Does anyone know of a library (any language, though preferably python/R/matlab) for parametric curve fitting, i.e. if you have a set of points in the plane {(x_i,y_i)} you can find parameter estimates for two (polynomial) functions y=f_y(t) and x=f_x(t) for some (arc-length?) parametrization t? This is especially useful if you have some multi-valued function (e.g. a circle) for which regression wouldn't work.
There are a number of papers detailing algorithms (e.g. 'Parametric Curve Fitting', Grossman 1971) but I can't find any corresponding software that would save a lot of time coding up.
For future reference, I ended up using the princurve library in R based on principal curves by Trevor Hastie.
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I am using OpenCascade to import STEP/IGES as meshes in my software. Works nicely.
But I need small triangles, and the one I get are sometimes very large (in flat area), or very elongated (eg. when meshing a cylinder). The best would be to split triangle's edge bigger than some absolute value. Avoiding T vertices, too.
I was'nt able to google anything about that... So, currently, I pass the mesh to OpenMesh, apply the OpenMesh::Subdivider::Uniform::LongestEdgeT operator, then pass it back to my software. Tedious and costly when I manage several M triangles...
Questions:
Is there an equivalent in OpenCascade ?
Or a simple code snipet to implement my own loop to do so ?
Thanks !
The meshing algorithm BRepMesh_IncrementalMesh coming with Open CASCADE Technology is mainly focused on two usage scenarios:
Visualization in 3D Viewer. Large and prolonged triangles make no harm to presentation, as vertex normals ensure proper smooth shading. Deflection parameters allows managing presentation quality.
Computing Algorithms using triangulation as approximation (to speed up calculations compared to the same algorithm working on exact geometry). In this case, deflection parameters determine the target precision of the algorithm. Large and prolonged triangles should not cause problems here, as deviation from exact geometry is controlled by meshing parameters.
There are, however, some categories of algorithms, where shape of mesh element is very important. Solvers (numerical simulation) make one of such categories, where unfortunate mesh elements may cause numerical instability or other issues. What exactly matters / cause issues depend on specific algorithm - this may include element skewness, element aspect ratio, element size and elements grid. Some solvers work much better on quads rather than on triangles.
If you take a look onto meshing result of BRepMesh_IncrementalMesh algorithm, you may notice that not only large prolonged triangles, but entire mesh structure is somewhat suboptimal for solver algorithms:
There are several options you may consider:
Triangulation refinement algorithm. Such algorithm processes existing triangulation and tries healing some properties like skewness. This what does OpenMesh from your question, I suppose. Such postprocessing algorithm might give satisfactory results at good performance, but final result will dramatically depend on properties of original meshing algorithm. For the moment, OCCT doesn't have any refinement tool, although it is possible writing such algorithm on your own (I cannot give you a small code snippet, because such algorithm is not that small an trivial as it may look from a first glance).
Consider alternative meshing algorithm. Probably incomplete list:
Express Mesh by Open Cascade (hence, working directly on OCCT shapes). This tool generates triangulation having nice grid-alike structure (for smooth surfaces), configurable element size and quad-dominant generation option. This is a commercial product though.
Netgen mesher. This open source tool provides bindings to OCCT, and although it is focused on 3D tetrahedral mesh generation, it may be also used for generating a common triangular mesh. I cannot say something good about this tool - it was rather slow and unstable when I've seen its work many years ago.
MeshGems surface meshing. Another commercial tool providing an interface to OCCT. Never worked with this product, so cannot share any opinion on it.
Consider improving BRepMesh_IncrementalMesh. As OCCT is an open source framework, you may consider extending its meshing algorithm with more parameters and contribute to the project.
I am a civil engineering grad student working on survival modeling of bridges. I am using SAS with Kaplan-Meier method to analyze the data. I would like to overlay the survival probability plot with a smooth curve using either Kernel smoothing or Bezier smoothing. But since these methods are not available in statistic packages, I exported the survival probability to an excel in order to produce it manually (I may be wrong on doing this). After several attempt I could not figure out on how to do it in Excel.
I am attaching an excel file image with data and the Kaplan Meier curve I am working and would be grateful if someone could help me by coding either the kernel smoothing or Bezier smoothing steps and overlay the curve and repost the file here to me. Or, more effeciently provide me with an excel add in to do this or a SAS code to do this.
If a parametric model providing a smoothed description of the survival data isn't appropriate, you could use smoothing splines or loess curves, compared on this page for example. For smoothing, multiple Bézier curves are combined to form one type of spline, and loess is one form of a kernel smoother. These are easily implemented in standard statistical software packages; for example, in SAS TRANSREG provides splines and LOESS implements that locally-weighted regression.*
That said, baseline survival curves in clinical studies are typically presented as the type of step function that you display, without smoothing. Also, it seems that your underlying data might only provide information at 1-year intervals. If so, then presenting a smooth curve might be somewhat misrepresenting the smoothness of your data. If you do have individual event and censoring times for each bridge, then your smoothed curve should be based on the survival probabilities at each event time to provide the closest representation of your data.
*I would be wary of using Excel for any serious statistical work. I have found it too easy to make unforced errors that avoid detection in Excel.
I’d like to perform a surface parametrization of a triangle mesh (for the purpose of texture mapping).
I tried using some of CGAL’s algorithms, e.g. ARAP, Discrete Conformal Map etc.
The problem is that the surface parameterization methods proposed by CGAL only deal with meshes which are homeomorphic (topologically equivalent) to discs.
Meshes with arbitrary topology can be parameterized, provided that the user specifies a cut graph (a set of edges), which defines the border of a topological disc.
So the problem now becomes – how to cut the graph properly (using CGAL’s interface).
I found a similar question from 3 years ago that went unanswered.
P.S.
If someone can point me to a different library that can do the job, that’ll be just as great.
Thanks.
This is for self-study of N-dimensional system of linear homogeneous ordinary differential equations of the form:
dx/dt=Ax
where A is the coefficient matrix of the system.
I have learned that you can check for stability by determining if the real parts of all the eigenvalues of A are negative. You can check for oscillations if there are any purely imaginary eigenvalues of A.
The author in the book I'm reading then introduces the Routh-Hurwitz criterion for detecting stability and oscillations of the system. This seems to be a more efficient computational short-cut than calculating eigenvalues.
What are the advantages of using Routh-Hurwitz criteria for stability and oscillations, when you can just find the eigenvalues quickly nowadays? For instance, will it be useful when I start to study nonlinear dynamics? Is there some additional use that I am completely missing?
Wikipedia entry on RH stability analysis has stuff about control systems, and ends up with a lot of equations in the s-domain (Laplace transforms), but for my applications I will be staying in the time-domain for the most part, and just focusing fairly narrowly on stability and oscillations in linear (or linearized) systems.
My motivation: it seems easy to calculate eigenvalues on my computer, and the Routh-Hurwitz criterion comes off as sort of anachronistic, the sort of thing that might save me a lot of time if I were doing this by hand, but not very helpful for doing analysis of small-fry systems via Matlab.
Edit: I've asked this at Math Exchange, which seems more appropriate:
https://math.stackexchange.com/questions/690634/use-of-routh-hurwitz-if-you-have-the-eigenvalues
There is an accepted answer there.
This is just legacy educational curriculum which fell way behind of the actual computational age. Routh-Hurwitz gives a very nice theoretical basis for parametrization of root positions and linked to much more abstract math.
However, for control purposes it is just a nice trick that has no practical value except maybe simple transfer functions with one or two unknown parameters. It had real value when computing the roots of the polynomials were expensive or even manual. Today, even root finding of polynomials is based on forming the companion matrix and computing the eigenvalues. In fact you can basically form a meshgrid and check the stability surface by plotting the largest real part in a few minutes.
I've been trying to develop a simple app that calculates the equation of a curve using information given by the user.
For example, let's suppose the user has the equation of a circle and the equation of an ellipse, and he wants to know the intersection points.
Mathematically speaking, this is a quite simple problem, but i can't figure out how to tell Xcode to solve that system.
I've looked into the Accelerate framework, and i found the "dgesv" function of Lapack. This would be a perfect solution for a system of lines, but what about more complex systems like the one i've stated before?
I was even wondering how to calculate the tangent line of a curve, and other similar geometry problems.
For example, let's suppose the user has the equation of a circle and the equation of an ellipse, and he wants to know the intersection points. Mathematically speaking, this is a quite simple problem, but i can't figure out how to tell Xcode to solve that system.
The problem of intersecting a circle and an ellipse in general involves solving a fourth degree polynomial equation. You can break it down to third degree, but square roots alone won't suffice. So I'd say that's not really a “quiet simple problem”. That being said, some numeric libraries do support algorithms for finding the real or complex roots of a univariate polynomial. So you'd eliminate one of your variables (e.g. using resultants or Gröbner bases) and then feed the resulting polynomial to that. Not sure what libraries would be most suitable in the Objective C world. But a question asking to suggest a specific library would be deemed off-topic anyway, so use the keywords I mentioned to find something that suites your needs.
I was even wondering how to calculate the tangent line of a curve, and other similar geometry problems.
The tangent direction is simply orthogonal to the gradient of the implicit form. In other words, describe your curve not parametrically as x=f(t) and y=g(t) but instead implicitely as f(x,y)=0. In case of a circle that would look like x2+y2+ax+by+c=0. Then compute the partial derivatives with respect to x and y. That's 2x+a and 2y+b. So (2x+a, 2y+b) is perpendicular to the circle, and (2y+b, -2x-a) is tangential. Not sure what you'd consider similar to this.