contour for scatter data

Question

Pegah 2023-2-27

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1920050-contour-for-scatter-data

编辑： Walter Roberson 2023-8-1

export.csv

Hi every one,

I have 3 columns of data X, Y, and T. I used "svd" function for T and now I need to visulaize T considering X, and Y. How could I use "contour" or "contourf" for this data? the problem is, I cann't use "meshgrid" function because the grid that I used is not organised.

Thanks in advance,

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Totanly 2023-8-1

编辑：Walter Roberson 2023-8-1

see this link, i think it will solve your problem

https://www.youtube.com/watch?v=66WOwdZ-Vpk

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Answer 1

Walter Roberson 2023-2-27

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https://ww2.mathworks.cn/matlabcentral/answers/1920050-contour-for-scatter-data#answer_1181200

编辑：Walter Roberson 2023-2-28

https://www.mathworks.com/matlabcentral/fileexchange/38858-contour-plot-for-scattered-data

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Walter Roberson 2023-2-28

在 MATLAB Online 中打开

export.csv

You have one value per face. If you color the face by interpolating the temperatures of the vertices, then you get

filename = 'export.csv';

xyzt = readmatrix(filename, 'Range', "7:512");

faceinfo = readmatrix(filename, 'Range', "515:969");

p = patch('Faces', faceinfo + 1, 'Vertices', xyzt(:,1:3), 'FaceColor', 'interp', 'FaceVertexCData', xyzt(:,4));

view(3)

It is worth looking at the scatter plot:

x = xyzt(:,1); y = xyzt(:,2); z = xyzt(:,3); t = xyzt(:,4);

figure

scatter3(x, y, z, [], t);

Now we can head towards interpolation. But there are two holes, so we need to avoid interpolating there (or at least not plot there if we do interpolate.)

One way to avoid plotting where there are holes is to create an occupancy map

F = scatteredInterpolant(x, y, t);
[dimmin, dimmax] = bounds([x,y], 1);
N = 50;
xvec = linspace(dimmin(1), dimmax(1), N+1);
yvec = linspace(dimmin(2), dimmax(2), N+2);
xbin = discretize(x, xvec);
ybin = discretize(y, yvec);
occupied = accumarray([ybin, xbin], 1) ~= 0;

occupied is now true for locations where data was found inside the grid point, and false for locations where no data was found inside the grid point

[XG, YG] = meshgrid(xvec(1:end-1), yvec(1:end-1));
TG = F(XG, YG);
whos XG YG TG occupied
  Name           Size            Bytes  Class      Attributes

  TG            51x50            20400  double               
  XG            51x50            20400  double               
  YG            51x50            20400  double               
  occupied      51x50             2550  logical              
figure
TG(~occupied) = nan;

nan out the unoccupied interpolated locations, and show them as pcolor

pcolor(XG, YG, TG)

That is fairly sparse. This goes back to the fair distance between locations because there is only one temperature per element.

We can proceed from here to contour...

figure

contourf(XG, YG, TG); colorbar

xlabel('x'); ylabel('y');

but with the data being so sparse, the contour does not look good.

You could reduce the resolution by a factor of 5 or so so that the samples were beside each other -- but the resulting map would only be about 10 x 10, which is not very useful.

Or you could change the way you calculate occupancy. If you put all the vertex lists together separated by nan and then probe each point in XG YG using inpolygon() then you could probably do something useful. At the moment I do not know if there are any better ways.

Voss 2023-3-7

在 MATLAB Online 中打开

export.csv

@Pegah: Include 'EdgeColor','none' in the call to patch():

filename = 'export.csv';

xyzt = readmatrix(filename, 'Range', "7:512");

faceinfo = readmatrix(filename, 'Range', "515:969");

p = patch( ...

'Faces', faceinfo + 1, ...

'Vertices', xyzt(:,1:3), ...

'FaceColor', 'interp', ...

'FaceVertexCData', xyzt(:,4), ...

'EdgeColor', 'none');

view(3)

Pegah 2023-3-7

It works, Thank you so much.

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Answer 2

John D'Errico 2023-2-28

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https://ww2.mathworks.cn/matlabcentral/answers/1920050-contour-for-scatter-data#answer_1181315

编辑：John D'Errico 2023-2-28

在 MATLAB Online 中打开

tricontour.m

Let me give an example of how to use a tricontouring tool for scattered data.

x = rand(100,1);

y = rand(100,1);

zfun = @(x,y) sin(x + y).*cos(x-2*y) - x;

z = zfun(x,y);

% Note the use of an alpha shape to generate the triangulation.

% This is important, as otherwise, there will be significant edge

% artifacts.

S = alphaShape(x,y);

S.Alpha = .3;

plot(S)

That is merely the triangulation. Again, it is based purely on the variables x and y.

trisurf(S.alphaTriangulation,x,y,z)

tricontour(S.alphaTriangulation,x,y,z,10)

hold on

plot(x,y,'ro')

colorbar

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contour for scatter data

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