Skip to main content

NURBS curve and surface evaluation library in native Python

Project description

# Non-Uniform Rational Basis Spline (NURBS) Python Package

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.815011.svg)](https://doi.org/10.5281/zenodo.815011)

[![Documentation Status](https://readthedocs.org/projects/nurbs-python/badge/?version=latest)](http://nurbs-python.readthedocs.io/en/latest/?badge=latest)

## Introduction

This project aims to implement the NURBS curve and surface computation algorithms in native Python with [minimum possible dependencies](#minimum-requirements).

Currently, the Curve and Surface classes can be used for data storage and evaluation of B-Spline and NURBS curves and surfaces. Additionally, Grid class can be used to generate simple 2D control point grids for use with the Surface class.

## For Researchers

I would be glad if you cite this repository using the DOI provided as a badge at the top.

## Example Scripts

Please see [NURBS-Python Examples](https://github.com/orbingol/NURBS-Python_Examples) repository for example scripts and figures.

## Description of the Package

### Algorithms

NURBS-Python currently implements the following algorithms from The NURBS Book by Piegl & Tiller:

  • Algorithm A2.1: FindSpan

  • Algorithm A2.2: BasisFuns

  • Algorithm A2.3: DersBasisFuns

  • Algorithm A3.1: CurvePoint

  • Algorithm A3.2: CurveDerivsAlg1

  • Algorithm A3.3: CurveDerivCpts

  • Algorithm A3.4: CurveDerivsAlg2

  • Algorithm A3.5: SurfacePoint

  • Algorithm A3.6: SurfaceDerivsAlg1

  • Algorithm A4.1: CurvePoint (from weighted control points)

  • Algorithm A4.3: SurfacePoint (from weighted control points)

### Data Structure

The data structure in Curve and Surface classes is implemented using [Python properties](https://docs.python.org/2/library/functions.html#property). The following table shows the properties defined in these classes:

Curve Properties | Surface Properties | Notes |
:—: | :—: | :—: |
degree | degree_u | Degree of the curve/surface|
| degree_v | |
knotvector | knotvector_u | Knot vectors|
| knotvector_v | |
ctrlpts | ctrlpts | 1D array of control points |
| ctrlpts2D | 2D array of control points in _[u][v]_ format |
ctrlptsw | ctrlptsw | 1D array of weighted control points |
weights | weights | Weights vector |
delta | delta | Evaluation delta for knots |
curvepts | surfpts | Evaluated points |

### Evaluation Methods

After setting the required parameters, the curve or the surface can be evaluated using evaluate() or evaluate_rational() methods. Then, the evaluated curve points can be obtained from curvepts property and the evaluated surface points can be obtained from surfpts property. The curve and surface derivatives can be evaluated using derivatives() method. An easy way to get 1st derivatives using tangent() method is available in both classes.

Surface class has methods for transposing the surface by swapping U and V directions, tranpose(), and finding surface normals, normal().

### Reading Control Points

Both classes have read_ctrlpts() and read_ctrlptsw() methods for reading control points and weighted control points, respectively, from a text file. The details on the file format are explained in [FORMATS.md](FORMATS.md) file.

### Additional Features

utilities module has some extra features for several mathematical operations:

  • autogen_knotvector() generates a uniform knot vector according to the input degree and number of control points

  • normalize_knotvector() normalizes the knot vector between 0 and 1

  • cross_vector() computes the cross production of the input vectors

  • normalize_vector() generates a unit vector from the input vector

Other functions in the utilities module are used as helper functions in evaluation methods of Curve and Surface classes.

### 2D Grid Generation

Grid module is capable of generating simple 2D control point grids for use with the Surface class. Please check [ex_grid01.py](https://github.com/orbingol/NURBS-Python_Examples/blob/master/ex_grid01.py) file and the documentation for details on how to use the Grid class and its features.

## Minimum Requirements

One of the major goals of this project is implementing all these algorithms with minimum dependencies. Currently, the NURBS package can run with plain Python and therefore, it has no extra dependencies, like NumPy or similar. The code was tested with Python versions 2.7.12 and 3.5.3.

On the other hand, the plotting part of the examples requires Matplotlib installed in your Python distribution. If you don’t need any plotting, you basically won’t need Matplotlib at all.

## Issues and Reporting

If you have any questions related to the NURBS-Python package, please don’t hesitate to contact the author by email or creating a new issue.

## Author

## Contributors

## License

[MIT](LICENSE)

## Acknowledgments

I would like to thank my PhD adviser, [Dr. Adarsh Krishnamurthy](https://www.me.iastate.edu/faculty/?user_page=adarsh), for his guidance and supervision throughout the course of this project. If you are interested in this Python package, please have a look at [our research group’s web page](http://web.me.iastate.edu/idealab/) for more projects and contact information.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

NURBS_Python-2.3.7-py3-none-any.whl (22.9 kB view details)

Uploaded Python 3

NURBS_Python-2.3.7-py2-none-any.whl (22.9 kB view details)

Uploaded Python 2

File details

Details for the file NURBS_Python-2.3.7-py3-none-any.whl.

File metadata

File hashes

Hashes for NURBS_Python-2.3.7-py3-none-any.whl
Algorithm Hash digest
SHA256 e35f29efb6398ceb630c5ecc2a9a9f0add8ba95981774b46fb200ecaeffc7e6d
MD5 c1f1df3395feca364208170a58510474
BLAKE2b-256 dca5923592cfde301229d974e3003fdc2d5d64c5169b1d68c2433c9098416045

See more details on using hashes here.

File details

Details for the file NURBS_Python-2.3.7-py2-none-any.whl.

File metadata

File hashes

Hashes for NURBS_Python-2.3.7-py2-none-any.whl
Algorithm Hash digest
SHA256 cfd75b194608bd1ea7769a1e5700bcfce768793006dff8a0cf1617901bc6e901
MD5 f46234d126c21077adaf23cbb3d2fb09
BLAKE2b-256 adf78ca7979b5ebf37a908971bbe3d18d5db01a44468fa8526e7e6bec979c73e

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page