curve-app | Vector drawing desktop application | Graphics library

 by   benogle JavaScript Version: v0.0.1 License: No License

kandi X-RAY | curve-app Summary

kandi X-RAY | curve-app Summary

curve-app is a JavaScript library typically used in User Interface, Graphics, JavaFX applications. curve-app has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Vector drawing desktop application
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            kandi-support Support

              curve-app has a low active ecosystem.
              It has 128 star(s) with 7 fork(s). There are 4 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 1 open issues and 0 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of curve-app is v0.0.1

            kandi-Quality Quality

              curve-app has 0 bugs and 0 code smells.

            kandi-Security Security

              curve-app has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              curve-app code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              curve-app does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              curve-app releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.
              curve-app saves you 81 person hours of effort in developing the same functionality from scratch.
              It has 208 lines of code, 0 functions and 19 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

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            curve-app Key Features

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            curve-app Examples and Code Snippets

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            Community Discussions

            QUESTION

            Understanding Curve Global Approximation algorithm
            Asked 2019-Oct-26 at 20:37
            Problem description

            I am trying to understand and implement the Curve Global Approximation, as proposed here:

            https://pages.mtu.edu/~shene/COURSES/cs3621/NOTES/INT-APP/CURVE-APP-global.html

            To implement the algorithm it is necessary to calculate base function coefficients, as described here:

            https://pages.mtu.edu/~shene/COURSES/cs3621/NOTES/spline/B-spline/bspline-curve-coef.html

            I have trouble wrapping my head around some of the details.

            1. First there is some trouble with variable nomenclature. Specifically I am tripped up by the fact there is as function parameter as well as input and . Currently I assume, that first I decide how many knot vectors I want to find for my approximation. Let us say I want 10. So then my parameters are:

            I assume this is what is input parameter in the coefficient calculation algorithm?

            1. The reason this tripped me up is because of the sentence:

            Let u be in knot span

            If input parameter was one of the elements of the knot vector , then there was no need for an interval. So I assume is actually one of these elements ( ?), defined earlier:

            Is that assumption correct?

            1. Most important question. I am trying to get my N to work with the first of the two links, i.e. the implementation of the Global Curve Approximation. As I look at the matrix dimensions (where P, Q, N dimensions are mentioned), it seems that N is supposed to have n rows and h-1 columns. That means, N has rows equal to the amount of data points and columns equal to the curve degree minus one. However when I look at the implementation details of N in the second link, an N row is initialized with n elements. I refer to this:

            Initialize N[0..n] to 0; // initialization

            But I also need to calculate N for all parameters which correspond to my parameters which in turn correspond to the datapoints. So the resulting matrix is of ddimension ( n x n ). This does not correspond to the previously mentioned ( n x ( h - 1 ) ).

            To go further, in the link describing the approximation algorithm, N is used to calculate Q. However directly after that I am asked to calculate N which I supposedly already had, how else would I have calculated Q? Is this even the same N? Do I have to calculate a new N for the desired amount of control points?

            Conclusion

            If somebody has any helpful insight on this - please do share. I aim to implement this using C++ with Eigen for its usefulness w.r.t. to solving M * P = Q and matrix calculations. Currently I am at a loss though. Everything seems more or less clear, except for N and especially its dimensions and whether it needs to be calculated multiple times or not.

            Additional media

            In the last image it is supposed to say, "[...] used before in the calculation of Q"

            ...

            ANSWER

            Answered 2019-Oct-26 at 20:37

            The 2nd link is telling you how to compute the basis function of B-spline curve at parameter u where the B-spline curve is defined by its degree, knot vector [u0,...um] and control points. So, for your first question, if you want to have 10 knots in your knot vector, then the typical knot vector will look like:

            [0, 0, 0, 0, 0.3, 0.7, 1, 1, 1, 1]

            This will be a B-spline curve of degree 3 with 6 control points.

            For your 2nd question, The input parameter u is generally not one of the knots [u0, u1,...um]. Input parameter u is simply the parameter we would like to evaluate the B-spline curve at. The value of u actually varies from 0 to 1 (assuming the knot vector ranges is also from 0 to 1).

            For your 3rd questions, N (in the first link) represents a matrix where each element of this matrix is a Ni,p(tj). So, basically the N[] array computed from 2nd link is actually a row vector of the matrix N in the first link.

            I hope my answers have cleared out some of your confusions.

            Source https://stackoverflow.com/questions/58562439

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