I have a set of points in 2d space as vertices. To find MST i want to implemente boruvka's algorithm with kdtree in python. How can i do that? Where should i use kdtree in the algorithm?
I know the algorithm but don't khow how to implement it with kdtree datastructure.
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I am working on a Configuration Space subject (C-Space) for a 6 DOF robot arm.
From a simulation I can get a point cloud that define my C-Space.
From this C-Space, I would like to be able to know if a robot configuration (set of joints angles), is inside the C-Space or not.
So I would like to define a 6 dimensions model from my C-Space, like a combination of a lot of convex hull with a given radius.
And then, I would like to create or use a function that give me if my configuration is inside one of the convex hull (so inside the C-Space, which that means that the configuration is in collision).
Do you have any ideas ?
Thanks a lot.
The question is not completely clear yet. I am guessing that you have a point cloud from a laser scanner and would like to approximate the output of the point cloud with a set of convex objects to perform collision query later.
If the point clouds is already clustered into sets, convex hull for each set can be found fairly quickly using the quick-hull algorithm.
If you want to also find the clusters, then a convex decomposition algorithm, like the Volumetric Hierarchical Approximate Convex Decomposition maybe what you are looking for. However, there may need to be an intermediate step to transform the point cloud into a mesh object to pass as an input to V-HACD.
I'm trying to annotate some data for 3D instance segmentation. While it's fairly straightforward to draw masks for each 2D plane, it's not obvious how to connect the same "instances" together post-annotation (ie. connect the "red" masks together, connect the "blue" masks together) without laboriously making sure the instances are instance-matched (ie. colour-coded to make sure "red" masks always connect with "red" masks).
A naive approach I have thought of is to make many 2D segmentation masks, and calculate the center of mass for each object detected. I can later re-assign the instances based on the closest matching center of mass, but I worry this would inadvertently generate "crossed-over" segmentation instances (illustrated below). What are some high-throughput strategies to generate 3D annotations?
The boundary of your 2-d slices could be used as constraints to obtain the optimal 3-d surface, as proposed in 1.
However, I think it is easier to generate 3-d labels from markers, such as 2. Its implementation is available in here (Fill free open an issue if you encounter any problems :P).
Also, the napari package could be useful to develop the GUI without much effort.
[1] Grady, Leo. "Minimal surfaces extend shortest path segmentation methods to 3D." IEEE Transactions on Pattern Analysis and Machine Intelligence 32.2 (2008): 321-334.
[2] Falcão, Alexandre X., and Felipe PG Bergo. "Interactive volume segmentation with differential image foresting transforms." IEEE Transactions on Medical Imaging 23.9 (2004): 1100-1108.
You can use 3D Slicer's Segment Editor. It is free, open-source, has many built-in tools, and customizable/extensible in Python or C++ (you can plug in your own segmentation method with minimal effort). To solve a segmentation task, typically you first figure out a good segmentation workflow (what tools to use, in what combination and what parameters) using interactive GUI, then if necessary you can make it semi-automatic or fully automatic using Python scripting.
You can create a segmentation by contouring every image slice, but it would be too tedious. Instead, you can use 3D region growing (Grow from seeds effect) or segment on just a few slices and interpolate between them (Fill between slices effect).
This is essentially a question on fundamentals, and whether or not there is a more efficient way to achieve what I am looking for. I have built a working fluid dynamics calculator in Excel to find the flow rates required for a target pressure loss, the optimisation is handled using Solver but it's very clunky and not user friendly.
I'm trying to replicate the function in Octave since it's widely used here, but I am a complete beginner; I'm probably missing something obvious. I can easily enter all of the math for a single iteration via a series of functions, but my excel file required using the 'Solver' macro, and I'm unsure how to efficiently replicate this in Octave.
I am aware that linprog (in matlab) and glpk (octave) can be used to solve systems of linear equations.
I have a series of nested equations which are all dependant on a single matrix, Q (flow rates at various locations). Many other inputs are required, but they either remain constant throughout calculation (e.g. system geometry) or are dictated by Q (e.g. Reynolds number and loss coefficients). In trying to simplify my problem I have settled on two steps:
Write code to solve my problem, input: Q matrix, output: pressure loss matrix
Create a loop that iterates different Q matrices until some conditions for the pressure loss matrix are met.
I don't think it will be practical to get my expressions into the form of A*x = B (in order to use glpk) given the complexity. In excel, I can point solver at a Q value that drives a multitude of equations that impact pressure loss, and it will find the value I need to achieve a target. How can I most efficiently replicate this functionality in Octave?
First off all Solver is not a macro. Pretty far from.
So, you're going to replicate a comprehensive "What-If" Analysis Plug-in -- so complex in fact, that Microsoft chose to contract a 3rd Party company of experts to develop the tool and provide support for it (successfully based on the 1.2 Billion copies they've distributed).
And you're going to this an inferior coding language that you're a complete beginner with? Cool. I'd like to see this!
Cool. Here's a checklist of Solver's features, so you don't miss anything:
Good Luck!
More Information:
Wikipedia : Solver
Office.com : Define and Solve a Problem by using Solver
Frontline: Official Solver Page: http://solver.com
AppSource.Microsoft.com : Solver (with Video)
Frontline:L Solver International Manazine
So, I'm wondering how to implement a STFT in Julia, possibly using a Hamming window. I can't find anything on the internet.
What is the best way to do it? I'd prefer not to use Python libraries, but pure native Julia if possible. Maybe it's a feature still being developed in Juila...?
Thanks!
I'm not aware of any native STFT implementation in Julia. As David stated in his comment, you will have to implement this yourself. This is fairly straightforward:
1) Break up your signal into short time sections
2) Apply your window of choice (in your case, Hamming)
3) Take the FFT using Julia's fft function.
All of the above are fairly standard mathematical operations and you will find a lot of references online. The below code generates a Hamming window if you don't have one already (watch out for Julia's 1-indexing when using online references as a lot of the signal processing reference material likes to use 0-indexing when describing window generation).
Wb = Array(Float64, N)
## Hamming Window Generation
for n in 1:N
Wb[n] = 0.54-0.46cos(2pi*(n-1)/N)
end
You could also use the Hamming window function from DSP.jl.
P.S If you are running Julia on OS X, check out the Julia interface to Apple's Accelerate framework. This provides a very fast Hamming window implementation, as well as convolution and elementwise multiplication functions that might be helpful.
I would like to obtain ALL the real roots of a system of 8 polynomial equations in 8 variables, where the maximum degree of each equation is 3. Is this doable? What is the best software to do this?
You will need to use a multi-dimensional root finding method.
Newton's should be fairly easy to implement (simple algorithm) and you may find some details about this and other methods here.
As far as I understand, it's not likely to be straightforward to solve 8 simultaneous cubic equations with a very general direct method.
Depending on your problem, you may also want to check if it's analogous to the problem of fitting a cubic spline to a set of points. If it is, then a simple direct algorithm is available here.
For the initial conditions, you might need to use well-dispersed random starting points in your domain of interest. You could use sobol sequences or some other low-discrepancy random number generator to efficiently generate random numbers to fill-out the space under consideration.
You may also consider moving this question to math exchange where you might have a better chance of having this answered correctly.