plot of all the datas in the same plot. My code is given below:

[x,y] = meshgrid(X,Y);
z= [Z1,Z2,Z3,Z4,Z5,Z6,Z7];
Z = zeros(length(x),length(y)) ;
for i = 1:length(x)
    for j = 1:length(y)
        if i==j % z data exist for only for x(n) y(n) location, n = 1,2,3...
        Z(i,j) = z(i);
        end
    end
end
contourf(x,y,Z)
colorbar

But I am not getting the correct result. Kindly help.

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Answer 1

Star Strider 2022-11-26

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1864048-how-to-get-contour-plot-of-multiple-datas-in-the-same-plot#answer_1112673

I did something like this recently in Contourf plot of magnitude of transferfunction along trajectory. See if you can adapt that approach to your problem.

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Vaswati Biswas 2022-11-27

Thanks for your reply. But In this case one function is plotted but in my case I have different data therefore when I am trying to incoparate this approch I am getting the error "Data dimensions must agree".

Star Strider 2022-11-27

在 MATLAB Online 中打开

They of course must agree.

You can use interp1 to make their lengths agree.

Example —

X1 = 0:0.1:10;

Z1 = X1.*exp(-0.75*X1)

Z1 = 1×101

0 0.0928 0.1721 0.2396 0.2963 0.3436 0.3826 0.4141 0.4390 0.4582 0.4724 0.4821 0.4879 0.4904 0.4899 0.4870 0.4819 0.4750 0.4666 0.4570 0.4463 0.4347 0.4225 0.4098 0.3967 0.3834 0.3699 0.3564 0.3429 0.3295

X2 = 0:0.5:20;

Z2 = sin(2*pi*X2*5)

Z2 = 1×41

1.0e-13 * 0 0.0061 -0.0122 0.0539 -0.0245 -0.0049 -0.1078 0.0784 -0.0490 0.0196 0.0098 0.2450 -0.2156 -0.0980 -0.1568 0.1274 -0.0980 0.0686 -0.0392 0.0098 0.0196 -0.0490 -0.4900 0.4606 -0.4312 -0.1667 0.1961 0.3430 -0.3136 -0.2843

X3 = 0:25;

Z3 = exp(-0.1*X3) .* cos(2*pi*3*X3)

Z3 = 1×26

1.0000 0.9048 0.8187 0.7408 0.6703 0.6065 0.5488 0.4966 0.4493 0.4066 0.3679 0.3329 0.3012 0.2725 0.2466 0.2231 0.2019 0.1827 0.1653 0.1496 0.1353 0.1225 0.1108 0.1003 0.0907 0.0821

N = 200;

xfcn = @(x) linspace(min(x), max(x), N); % Create Independent Interpolation Variable Vector For Each Vector

Z1m = interp1(X1, Z1, xfcn(X1)); % Interpolate To Same Lengths

Z2m = interp1(X2, Z2, xfcn(X2)); % Interpolate To Same Lengths

Z3m = interp1(X3, Z3, xfcn(X3)); % Interpolate To Same Lengths

figure

contourf([Z1m; Z2m; Z3m])

colormap(turbo)

Here, they’re interpolated to the same length, then (since they’re row vectors in this example), vertically concatenated to create a matrix, and then presented as that matrix to contourf.

.

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