I have a 2D scatter plot in MATLAB. Is it possible to interpolate the scatter plot to create an area plot?
If you're simply trying to draw one large filled polygon around your entire set of scattered points, you can use the function CONVHULL to find the convex hull containing your points and the function PATCH to display the convex hull:
x = rand(1,20); %# 20 random x values
y = rand(1,20); %# 20 random y values
hullPoints = convhull(x,y); %# Find the points defining the convex hull
patch(x(hullPoints),y(hullPoints),'r'); %# Plot the convex hull in red
hold on; %# Add to the existing plot
scatter(x,y); %# Plot your scattered points (for comparison)
And here's the resulting figure:
Scatter is generally used to represent data where you can't use a line graph, i.e., where each x might have many different y values, so you can't convert directly to an area graph--it would be meaningless. If your data actually is representable as a line graph, then pass it to area directly.
So I'm not quite sure what you want, but here are some possibilities:
You could create a Voronoi diagram based on your points. This will show a region near your points showing which points are closer to a specific point: voronoi(x,y), or see the help.
You could bucket or quantize your data somehow, making it fit into a grid, and then plot the grid. This could also be considered a histogram, so read up on that.
You could just use larger scatter markers (scatter(x,y,scale) where scale is the same dimensions as x and y).
Related
I want to visualize 4 vectors of scattered data with a surface plot. 3 vectors should be the coordinates. In addition the 4th vector should represent a surface color.
My first approach was to plot this data (xk,yk,zk,ck) using
scatHand = scatter3(xk,yk,zk,'*');
set(scatHand, 'CData', ck);
caxis([min(ck), max(ck)])
As a result I get scattered points of different color. As these points lie on the surface of a hemisphere it ist possible to get colored faces instead of just points. I replace the scattered points by a surface using griddata to first build an approximation
xk2=sort(unique(xk));
yk2=sort(unique(yk));
[xxk, yyk]=meshgrid(xk2, yk2);
zzk=griddata(xk,yk,zk,xxk,yyk,'cubic');
cck=griddata(xk,yk,clr,xxk,yyk,'cubic');
surf(xxk,yyk,zzk,cck);
shading flat;
This is already nearly what I want except that the bottom of the hemisphere is ragged. Of course if I increase the interpolation point numbers it gets better but than the handling of the plot gets also slow. So I wonder if there is an easy way to force the interpolation function to do a clear break. In addition it seems that the ragged border is because the value of zzk gets 'NaN' outside the circle the hemisphere shares with the z=0-plane.
The red points at the top are the first several entries of the original scattered data.
You can set the ZLim option to slice the plotted values within a certain range.
set(gca, 'Zlim', [min_value max_value])
I'd like to plot grouped data in a scatter plot with marginal histograms using Matlab. There is a function to do just that: scatterhist.
However, I'm dealing with some visualization issues: I have a large number of data points and many points are printed one over another (changing marker type and marker size doesn't help).
Therefore, I'd prefer to substitute individual points from each group with their convex hull; i.e., if I have 5 groups, the plot would show 5 convex hulls and keep marginal histograms generated from the original data points. Do you know if there is an easy way to do this or if this has already been implemented?
In the end, I came up with the following:
Use scatterhist to create the scatter plot with marginal histograms.
hold on
Use convhull to get the convex hull for each group of points.
Use fill to draw the convex hull.
I need to ask help in plotting a 3D volume in MATLAB. My data set includes the X, Y, Z coordinate and corresponding intensity value, V.
I was using pcolor (X,Y,V) and shading interp while i was doing the 2D images but then I got stuck when I am about to create the 3D images. I have tried scatter3, smooth3 and slice but it seems it does not fit the function I need.
My goal is to plot the 3D grid with the corresponding intensity value per coordinate and apply shading interp between these points.
I am really new in 3D plotting and I would really appreciate any help in achieving my goal. Thank you so much!
Here are some example of images that I am trying to create
(source: ndt.net)
(source: www.bam.de)
I have a solution for your first example, in which you show three cross-sections of the volume data. With this solution you can essentially plot several pcolor with different orientations, in one same 3D figure.
First, you need the data for the different slices. This is exactly what you had when you did pcolor(X,Y,V) to plot the cross section in 2D, where X, Y and V are two dimensional matrices. You will also need to create a matrix Z with the z-position of the slice (e.g. Z = zeros(size(X)) + z0). Then you use the command surface(X,Y,Z,V) to plot the cross section image in 3D, where X,Y,Z are the positions, and V is the color (value of the function).
Repeat this procedure for all the other slices, using appropriate X,Y,Z,V matrices for each slice. For slices oriented on other axes you will have to define X,Y,Z accordingly. Remember to use "hold on" after the first surface command, so that all the slices are plotted.
Example:
surface(X1,Y1,Z1,V1); shading interp; hold on
surface(X2,Y2,Z2,V2); shading interp;
surface(X3,Y3,Z3,V3); shading interp;
colormap(jet(2048)); caxis([0,3]); % color axis colormap and scaling
axis equal; xlabel('X'); ylabel('Y'); zlabel('Z') % X,Y,Z scaling and label
Result figure here:
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 have a large (~60,000) set of triplet data points representing x,y, and z coordinates, which are scattered throughout a Cartesian volume.
I'm looking for a way to use Matlab to visualize the non-convex shape/volume described by the maximum extent of the points.
I can of course visualize the individual points using scatter3, but given the large number of points the details of the shape are obscured by the noise of the dots.
As an analogy, imagine that you filled a hour glass with spheres of random sizes such as BBs, ping pong balls, and kix and then were given the coordinates of the center of each of each object. How would you take those coordinates and visualize the shape of the hour glass containing them?
My example uses different sized objects because the spacing between data points is non-uniform and effectively random; it uses an hourglass because the shape is non-convex.
If your surface enclosing the points can be described as a convex polyhedron (i.e. like the surface of a cube or a dodecahedron, without concave pits or jagged pointy parts), then I would start by creating a 3-D Delaunay triangulation of the points. This will fill the volume around the points with a series of tetrahedral elements with the points as their vertices, and you can then find the set of triangular faces that form the outer shell of the volume using the convexHull method of the DelaunayTri class.
Here's an example that generates 200 random points uniformly distributed within the unit cube, creates a tetrahedral mesh for these points, then finds the 3-D convex hull for the volume:
interiorPoints = rand(200,3); %# Generate 200 3-D points
DT = DelaunayTri(interiorPoints); %# Create the tetrahedral mesh
hullFacets = convexHull(DT); %# Find the facets of the convex hull
%# Plot the scattered points:
subplot(2,2,1);
scatter3(interiorPoints(:,1),interiorPoints(:,2),interiorPoints(:,3),'.');
axis equal;
title('Interior points');
%# Plot the tetrahedral mesh:
subplot(2,2,2);
tetramesh(DT);
axis equal;
title('Tetrahedral mesh');
%# Plot the 3-D convex hull:
subplot(2,2,3);
trisurf(hullFacets,DT.X(:,1),DT.X(:,2),DT.X(:,3),'FaceColor','c')
axis equal;
title('Convex hull');
You could treat your data as a sample from a three-dimensional probability density, and estimate that density on a grid, e.g. via a 3d histogram, or better a 3d kernel density estimator. Then apply a threshold and extract the surface using isosurface.
Unfortunately, hist3 included in the Statistics Toolbox is (despite its name) just a 2d histogram, and ksdensity works only with 1d data, so you would have to implement 3d versions yourself.