I would like to look at two dimensional data in a time-series - the first idea I had was to use a scatter plot, where you can easily explore timepoint-to-timepoint. Is there a function I could use for this? I looked at scatter3 but it can only plot perfectly-cubic data, not as below:
e.g.
data=rand(5,5,3);
scatter3(data(1,:,:),data(:,1,:),data(:,:,1)) %throws an error
thanks
edit: Originally I had something like >this< in mind
scatter3 seems to be for 3D plots, but you say your data is 2D.
For a simple time-series graph you could presumably even just use plot:
figure
nPoints = 25;
dataX = 1:nPoints;
dataY = rand(1,nPoints);
plot(dataX,dataY, 'o-')
However, the example you give in your link looks like something else, so it seems like scatter (rather than scatter3) might be what you're after. Maybe something like this?
figure
nPoints = 25;
dataX = 1:nPoints;
dataY = rand(1,nPoints);
dataArea = rand(1,nPoints)*100;
dataColours = rand(nPoints,3);
scatter(dataX,dataY,dataArea,dataColours)
EDIT:
I think I understand better what you mean, sorry I didn't see the buttons at the bottom of the link, but correct me if I'm wrong. So you have a set of XY coordinates for multiple objects at different points in time, and ideally you want to plot how the XY coordinates of each object (in 2 dimensions) change over time (in 3 dimensions). Your initial approach in using scatter3 was to try and make a simple 3d graph, but maybe ideally you want a 2d graph that can be either animated or interactive, to change the time point displayed at any given time?
Going back to your original question, I think the issue with your attempt to use scatter3 (or plot3 might be useful too) is I'm not sure what your dummy data would represent. You created data as a 5x5x3 matrix, and I assume that might represent 25 data points, at 3 different time intervals? However, which data would represent the X and which the Y coordinates? It would work with something like the following, where each variable represents the X/Y/Z coordinates of 6 objects (columns) at 5 different time points (rows)
myX = rand(5,6,1);
myY = rand(5,6,1);
% I'm making each time point increase linearly.
myZ = repmat((1:size(myX,1))', 1, size(myX,2));
plot3(myX, myY, myZ, 'o-')
grid on
% Note I find the default dimensions it treats as X, Y and Z unintuitive (e.g. the Z dimension is the vertical dimension), but one could swap the input arguments around to overcome this.
However, especially if you have a lot of points, I'm not sure how clear a graph like this will be, especially compared to the example in your link.
Instead it seems like you ideally want only the XY coordinates of all objects to be plotted for only one time point at once, and a way to cycle through each time point sequentially. This seems trickier, and maybe someone else will be able to answer better than I have. A couple more questions though that might be useful:
How much do you care about the smoothness of the transition. In the example link the circles move smoothly from one position to another, rather than just jumping/teleporting between points.
Ideally do you want a function that would produce an 'animation', cycling through all the time points from begining to end, or a way of manually specifying/changing which time point is being displayed. If the former, maybe this function would be useful (though I've tried it myself yet) https://uk.mathworks.com/matlabcentral/fileexchange/42305-multicomet
Related
Is it possible to graphically represent linear transformations in MATLAB like the one shown below?
In particular, I'm looking for a way to represent the way a coordinate grid might become squished, stretched, or rotated after a matrix multiplication has been performed. I've tried using meshgrid(x,y) to plot the grid but I've been having trouble getting it to display properly. Ideally, it would be nice to see the way that the transformation acts on a unit square (and, if possible, to see the transformation being performed in a continuous fashion, where you can see the graphs morphing into each other). However, I'm willing to sacrifice these last two requirements.
Here is the code I have used so far:
x = -10:2:10;
y = -10:2:10;
[X,Y] = meshgrid(x,y);
plot(X,Y)
For some reason, only vertical lines have been plotted. (The documentation says it will display a square grid.)
please read the plot doc.
the problem is that what you are doing is trying to plot mash matrixes in a plot not by using mesh or something like that.
you may find this very helpful.
I have this x y data:
I would like to increase the density of points by closing small gaps. How do i go about. I still want to preserve the structure of the points.
It depends on what you mean by 'closing gaps'.
if you mean that you want to make the data seem more grouped without actually adding more data points, then you might find the 'LineWidth' argument to be useful. If used currectly it increases the width of each marker in the scatter plot, which will make the data seem more grouped and with less gaps.
to use it, write the scatter code line as follows:
scatter(X, Y, 'LneWidth', width_number)
replace 'width_number' with different values and see the effect.
I have 8 plots which I want to implement in my Matlab code. These plots originate from several research papers, hence, I need to digitize them first in order to be able to use them.
An example of a plot is shown below:
This is basically a surface plot with three different variables. I know how to digitize a regular plot with just X and Y coordinates. However, how would one digitize a graph like this? I am quite unsure, hence, the question.
Also, If I would be able to obtain the data from this plot. How would you be able to utilize it in your code? Maybe with some interpolation and extrapolation between the given data points?
Any tips regarding this topic are welcome.
Thanks in advance
Here is what I would suggest:
Read the image in Matlab using imread.
Manually find the pixel position of the left bottom corner and the upper right corner
Using these pixels values and the real numerical value, it is simple to determine the x and y value of every pixel. I suggest you use meshgrid.
Knowing that the curves are in black, then remove every non-black pixel from the image, which leaves you only with the curves and the numbers.
Then use the function bwareaopen to remove the small objects (the numbers). Don't forget to invert the image to remove the black instead of the white.
Finally, by using point #3 and the result of point #6, you can manually extract the data of the graph. It won't be easy, but it will be feasible.
You will need the data for the three variables in order to create a plot in Matlab, which you can get either from the previous research or by estimating and interpolating values from the plot. Once you get the data though, there are two functions that you can use to make surface plots, surface and surf, surf is pretty much the same as surface but includes shading.
For interpolation and extrapolation it sounds like you might want to check out 2D interpolation, interp2. The interp2 function can also do extrapolation as well.
You should read the documentation for these functions and then post back with specific problems if you have any.
So basically, the graph labeled "Thermal Wind" has an extreme value that compresses the y-values for all the other plots, making it much harder to see any of the individual variations in the other plots. Is there a way to neatly cut off this extreme value? I could just rescale the y limit to a maximum of 40, but then this looks ugly.
As for the alternative I've tried - it's here:
I would recommend trying to plot it on a log scale. The function you'll want to consider using is semilogx, though for completeness I recommend also reading the help file on loglog.
Alternately, you could use subplot to generate multiple plots, one of which is zoomed into a region of interest.
Are the outlier points errors in the data, or do they represent extreme cases?
If they are not valid data, just manually exclude them from the data, plot the graph, and include a text clarification when describing the graph. If they are valid data, then trimming them would misrepresent the data, which isn't a good thing.
Graphs of data aren't art: their main goal isn't to be pretty; it's to provide a useful visualization of data. There are some minimum requirements on appearance, however: the axes have to be labeled, the units have to be meaningful, the different curves have to be visually distinct, etc. As long as your graph has these things, you shouldn't expect to lose marks for presentation.
There are two approaches that I use:
One approach would be transform the data so it will fill the plot nicely. Make the transform so that it wouldn't touch the range - say -10 to +10. In your case you could choose it so that 100 transforms to +15 and -100 to -15.
For clarity you need to then also set and label the y ticks appropriately. And for nice style make sure the line changes slope when it goes over the border.
I plot the data as is. But set the axis limits say from -10 to +10. Where points lay outside I place upwards and downwards triangles along the border to mark in which direction the "outliers" would be. Obviously this is only good when there aren't too many.
I've a 256x256 projection matrix. each row is a projection taken with equal angles. i need to generate the original image with backprojection using matlab and I am not really familiar with matlab. Can you suggest me any code samples or alghorithms? I've found some similar codes i couldn't generate the original image using them.
This should be relatively simple with the iradon command, if you have the Image Processing Toolbox. If you don't, it will be a bit tougher because you need to roll your own version of that. Apparently you can't use this, but for what it's worth I get an image if I use:
I = iradon(Pteta',linspace(0,179,size(Pteta,1));
So, how can you do this yourself? I'll try to help you along the way without giving you the answer - this is homework after all!
First, think about your 0-degree projection. Imagine the axis you're projecting on has units 1,256. Now imagine back projection of these coordinates across your image, it would look something like this:
Similarly, think a 90-degree projection would look like this:
Cool, we can get these matrices by using [X, Y] = meshgrid(1:256);, but what about off-axis projections? Just think of the distance along some angled line, like converting polar/Cartesian coordinates:
theta = 45 % projection angle in degrees
t = X*cosd(theta) + Y*sind(theta);
And it works!
There is a problem, though! Notice the values go up over 350 now? Also it's sort of off-center. The coordinates now exceed the length of our projections because the diagonal of a square is longer than the side. I'll leave it to you to figure out how to resolve this, but figure the final image will be smaller than the initial projections, and you may need to use different units (-127 to 128 instead of 1 to 256).
Now you can just index your projections for those angles to backproject the actual values across the image. Here we have a second problem, though, because the values are not integers! We could just round them, this is called nearest-neighbor interpolation, but it doesn't give the best results.
proj = Pteta(angle,:);
% add projection filtering here
t = X*cosd(theta) + Y*sind(theta);
% do some rounding/interpolating to make t all integers
imagesc(proj(t));
For our off-center version, that gives us this image, or something similar:
Now you just need to do this for every angle, and add them all up.