I'm trying to plot many 2D plots (x,y).
But...
each 2D plot is for a constant z.
So really my data is (x,y,z) but not z(x,y), which I believe are the requirements for using the "surf" command.
Could anyone help with this?
Example,
x = velocity
y = drag
I have multiple runs of y(x) for a constant temperature, z.
I just want to plot each (x,y) along a 3rd axis, temperature z.
Ideally I'd also want some sort of contour between the (x,y) plots so I can show the peaks/troughs etc.
Any help would be great.
If the runs are not independent (there is some trend over multiple runs) then it may make sense to use surf. You then need to construct your data such you have an X,Y, and Z - in this case I'd suggest you use the drag measurements as your Z (height).
Assuming that you have all the drag/velocity data in drag and velocity which are both of size [data points x number of runs]:
% construct matrix of run numbers
runs = repmat(1:numruns, [1, datapoints]);
runs = reshape(runs, datapoints, numruns);
% plot and label
surf(runs,velocity,drag);
xlabel('runs')
ylabel('velocity')
zlabel('drag')
Related
I have a function f(x,y,t), where for each time point t I have a 2D line plot of x vs. y. I would like to somehow stack all these 2D plots next to each other so that I see the evolution in time t (but not an animation, just a stationary plot). Both x and y variables hold 100 values, and I repeat the calculation t=3500 times. I've organized the output of a function in a 2x100x3500 matrix.
I know that there is a very simple way to make a 3D plot of this matrix, but I have big trouble finding it. Any help is appreciated.
Use waterfall
figure
[X,Y,Z] = peaks(30);
waterfall(X,Y,Z)
I want to build a contourf plot of a certain aspect in my Plate. The plate is divided in triangle elements, which I have the coordinates (x,y) of each knot of the triangle.
So, How can I make a meshgrid for my knots so I can make my contourf plot?? I have the coordinates of everything and have the value of my function Z in each knot. (I'm a beginner in Matlab, sorry for this "basic" question)
If your goal is just to visualise the triangles then there is another way that's probably simpler and more robust (see the end of this post).
If you definitely need to generate contours then you will need to interpolate your triangular mesh over a grid. You can use the scatteredInterpolant class for this (documentation here). It takes the X and Y arguments or your triangular vertices (knots), as well as the Z values for each one and creates a 'function' that you can use to evaluate other points. Then you create a grid, interpolate your triangular mesh over the grid and you can use the results for the countour plot.
The inputs to the scatteredInterpolanthave to be linear column vectors, so you will probably need to reshape them using the(:)`notation.
So let's assume you have triangular data like this
X = [1 4; 8 9];
Y = [2 3; 4 5];
Z = [0.3 42; 16 8];
you would work out the upper and lower limits of your range first
xlimits = minmax(X(:));
ylimits = minmax(Y(:));
where the (:) notation serves to line up all the elements of X in a single column.
Then you can create a meshgrid that spans that range. You need to decide how fine that grid should be.
spacing = 1;
xqlinear = xlimits(1):spacing:xlimits(2);
yqlinear = ylimits(1):spacing:ylimits(2);
where linspace makes a vector of values starting at the first one (xlimits(1)) and ending at the third one (xlimits(2)) and separated by spacing. Experiment with this and look at the results, you'll see how it works.
These two vectors specify the grid positions in each dimension. To make an actual meshgrid-style grid you then call meshgrid on them
[XQ, YQ] = meshgrid(xqlinear, yqlinear);
this will produce two matrices of points. XQ holds the x-coordinates of every points in the grid, arranged in the same grid. YQ holds the y-coordinates. The two need to go together. Again experiment with this and look at the results, you'll see how it works.
Then you can put them all together into the interpolation:
F = scatteredInterpolant(X(:), Y(:), Z(:));
ZQ = F(XQ, YQ);
to get the interpolated values ZQ at each of your grid points. You can then send those data to contourf
contourf(XQ, YQ, ZQ);
If the contour is too blocky you will probably need to make the spacing value smaller, which will create more points in your interpolant. If you have lots of data this might cause memory issues, so be aware of that.
If your goal is just to view the triangular mesh then you might find trimesh does what you want or, depending on how your data is already represented, scatter. These will both produce 3D plots with wireframes or point clouds though so if you need contours the interpolation is the way to go.
I need to plot a 3D figure with each data point colored with the value of a 4th variable using a colormap. Lets say I have 4 variables X,Y,Z and W where W = f(X,Y,Z). I want a 3D plot with X, Y, and Z as the three axis. The statement scatter3(X,Y,Z,'filled','b') gives me a scatter plot in 3D but I want to incorporate the value of Z in the graph by representing the points as an extra parameter (either with different areas :bigger circles for data points with high value of Z and small circles for data points with low value of Z or by plotting the data points with different colors using a colormap). However, I am a novice in MATLAB and dont really know how to proceed. Any help will be highly appreciated.
Thanks in advance!
So just use z for the size vector (4th input) as well as the color vector (5th input):
z = 10*(1:pi/50:10*pi);
y = z.*sin(z/10);
x = z.*cos(z/10);
figure(1)
scatter3(x,y,z,z,z)
view(45,10)
colorbar
The size vector needs to be greater 0, so you may need to adjust your z accordingly.
You are already nearly there... simply use
scatter3(X,Y,Z,s,W);
where s is the point size (scalar, e.g. 3) and W is a vector with your W values.
You might also want to issue an
set(gcf, 'Renderer','OpenGL');
where gcf gets your current figure you are plotting in to significantly increase responsiveness when scattering a lot of data.
I am looking for help for my particular problem.
I have a contour plot created from XYZ data. This plot contains 2 broad peaks with one more intense than the other.
When the most intense peak is aligned with the Y axis, I can perform a fitting of every YZ curve at each X values. I usually do a gaussian fit to plot the peak center on the same graph.
In some cases I need to perform the same fitting but no along the Y axis direction (in this case I just plot YZ scan at every different X values) but along another arbitrary direction.
For the moment the only way I found is the following:
-plot the contour plot and find for the position of the most intense peak
-if the position is not aligned with the Y axis, then rotate all the datas and plot again the contour
-perform the YZ gaussian fit for every X value
- Rotate the resulting XY position to go back to the original plot
-plot the XY position as a line on the original contour plot
this is quite long and requires a lot of memory. i would like ot know if there is a more elegant/faster way.
Thanks for your help
David
I take it you want to extract data from the (x,y,z) data along some arbitrary line in order to make a fit. A contour plot will show only part of the data, the full z(x,y) data can be shown with imagesc etc. Say you want the data along line defined by two points (x1,y1) -> (x2,y2). According to the eq of the line, the line y=a*x+b the slope a is (y2-y1)/(x2-x1) and b=y1-a*x1. For example, I'll select (x,y) coordinates in the following contour:
Create data and end points:
m=peaks(100);
x1=11 ; x2=97;
y1=66; y2=40;
Thus the line parameters are:
a=(y2-y1)/(x2-x1);
b=y1-a*x1;
and the line is:
x=x1:x2;
y=round(a*x+b);
select the proper (x,y) elements using linear indexing:
ind=sub2ind(size(m),y,x)
plot:
subplot(2,1,1)
contour(m,10); hold on
line([x1 x2],[y1 y2],'Color',[1 0 0]);
subplot(2,1,2)
plot(m(ind))
You can now use vec=m(ind) to fit your function.
I've run simulations which have given me data points corresponding to X number of different radii, and Y number of angles each one was evaluated at. This means that I have X times Y data points which I need to plot.
I am currently plotting it in an non-ideal fashion: I am using the x and y axes as the r and theta axes. This means that my data appears as a sinusoidal trend which increases with radius on a Cartesian grid, not the circle which it physically represents. This is how I am currently plotting my data:
surf(r_val, th_val, v_val);
What I wish to do is plot my data on a cylindrical axis, such like that of the function polar(), but in R3 space. I would rather not download a toolbox, or modify the existing polar function; if there is no other solution then I will obviously end up doing this anyways.
Thanks for your help!
G.
Also, I am using Matlab 2012a
EDIT:
r_val = 1x8 vector containing unique radii
th_val = 1x16 vector containing unique angles
v_val = 8x16 matrix containing voltages corresponding to each position
NOTE: (after answered)
The truly ideal solution does not exist to this problem, as Matlab currently supports no true polar axes methods. Resource found here.
You should transform your coordinates to Cartesian coordinates before plotting them. MATLAB has builtin functions for perfroming coordiante transformations. See, for example pol2cart, which transforms polar or cylindrical coordinates to Cartesian coordinates. In your case you would simply use something like:
[x, y] = pol2cart(th_val, r_val);
surf(x, y, v_val);
Edit: Given that th_val and r_val are vectors of differing lengths it is necessary to first create a grid of points before calling pol2cart, along the lines of:
[R, T] = meshgrid(r_val, th_val);
[x, y] = pol2cart(T, R);
surf(x, y, v_val);