MATLAB更新trisurf手柄

Question

I'm using a Delaunay triangularization to convert a scatter plot to a surface. To animate this plot, I want to update the trisurf handle instead of creating a new trisurf plot to reduce overhead and to increase the plotting speed.

Basically, in a for loop, I want to update the properties of the trisurf handle h to obtain the same plot that calling trisurf again would yield.

MWE

x = linspace(0,1,11); 
y = x;
[X,Y] = meshgrid(x,y);
mag = hypot(X(:),Y(:)); % exemplary magnitude
T = delaunay(X(:),Y(:));

z = 0

h = trisurf(T, X(:), Y(:), z*ones(size(X(:))), mag, 'FaceColor', 'interp'); view([-90 90]);

for i = 1:10
    % Compute new values for X, Y, z, and mag
    % -> Update properties of handle h to redraw the trisurf plot instead
    %    of recalling the last line before the for loop again, e.g.,
    % h.FaceVertexCData = ...
    % h.Faces = ...
    % h.XData = ...
end

Answer 1

You can change a few properties of the Patch object returned by trisurf() :

for i = 1:9
  % Compute new values for X, Y, z, and mag
  % As an example:
  x = linspace(0,1,11-i);
  y = x;
  [X,Y] = meshgrid(x,y);
  mag = hypot(X(:),Y(:));
  T = delaunay(X(:),Y(:));

  z = i;
  Z = z*ones(size(X)); %we could have just called `meshgrid()` with 3 arguments instead
  % End recomputation

  % Update trisurf() patch: option 1
  set( h, 'Faces',T, 'XData',X(T).', 'YData',Y(T).', 'ZData',Z(T).', 'CData',mag(T).' );
  pause(0.25); %just so we can see the result
  % Update trisurf() patch: option 2
  set( h, 'Faces',T, 'Vertices',[X(:) Y(:) Z(:)], 'FaceVertexCData',mag(:) );
  pause(0.25); %just so we can see the result
end

where z is assumed to always be a scalar, just like in the original call to trisurf() .

• Q: Are these options equally fast?
• A: I have run some tests (see code below) on my computer (R2019a, Linux) and found that, when the number of x/y-positions is a random number between 2 and 20, multiple set() calls using Vertices can be some 20% faster than set() calls using XData and related properties, and that these strategies are about an order of magnitude faster than multiple trisurf() calls. When the number of x/y-positions is allowed to vary from 2 to 200, however, run times are about the same for the three approaches.

Nruns=1e3;
Nxy_max=20;

for i=1:Nruns
  if i==round(Nruns/10)
    tic(); %discard first 10% of iterations
  end
  x = linspace(0,1,randi(Nxy_max-1)+1); %randi([2,Nxy_max]) can be a bit slower
  [X,Y,Z] = meshgrid(x,x,randn());
  mag = hypot(X(:),Y(:));
  T = delaunay(X(:),Y(:));
  trisurf(T, X(:), Y(:), Z(:), mag, 'FaceColor', 'interp');
  view([-90 90]);
end
tmean_trisurf=1e3*toc()/(Nruns-round(Nruns/10)+1), %in [ms]

h=trisurf(T, X(:), Y(:), Z(:), mag, 'FaceColor', 'interp');
view([-90 90]);

for i=1:Nruns
  if i==round(Nruns/10)
    tic();
  end
  x = linspace(0,1,randi(Nxy_max-1)+1);
  [X,Y,Z] = meshgrid(x,x,randn());
  mag = hypot(X(:),Y(:));
  T = delaunay(X(:),Y(:));
  set( h, 'Faces',T, 'XData',X(T).', 'YData',Y(T).', 'ZData',Z(T).', 'CData',mag(T).' );
end
tmean_xyzdata=1e3*toc()/(Nruns-round(Nruns/10)+1), %in [ms]

for i=1:Nruns
  if i==round(Nruns/10)
    tic();
  end
  x = linspace(0,1,randi(Nxy_max-1)+1);
  [X,Y,Z] = meshgrid(x,x,randn());
  mag = hypot(X(:),Y(:));
  T = delaunay(X(:),Y(:));
  set( h, 'Faces',T, 'Vertices',[X(:) Y(:) Z(:)], 'FaceVertexCData',mag(:) );
end
tmean_vertices=1e3*toc()/(Nruns-round(Nruns/10)+1), %in [ms]