I have 3D space. And I know for example N points in this space (x1,y1,z1), (x2,y2,z2)..., (xn,yn,zn). I want to interplolate points, that is different from this. How can I do this in Matlab?
interp3 may help you. Here is the documentation.
As always, there are questions left unanswered by your one line query.
If the data is of the form where there is a functional relationship z(x,y), (or y(x,z) or x(y,z)) then you might potentially be able to use one of the interpolation tools. Thus, suppose you have data that lies on a lattice in the (x,y) plane, thus some value of z at each point in that lattice. In this case, you can use interp2.
Alternatively, if the data is scattered, but there is some single valued functional relationship z(x,y) that you don't have, but it is some continuous function. Infinite first derivatives are a problem too here. In this case, assuming that you have data that at least fills some convex domain in the (x,y) plane, you can still interpolate a value of z. For this, use griddata, or TriScatteredInterp. Or you might use my own gridfit tool, as found on the file exchange.
Next, the way you describe the data, I'm not at all positive that you have something in one of the above forms. For example, if your data lies along some curved path in this 3D domain, and you wish to interpolate points along that curved arc can be done using my interparc tool, also found on the file exchange.
One last case that people often seem to have when they talk about interpolation of a spatial set like this, is just a general surface, that they wish to build a neatly interpolated, smooth surface. It might be something as simple as the surface of a sphere, or something wildly more complex. (These things are never simple.) For this, you might be able to use a convex hull to approximate something, if it is a closed convex surface. More complex surfaces might require a tool like CRUST, although I have no implementation of it I can offer to you. Google will help you there, if that is what you need.
The point of all this is, how you interpolate your data depends on what form the data is in, what it represents, and the shape of the relationship you will be interpolating.
Related
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, this is going to be pretty hard for me to explain, or try to detail out since I only think I know what I'm asking, but I could be asking it with bad wording, so please bear with me and ask questions if need-be.
Currently I have a 3D vector field that's being plotted which corresponds to 40 levels of wind vectors in a 3D space (obviously). These are plotted in 3D levels and then stacked on top of each other using a dummy altitude for now (we're debating how to go about pressure altitude conversion most accurately--not to worry here). The goal is to start at a point within the vector space, modeling that point as a particle that can experience physics, and iteratively go through the vector field reacting to the forces, thus creating a trajectory of sorts through the vector field.
Currently what I'm trying to do is whip up code that would allow me to to start a point within this field and calculate the forces that the particle would feel at that point and then establish a resultant force vector that would indicate the next path of movement throughout the vector space.
Right now I'm stuck in the theoretical aspects of the code, as I'm trying to think through how the particle would feel vectors at a distance.
Any suggestions on ways to attack this problem within MatLab or relevant equations to use?
In order to run my code, you'll need read_grib.r4 and to compile that mex file here is a link to a zip with the code and the required files.
https://www.dropbox.com/s/uodvixdff764frq/WindSim_StackOverflow_Files.zip
I would try to interpolate the wind vector from the adjecent ones. You seem to have a regular grid, that should be no problem. (You can use interp3 for this)
Afterwards, you can use any differential-equation solver for your problem, as you have basically a field of gradients and an initial value. Forward euler would be the simplest one but need a small step size. (N.B.: Your field should be a gradient field)
You may read about this in Wikipedia: http://en.wikipedia.org/wiki/Vector_field#Flow_curves
In response to comment #1:
Yes. In a regular grid, any (arbitrary chosen) point will have eight neighbors. interp3 will so a trilinear interpolation to determine an interpolated gradient vector.
If you use forward-euler, you will then move a small distance in that direction. There you interpolate a gradient and go a small step into this new direction and so on. What happens are two things:
You get a series of points that lie on a streamline and thus form the trajectory of a particle moving along the field
Get large errors, the further you move and the larger the step size is. Use a small step size or use a better solver (Runge-Kutta comes to my mind)
If all you want is plotting, then the streamline function might help.
I am working with structured 2.5D and unstructured 3D data, which generally is available in (X,Y,Z) coordinates, i.e. point clouds. Now I want to impose a regular voxel grid onto the data. This is not meant for visualization purposes, but rather for "cleaning" or fusing the data. I imagine cases, where e.g. 3 points fall within the volume of one voxel. Then I would aim at either just setting this voxel to "activated" and discarding the 3 original points or alternatively I would like to calculate the euclidian mean of the points and return the thus "cleaned" point cloud as an irregularly structured one again.
I hope I could make my intentions clear enough: It's not about visualization and not necessarily about using volumetric cubes instead of points. It's only about manipulating possibily irregular point clouds in a structured way.
I was thinking about kd-tree or octree based solutions in this context, but can anybody point me in the proper direction? Hinting at existing MATLAB implementations would be most appreciated.
If the data is irregularly spaced, what you want to use is something which both smooths and interpolates your data points. A very good method for doing so is Gaussian process regression. Here's an example for the same problem but in 2D.
I'm currently working with DICOM-RT files (which contain DICOM along with dose delivery data and structure set files). I'm mainly interested in the "structure set" file (i.e. RTSS.dcm), which contains the set of contour points for an ROI of interest. In particular, the contour points surround a tumor volume. For instance, a tumor would have a set of 5 contours, each contour being a set of points that encircle that slice of the tumor.
I'm trying to use MatLab to use these contour points to construct a tumor volume in a binary 3D matrix (0 = nontumor, 1=tumor), and need help.
One possible approach is to fill each contour set as a binary slice, then interpolate the volume between slices. So far I've used the fill or patch function to create binary cross-sections of each contour slice, but I'm having difficulty figuring out how to interpolate these binary slices into a 3D volume. None of the built-in functions appear to apply to this particular problem (although maybe I'm just using them wrong?). A simple linear interpolation doesn't seem appropriate either, since the edges of one contour should blend into the adjacent contour in all directions.
Another option would be to take the points and tesselate them (without making slices first). However, I don't know how to make MatLab only tesselate the surface of the tumor and not intersecting the tumor volume. Currently it seems to find triangles within the tumor. If I could get it into just a surface, I'm not sure how to take that and convert it into a binary 3D matrix volume either.
Does anyone have experience with either 3D slice interpolation OR tesselation techniques that might apply here? Or perhaps any relevant toolkits that exist? I'm stuck... :(
I'm open to approaches in other languages as well: I'm somewhat familiar with C# and Python, although I assumed MatLab would handle the matrix operations a little easier.
Thanks in advance!
I'm not sure from what program you're exporting your dicom-rt structure files, but I believe I found a more elegant solution for you, already described in an open-source software (GDCM, CMake, ITK) in an Insight journal article.
I was discussing a similar problem with one of our physicists, and we saw your solution. It's fine if whatever structure you're attempting to binarize has no concavities, but if so, they'll be rendered inaccurately.
This method is verified for dicom-rt structure sets from Eclipse and Masterplan. Hope it helps.
http://www.midasjournal.org/download/viewpdf/701/4
I think I found an answer in another post (here). Rather than trying to interpolate the "missing slices" between the defined contours, treating the contour points as a point cloud and finding the convex hull might be a more efficient way of doing it. This method created the binary 3D volume that I was after.
Here is the code I used, hope it might be helpful to those who need to work with DICOM-RT files:
function mask = DicomRT2BinaryVol(file)
points = abs(getContourPoints(file));
%%NOTE: The getContourPoints function simply reads the file using
%%'dicominfo' method and organizes the contour points into an n-by-3
%%matrix, each column being the X,Y,Z coordinates.
DT = DelaunayTri(points);
[X,Y,Z] = meshgrid(1:50,1:50,1:50);
simplexIndex = pointLocation(DT, X(:), Y(:), Z(:));
mask = ~isnan(simplexIndex);
mask = reshape(mask,size(X));
end
This method is a slightly modified version of the method posted by #gnovice in the link above.
iTk is an excellent library for this sort of thing: http://www.itk.org/
HTH
Is there any subroutine, in MATLAB, that takes in a list of points, and return me a good mesh that I can use to show to my colleagues, such as this?
Actually, all I need is just a simple 2D mesh generator that takes in a series of X, Y coordinates (that defines the boundary of the area), and give me back a list of elements that can mesh that area well. I can do the rest by using MATLAB command to interpolate the Z value.
Edit : I am not interested to use MATLAB to produce the above looking plot. I am interested in using a MATLAB library to obtain a list of elements so that when I plot those element myself (not in MATLAB itself; but in my own C# program), I can obtain this meshed surface.
PS: I know there is this DistMesh, but I am looking for something simpler - something built-in direct in MATLAB perhaps. And no, meshgrid is not mesh generation.
It sounds like you want to create a finite element mesh, starting with a set of points defining a boundary of a region and then generating a triangular mesh that creates more points within that region. I don't think there's a "simple" solution for this problem.
The closest "built-in" solution would probably be the Partial Differential Equation Toolbox, specifically some of the Geometry Algorithms like INITMESH and REFINEMESH.
The link you gave to DistMesh appears to be another good solution. There are also a few submissions on the MathWorks File Exchange that you could take a look at:
MESH2D by Darren Engwirda
Finite Element Toolbox 2.1 by Rasmus Anthin
That picture looks exactly like the one from the griddata documentation. The example in there looks like what you want.
SFTOOL will easily make the picture that you show.
A thin-plate spline, e.g., TPAPS, should also do the job.
I think the user-created 'gridfit' is the best I've come across for a single surface, much better/prettier than griddata.
Mesh generation as in Delaunay Triangulation + Steiner Points? There is a builtin Delaunay function in MATLAB.
If your surface is the z=f(x,y) form you can use:
http://www.advancedmcode.org/how-to-plot-a-coloured-surface-from-3d-scatter.html
If your surface is concave look for surface reconstruction on the same website.