开单纯形n维梯度噪声函数。
opensimplex的Python项目详细描述
OpenSimplex noise is an n-dimensional gradient noise function that was developed in order to overcome the patent-related issues surrounding Simplex noise, while continuing to also avoid the visually-significant directional artifacts characteristic of Perlin noise.
这只是kurt spencer原始代码的一个python端口，发布给 公共领域，并整齐地包装在一个包里。
用法
初始化：
>>> from opensimplex import OpenSimplex >>> tmp = OpenSimplex() >>> print (tmp.noise2d(x=10, y=10)) 0.732051569572
可选地，类接受种子值：
>>> tmp = OpenSimplex(seed=1) >>> print (tmp.noise2d(x=10, y=10)) -0.4790979022623557
种子必须是有效的python编号。它在内部用来产生一些 排列阵列，用于产生噪声。
如果没有提供，则类将默认使用0作为种子。
可用的类方法：
- opensimplex.noise2d（x，y）
- 从x，y坐标生成2d opensimplex噪声。
- opensimplex.noise3d（x，y，z）
- 从x、y、z坐标生成3d opensimplex噪声。
- opensimplex.noise4d（x，y，z，w）
- 从x、y、z、w坐标生成4d opensimplex噪声。
运行测试和基准：
首先制作一个virtualenv并安装开发要求：
virtualenv venv source venv/bin/activate pip install -r requirements.txt
然后简单地运行测试：
make test
- 或基本基准：
- 制定基准
常见问题解答
这与原始专利的区别是否足以避免任何真正的麻烦？
If you read the patent claims:
Claim #1 talks about the hardware-implementation-optimized gradient generator. Most software implementations of Simplex Noise don’t use this anyway, and OpenSimplex Noise certainly doesn’t.
Claim #2(&3&4) talk about using (x’,y’,z’)=(x+s,y+s,z+s) where s=(x+y+z)/3 to transform the input (render space) coordinate onto a simplical grid, with the intention to make all of the “scissor-simplices” approximately regular. OpenSimplex Noise (in 3D) uses s=-(x+y+z)/6 to transform the input point to a point on the Simplectic honeycomb lattice so that the simplices bounding the (hyper)cubes at (0,0,..,0) and (1,1,…,1) work out to be regular. It then mathematically works out that s=(x+y+z)/3 is needed for the inverse transform, but that’s performing a different (and opposite) function.
Claim #5(&6) are specific to the scissor-simplex lattice. Simplex Noise divides the (squashed) n-dimensional (hyper)cube into n! simplices based on ordered edge traversals, whereas OpenSimplex Noise divides the (stretched) n-dimensional (hyper)cube into n polytopes (simplices, rectified simplices, birectified simplices, etc.) based on the separation (hyper)planes at integer values of (x’+y’+z’+…).
Another interesting point is that, if you read all of the claims, none of them appear to apply to the 2D analogue of Simplex noise so long as it uses a gradient generator separate from the one described in claim #1. The skew function in Claim #2 only applies to 3D, and #5 explicitly refers to n>=3.
And none of the patent claims speak about using surflets / “spherically symmetric kernels” to generate the “images with texture that do not have visible grid artifacts,” which is probably the biggest similarity between the two algorithms.
- Kurt, on Reddit.
学分
- Kurt Spencer-原创作品
- 一个svensson-python端口和包作者
- creamycookie-清理和优化
许可证
当库尔特将原著公之于众时 正在使用麻省理工学院的许可证。详情请参阅文件许可证。
预期产量
二维噪声（带默认种子）：
3D噪音：
4D噪音：
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