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pypi package trails stable
Signed-off-by: Amith <amitharun3@gmail.com>
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‎LICENSE‎

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MIT License
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Copyright (c) 2022 Amith M
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Copyright (c) 2022 Amith225
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎MANIFEST.in‎

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# MANIFEST.in
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include docs/*
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include src/NPerlinNoise/*.toml
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include src/NPerlinNoise/*
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include snaps/*
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include tests/*
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include README_pypi.md

‎README.md‎

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# N Perlin Noise
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### A robust open source implementation of Perlin Noise Algorithm for N-Dimensions in Python.
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- A powerful and fast API for n-dimensional noise.
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- Easy hyper-parameters selection of octaves, lacunarity and persistence
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- A _powerful_ and _fast_ API for _n-dimensional_ noise.
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- Easy hyper-parameters selection of _octaves_, _lacunarity_ and _persistence_
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as well as complex and customizable hyper-parameters for n-dimension
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frequency, waveLength, warp(interpolation) and range.
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_frequency_, _waveLength_, _warp_(interpolation) and _range_.
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- Includes various helpful tools for noise generation and for procedural generation tasks
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such as customizable Gradient, Warp classes.
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- Implements custom PRNG generator for n-dimension and can be easily tuned.
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such as customizable _Gradient_, _Color Gradients_, _Warp_ classes.
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- Implements custom _PRNG_ generator for n-dimension and can be easily tuned.
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**Details**:
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- **Technology stack**:
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- **Status**: [v0.1.2-alpha](https://github.com/Amith225/NPerlinNoise/releases/tag/v0.1.2-alpha)
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- [CHANGELOG](docs/CHANGELOG.md)
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> **Status**: **`v0.1.2-alpha9`** Packaging Trails<br>
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> **All Packages**: [releases](https://github.com/Amith225/NPerlinNoise/releases)<br>
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> **PyPI**: [v0.1.2a10](https://test.pypi.org/project/NPerlinNoise/0.1.2a10/)<br>
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> [CHANGELOG](docs/CHANGELOG.md)<br>
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**Screenshots**:
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---
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## Dependencies
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- Python>=3.10.0
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- `Python>=3.10.0`
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for production dependencies see [Requirements](requirements.txt)<br>
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for development dependencies see [Dev-Requirements](requirements_dev.txt)
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## Installation
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for detailed instruction on installation see [INSTALLATION](docs/INSTALL.md).
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<a id="usage"></a>
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## Usage
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for detailed usage see [EXAMPLE](tests/main.py)
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## Getting involved
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- Looking for Contributors for WebApps
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- `Looking for Contributors for WebApps`
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- [Fork](https://github.com/Amith225/NPerlinNoise/fork) the repository
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and issue a [PR](https://github.com/Amith225/NPerlinNoise/pulls) to contribute
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READMEpypi.md renamed to README_pypi.md

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# N Perlin Noise
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### A robust open source implementation of Perlin Noise Algorithm for N-Dimensions in Python.
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- A powerful and fast API for n-dimensional noise.
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- Easy hyper-parameters selection of octaves, lacunarity and persistence
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- A _powerful_ and _fast_ API for _n-dimensional_ noise.
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- Easy hyper-parameters selection of _octaves_, _lacunarity_ and _persistence_
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as well as complex and customizable hyper-parameters for n-dimension
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frequency, waveLength, warp(interpolation) and range.
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_frequency_, _waveLength_, _warp_(interpolation) and _range_.
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- Includes various helpful tools for noise generation and for procedural generation tasks
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such as customizable Gradient, Warp classes.
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- Implements custom PRNG generator for n-dimension and can be easily tuned.
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such as customizable _Gradient_, _Color Gradients_, _Warp_ classes.
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- Implements custom _PRNG_ generator for n-dimension and can be easily tuned.
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**Details**:
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- **Technology stack**:
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- **Status**: [v0.1.2-alpha](https://github.com/Amith225/NPerlinNoise/releases/tag/v0.1.2-alpha)
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- [CHANGELOG](https://raw.github.com/Amith225/NPerlinNoise/master/docs/CHANGELOG.md)
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> **Status**: **`v0.1.2-alpha9`** Packaging Trails<br>
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> **All Packages**: [releases](https://github.com/Amith225/NPerlinNoise/releases)<br>
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> **PyPI**: [](https://github.com/Amith225/NPerlinNoise/blob/master/)<br>
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> [CHANGELOG](https://github.com/Amith225/NPerlinNoise/blob/master/docs/CHANGELOG.md)<br>
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**Screenshots**:
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---
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## Dependencies
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- Python>=3.10.0
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- `Python>=3.10.0`
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for production dependencies see [Requirements](https://raw.github.com/Amith225/NPerlinNoise/master/requirements.txt)<br>
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for development dependencies see [Dev-Requirements](https://raw.github.com/Amith225/NPerlinNoise/master/requirements_dev.txt)
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## Installation
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for detailed instruction on installation see [INSTALLATION](https://raw.github.com/Amith225/NPerlinNoise/master/docs/INSTALL.md).
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for detailed instruction on installation see [INSTALLATION](https://github.com/Amith225/NPerlinNoise/blob/master/docs/INSTALL.md).
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<a id="usage"></a>
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## Usage
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for detailed usage see [EXAMPLE](https://raw.github.com/Amith225/NPerlinNoise/master/tests/main.py)
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for detailed usage see [EXAMPLE](https://github.com/Amith225/NPerlinNoise/blob/master/tests/main.py)
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## How to test the software
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- To test overalls run [main](https://raw.github.com/Amith225/NPerlinNoise/master/tests/main.py)
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- To test Logical consistency run [testLogic](https://raw.github.com/Amith225/NPerlinNoise/master/tests/testLogic.py)
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- To test Profile Benchmarking run [testProfile](https://raw.github.com/Amith225/NPerlinNoise/master/tests/testProfile.py)
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- To test Visuals run [testVisuals](https://raw.github.com/Amith225/NPerlinNoise/master/tests/testVisuals.py)
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- To test Colors run [testCol](https://raw.github.com/Amith225/NPerlinNoise/master/tests/testCol.py)
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- To test overalls run [main](https://github.com/Amith225/NPerlinNoise/blob/master/tests/main.py)
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- To test Logical consistency run [testLogic](https://github.com/Amith225/NPerlinNoise/blob/master/tests/testLogic.py)
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- To test Profile Benchmarking run [testProfile](https://github.com/Amith225/NPerlinNoise/blob/master/tests/testProfile.py)
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- To test Visuals run [testVisuals](https://github.com/Amith225/NPerlinNoise/blob/master/tests/testVisuals.py)
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- To test Colors run [testCol](https://github.com/Amith225/NPerlinNoise/blob/master/tests/testCol.py)
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to see all tests see [Tests](https://raw.github.com/Amith225/NPerlinNoise/master/tests)
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to see all tests see [Tests](https://github.com/Amith225/NPerlinNoise/blob/master/tests)
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## Known issues
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- **_`No Known Bugs`_**
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- **_`NPerlin.findBounds is bottleneck`_**
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## Getting help
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- Check [main.py](https://raw.github.com/Amith225/NPerlinNoise/master/tests/main.py) for detailed usage
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- Check [docs](https://raw.github.com/Amith225/NPerlinNoise/master/docs) for all documentations
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- Check [main.py](https://github.com/Amith225/NPerlinNoise/blob/master/tests/main.py) for detailed usage
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- Check [docs](https://github.com/Amith225/NPerlinNoise/blob/master/docs) for all documentations
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- Check [Usage](#usage) Section
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If you have questions, concerns, bug reports, etc,
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## Getting involved
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- Looking for Contributors for WebApps
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- `Looking for Contributors for WebApps`
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- [Fork](https://github.com/Amith225/NPerlinNoise/fork) the repository
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and issue a [PR](https://github.com/Amith225/NPerlinNoise/pulls) to contribute
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General instructions on _how_ to contribute [CONTRIBUTING](https://raw.github.com/Amith225/NPerlinNoise/master/docs/CONTRIBUTING.md).
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General instructions on _how_ to contribute [CONTRIBUTING](https://github.com/Amith225/NPerlinNoise/blob/master/docs/CONTRIBUTING.md).
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----
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## Open source licensing info
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1. [TERMS](https://raw.github.com/Amith225/NPerlinNoise/master/docs/TERMS.md)
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2. [LICENSE](https://raw.github.com/Amith225/NPerlinNoise/master/LICENSE)
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1. [TERMS](https://github.com/Amith225/NPerlinNoise/blob/master/docs/TERMS.md)
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2. [LICENSE](https://github.com/Amith225/NPerlinNoise/blob/master/LICENSE)
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3. [CFPB Source Code Policy](https://github.com/cfpb/source-code-policy/)
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----
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## Credits and references
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1. Inspired from [The Coding Train](https://www.youtube.com/channel/UCvjgXvBlbQiydffZU7m1_aw) -> [perlin noise](https://thecodingtrain.com/challenges/24-perlin-noise-flow-field)
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2. hash function by [xxhash](https://github.com/Cyan4973/xxHash)
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inspired the [rand3](https://raw.github.com/Amith225/NPerlinNoise/master/src/NPerlinNoise/tools.py) algo
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and ultimately helped for O(1) time complexity n-dimensional random generator [NPrng](https://raw.github.com/Amith225/NPerlinNoise/master/src/NPerlinNoise/tools.py)
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inspired the [rand3](https://github.com/Amith225/NPerlinNoise/blob/master/src/NPerlinNoise/tools.py) algo
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and ultimately helped for O(1) time complexity n-dimensional random generator [NPrng](https://github.com/Amith225/NPerlinNoise/blob/master/src/NPerlinNoise/tools.py)
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3. [StackOverflow](https://stackoverflow.com/) for helping on various occasions throughout the development

‎docs/CHANGELOG.md‎

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## v0.1.0@alpha:
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## v0.1.0-alpha:
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- first public release
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- docs pending
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## v0.1.[1,2]-alpha:
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- bugs fixed
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- docs implemented
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- readability improved
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## v0.1.2-alpha[x]:
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- packaging trails

‎pyproject.toml‎

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[project]
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name = "NPerlinNoise"
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version = "0.1.2-alpha4"
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version = "0.1.2-alpha10"
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description = "A robust open source implementation of Perlin Noise Algorithm for N-Dimensions"
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readme = "READMEpypi.md"
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readme = "README_pypi.md"
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authors = [{ name = "Amith225", email = "amitharun3@gmail.com" }]
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license = { file = "LICENSE" }
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classifiers = [

‎readme.py‎

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start_ += len(sub)
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with open('README.md', 'r') as file:
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content = file.read()
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def get_embed_readme():
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with open('README.md', 'r') as _file:
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content = _file.read()
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_content = ""
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ignore = ["#usage"]
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base = 'https://raw.github.com/Amith225/NPerlinNoise/master/'
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for start, end in zip(find_all(content, '['), find_all(content, ']')):
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alt_text = content[start:end]
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link = content[_start:_end]
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if not link.startswith('http') and link not in ignore: link = base + link
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_content += alt_text + f"({link})"
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_content += content[_end + 1:]
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_content = ""
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_end = -1
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ignore = ["#usage"]
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raw = ['.png', '.txt']
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base_raw = 'https://raw.github.com/Amith225/NPerlinNoise/master/'
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base = 'https://github.com/Amith225/NPerlinNoise/blob/master/'
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for start, end in zip(find_all(content, '['), find_all(content, ']')):
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_content += content[_end + 1:start]
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end += 1
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alt_text = content[start:end]
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_start, _end = end + 1, content.find(')', end)
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link = content[_start:_end]
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if not link.startswith('http') and link not in ignore:
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link = (base_raw if any(link.endswith(r) for r in raw) else base) + link
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_content += alt_text + f"({link})"
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_content += content[_end + 1:]
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with open('READMEpypi.md', 'w+') as file:
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file.write(_content)
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return _content
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with open('README_pypi.md', 'w+') as file:
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file.write(get_embed_readme())

‎setup.py‎

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from setuptools import setup
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setup()
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setup(include_package_data=True)

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