I generated a 3D surface in matlab.
clear all;
close all;
clc;
x = [1:0.1:5];
y=[1:50];
[n1 n2] = size(x);
[m1, m2] = size(y);
for i = 1 : m2
for j = 1 : n2
z(i,j) = (x(1,j)) / (y(1,i));
end
end
[x, y] = meshgrid(x, y);
surf(y, x, z)
colorbar
xlabel('x')
ylabel('y')
zlabel('z')
I got the following plotted surface:
I am interested now to rotate the cube of a quarter turn in the clockwise direction. I know that I can use the "rotate3d on" and choose the best Az and EI, but I didn't understand what are Az and EI and how should be equal to respond to my need?
For example:
There also another function called camroll(). But I don't know what must the value in () in order to rotate the cube of a quarter turn in the clockwise direction. Does 90 degree is the correct answer?
Any help will be very appreciated!
To rotate the figure axis you can use the view command:
surf(rand(20))
for az = 360:-1:-0
view(az, 30)
pause(0.01)
end
Azimus has values in between 0° and 360°. To turn the axis figure by 90° in counter-clockwise direction you oculd choose the azimuths of: [270, 180, 90, 0]
as #ASantosRibeiro mentioned, the rotate function will produce the output you want. As an aside, here is a vectorized version of your code, which you might find useful if you have a significantly larger data set.
x = 1:0.1:5;
y=1:50;
X = repmat(x',1,size(y,2)).';
Y = repmat(y',1,size(x,2));
Z = X./Y;
figure
hSurf = surf(Y,X,Z);
rotate(hSurf,[1,0,0],45) % As proposed by #ASantosRibeiro
colorbar
xlabel('x')
ylabel('y')
zlabel('z')
The repmat function is used to replicate both x and y in order to form X and Y with correct sizes to be allowed to divide one by the other in order to form Z. This operation is quite similar to the call to meshgrid in your code. For a small dataset like the one in your example the running times are similar with both methods (on my laptop), however when I use x = 1:0.1:500 and y = 1:500 for example the vectorized version takes 3x less time, so it might be worthwhile to look at it.
Hope that helps you!
Related
I am trying to create an animated plot of triangles and the end result should be ten triangles followed by two bigger triangles followed by a straight line. Using the matlab documentation, I ended up having this, which results an animated sin plot:
h = animatedline;
axis([0 4*pi -1 1])
x = linspace(0,4*pi,2000);
for k = 1:length(x)
y = sin(x(k));
addpoints(h,x(k),y);
drawnow
end
The problem is that the plot is really slow and as soon as I changed y=sin(x(k)) to a triangular form, it got even worst. Is there a better way to do an animated plot or at least to adjust the speed? (if the speed is not dependant on the computer)
You can speed it up a little by
Computing a y vector at once, instead of computing each value in the loop.
Updating the XData and YData properties of a plot, instead of using animatedline.
The code becomes:
h = plot(NaN,NaN);
axis([0 4*pi -1 1])
x = linspace(0,4*pi,2000);
y = sin(x);
for k = 1:length(x)
set(h, 'XData', x(1:k), 'YData', y(1:k))
drawnow
end
The gain in speed is small, though. If you need more speed you probably need to decrease the number of points.
You can examine comet function to animate the curve:
t = linspace(0,4*pi,2000);
comet(t, sin(t));
It would be smooth and easier to animate a curve (see its documentation).
Also, for 3d curves you can use comet3.
I want to plot smooth contour plot from X Y Z matrix.
sf = fit([X Y] Z, 'poly23');
plot(sf);
I have not enought smooth curve..
What I need?
You can use the functions such as griddata and csaps. Together they will lead you to the result as smooth as you wish. The first function adds additional points to your data matrix set. The second one makes the result smoother. The example of the code is below. In the example smoothing is done first in X direction and then in Y direction. Try to play around with the resolution and smoothing_parameter (the current set of these parameters should be OK though).
x = min_x:step_x:max_x;
y = min_y:step_y:max_y;
resolution = 10;
xg = min_x:(step_x/resolution):max_x;
yg = min_y:(step_y/resolution):max_y;
[X,Y] = meshgrid(x,y);
[XG,YG] = meshgrid(xg,yg);
smoothing_parameter = 0.02;
fitted = griddata(X,Y,Z,XG,YG,'cubic');
fitted_smoothed_x = csaps(xg,fitted,smoothing_parameter,xg);
fitted_smoothed_xy = csaps(yg,fitted_smoothed_x',smoothing_parameter,yg);
surf(XG,YG,fitted_smoothed_xy');
EDIT: If you want to get just a contour plot, you can do, for example, as presented below. As I don't have the real data, I will use build-in function peaks to generate some.
[X,Y,Z] = peaks(30);
figure
surfc(X,Y,Z)
view([0 90])
zlim([-10 -8])
Here you just look at your contour plot from above being below the surface.
MATLAB's surf command allows you to pass it optional X and Y data that specify non-cartesian x-y components. (they essentially change the basis vectors). I desire to pass similar arguments to a function that will draw a line.
How do I plot a line using a non-cartesian coordinate system?
My apologies if my terminology is a little off. This still might technically be a cartesian space but it wouldn't be square in the sense that one unit in the x-direction is orthogonal to one unit in the y-direction. If you can correct my terminology, I would really appreciate it!
EDIT:
Below better demonstrates what I mean:
The commands:
datA=1:10;
datB=1:10;
X=cosd(8*datA)'*datB;
Y=datA'*log10(datB*3);
Z=ones(size(datA'))*cosd(datB);
XX=X./(1+Z);
YY=Y./(1+Z);
surf(XX,YY,eye(10)); view([0 0 1])
produces the following graph:
Here, the X and Y dimensions are not orthogonal nor equi-spaced. One unit in x could correspond to 5 cm in the x direction but the next one unit in x could correspond to 2 cm in the x direction + 1 cm in the y direction. I desire to replicate this functionality but drawing a line instead of a surf For instance, I'm looking for a function where:
straightLine=[(1:10)' (1:10)'];
my_line(XX,YY,straightLine(:,1),straightLine(:,2))
would produce a line that traced the red squares on the surf graph.
I'm still not certain of what your input data are about, and what you want to plot. However, from how you want to plot it, I can help.
When you call
surf(XX,YY,eye(10)); view([0 0 1]);
and want to get only the "red parts", i.e. the maxima of the function, you are essentially selecting a subset of the XX, YY matrices using the diagonal matrix as indicator. So you could select those points manually, and use plot to plot them as a line:
Xplot = diag(XX);
Yplot = diag(YY);
plot(Xplot,Yplot,'r.-');
The call to diag(XX) will take the diagonal elements of the matrix XX, which is exactly where you'll get the red patches when you use surf with the z data according to eye().
Result:
Also, if you're just trying to do what your example states, then there's no need to use matrices just to take out the diagonal eventually. Here's the same result, using elementwise operations on your input vectors:
datA = 1:10;
datB = 1:10;
X2 = cosd(8*datA).*datB;
Y2 = datA.*log10(datB*3);
Z2 = cosd(datB);
XX2 = X2./(1+Z2);
YY2 = Y2./(1+Z2);
plot(Xplot,Yplot,'rs-',XX2,YY2,'bo--','linewidth',2,'markersize',10);
legend('original','vector')
Result:
Matlab has many built-in function to assist you.
In 2D the easiest way to do this is polar that allows you to make a graph using theta and rho vectors:
theta = linspace(0,2*pi,100);
r = sin(2*theta);
figure(1)
polar(theta, r), grid on
So, you would get this.
There also is pol2cart function that would convert your data into x and y format:
[x,y] = pol2cart(theta,r);
figure(2)
plot(x, y), grid on
This would look slightly different
Then, if we extend this to 3D, you are only left with plot3. So, If you have data like:
theta = linspace(0,10*pi,500);
r = ones(size(theta));
z = linspace(-10,10,500);
you need to use pol2cart with 3 arguments to produce this:
[x,y,z] = pol2cart(theta,r,z);
figure(3)
plot3(x,y,z),grid on
Finally, if you have spherical data, you have sph2cart:
theta = linspace(0,2*pi,100);
phi = linspace(-pi/2,pi/2,100);
rho = sin(2*theta - phi);
[x,y,z] = sph2cart(theta, phi, rho);
figure(4)
plot3(x,y,z),grid on
view([-150 70])
That would look this way
I know the locations of spheres (center and radius) in a box. I want to extract cross sections. I am able to plot the spheres placed in a cube using the following Matlab code:
[X,Y,Z] = sphere;
for SpNum = 1:NumSpheres
surf( X*Radius(SpNum)+Center(SpNum,1), Y*Radius(SpNum)+Center(SpNum,2), Z*Radius(SpNum)+Center(SpNum,3), ...
'FaceColor','r' );
%shading interp;
hold on;
end
axis tight; daspect([1 1 1]);
In the above code, each sphere could have different radius and they do not overlap (so the centers are also different).
The above code does not however generate cross sections. I want to extract cross sections similar to what we get from say X-ray CT data: a series of images in the Z-direction. I think 'interp2/interp3' and 'slice' functions are the relevant functions, but I am not sure how to use them to generate the cross sections. I would appreciate if anyone could give pointers or provide some sample code for my problem?
-- Thanks in advance.
Update:
I tried using meshgrid to generate the grid points followed by the function F(X,Y,Z) as follows:
[X,Y,Z] = meshgrid(1:100,1:100,1:100);
F = zeros(size(X),'uint8');
for SpNum = 1:NumSpheres
F( sqrt((X - Center(SpNum,1)).^2 + (Y - Center(SpNum,2)).^2 + (Z - Center(SpNum,3)).^2) <= Radius(SpNum) ) = 1;
end
surf(F);
followed by:
z = 1;
I = interp3(X, Y, Z, X*Radius(SpNum)+Center(SpNum,1), Y*Radius(SpNum)+Center(SpNum,2), Z*Radius(SpNum)+Center(SpNum,3), z, 'spline');
figure, imshow(I);
I know that interp3 is the function to use since it interpolates the values of the function F(X,Y,Z) which represent the spheres at different location within a bounded box (say 1:100, 1:100, 1:100). The interpolated values at particular 'z' (= 1, 2, 3... 100) should give me 100 cross sections (in the form of 2-D images).
The flaw is in the function F itself, since 'surf' throws an error saying that F should be an array - "CData must be an M-by-N matrix or M-by-N-by-3 array".
Can anyone please help.
I finally figured it. For the benefit of others, here is the code.
% A 3-D matrix 'F' which has its value at particular coordinate set to 255 if it belongs to any one of the spheres and 0 otherwise.
[X,Y,Z] = meshgrid(1:100,1:100,1:100);
F = zeros(size(X));
for SpNum = 1:NumSpheres
F( sqrt((X - Center(SpNum,1)).^2 + (Y - Center(SpNum,2)).^2 + (Z - Center(SpNum,3)).^2) <= Radius(SpNum) ) = 255;
end
% Extract cross sections from F using interp3 function along the z-axis.
I = zeros(size(X));
for z = 1:100
I(:,:,z) = interp3(X, Y, Z, F, 1:100, (1:100)', z, 'spline');
end
implay(I,4);
You could test and visualize the output by setting Center (a 3-D vector) and Radius of each sphere (some arbitrary NumSpheres) to some random values. The above code will display a window with cross-sections.
Previously, I was trying to use 'surf' to render the spheres which is not right. To render, you have to use the first code snippet. Another mistake I made was using a row vector for the 6th argument instead of column vector.
Hope this helps.
--
Cheers,
Ram.
i just started with my master thesis and i already am in trouble with my capability/understanding of matlab.
The thing is, i have a trajectory on a surface of a planet/moon whatever (a .mat with the time, and the coordinates. Then i have some .mat with time and the measurement at that time.
I am able to plot this as a color coded trajectory (using the measurement and the coordinates) in scatter(). This works awesomely nice.
However my problem is that i need something more sophisticated.
I now need to take the trajectory and instead of color-coding it, i am supposed to add the graph (value) of the measurement (which is given for each point) to the trajectory (which is not always a straight line). I will added a little sketch to explain what i want. The red arrow shows what i want to add to my plot and the green shows what i have.
You can always transform your data yourself: (using the same notation as #Shai)
x = 0:0.1:10;
y = x;
m = 10*sin(x);
So what you need is the vector normal to the curve at each datapoint:
dx = diff(x); % backward finite differences for 2:end points
dx = [dx(1) dx]; % forward finite difference for 1th point
dy = diff(y);
dy = [dy(1) dy];
curve_tang = [dx ; dy];
% rotate tangential vectors 90° counterclockwise
curve_norm = [-dy; dx];
% normalize the vectors:
nrm_cn = sqrt(sum(abs(curve_norm).^2,1));
curve_norm = curve_norm ./ repmat(sqrt(sum(abs(curve_norm).^2,1)),2,1);
Multiply that vector with the measurement (m), offset it with the datapoint coordinates and you're done:
mx = x + curve_norm(1,:).*m;
my = y + curve_norm(2,:).*m;
plot it with:
figure; hold on
axis equal;
scatter(x,y,[],m);
plot(mx,my)
which is imo exactly what you want. This example has just a straight line as coordinates, but this code can handle any curve just fine:
x=0:0.1:10;y=x.^2;m=sin(x);
t=0:pi/50:2*pi;x=5*cos(t);y=5*sin(t);m=sin(5*t);
If I understand your question correctly, what you need is to rotate your actual data around an origin point at a certain angle. This is pretty simple, as you only need to multiply the coordinates by a rotation matrix. You can then use hold on and plot to overlay your plot with the rotated points, as suggested in the comments.
Example
First, let's generate some data that resembles yours and create a scatter plot:
% # Generate some data
t = -20:0.1:20;
idx = (t ~= 0);
y = ones(size(t));
y(idx) = abs(sin(t(idx)) ./ t(idx)) .^ 0.25;
% # Create a scatter plot
x = 1:numel(y);
figure
scatter(x, x, 10, y, 'filled')
Now let's rotate the points (specified by the values of x and y) around (0, 0) at a 45° angle:
P = [x(:) * sqrt(2), y(:) * 100] * [1, 1; -1, 1] / sqrt(2);
and then plot them on top of the scatter plot:
hold on
axis square
plot(P(:, 1), P(:, 2))
Note the additional things have been done here for visualization purposes:
The final x-coordinates have been stretched (by sqrt(2)) to the appropriate length.
The final y-coordinates have been magnified (by 100) so that the rotated plot stands out.
The axes have been squared to avoid distortion.
This is what you should get:
It seems like you are interested in 3D plotting.
If I understand your question correctly, you have a 2D curve represented as [x(t), y(t)].
Additionally, you have some value m(t) for each point.
Thus we are looking at the plot of a 3D curve [x(t) y(t) m(t)].
you can easily achieve this using
plot3( x, y, m ); % assuming x,y, and m are sorted w.r.t t
alternatively, you can use the 3D version of scatter
scatter3( x, y, m );
pick your choice.
Nice plot BTW.
Good luck with your thesis.