Extract the information of inside and outside of a contourf

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Roberto Tumolo 2024-7-26

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评论： Steve 2024-10-11

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Hello everyone,

I have a single line of code, quite simple, thanks to which I get (visually) exactly what I need:

h=figure; [pippo1, pippo2]=contourf(AZ,EL,FF, [0, 0]);

where the input arguments have size 301x301. The plot I get is like this:

As you can see, the outside of the contour is not only the big cyan area, but also some small areas in the white (not filled) polygon. I need the coordinates of the outside polygon (the whole cyan region) but I couldn't extract them from either "h" or "pippo2". Please note, I DON'T NEED the coordinates of the single contours, because if I extract XDATA and YDATA they lose the information about "the inside" and the "outside". I need to extract, in some way, a single contour that represents the whole cyan area, and another one that represents the complementary (white) one.

Thanks a lot!

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Answer 1

Garmit Pant 2024-8-9

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Hi Roberto

To extract the regions based on the output of the “contourf” function, you can use the contour matrix output of the “contour” function.

“contour” outputs a contour matrix that defines the contour lines. It is of the following form:

You can extract the line data from the matrix and then use “inpolygon” function to create binary masks to extract the regions inside and out the contour lines. The following code snippet demonstrates how to extract regions inside the contour lines:

% Generate sample data

[X, Y, Z] = peaks;

% Generate contour data

M = contourf(X, Y, Z, [0, 0]);

% Extract regions from contour data

r1 = M(:, 2:M(2, 1) + 1);

r2 = M(:, M(2, 1) + 1 + 2:M(2, 1) + 1 + 2 + M(2, M(2, 1) + 1 + 1) - 1);

r3 = M(:, M(2, 1) + 1 + 2 + M(2, M(2, 1) + 1 + 1) + 1:end);

% Plot the regions

figure;

plot(r1(1, :), r1(2, :), 'o');

hold on;

plot(r2(1, :), r2(2, :), 'o');

plot(r3(1, :), r3(2, :), 'o');

xlim([-3 3]);

ylim([-3 3]);

hold off;

% Create masks for each region

in_mask1 = inpolygon(X, Y, r1(1, :), r1(2, :));

in_mask2 = inpolygon(X, Y, r2(1, :), r2(2, :));

in_mask3 = inpolygon(X, Y, r3(1, :), r3(2, :));

% Combine the masks

in_maskmain = in_mask1 | in_mask2 | in_mask3;

% Visualize the combined mask

figure;

surf(X, Y, int8(in_maskmain));

title('Combined Mask');

xlabel('X');

ylabel('Y');

zlabel('Mask');

% Initialize the masked Z matrix with zeros (or another value like NaN)

Z_masked = zeros(size(Z)); % or Z_masked = NaN(size(Z));

% Assign the heights from Z within the region to Z_masked

Z_masked(in_maskmain) = Z(in_maskmain);

% Visualize the masked Z data

figure;

M2 = contourf(X, Y, Z_masked);

title('Masked Z Data');

xlabel('X');

ylabel('Y');

For further understanding, kindly refer to the following MathWorks Documentation:

Refer to the ‘Output Arguments’ section to understand the outputs generated: https://www.mathworks.com/help/releases/R2023b/matlab/ref/contourf.html
Refer to the ‘Examples’ section: https://in.mathworks.com/help/releases/R2023b/matlab/ref/inpolygon.html

I hope you find the above explanation and suggestions useful!

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DGM 2024-10-11

编辑：DGM 2024-10-11

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If the goal is to create a mask, why are we even using contour()?

% Generate sample data
[X, Y, Z] = peaks;
% there's the mask.
mask = Z > 0;

There's no need for a second mask. Just use ~mask.

Steve 2024-10-11

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contourf_mask_polygonsofmaskedarea.PNG

Hello,

thank you for your reply.

I have the goal to show the contourf-plot and afterwards i want to intersect it with other contourf-plots. (the part below a certain level)

Using masks is not giving me the same results, because i dont know exactly how contourf is interpolating and smoothing Data.

One example - Code:

% Give Data
X = [8, 12, 16, 20, 24, 28, 32];
Y = [8, 20, 32, 44, 56, 68, 80];
Z1 = [0.2795, 0.2799, 0.2814, 0.2817, 0.2800, 0.2809, 0.2808;
    0.2786, 0.3040, 0.3093, 0.3113, 0.3130, 0.3131, 0.3118;
    0.2824, 0.2908, 0.2975, 0.2997, 0.2992, 0.2922, 0.2930;
    0.2864, 0.2847, 0.2871, 0.2798, 0.2753, 0.2714, 0.2686;
    0.2850, 0.2776, 0.2802, 0.2699, 0.2684, 0.2680, 0.2681;
    0.2851, 0.2830, 0.2732, 0.2684, 0.2678, 0.2684, 0.2688;
    0.2857, 0.2824, 0.2747, 0.2688, 0.2668, 0.2690, 0.2687];
figure
level = 0.28;
[C,h] = contourf(X,Y,Z1,[level level]);
hold on
% Interpolation
[Xq, Yq] = meshgrid(8:0.05:32, 8:0.05:80); % Feinere Auflösung
% Zq = interp2(X, Y, Z1, Xq, Yq, 'linear'); 
% Zq = interp2(X, Y, Z1, Xq, Yq, 'cubic'); 
% Zq = interp2(X, Y, Z1, Xq, Yq, 'makima'); 
% Zq = interp2(X, Y, Z1, Xq, Yq, 'spline'); 
Zq = griddata(X, Y, Z1, Xq, Yq, 'linear'); 
% Zq = griddata(X, Y, Z1, Xq, Yq, 'cubic'); 
% Zq = griddata(X, Y, Z1, Xq, Yq, 'v4'); 
% Zq = griddata(X, Y, Z1, Xq, Yq, 'natural'); 
mask_Zq = Zq <= level;
% Indizes der 1en finden
[row, col] = find(mask_Zq == 1);
% Plot mask_Zq
figure;
hold on;
scatter(Xq(mask_Zq), Yq(mask_Zq), 10, 'k', 'filled');
contour(X,Y,Z1,[level level])
hold on
% Adjust Axes
xlim([min(min(X)),max(max(X))])
ylim([min(min(Y)),max(max(Y))])
% Find separate Areas
[B, L] = bwboundaries(mask_Zq, 'noholes');
% Plot separate Areas as Polygons
figure;
hold on;
for k = 1:length(B)
    boundary = B{k};
    fill(boundary(:,2), boundary(:,1), rand(1,3), 'FaceAlpha', 0.5);
end
hold off;

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Extract the information of inside and outside of a contourf

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Extract the information of inside and outside of a contourf

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