Metadata-Version: 2.1
Name: opensimplex
Version: 0.4.3
Summary: OpenSimplex n-dimensional gradient noise function.
Home-page: https://github.com/lmas/opensimplex
Download-URL: https://github.com/lmas/opensimplex/releases
Author: Alex
Author-email: opensimplex@larus.se
License: MIT
Keywords: opensimplex simplex noise 2D 3D 4D
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE


OpenSimplex Noise
================================================================================

[![build-status](https://github.com/lmas/opensimplex/workflows/Tests/badge.svg?branch=master)](https://github.com/lmas/opensimplex/actions)
[![pypi-version](https://badge.fury.io/py/opensimplex.svg)](https://pypi.org/project/opensimplex/)

        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.

This is merely a python port of Kurt Spencer's original code (released to the public domain) and neatly wrapped up in a
package.

Status
================================================================================

The `master` branch contains the latest, potentially unstable, code.

For the latest stable version, please refer to the [version tags](https://github.com/lmas/opensimplex/tags) for now.

This version has been tested with **Python 3.8, 3.9, 3.10 on Linux, MacOS and Windows**.

Updates for **v0.4+**:

- Adds a hard dependency on 'Numpy', for array optimizations aimed at heavier workloads.
- Adds optional dependency on 'Numba', for further speed optimizations using caching (currently untested due to issues
  with llvmlite).
- Adds typing support.
- General refactor and cleanup of the library, tests and docs.
- **Breaking changes: API function names has been modified.**

Usage
================================================================================

**Installation:**

        pip install opensimplex

**Basic usage:**

        >>> import opensimplex
        >>> opensimplex.seed(1234)
        >>> n = opensimplex.noise2(x=10, y=10)
        >>> print(n)
        0.580279369186297

For more advanced examples, see the files in the `tests` and `example` directory.

**Available functions:**

*opensimplex.seed(seed)*

        Seeds the underlying permutation array (which produces different outputs), using a 64-bit integer number.
        If no value is provided, a static default will be used instead.

        seed(13)

*random_seed()*

        Works just like seed(), except it uses the system time (in ns) as a seed value.
        Not guaranteed to be random so use at your own risk.

        random_seed()

*opensimplex.noise2(x, y)*

        Generate 2D OpenSimplex noise from X,Y coordinates.
        :param x: x coordinate as float
        :param y: y coordinate as float
        :return: generated 2D noise as float, between -1.0 and 1.0

        >>> noise2(0.5, 0.5)
        -0.43906247097569345

*opensimplex.noise2array(x, y)*

        Generates 2D OpenSimplex noise using Numpy arrays for increased performance.
        :param x: numpy array of x-coords
        :param y: numpy array of y-coords
        :return: 2D numpy array of shape (y.size, x.size) with the generated noise for the supplied coordinates

        >>> rng = numpy.random.default_rng(seed=0)
        >>> ix, iy = rng.random(2), rng.random(2)
        >>> noise2array(ix, iy)
        array([[ 0.00449931, -0.01807883],
           [-0.00203524, -0.02358477]])

*opensimplex.noise3(x, y, z)*

        Generate 3D OpenSimplex noise from X,Y,Z coordinates.
        :param x: x coordinate as float
        :param y: y coordinate as float
        :param z: z coordinate as float
        :return: generated 3D noise as float, between -1.0 and 1.0

        >>> noise3(0.5, 0.5, 0.5)
        0.39504955501618155

*opensimplex.noise3array(x, y, z)*

        Generates 3D OpenSimplex noise using Numpy arrays for increased performance.
        :param x: numpy array of x-coords
        :param y: numpy array of y-coords
        :param z: numpy array of z-coords
        :return: 3D numpy array of shape (z.size, y.size, x.size) with the generated noise for the supplied coordinates

        >>> rng = numpy.random.default_rng(seed=0)
        >>> ix, iy, iz = rng.random(2), rng.random(2), rng.random(2)
        >>> noise3array(ix, iy, iz)
        array([[[0.54942818, 0.54382411],
            [0.54285204, 0.53698967]],
           [[0.48107672, 0.4881196 ],
            [0.45971748, 0.46684901]]])

*opensimplex.noise4(x, y, z, w)*

        Generate 4D OpenSimplex noise from X,Y,Z,W coordinates.
        :param x: x coordinate as float
        :param y: y coordinate as float
        :param z: z coordinate as float
        :param w: w coordinate as float
        :return: generated 4D noise as float, between -1.0 and 1.0

        >>> noise4(0.5, 0.5, 0.5, 0.5)
        0.04520359600370195

*opensimplex.noise4array(x, y, z, w)*

        Generates 4D OpenSimplex noise using Numpy arrays for increased performance.
        :param x: numpy array of x-coords
        :param y: numpy array of y-coords
        :param z: numpy array of z-coords
        :param w: numpy array of w-coords
        :return: 4D numpy array of shape (w.size, z.size, y.size, x.size) with the generated noise for the supplied
        coordinates

        >>> rng = numpy.random.default_rng(seed=0)
        >>> ix, iy, iz, iw = rng.random(2), rng.random(2), rng.random(2), rng.random(2)
        >>> noise4array(ix, iy, iz, iw)
        array([[[[0.30334626, 0.29860705],
             [0.28271858, 0.27805178]],
            [[0.26601215, 0.25305428],
             [0.23387872, 0.22151356]]],
           [[[0.3392759 , 0.33585534],
             [0.3343468 , 0.33118285]],
            [[0.36930335, 0.36046537],
             [0.36360679, 0.35500328]]]])

**Running tests and benchmarks:**

Setup a development environment:

        make dev
        source devenv/bin/activate
        make deps

And then run the tests:

        make test

Or the benchmark:

        make benchmark

FAQ
================================================================================

- Is this relevantly different enough to avoid any real trouble with the
original patent?

    > If you read the [patent
    > claims](http://www.google.com/patents/US6867776):
    >
    > 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](https://www.reddit.com/r/proceduralgeneration/comments/2gu3e7/like_perlins_simplex_noise_but_dont_like_the/ckmqz2y)

Credits
================================================================================

- Kurt Spencer - Original work
- Alex - Python port and package author

- Owen Raccuglia - Test cases, [Go Module](https://github.com/ojrac/opensimplex-go)
- /u/redblobgames - Fixed conversion for Java's long type, see [Reddit](https://old.reddit.com/r/proceduralgeneration/comments/327zkm/repeated_patterns_in_opensimplex_python_port/cq8tth7/)

And all the other Github [Contributors](https://github.com/lmas/opensimplex/graphs/contributors) and [Bug Hunters](https://github.com/lmas/opensimplex/issues?q=)!

Thanks!

License
================================================================================

While the original work was released to the public domain by Kurt, this package is using the MIT license.

Please see the file LICENSE for details.

Expected Output
================================================================================

Example images visualising 2D, 3D and 4D noise on a 2D plane, using the default seed:

**2D noise**

![image](images/noise2d.png)

**3D noise**

![image](images/noise3d.png)

**4D noise**

![image](images/noise4d.png)
