Metadata-Version: 2.1
Name: NPerlinNoise
Version: 0.1.3a0
Summary: A robust open source implementation of Perlin Noise Algorithm for N-Dimensions
Author-email: Amith225 <amitharun3@gmail.com>
License: MIT License
        
        Copyright (c) 2022 Amith225
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/Amith225/NPerlinNoise
Keywords: perlin-noise,perlin,n-dimensional,numpy-perlin,numpy-noise,noise,noise-algorithm,performance
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

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# N Perlin Noise

### A robust open source implementation of Perlin Noise Algorithm for N-Dimensions in Python.
- A _powerful_ and _fast_ API for _n-dimensional_ noise.
- Easy hyper-parameters selection of _octaves_, _lacunarity_ and _persistence_
  as well as complex and customizable hyper-parameters for n-dimension
  _frequency_, _waveLength_, _warp_(interpolation) and _range_.
- Includes various helpful tools for noise generation and for procedural generation tasks
  such as customizable _Gradient_, _Color Gradients_, _Warp_ classes.
- Implements custom _PRNG_ generator for n-dimension and can be easily tuned.

**Details**:
- **Technology stack**:
  > **Status**: **`v0.1.3-alpha`** Ready for public PyPI release<br>
  > **All Packages**: [releases](https://github.com/Amith225/NPerlinNoise/releases)<br>
  > **PyPI**: [v0.1.3a0](https://pypi.org/project/NPerlinNoise/0.1.3a0/)<br>
  > [CHANGELOG](https://github.com/Amith225/NPerlinNoise/blob/master/docs/CHANGELOG.md)<br>

**Screenshots**:
- raw<br>
  ![raw](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/raw.png)
- wood<br>
  ![wood](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/wood.png)
- hot nebula<br>
  ![hot nebula](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/hot_nebula.png)
- island<br>
  ![island](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/island.png)
- land<br>
  ![land](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/land.png)
- marble fractal<br>
  ![marble fractal](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/marble_fractal.png)
- patch<br>
  ![patch](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/patch.png)
- color patch<br>
  ![color patch](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/color_patch.png)
- ply1<br>
  ![ply1](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/ply1.png)
- ply2<br>
  ![ply2](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/ply2.png)
- stripes<br>
  ![stripes](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/stripes.png)
- warp<br>
  ![warp](https://raw.github.com/Amith225/NPerlinNoise/master/snaps/warp.png)

---

## Dependencies
- `Python>=3.10.0`

for production dependencies see [Requirements](https://raw.github.com/Amith225/NPerlinNoise/master/requirements.txt)<br>
for development dependencies see [Dev-Requirements](https://raw.github.com/Amith225/NPerlinNoise/master/requirements_dev.txt)

## Installation
for detailed instruction on installation see [INSTALLATION](https://github.com/Amith225/NPerlinNoise/blob/master/docs/INSTALL.md).

<a id="usage"></a>
## Usage
for detailed usage see [EXAMPLE](https://github.com/Amith225/NPerlinNoise/blob/master/tests/main.py)

## How to test the software
- To test overalls run [main](https://github.com/Amith225/NPerlinNoise/blob/master/tests/main.py)
- To test Logical consistency run [testLogic](https://github.com/Amith225/NPerlinNoise/blob/master/tests/testLogic.py)
- To test Profile Benchmarking run [testProfile](https://github.com/Amith225/NPerlinNoise/blob/master/tests/testProfile.py)
- To test Visuals run [testVisuals](https://github.com/Amith225/NPerlinNoise/blob/master/tests/testVisuals.py)
- To test Colors run [testCol](https://github.com/Amith225/NPerlinNoise/blob/master/tests/testCol.py)

to see all tests see [Tests](https://github.com/Amith225/NPerlinNoise/blob/master/tests)

## Known issues
- **_`No Known Bugs`_**
- **_`NPerlin.findBounds is bottleneck`_**

## Getting help
- Check [main.py](https://github.com/Amith225/NPerlinNoise/blob/master/tests/main.py) for detailed usage
- Check [docs](https://github.com/Amith225/NPerlinNoise/blob/master/docs) for all documentations
- Check [Usage](#usage) Section

If you have questions, concerns, bug reports, etc,
please file an [issue](https://github.com/Amith225/NPerlinNoise/issues) in this repository's Issue Tracker or
open a [discussion](https://github.com/Amith225/NPerlinNoise/discussions/7) in this repository's Discussion section.


## Getting involved
- `Looking for Contributors for WebApps`
- [Fork](https://github.com/Amith225/NPerlinNoise/fork) the repository
  and issue a [PR](https://github.com/Amith225/NPerlinNoise/pulls) to contribute

General instructions on _how_ to contribute  [CONTRIBUTING](https://github.com/Amith225/NPerlinNoise/blob/master/docs/CONTRIBUTING.md).

----

## Open source licensing info
1. [TERMS](https://github.com/Amith225/NPerlinNoise/blob/master/docs/TERMS.md)
2. [LICENSE](https://github.com/Amith225/NPerlinNoise/blob/master/LICENSE)
3. [CFPB Source Code Policy](https://github.com/cfpb/source-code-policy/)

----

## Credits and references
1. Inspired from [The Coding Train](https://www.youtube.com/channel/UCvjgXvBlbQiydffZU7m1_aw) -> [perlin noise](https://thecodingtrain.com/challenges/24-perlin-noise-flow-field)
2. hash function by [xxhash](https://github.com/Cyan4973/xxHash)
   inspired the [rand3](https://github.com/Amith225/NPerlinNoise/blob/master/src/NPerlinNoise/tools.py) algo
   and ultimately helped for O(1) time complexity n-dimensional random generator [NPrng](https://github.com/Amith225/NPerlinNoise/blob/master/src/NPerlinNoise/tools.py)
3. [StackOverflow](https://stackoverflow.com/) for helping on various occasions throughout the development
