I am running autonomous underwater vehicle missions which give me lat, long, depth, and temperature data that I am trying to create 3D interpolations with. I am able to create the 3D model of the environment but I am trying to have the color fill be the interpolated temperature at each associated position.
The image below is the 3d depth chart I get that I want to have the fill color be the temperatures at those locations:
I have tried using colormap where surf(X, Y, Z, C) and C is the temperature data but that does not work.
This is what I have for code where VPSA is my data set and X = longitude, y = latitude, Z = depth, and C = temperature
%Making Variables:
X = VPSA {:,1};
Y = VPSA {:,2};
Z = VPSA {:,3};
C = VPSA {:,4};
%Three axis plot
%Plotting Variable with coordinates
xi = linspace(min(X),max(X),1000);
yi = linspace(min(Y),max(Y),1000);
[Xi,Yi] = meshgrid(xi,yi);
Zi = griddata(X,Y,Z, Xi,Yi);
mesh (Xi,Yi,-Zi)
xlabel('Latitude')
ylabel('Longitude')
zlabel('Depth')
UPDATE: I added the following lines of code
Ci = griddata(X,Y,C,Xi,Yi);
mesh(Xi,Yi,-Zi,Ci)
to get the following figure, but it is so hard to tell what is going on, I wonder if there is a way to smooth the interpolation out into a box so it isn't as jagged.
Thank you!!
I am assuming that the original X,Y,Z,C data points are matched, and you can use the command mesh(X,Y,Z,C) to get a usable plot. Now you are looking to do the same, but with an interpolated dataset.
In this case, you only need to give the mesh command a the color data C.
C also needs to be interpolated, so maybe something like:
Ci = griddata(X,Y,C,Xi,Yi); % Interpolate C the same way you did for Z
mesh(Xi,Yi,-Zi,Ci)
Edit: Apologies for the previous incorrect answer.
It's hard to know exactly what you mean by "does not work" or what the nature of your temperature matrix C is, but the specification for C is as follows:
same size as Z (more specifically, each element in Z corresponds to each element of C in the same position)
each element in C is an integer, corresponding to a position in the current colormap. (which means if you update your colormap, your surface plot will update accordingly)
The integers can be inspected / changed after plotting, via the cdata property of your surface plot.
You may have to change whether your plot treats your colormap as scaled or direct (i.e. the cdatamapping). Have a look at your object's properties to find out.
Related
Trisurf(f,x,y,z) function can draw a patch object. How can I add datatip to this object at the known vertices index with code?
I tried to use the following codes in 2017b
cursorMode = datacursormode(gcf);
hdtip = cursorMode.createDatatip(h_surf); %h_surf is the handle of trisurf
hdtip.Cursor.Position=pos; %this commond seems cannot find the exact vertex and depends on the view angle, where pos is the vertex coordinates.
hdtip.Cursor.DataIndex=idx; %try to use this command to assign the vertex index, but not successful.DataIndex seems not the vertex index.
Accordint to #Woffie's suggestions, I tuned a little bit to reproduce the error with different view angle in 2017b and 2019b.
First to enable view([180,0]); line. Run the code to get the correct result. And then without closing the figure,comment this line, only remain view([0,0]); to get the wrong result. But if add Close all to the front of the script, should get the correct result each time. (But still don't know the reason, whether it's a bug or not and how to avoid it smartly.)
It might be good to note, in 2D plots, this usually is not a problem since one doesn't usually change the view.
ngrid=1000;%actualy doesnot matter.
x=linspace(-3,3,ngrid+1);
y=linspace(-3,3,ngrid+1);
[x,y] = meshgrid(x,y);
tri = delaunay(x,y);
z = peaks(x,y);
x=(x+3)/6;y=(y+3)/6;
h_surf = trisurf(tri,x,y,z,'EdgeAlpha',0.1);
x0=0.25;y0=0.44;%the above normlization are not necessary but easier to setup the query node.
z0=z(round(y0*ngrid)+1,round(x0*ngrid)+1);
pos=[x0,y0,z0];
view([0,0]);
view([180,0]); %toggle between these two view angles to see the difference.
cursorMode = datacursormode(gcf);
hdtip = cursorMode.createDatatip(h_surf);
hdtip.Cursor.Position = pos;
hdtip.Cursor.Position
BTW, I also figured out how to use hdtip.Cursor.DataIndex=idx. Basically, DataIndex here is the linear index for h_surf.XData(or YData or ZData), while the XData, YData, ZData are mxn matrice as the node/vertex coordinates of all faces in h_surf, where m is the number of nodes in each face and n is the number of the faces. So it will be straightforward to convert to node index into DataIndex.
I'll use the trisurf example from the docs to create a mesh:
[x,y] = meshgrid(1:15,1:15);
tri = delaunay(x,y);
z = peaks(15);
h_surf = trisurf(tri,x,y,z);
Now the tri matrix contains the indices within x, y, and z for each vertex in the mesh. We can choose a vertex index idx, and the corresponding value from each of the x/y/z arrays to use as the .Position property of your data cursor. Contrary to your comment, the Position property does not depend on the view angle, it is the position relative to the axes (and therefore your data).
So
idx = 123; % intex within triangulation "tri"
xcurs = x(tri(idx)); % = 5 in this example
ycurs = y(tri(idx)); % = 6 in this example
zcurs = z(tri(idx)); % = 0.65233 in this example
cursorMode = datacursormode(gcf);
hdtip = cursorMode.createDatatip(h_surf);
hdtip.Cursor.Position = [xcurs,ycurs,zcurs];
You could determine idx however you want, then use a common idx to extract the aligned x, y, and z coordinates.
Two different views to show the data tip stays in place:
This example was run using MATLAB R2017b.
I'd like to create a heat map to analyze the porosity of some specimens that I have 3D-printed. the X-Y coordinates are fixed since they are the positions in which the specimens are printed on the platform.
Heatmap:
Tbl = readtable('Data/heatmap/above.csv');
X = Tbl(:,1);
Y = Tbl(:,2);
porosity = Tbl(:,3);
hmap_above = heatmap(Tbl, 'X', 'Y', 'ColorVariable', 'porosity');
The first question is: how can I sort the Y-axis of the plot? since it goes from the lower value (top) to the higher value (bottom) and I need it the other way around.
The second question is: I only have around 22 data points and most of the chart is without color, so I'd like to get a smoother heatmap without the black parts.
The data set is quite simple and is shown below:
X
Y
porosity
74.4615
118.3773
0.039172163
84.8570
69.4699
0.046314637
95.2526
20.5625
0.041855213
105.6482
-28.3449
0.049796110
116.0438
-77.2522
0.045010692
25.5541
107.9817
0.038562053
35.9497
59.0743
0.041553065
46.3453
10.1669
0.036152061
56.7408
-38.7404
0.060719664
67.1364
-87.6478
0.037756115
-23.3533
97.5861
0.052840845
-12.9577
48.6787
0.045216851
-2.5621
-0.2286
0.033645353
7.8335
-49.1360
0.030670865
18.2290
-98.0434
0.024952472
-72.2607
87.1905
0.036199237
-61.8651
38.2831
0.026725885
-51.4695
-10.6242
0.029212058
-41.0739
-59.5316
0.028572611
-30.6783
-108.4390
0.036796151
-121.1681
76.7949
0.031688096
-110.7725
27.8876
0.034619855
-100.3769
-21.0198
0.039070101
-89.9813
-69.9272
NaN
-79.5857
-118.8346
NaN
If you want to assign color to the "black parts" you will have to interpolate the porosity over a finer grid than you currently have.
The best tool for 2D interpolation over a uniformly sampled grid is griddata
First you have to define the X-Y grid you want to interpolate over, and choose a suitable mesh density.
% this will be the number of points over each side of the grid
gridres = 100 ;
% create a uniform vector on X, from min to max value, made of "gridres" points
xs = linspace(min(X),max(X),gridres) ;
% create a uniform vector on Y, from min to max value, made of "gridres" points
ys = linspace(min(Y),max(Y),gridres) ;
% generate 2D grid coordinates from xs and ys
[xq,yq]=meshgrid(xs,ys) ;
% now interpolate the pososity over the new grid
InterpolatedPorosity = griddata(X,Y,porosity,xq,yq) ;
% Reverse the Y axis (flip the `yq` matrix upside down)
yq = flipud(yq) ;
Now my version of matlab does not have the heatmap function, so I'll just use pcolor for display.
% now display
hmap_above = pcolor(xq,yq,InterpolatedPorosity);
hmap_above.EdgeColor = [.5 .5 .5] ; % cosmetic adjustment
colorbar
colormap jet
title(['Gridres = ' num2str(gridres)])
And here are the results with different grid resolutions (the value of the gridres variable at the beginning):
Now you could also ask MATLAB to further graphically smooth the domain by calling:
shading interp
Which in the 2 cases above would yield:
Notes: As you can see on the gridres=100, you original data are so scattered that at some point interpolating on a denser grid is not going to produce any meaningful improvment. No need to go overkill on your mesh density if you do not have enough data to start with.
Also, the pcolor function uses the matrix input in the opposite way than heatmap. If you use heatmap, you have to flip the Y matrix upside down as shown in the code. But if you end up using pcolor, then you don't need to flip the Y matrix.
The fact that I did it in the code (to show you how to do) made the result display in the wrong orientation for a display with pcolor. Simply comment the yq = flipud(yq) ; statement if you stick with pcolor.
Additionally, if you want to be able to follow the isolevels generated by the interpolation, you can use contour to add a layer of information:
Right after the code above, the lines:
hold on
contour(xq,yq,InterpolatedPorosity,20,'LineColor','k')
will yield:
I have a number of 2d probability mass functions from 2 categories. I am trying to plot the contours to visualise them (for example at their half height, but doesn't really matter).
I don't want to use contourf to plot directly because I want to control the fill colour and opacity. So I am using contourc to generate xy coordinates, and am then using fill with these xy coordinates.
The problem is that the xy coordinates from the contourc function have strange numbers in them which cause the following strange vertices to be plotted.
At first I thought it was the odd contourmatrix format, but I don't think it is this as I am only asking for one value from contourc. For example...
contourmatrix = contourc(x, y, Z, [val, val]);
h = fill(contourmatrix(1,:), contourmatrix(2,:), 'r');
Does anyone know why the contourmatrix has these odd values in them when I am only asking for one contour?
UPDATE:
My problem seems might be a failure mode of contourc when the input 2D matrix is not 'smooth'. My source data is a large set of (x,y) points. Then I create a 2D matrix with some hist2d function. But when this is noisy the problem is exaggerated...
But when I use a 2d kernel density function to result in a much smoother 2D function, the problem is lessened...
The full process is
a) I have a set of (x,y) points which form samples from a distribution
b) I convert this into a 2D pmf
c) create a contourmatrix using contourc
d) plot using fill
Your graphic glitches are because of the way you use the data from the ContourMatrix. Even if you specify only one isolevel, this can result in several distinct filled area. So the ContourMatrix may contain data for several shapes.
simple example:
isolevel = 2 ;
[X,Y,Z] = peaks ;
[C,h] = contourf(X,Y,Z,[isolevel,isolevel]);
Produces:
Note that even if you specified only one isolevel to be drawn, this will result in 2 patches (2 shapes). Each has its own definition but they are both embedded in the ContourMatrix, so you have to parse it if you want to extract each shape coordinates individually.
To prove the point, if I simply throw the full contour matrix to the patch function (the fill function will create patch objects anyway so I prefer to use the low level function when practical). I get the same glitch lines as you do:
xc = X(1,:) ;
yc = Y(:,1) ;
c = contourc(xc,yc,Z,[isolevel,isolevel]);
hold on
hp = patch(c(1,1:end),c(2,1:end),'r','LineWidth',2) ;
produces the same kind of glitches that you have:
Now if you properly extract each shape coordinates without including the definition column, you get the proper shapes. The example below is one way to extract and draw each shape for inspiration but they are many ways to do it differently. You can certainly compact the code a lot but here I detailed the operations for clarity.
The key is to read and understand how the ContourMatrix is build.
parsed = false ;
iShape = 1 ;
while ~parsed
%// get coordinates for each isolevel profile
level = c(1,1) ; %// current isolevel
nPoints = c(2,1) ; %// number of coordinate points for this shape
idx = 2:nPoints+1 ; %// prepare the column indices of this shape coordinates
xp = c(1,idx) ; %// retrieve shape x-values
yp = c(2,idx) ; %// retrieve shape y-values
hp(iShape) = patch(xp,yp,'y','FaceAlpha',0.5) ; %// generate path object and save handle for future shape control.
if size(c,2) > (nPoints+1)
%// There is another shape to draw
c(:,1:nPoints+1) = [] ; %// remove processed points from the contour matrix
iShape = iShape+1 ; %// increment shape counter
else
%// we are done => exit while loop
parsed = true ;
end
end
grid on
This will produce:
I have a matrix containing the temperature value for a set of GPS coordinates. So my matrix looks like this :
Longitude Latitude Value
--------- -------- -----
12.345678 23.456789 25
12.345679 23.456790 26
%should be :
% x y z
etc.
I want to convert this matrix into a human-viewable plot like a color plot (2D or 3D), how can I do this?
3D can be something like this :
or just the 2-D version of this (looking from top z-axis).
What Have I Tried
I know MATLAB has surf and mesh functions but I cannot figure out how to use them.
If I call
surf(matrix(:,1) , matrix(:,2) , matrix(:,3));
I get the error :
Error using surf (line 75)
Z must be a matrix, not a scalar or vector
Thanks in advance for any help !
P.S : It would also be great if there is a function that "fills" the gaps by interpolation (smoothing, whatever :) ). Since I have discrete data, it would be more beautiful to represent it as a continous function.
P.S 2 : I also want to use plot_google_map in the z=0 plane.
A surprisingly hard-to-find answer. But I'm lucky that somebody else has asked almost the same question here.
I'm posting the answer that worked for me :
x = matrix(:,1);
y = matrix(:,2);
z = matrix(:,3);
xi=linspace(min(x),max(x),30)
yi=linspace(min(y),max(y),30)
[XI YI]=meshgrid(xi,yi);
ZI = griddata(x,y,z,XI,YI);
contourf(XI,YI,ZI)
which prints a nice color map.
One option that avoids unnecessarily gridding your data would be to compute the Delaunay triangulation of the scattered data points and then using a command like trisurf to plot the data. Here's an example:
N=50;
x = 2*pi*rand(N,1);
y = 2*pi*rand(N,1);
z = sin(x).*sin(y);
matrix = [x y z];
tri = delaunay(matrix(:,1),matrix(:,2));
trisurf(tri,matrix(:,1),matrix(:,2),matrix(:,3))
shading interp
Suppose your matrix is nx3. Then you can create the grid as follows:
xMin=min(myMat(:,1));
xMax=max(myMat(:,1));
yMin=min(myMat(:,2));
yMax=max(myMat(:,2));
step_x=0.5; %depends on your data
[xGrid,yGrid]=meshgrid(xMin:step_x:xMax,yMin:step_y:yMax);
Now, put your data in the third column to the appropriate indices, in the new matrix say, valMat.
You can use surf now as follows:
surf(xGrid,yGrid,valMat);
If you want interpolation, you can convolve a Gaussian kernel (maybe 3x3) with valMat.
I'm trying to make a color plot in matlab using output data from another program. What I have are 3 vectors indicating the x-position, y-yposition (both in milliarcseconds, since this represents an image of the surroundings of a black hole), and value (which will be assigned a color) of every point in the desired image. I apparently can't use pcolor, because the values which indicate the color of each "pixel" are not in a matrix, and I don't know a way other than meshgrid to create a matrix out of the vectors, which didn't work due to the size of the vectors.
Thanks in advance for any help, I may not be able to reply immediately.
If we make no assumptions about the arrangement of the x,y coordinates (i.e. non-monotonic) and the sparsity of the data samples, the best way to get a nice image out of your vectors is to use TriScatteredInterp. Here is an example:
% samplesToGrid.m
function [vi,xi,yi] = samplesToGrid(x,y,v)
F = TriScatteredInterp(x,y,v);
[yi,xi] = ndgrid(min(y(:)):max(y(:)), min(x(:)):max(x(:)));
vi = F(xi,yi);
Here's an example of taking 500 "pixel" samples on a 100x100 grid and building a full image:
% exampleSparsePeakSamples.m
x = randi(100,[500 1]); y = randi(100,[500 1]);
v = exp(-(x-50).^2/50) .* exp(-(y-50).^2/50) + 1e-2*randn(size(x));
vi = samplesToGrid(x,y,v);
imagesc(vi); axis image
Gordon's answer will work if the coordinates are integer-valued, but the image will be spare.
You can assign your values to a matrix based on the x and y coordinates and then use imagesc (or a similar function).
% Assuming the X and Y coords start at 1
max_x = max(Xcoords);
max_y = max(Ycoords);
data = nan(max_y, max_x); % Note the order of y and x
indexes = sub2ind(size(data), max_y, max_x);
data(indexes) = Values;
imagesc(data); % note that NaN values will be colored with the minimum colormap value