I am trying to program a matlab code in R2012a that will allow a color representation of temperature change on a surface based on different corresponding height and temperature measurements. Then end goal would be to combine multiple images together to get some sort of a jpeg. The temperatures I recorded were time specific which is why it would be beneficial in the end to have a visual depicting this temperature change over time.
It has been a little bit of time since I have used matlab, and I have never created something This complicated. It has me a little over whelmed and wondering where to start.
Thanks for any and all advice!
You might want to look at the avifile documentation for details on how to create a movie from a series of frames, there is a nice example that should make it clear enough.
Now to generate the frames needed for the avi file. Here, the meshgrid and surf functions come in handy. meshgrid generates two coordinate matrices with x and y coordinates for every point on your plate, surf then allows you to plot a surface with colors given by the temperatures at these positions. For example:
[x,y] = meshgrid(1:5,1:10) % generate a 10*5 plate with step size 1
z = rand(10,5) % height of the plate for a given position
c = rand(10,5) % color values, these should be your (time-dependent) temperatures
surf(x,y,z,c)
It is not clear to me whether you actually have a curved surface (requiring three coordinates to define each position), or just a simple plate. If it is just a simple plate, you can just set z = ones(size(x)) to obtain an uniform height.
Related
Explanation of the problem:
I have points with (x,y,z) coordinates at two+ distinct times. For convenience, they can be imagined as irregularly spaced points along the surface of an inverted paraboloid.
There is some minimal thickness to the paraboloid. The paraboloid changes shape slightly as time proceeds (like a balloon inflating) and when it does so, all of the points move.
By substracting the coordinates at time2 - time1, I can get the displacement vectors at each point.
It is important to note (and I suspect this might be the source of the problem) that at the first time point, the x and y coordinates range from 0 to 2000, and the z coordinates are all within a narrower range - say 350 to 450. During the deformation, each point has an x component of displacement, y component, and z component.
The x and y components are small (~50 at most), while the z component is the largest (goes up to 400 near the center, much less near the edges).
Using weighted moving least squares at the location of each point, I am trying to fit the components of displacements to a second degree polynomial surface in terms of the original x,y,z coordinates of the point: eg.
x component of
displacement = ax^2 + bxy + cx + dy^2.. + hz^2 + iz + j
I use the lsqr function in MATLAB,like so, looping through each point for each time interval:
Ux = displacements{k,1}(:,1);
Cx = lsqr((adjust_B_matrix'*W*adjust_B_matrix),(adjust_B_matrix'*W*Ux),1e-7,10000);
W is the weight matrix, and adjust_B_matrix is the matrix of all (x,y,z) coordinates at time 1, shifted so that they're all centered around the point at which I'm trying to fit the function.
What is going wrong?
It's just not working -- once I have the functions, they're re-centered around the actual coordinates of the points.
But once I plot the resulting points (initial pointx + displacementx, initial pointy + displacementy, initial pointz + displacementz) by plugging in the coordinates at time 1 into the now-discovered functions, it just spits out a surface that looks just like the surface at time 1.
What might be going wrong? Things I have tried:
It's not an issue with the code itself- I generated 'fake' data using a grid of points and it worked perfectly. The predicted locations were superimposed with the actual coordinates and I was able to get back the function I started with. But in my trial example, I used x,y,z from 0 to 5, evenly spaced.
Global fitting works (but I need local fitting...).
I tried MATLAB's curve fitting toolbox and just tried to fit one of the displacements to only x and y coordinates, globally. It worked perfectly.
I think I shouldn't have a singular matrix issue because I use a large radius (around 75-80) points in the calculations, somewhat dispersed in 3D space.
Suspicions:
I think it has to do with the uneven distribution of initial (x,y,z) coordinates, but I don't know why or how to fix the issue, or even what method I can use.
If you read this far, thank you so much. Any advice would be greatly appreciated.
Figure for reference:
green = predicted points at time 2. Overlapping mostly with red, the actual coordinates of the points at time 1.
blue is the correct coordinates of points at time 2 (this is where the green ones should be if things were working).
image
Updated link for files:
http://a.tmp.ninja/eWfkNmFZyTFk.zip
Contents - code, sample data (please load the .mat files).
I can't actually access the code you posted, so here's some general suggestions.
It does look like the curve fitting toolbox has tools that do exactly what you are looking for, checkout the bottom of this page: https://www.mathworks.com/help/curvefit/polynomial.html#bt9ykh.
It looks like for whatever your learned function for the displacement is just very small or zero everywhere. I suspect the issue is just a minor typo/error on your part somewhere in your pipeline, possibly translating what you have to work with the fit function will reveal the issue.
This really shouldn't be the issue, but in the future if you had much more unbalanced data you could normalize it all before fitting (x_norm = (x - x_mu)/x_std).
Also, I don't think this is your problem either, but you can check if your matrix is close to singular by checking the condition number using the cord() function. So you could check cond(adjust_B_matrix'Wadjust_B_matrix). Second, If you check the documentation for lsqr there is an option to get a debug return flag, that is worth checking too.
I am trying to write a MATLAB script to give me a contour map. The contour map must be created from inputs that I generated from 100 images.
The story is like this:
I have 100 images on which I ran an image processing algorithm for optimization. Now, I got their energy curves. So, I have 100 energy curves. I want to create a contour map that will show me where the points are denser on the plot. (the energy curves are plotted as energy vs. iteration with fixed number of iterations)
The following is my variable:
energy(iteration,numImages)
Hope I explained it well.
Thanks in advance.
I interpret your question to boil down to how can I create a surface plot with colors according to the energy found in energy. I would solve this by using the contour function with a grid generated using meshgrid. If each image is described in 1000 data points with 100 files the plot can be generated as follows:
% using stuff as random junk instead of energy
numPoints = 1000;
numFiles = 100;
stuff = rand(1000,100); % replace with actual information
[X, Y] = meshgrid(1:numFiles, 1:numPoints);
contour(X,Y,stuff);
You can also create a 3D surface plot using surf and the same logic.
From what i see of you graph (and using the comments also), one possible way is to use plot3 to plot a line in 3D for every plot.
For doing so, you can use something like this code:
x=(0:0.01:1)';
aexp=zeros(100,numel(x));
hold on
for ii=1:100;
% aexp(ii,:)=exp((-x+ii/10)); %exponential
aexp(ii,:)=exp(-(x-ii/100).^2); %~gaussian
% aexp(ii,:)= x*ii; %linear increase
plot3(x,aexp(ii,:),ii*ones(1,numel(x)));
end
% set(gca,'yscale','log'); % uncomment if you need logscale.
giving
I have a few options of plot. It always plot from the XY view. I changed by hand, but you can use the view command. Notice that i used a simple counter to make the spacing in the z direction.
In a similar manner, you can plot using the contour. For my code, after the data have been generated in the for loop, remove/comment the plot3 and add:
contour(aexp) %outside the for loop,
giving
Notice that i have not really take care what i'm plotting. You can find more info on contour in the Matlab page .
You commented that the x-axis should be number of iterations, y-axis should be energy and z-axis should be the information containing how many lines are passing through from some areas. For this, make a qq variable, being it qq=number_of_lines(number of iterations,energy) . Make a discrete grid for the energy if you don't have one. Number of iterations is probably discrete anyway. The function is you who need to devise, but i would go for something which checks the number of lines for every energy and every iteration. In this case you will have the z-function that depends on y and x, that is the case to use contour or surface.
My function above make a line for every ii point, to have a 3d function. An edition for another extra loop is not hard. Just remember to have the same regular grid for every point, otherwise you will have trouble.
I want to assign vector to a contourf graph, in order to show the direction and magnitude of wind.
For this I am using contourf(A) and quiver(x,y), where as A is a matrix 151x401 and x,y are matrices with the same sizes (151x401) with magnitude and direction respectively.
When I am using large maps i get the position of the arrows but they are to densily placed and that makes the graph look bad.
The final graph has the arrows as desired, but they are to many of them and too close, I would like them to be more scarce and distributed with more gap between them, so as to be able to increase their length and at the same time have the components of the contour map visible.
Can anyone help , any pointers would be helpful
i know its been a long time since the question was asked, but i think i found a way to make it work.
I attach the code in case someone encounters the same issues
[nx,ny]= size(A) % A is the matrix used as base
xx=1:1:ny; % set the x-axis to be equal to the y
yy=1:1:nx; % set the y-axis to be equal to the x
contourf(xx,yy,A)
hold on, delta = 8; %delta is the distance between arrows)
quiver(xx(1:delta:end),yy(1:delta:end),B(1:delta:end,1:delta:end),C(1:delta:end,1:delta:end),1) % the 1 at the end is the size of the arrows
set(gca,'fontsize',12);, hold off
A,B,C are the corresponding matrices ones want to use
I am trying to plot x and y velocities using quiver function in MATLAB.
I have x,y,u and v arrays(with their usual meanings) with dimension 100x100
So, the result is my quiver plot is dense and I cannot see the arrows unless I zoom in.
Somewhat like this: quiver not drawing arrows just lots of blue, matlab
Take a look at my plot:
Is there any way to make quiver plot less dense(and with bigger arrows)? I am planning to clip x-axis range to 0-4. But anything apart from that?
I cannot make my mesh less dense for accuracy concerns. I am, however willing to ignore some fine data points if that's required to make the plot look better.
You can plot a reduced number of arrows by plotting, for example, (assuming your data are in arrays)
quiver(x(1:2:end,1:2:end),y(1:2:end,1:2:end),u(1:2:end,1:2:end),v(1:2:end,1:2:end))
where the 2 in this example means we plot only a quarter as many arrows. You can of course change it, as long as you change all of the 2's so that the arrays are all appropriately sized.
If you want to change the length of the arrows there are two options. Firstly, you can use the scale option scale=2 to scale the arrows by the amount specified, or you can normalise the velocities if you want to have all the arrows the same length. You do lose information doing that, because you can't compare the magnitude of the velocity by looking at the arrows, but it may be useful in some situations. You can do this by dividing u and v both by sqrt(u.^2+v.^2) (at the points you wish to plot arrows at.
Hope that helps and sets everything out nicely.
You need to make your interval value a bit larger in order to make your matrix more sparse.
This is very dense:
1:0.0001:100
This is very sparse:
1:1:100
EDIT:
If you have the Image Processing Toolkit you can use the imresize function to reduce the matrix resolution:
newMat = imresize(oldMat, newSize);
And if you don't have the Toolbox then you can resize in a similar manner to this example using interp2 Interpolation:
orgY = 1:size(oldMat,1);
orgX = 1:size(oldMat,2);
[orgX,orgY] = meshgrid(orgX ,orgY);
newY = linspace(1,size(mat,1),newHeight);
newX = linspace(1,size(mat,2),newWidth);
[newX,newY] = meshgrid(newX,newY);
newMat = interp2(orgX,orgY,mat,newX,newY);
And thanks to #David, if you want to just strip out some individual points you can simply do:
xPlot=x(1:2:end)
I have taken readings on my software defined radio. I have three quantities, frequency, power and error rate. I would like to keep frequency on my x-axis as the latter quantities are measured with respect to frequency. Now I need to plot them on a single curve so that i can see frequency vs power and frequency vs error rate in one shot. I have been looking at matplotlib for quite some time but I see the 3d scatter plots and they seem to not convey the picture i want. So my question is
Is 3d plot useless in my case and I better plot 2 graphs and join the y axes of both graphs together so that I could see both the graphs on top of each other.
Is there a way in python or matlab such that you plot a 3d curve and then if you move the cursor you can see frequency vs power and if you move the cursor the other way you can see frequency vs error rate.
Any other ideas on how i can represent my readings better will be helpful.
When two sets of data share the same dependant variable (x-axis), it is common in scientific literature at least to plot them both in the following manner to save space:
This is preferred to twining the xaxis which can cause confusion for some people. It works especially well if features are to be compared.
This plot was generated using:
import pylab as py
x = py.linspace(0,10,1000)
y = py.sin(x)
z = py.sinc(x)
ax1 = py.subplot(211)
ax1.plot(x,y)
ax1.set_xticklabels([]) # Remove the xticks from the top figure
ax1.set_yticks(ax1.get_yticks()[1:]) # Stops the y axis overlapping
ax2 = py.subplot(212)
ax2.plot(x,z)
py.subplots_adjust(hspace=0.0)
py.show()
I believe there is a better way to remove the xticklabels but I can't remember it at the moment. Be careful not to just py.set_xticks([]) as if you then ask for a grid the top plot has no ticks.