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
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]
Related
I want to create a graphic representation. I have a sphere with radius of 50. I need to create two different filled capacities when it is one-fourth and three-fourths of its total capacity.
What I already have is this:
[x,y,z] = sphere();
r = 50;
surf( r*x, r*y, r*z ) % sphere with radius 50 centred at (0,0,0)
You could create the spere in two parts. Take a look at the following example:
% First part -- 0 to pi/2
theta = linspace(0,pi/2);
phi = linspace(-pi/2,pi/2);
[theta, phi] = meshgrid(theta, phi);
rho = 50;
[x, y, z] = sph2cart(theta, phi, rho);
surf(x,y,z, 'EdgeColor', 'b');
% Second part -- 90 to 360
hold on;
theta = linspace(pi/2,2*pi);
[theta, phi] = meshgrid(theta, phi);
[x, y, z] = sph2cart(theta, phi, rho);
surf(x,y,z, 'EdgeColor', 'r');
hold off;
It produces a graph like the following.
I need to extract the isoline coordinates of a 4D variable from a 3D surface defined using a triangulated mesh in MATLAB. I need the isoline coordinates to be a ordered in such a manner that if they were followed in order they would trace the path i.e. the order of the points a 3D printer would follow.
I have found a function that can calculate the coordinates of these isolines (see Isoline function here) but the problem is this function does not consider the isolines to be joined in the correct order and is instead a series of 2 points separated by a Nan value. This makes this function only suitable for visualisation purposes and not the path to follow.
Here is a MWE of the problem of a simplified problem, the surface I'm applying it too is much more complex and I cannot share it. Where x, y and z are nodes, with TRI providing the element connectivity list and v is the variable of which I want the isolines extracted from and is not equal to z.
If anyone has any idea on either.....
A function to extract isoline values in the correct order for a 3D tri mesh.
How to sort the data given by the function Isoline so that they are in the correct order.
.... it would be very much appreciated.
Here is the MWE,
% Create coordinates
[x y] = meshgrid( -10:0.5:10, -10:0.5:10 );
z = (x.^2 + y.^2)/20; % Z height
v = x+y; % 4th dimension value
% Reshape coordinates into list to be converted to tri mesh
x = reshape(x,[],1); y = reshape(y,[],1); z = reshape(z,[],1); v = reshape(v,[],1);
TRI = delaunay(x,y); % Convertion to a tri mesh
% This function calculates the isoline coordinates
[xTows, yTows, zTows] = IsoLine( {TRI,[x, y, z]}, v, -18:2:18);
% Plotting
figure(1); clf(1)
subplot(1,2,1)
trisurf(TRI,x,y,z,v)
hold on
for i = 1:size(xTows,1)
plot3( xTows{i,1}, yTows{i,1}, zTows{i,1}, '-k')
end
hold off
shading interp
xlabel('x'); ylabel('y'); zlabel('z'); title('Isolines'), axis equal
%% This section is solely to show that the isolines are not in order
for i = 1:size(xTows,1)
% Arranging data into colums and getting rid of Nans that appear
xb = xTows{i,1}; yb = yTows{i,1}; zb = zTows{i,1};
xb = reshape(xb, 3, [])'; xb(:,3) = [];
yb = reshape(yb, 3, [])'; yb(:,3) = [];
zb = reshape(zb, 3, [])'; zb(:,3) = [];
subplot(1,2,2)
trisurf(TRI,x,y,z,v)
shading interp
view(2)
xlabel('x'); ylabel('y'); zlabel('z'); title('Plotting Isolines in Order')
axis equal; axis tight; hold on
for i = 1:size(xb,1)
plot3( [xb(i,1) xb(i,2)], [yb(i,1) yb(i,2)], [zb(i,1) zb(i,2)], '-k')
drawnow
end
end
and here is the function Isoline, which I have slightly adpated.
function [xTows, yTows, zTows] = IsoLine(Surf,F,V,Col)
if length(Surf)==3 % convert mesh to triangulation
P = [Surf{1}(:) Surf{2}(:) Surf{3}(:)];
Surf{1}(end,:) = 1i;
Surf{1}(:,end) = 1i;
i = find(~imag(Surf{1}(:)));
n = size(Surf{1},1);
T = [i i+1 i+n; i+1 i+n+1 i+n];
else
T = Surf{1};
P = Surf{2};
end
f = F(T(:));
if nargin==2
V = linspace(min(f),max(f),22);
V = V(2:end-1);
elseif numel(V)==1
V = linspace(min(f),max(f),V+2);
V = V(2:end-1);
end
if nargin<4
Col = 'k';
end
H = NaN + V(:);
q = [1:3 1:3];
% -------------------------------------------------------------------------
% Loop over iso-values ----------------------------------------------------
xTows = [];
yTows = [];
zTows = [];
for k = 1:numel(V)
R = {[],[]};
G = F(T) - V(k);
C = 1./(1-G./G(:,[2 3 1]));
f = unique(T(~isfinite(C))); % remove degeneracies by random perturbation
F(f) = F(f).*(1+1e-12*rand(size(F(f)))) + 1e-12*rand(size(F(f)));
G = F(T) - V(k);
C = 1./(1-G./G(:,[2 3 1]));
C(C<0|C>1) = -1;
% process active triangles
for i = 1:3
f = any(C>=0,2) & C(:,i)<0;
for j = i+1:i+2
w = C(f,q([j j j]));
R{j-i} = [R{j-i}; w.*P(T(f,q(j)),:)+(1-w).*P(T(f,q(j+1)),:)];
end
end
% define isoline
for i = 1:3
X{i} = [R{1}(:,i) R{2}(:,i) nan+R{1}(:,i)]';
% X{i} = [R{1}(:,i) R{2}(:,i)]'; % Changed by Matt
X{i} = X{i}(:)';
end
% plot isoline
if ~isempty(R{1})
% hold on
% H(k) = plot3(X{1},X{2},X{3},Col);
% Added by M.Thomas
xTows{k,1} = X{1};
yTows{k,1} = X{2};
zTows{k,1} = X{3};
end
end
What you will notice is that the isolines (xTows, yTows and zTows) are not in order there "jump around" when plotted sequentially. I need to sort the tows so that they give a smooth plot in order.
I would like to plot a sine curve in Matlab. But I want it blue for the positive values and red for the negative values.
The following code just makes everything red...
x = [];
y = [];
for i = -180 : 180
x = [x i];
y = [y sin(i*pi/180)];
end
p = plot(x, y)
set(p, 'Color', 'red')
Plot 2 lines with different colours, and NaN values at the positive/negative regions
% Let's vectorise your code for efficiency too!
x = -pi:0.01:pi; % Linearly spaced x between -pi and pi
y = sin(x); % Compute sine of x
bneg = y<0; % Logical array of negative y
y_pos = y; y_pos(bneg) = NaN; % Array of only positive y
y_neg = y; y_neg(~bneg)= NaN; % Array of only negative y
figure; hold on; % Hold on for multiple plots
plot(x, y_neg, 'b'); % Blue for negative
plot(x, y_pos, 'r'); % Red for positive
Output:
Note: If you're happy with scatter plots, you don't need the NaN values. They just act to break the line so you don't get join-ups between regions. You could just do
x = -pi:0.01:pi;
y = sin(x);
bneg = y<0;
figure; hold on;
plot(x(bneg), y(bneg), 'b.');
plot(x(~bneg), y(~bneg), 'r.');
Output:
This is so clear because my points are only 0.01 apart. Further spaced points would appear more like a scatter plot.
I like to visualize conics with Matlab or Octave. The (general) conic is given by the equation 0 = ax² + bxy + cy² +dxz +eyz+f*z² for a point p=(x,y,z). How can I plot this with Matlab or octave if I know the parameters a,b,c,d,e and f? Or respectively, how can I find the points that satisfy this equation?
Since you are asking for the conics, I understand that you are probably referring to the 2D contours of the general conic equation. I will also cover how to visualize this equation in other different ways.
For all the following examples, I have set the conic constants so that I obtain a hiperboloid.
My code is written in MATLAB syntax. If you are using Octave, it might differ slightly.
Visualizing 2D Conics with CONTOUR
I have isolated z in terms of x and y, from the general conic equation:
z = (1/2)*(-d*x-e*y±sqrt(-4*a*f*x.^2-4*b*f*x.*y-4*c*f*y.^2+d^2*x.^2+2*d*e*x.*y+e^2*y.^2))/f;
Since z is a piecewise function due to (± sqrt), I need to make sure that I plot both hemispheres. I designate z1 for +sqrt, and z2 for -sqrt.
Finally, I plot the contours for z1 and z2 that will yield the set of conics in 2D. This conics will be circles of different radius.
Code:
clear all;
clc;
% Conic constants.
a = 1;
b = 0;
c = 1;
d = 0;
e = 0;
f = -1;
% Value for x and y domain.
v = 10;
% Domain for x and y.
x = linspace(-v,v);
y = linspace(-v,v);
% Generate a 2D mesh with x and y.
[x,y] = meshgrid(x,y);
% Isolate z in terms of x and y.
z1 = (1/2)*(-d*x-e*y+sqrt(-4*a*f*x.^2-4*b*f*x.*y-4*c*f*y.^2+d^2*x.^2+2*d*e*x.*y+e^2*y.^2))/f;
z2 = (1/2)*(-d*x-e*y-sqrt(-4*a*f*x.^2-4*b*f*x.*y-4*c*f*y.^2+d^2*x.^2+2*d*e*x.*y+e^2*y.^2))/f;
% Find complex entries in z.
i = find(real(z1)~=z1);
j = find(real(z2)~=z2);
% Replace complex entries with NaN.
z1(i) = NaN;
z2(j) = NaN;
figure;
subplot(1,2,1);
% Draw lower hemisphere.
contour(x,y,z1,'ShowText','on');
% Adjust figure properties.
title('2D Conics: Lower hemishphere');
xlabel('x-axis');
ylabel('y-axis');
axis equal;
grid on;
box on;
axis([-10 10 -10 10]);
subplot(1,2,2);
% Draw upper hemisphere.
contour(x,y,z2,'ShowText','on');
hold off;
% Adjust figure properties.
title('2D Conics: Upper hemishphere');
xlabel('x-axis');
ylabel('y-axis');
axis equal;
grid on;
box on;
axis([-10 10 -10 10]);
Output:
Visualizing 3D Conics with CONTOUR3
Same as on the previous example, but now we plot the set of conics in 3D.
Code:
clear all;
clc;
% Conic constants.
a = 1;
b = 0;
c = 1;
d = 0;
e = 0;
f = -1;
% Value for x and y domain.
v = 10;
% Domain for x and y.
x = linspace(-v,v);
y = linspace(-v,v);
% Generate a 2D mesh with x and y.
[x,y] = meshgrid(x,y);
% Isolate z in terms of x and y.
z1 = (1/2)*(-d*x-e*y+sqrt(-4*a*f*x.^2-4*b*f*x.*y-4*c*f*y.^2+d^2*x.^2+2*d*e*x.*y+e^2*y.^2))/f;
z2 = (1/2)*(-d*x-e*y-sqrt(-4*a*f*x.^2-4*b*f*x.*y-4*c*f*y.^2+d^2*x.^2+2*d*e*x.*y+e^2*y.^2))/f;
% Find complex entries in z.
i = find(real(z1)~=z1);
j = find(real(z2)~=z2);
% Replace complex entries with NaN.
z1(i) = NaN;
z2(j) = NaN;
% Lower hemisphere. Draw 20 conics.
contour3(x,y,z1,20);
hold on;
% Upper hemisphere. Draw 20 conics.
contour3(x,y,z2,20);
hold off;
% Adjust figure properties.
title('3D Conics');
xlabel('x-axis');
ylabel('y-axis');
zlabel('z-axis');
axis equal;
grid on;
box on;
axis([-10 10 -10 10 -10 10]);
Output:
Visualizing Quadrics with ISOSURFACE
I have isolated f in terms of x, y and z, from the general conic equation, and renamed it to f_eq:
f_eq = -(a*x.^2+b*x.*y+c*y.^2+d*x.*z+e*y.*z)./z.^2;
Finally, I obtain the set of points that satisfy the equation f_eq = f, which is in fact an isosurface that yields a quadric; in this example a hiperboloid.
Code:
clear all;
clc;
% Conic constants.
a = 1;
b = 0;
c = 1;
d = 0;
e = 0;
f = -1;
% Value for x, y and z domain.
v = 10;
% Domain for x ,y and z.
x = linspace(-v,v);
y = linspace(-v,v);
z = linspace(-v,v);
% Generate a 3D mesh with x, y and z.
[x,y,z] = meshgrid(x,y,z);
% Evaluate function (3D volume of data).
f_eq = -(a*x.^2+b*x.*y+c*y.^2+d*x.*z+e*y.*z)./z.^2;
% Draw the surface that matches f_eq = f.
p = patch(isosurface(x,y,z,f_eq,f));
isonormals(x,y,z,f_eq,p)
p.FaceColor = 'red';
p.EdgeColor = 'none';
% Adjust figure properties.
title('Quadric');
xlabel('x-axis');
ylabel('y-axis');
zlabel('z-axis');
axis equal;
grid on;
box on;
axis([-10 10 -10 10 -10 10]);
camlight left;
lighting phong;
Output:
I'd like to draw a curve on an empty (semilog-y) graph by clicking the points I want it to run through, on the X-Y plane.
Is there a function for this?
edit: I'm trying to do this by obtaining the position of last pointer click -
axis([0 3000 0 1000]);
co=get(gcf, 'CurrentPoint');
It seems to return the cursor position at the time of execution, but it does not change later.
edit2: Here's what works for me. The actual drawing I can do by using the arrays of points collected.
clear
clc
h=plot(0);
grid on;
xlim([0 3000]);
ylim([0 1000]);
datacursormode on;
% Enlarge figure to full screen.
screenSize = get(0,'ScreenSize');
set(gcf, 'units','pixels','outerposition', screenSize);
hold on;
% Print the x,y coordinates - will be in plot coordinates
x=zeros(1,10); y=zeros(1,10);
for p=1:10;
[x(p),y(p)] = ginput(1) ;
% Mark where they clicked with a cross.
plot(x(p),y(p), 'r+', 'MarkerSize', 20, 'LineWidth', 3);
% Print coordinates on the plot.
label = sprintf('(%.1f, %.1f)', x(p), y(p));
text(x(p)+20, y(p), label);
end
Not really, but now there is:
function topLevel
%// parameters
xrange = [0 100];
yrange = [1e-4 1e4];
%// initialize figure, plot
figure, clf, hold on
plot(NaN, NaN);
axis([xrange yrange]);
set(gca, 'YScale', 'log')
t = text(sum(xrange)/2, sum(yrange)/2, ...
'<< Need at least 3 points >>',...
'HorizontalAlignment', 'center');
%// Main loop
xs = []; p = [];
ys = []; P = [];
while true
%// Get new user-input, and collect all of them in a list
[x,y] = ginput(1);
xs = [xs; x]; %#ok<AGROW>
ys = [ys; y]; %#ok<AGROW>
%// Plot the selected points
if ishandle(p)
delete(p); end
p = plot(xs, ys, 'rx');
axis([xrange yrange]);
%// Fit curve through user-injected points
if numel(xs) >= 3
if ishandle(t)
delete(t); end
%// Get parameters of best-fit in a least-squares sense
[A,B,C] = fitExponential(xs,ys);
%// Plot the new curve
xp = linspace(xrange(1), xrange(end), 100);
yp = A + B*exp(C*xp);
if ishandle(P)
delete(P); end
P = plot(xp,yp, 'b');
end
end
%// Fit a model of the form y = A + B·exp(C·x) to data [x,y]
function [A, B, C] = fitExponential(x,y)
options = optimset(...
'maxfunevals', inf);
A = fminsearch(#lsq, 0, options);
[~,B,C] = lsq(A);
function [val, B,C] = lsq(A)
params = [ones(size(x(:))) x(:)] \ log(abs(y-A));
B = exp(params(1));
C = params(2);
val = sum((y - A - B*exp(C*x)).^2);
end
end
end
Note that as always, fitting an exponential curve can be tricky; the square of the difference between model and data is exponentially much greater for higher data values than for lower data values, so there will be a strong bias to fit the higher values better than the lower ones.
I just assumed a simple model and used a simple solution, but this gives a biased curve which might not be "optimal" in the sense that you need it to be. Any decent solution really depends on what you want specifically, and I'll leave that up to you ^_^