| Name | nPerlinNoise JSON |
| Version |
0.1.4a0
JSON |
| download |
| home_page | |
| Summary | A robust open source implementation of Perlin Noise Algorithm for N-Dimensions |
| upload_time | 2022-10-18 11:44:57 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.10 |
| 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. |
| keywords |
perlin-noise
perlin
n-dimensional
numpy-perlin
numpy-noise
noise
noise-algorithm
performance
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
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# nPerlinNoise
#### indexed on PyPI - [nPerlinNoise](https://pypi.org/project/nPerlinNoise)
#### repo on GitHub - [nPerlinNoise](https://github.com/Amith225/nPerlinNoise)
#### docs on ReadTheDocs - [](https://readthedocs.org/)
### 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**: **`v0.1.4-alpha`** focusing on all issues [Getting Involved](#contribute), follows PEP440<br>
> **All Packages**: [releases](https://github.com/Amith225/nPerlinNoise/releases)<br>
> [CHANGELOG](https://github.com/Amith225/nPerlinNoise/blob/master/docs/CHANGELOG.md)<br>
> ###### _Tested on Python 3.10, Windows 10_
- **Future work**:
> **optimization** for octave noise<br>
> writing **unit tests**<br>
> writing **API docs**<br>
> writing **pending docs**<br>
> writing **ReadTheDocs**<br>
> **blogging**<br>
> finishing left **in-code docs**<br>
> dimensional **octaves**<br>
---
**Screenshots**:
<div align="center">
</div>
---
## Dependencies
- `Python>=3.10.0`
for production dependencies see [Requirements](https://raw.github.com/Amith225/nPerlinNoise/master/requirements/requirements.txt)<br>
for development dependencies see [Dev-Requirements](https://raw.github.com/Amith225/nPerlinNoise/master/requirements/dev_requirements.txt)
## Installation
```shell
$ pip install nPerlinNoise
```
for detailed instruction on installation see [INSTALLATION](https://github.com/Amith225/nPerlinNoise/blob/master/docs/INSTALL.md).
<a id="usage"></a>
## Usage
**Setup**
```pycon
>>> import nPerlinNoise as nPN
>>> noise = nPN.Noise(seed=69420)
```
**Basic usage**
Get noise values at given n-dimensional coordinates by calling ```noise(...)```,<br>
coordinates can be single value, or an iterable
- ###### single value
> noise(..., l, m, n, ...)<br>
> where l, m, n, ..., are single values
```pycon
>>> noise(73)
array(0.5207113, dtype=float32)
>>> noise(73, 11, 7)
array(0.5700986, dtype=float32)
>>> noise(0, 73, 7, 11, 0, 3)
array(0.5222712, dtype=float32)
```
- ###### iterable
> noise(...., [l1, l2, ..., lx], [m1, m2, ..., mx], [n1, n2, ..., nx], ....)<br>
> where ...., are iterable of homogeneous-dimensions and lx, mx, nx, ..., are single values
> the output will be of same shape of input homogeneous-dimensions
```pycon
>>> noise([73, 49])
array([0.52071124, 0.6402224 ], dtype=float32)
>>> noise([73, 49], [2, 2])
array([0.4563121 , 0.63378346], dtype=float32)
>>> noise([[73], [49], [0]],
... [[2 ], [2 ], [2]],
... [[0 ], [1 ], [2]])
array([[0.4563121 ],
[0.6571784 ],
[0.16369209]], dtype=float32)
>>> noise([[1, 2], [2, 3]],
... [[1, 1], [1, 1]],
... [[2, 2], [2, 2]])
array([[0.08666219, 0.09778494],
[0.09778494, 0.14886124]], dtype=float32)
```
`noise(..., l, m, n, ...)` has same values with trailing dimensions having zero as coordinate
- ###### n-dimensionality
> noise(..., l, m, n) = noise(..., l, m, n, 0) = noise(..., l, m, n, 0, 0) = noise(..., l, m, n, 0, 0, ...)
```pycon
>>> noise(73)
array(0.5207113, dtype=float32)
>>> noise(73, 0)
array(0.5207113, dtype=float32)
>>> noise(73, 0, 0)
array(0.5207113, dtype=float32)
>>> noise(73, 0, 0, 0, 0)
array(0.5207113, dtype=float32)
```
grid mode allows for computing noise for every combination of coords<br>
use `noise(..., gridMode=True)` gridMode is key-word only argument, default=False<br>
the output will be of shape equal to the length(s) of coords in that order
- ###### gridMode
```pycon
>>> noise([73, 49], [2, 2], [0, 1], gridMode=True)
array([[[0.4563121 , 0.63378346],
[0.4563121 , 0.63378346]],
[[0.44594935, 0.6571784 ],
[0.44594935, 0.6571784 ]]], dtype=float32)
>>> noise([1, 20, 32, 64], [1, 1, 2], 0, [1, 2], gridMode=True)
array([[[[0.06459193, 0.5110498 , 0.669962 , 0.47636804],
[0.06459193, 0.5110498 , 0.669962 , 0.47636804],
[0.09864856, 0.5013973 , 0.62935597, 0.47954425]]],
[[[0.07678645, 0.50853723, 0.6778991 , 0.4679888 ],
[0.07678645, 0.50853723, 0.6778991 , 0.4679888 ],
[0.14069612, 0.47582665, 0.6663638 , 0.48764956]]]],
dtype=float32)
```
for detailed usage see [EXAMPLE](https://github.com/Amith225/nPerlinNoise/blob/master/scripts/main.py)
## API
- docs pending
## How to test the software
- 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`_**
- **_`noise(a, b, c, d, e, f, ...) is slow for single value coordinates`_**
## Getting help
- Check [main.py](https://github.com/Amith225/nPerlinNoise/blob/master/scripts/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
<a id="contribute"></a>
- `Looking for Contributors for feature additions`
- `Looking for Contributors for optimization` [#11](https://github.com/Amith225/nPerlinNoise/issues/11)
- `Looking for Contributors for unit testing` [#12](https://github.com/Amith225/nPerlinNoise/issues/12)
- `Looking for Contributors for ReadTheDocs` [#13](https://github.com/Amith225/nPerlinNoise/issues/13)
- `Looking for Contributors for WebApp` [#14](https://github.com/Amith225/nPerlinNoise/issues/14)
- `Looking for Contributors for API docs` [#15](https://github.com/Amith225/nPerlinNoise/issues/15)
- [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)
and [CODE OF CONDUCT](https://github.com/Amith225/nPerlinNoise/blob/master/CODE_OF_CONDUCT.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
4. [vnoise](https://github.com/plottertools/vnoise) and [opensimplex](https://github.com/lmas/opensimplex)
for ideas for README.md
5. docs derivative from [open-source-project-template](https://github.com/cfpb/open-source-project-template)
6. packaging help from [realpython](https://realpython.com/pypi-publish-python-package/)
**Maintainer**:
| <a href="https://github.com/Amith225"><img src="https://avatars.githubusercontent.com/u/75326634?v=4" height=250></a> |
|:-----------------------------------------------------------------------------------------------------------------------------------:|
| **[Amith M](https://www.linkedin.com/in/amith-m-17088b246/)** |
| [](https://instagram.com/amithm3 ) |
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"description": "<a href=\"https://github.com/Amith225/nPerlinNoise/blob/master/LICENSE\"></a>\n<a href=\"https://github.com/Amith225/nPerlinNoise\"></a>\n<a href=\"https://pypi.org/project/nPerlinNoise\"></a>\n<a href=\"https://github.com/Amith225/nPerlinNoise/releases/latest\"></a>\n<a href=\"https://github.com/Amith225/nPerlinNoise/releases\"></a>\n<a href=\"https://www.python.org/downloads/\"></a>\n<a href=\"#\"></a>\n\n[](https://forthebadge.com)\n[](https://forthebadge.com)\n[](https://forthebadge.com)\n[](https://forthebadge.com)\n[](https://forthebadge.com)\n\n# nPerlinNoise\n\n#### indexed on PyPI - [nPerlinNoise](https://pypi.org/project/nPerlinNoise)\n\n#### repo on GitHub - [nPerlinNoise](https://github.com/Amith225/nPerlinNoise)\n\n#### docs on ReadTheDocs - [](https://readthedocs.org/)\n\n### A robust open source implementation of Perlin Noise Algorithm for N-Dimensions in Python.\n\n- A _powerful_ and _fast_ API for _n-dimensional_ noise.\n- Easy hyper-parameters selection of _octaves_, _lacunarity_ and _persistence_\n as well as complex and customizable hyper-parameters for n-dimension\n _frequency_, _waveLength_, _warp_(interpolation) and _range_.\n- Includes various helpful tools for noise generation and for procedural generation tasks\n such as customizable _Gradient_, _Color Gradients_, _Warp_ classes.\n- Implements custom _PRNG_ generator for n-dimension and can be easily tuned.\n\n**Details**:\n\n- **Technology stack**:\n > **Status**: **`v0.1.4-alpha`** focusing on all issues [Getting Involved](#contribute), follows PEP440<br>\n > **All Packages**: [releases](https://github.com/Amith225/nPerlinNoise/releases)<br>\n > [CHANGELOG](https://github.com/Amith225/nPerlinNoise/blob/master/docs/CHANGELOG.md)<br>\n > ###### _Tested on Python 3.10, Windows 10_\n- **Future work**:\n > **optimization** for octave noise<br>\n > writing **unit tests**<br>\n > writing **API docs**<br>\n > writing **pending docs**<br>\n > writing **ReadTheDocs**<br>\n > **blogging**<br>\n > finishing left **in-code docs**<br>\n > dimensional **octaves**<br>\n\n---\n\n**Screenshots**:\n\n<div align=\"center\">\n\n\n\n\n\n\n\n\n\n\n\n</div>\n\n---\n\n## Dependencies\n\n- `Python>=3.10.0`\n\nfor production dependencies see [Requirements](https://raw.github.com/Amith225/nPerlinNoise/master/requirements/requirements.txt)<br>\nfor development dependencies see [Dev-Requirements](https://raw.github.com/Amith225/nPerlinNoise/master/requirements/dev_requirements.txt)\n\n## Installation\n\n```shell\n$ pip install nPerlinNoise\n```\n\nfor detailed instruction on installation see [INSTALLATION](https://github.com/Amith225/nPerlinNoise/blob/master/docs/INSTALL.md).\n\n<a id=\"usage\"></a>\n\n## Usage\n\n**Setup**\n\n```pycon\n>>> import nPerlinNoise as nPN\n>>> noise = nPN.Noise(seed=69420)\n```\n\n**Basic usage**\n\nGet noise values at given n-dimensional coordinates by calling ```noise(...)```,<br>\ncoordinates can be single value, or an iterable\n\n- ###### single value\n\n > noise(..., l, m, n, ...)<br>\n > where l, m, n, ..., are single values\n\n ```pycon\n >>> noise(73)\n array(0.5207113, dtype=float32)\n >>> noise(73, 11, 7)\n array(0.5700986, dtype=float32)\n >>> noise(0, 73, 7, 11, 0, 3)\n array(0.5222712, dtype=float32)\n ```\n\n- ###### iterable\n\n > noise(...., [l1, l2, ..., lx], [m1, m2, ..., mx], [n1, n2, ..., nx], ....)<br>\n > where ...., are iterable of homogeneous-dimensions and lx, mx, nx, ..., are single values\n > the output will be of same shape of input homogeneous-dimensions\n\n ```pycon\n >>> noise([73, 49])\n array([0.52071124, 0.6402224 ], dtype=float32)\n >>> noise([73, 49], [2, 2])\n array([0.4563121 , 0.63378346], dtype=float32)\n >>> noise([[73], [49], [0]],\n ... [[2 ], [2 ], [2]],\n ... [[0 ], [1 ], [2]])\n array([[0.4563121 ],\n [0.6571784 ],\n [0.16369209]], dtype=float32)\n >>> noise([[1, 2], [2, 3]],\n ... [[1, 1], [1, 1]],\n ... [[2, 2], [2, 2]])\n array([[0.08666219, 0.09778494],\n [0.09778494, 0.14886124]], dtype=float32)\n ```\n\n`noise(..., l, m, n, ...)` has same values with trailing dimensions having zero as coordinate\n\n- ###### n-dimensionality\n\n > noise(..., l, m, n) = noise(..., l, m, n, 0) = noise(..., l, m, n, 0, 0) = noise(..., l, m, n, 0, 0, ...)\n\n ```pycon\n >>> noise(73)\n array(0.5207113, dtype=float32)\n >>> noise(73, 0)\n array(0.5207113, dtype=float32)\n >>> noise(73, 0, 0)\n array(0.5207113, dtype=float32)\n >>> noise(73, 0, 0, 0, 0)\n array(0.5207113, dtype=float32)\n ```\n\ngrid mode allows for computing noise for every combination of coords<br>\nuse `noise(..., gridMode=True)` gridMode is key-word only argument, default=False<br>\nthe output will be of shape equal to the length(s) of coords in that order\n\n- ###### gridMode\n ```pycon\n >>> noise([73, 49], [2, 2], [0, 1], gridMode=True)\n array([[[0.4563121 , 0.63378346],\n [0.4563121 , 0.63378346]],\n \n [[0.44594935, 0.6571784 ],\n [0.44594935, 0.6571784 ]]], dtype=float32)\n >>> noise([1, 20, 32, 64], [1, 1, 2], 0, [1, 2], gridMode=True)\n array([[[[0.06459193, 0.5110498 , 0.669962 , 0.47636804],\n [0.06459193, 0.5110498 , 0.669962 , 0.47636804],\n [0.09864856, 0.5013973 , 0.62935597, 0.47954425]]],\n \n \n [[[0.07678645, 0.50853723, 0.6778991 , 0.4679888 ],\n [0.07678645, 0.50853723, 0.6778991 , 0.4679888 ],\n [0.14069612, 0.47582665, 0.6663638 , 0.48764956]]]],\n dtype=float32)\n ```\n\nfor detailed usage see [EXAMPLE](https://github.com/Amith225/nPerlinNoise/blob/master/scripts/main.py)\n\n## API\n\n- docs pending\n\n## How to test the software\n\n- To test Logical consistency run [testLogic](https://github.com/Amith225/nPerlinNoise/blob/master/tests/testLogic.py)\n- To test Profile Benchmarking run [testProfile](https://github.com/Amith225/nPerlinNoise/blob/master/tests/testProfile.py)\n- To test Visuals run [testVisuals](https://github.com/Amith225/nPerlinNoise/blob/master/tests/testVisuals.py)\n- To test Colors run [testCol](https://github.com/Amith225/nPerlinNoise/blob/master/tests/testCol.py)\n\nto see all tests see [Tests](https://github.com/Amith225/nPerlinNoise/blob/master/tests)\n\n## Known issues\n\n- **_`No Known Bugs`_**\n- **_`NPerlin.findBounds is bottleneck`_**\n- **_`noise(a, b, c, d, e, f, ...) is slow for single value coordinates`_**\n\n## Getting help\n\n- Check [main.py](https://github.com/Amith225/nPerlinNoise/blob/master/scripts/main.py) for detailed usage\n- Check [docs](https://github.com/Amith225/nPerlinNoise/blob/master/docs) for all documentations\n- Check [Usage](#usage) Section\n\nIf you have questions, concerns, bug reports, etc.\nplease file an [issue](https://github.com/Amith225/nPerlinNoise/issues) in this repository's Issue Tracker or\nopen a [discussion](https://github.com/Amith225/nPerlinNoise/discussions/7) in this repository's Discussion section.\n\n## Getting involved\n\n<a id=\"contribute\"></a>\n- `Looking for Contributors for feature additions`\n- `Looking for Contributors for optimization` [#11](https://github.com/Amith225/nPerlinNoise/issues/11)\n- `Looking for Contributors for unit testing` [#12](https://github.com/Amith225/nPerlinNoise/issues/12)\n- `Looking for Contributors for ReadTheDocs` [#13](https://github.com/Amith225/nPerlinNoise/issues/13)\n- `Looking for Contributors for WebApp` [#14](https://github.com/Amith225/nPerlinNoise/issues/14) \n- `Looking for Contributors for API docs` [#15](https://github.com/Amith225/nPerlinNoise/issues/15) \n- [Fork](https://github.com/Amith225/nPerlinNoise/fork) the repository\n and issue a [PR](https://github.com/Amith225/nPerlinNoise/pulls) to contribute\n\nGeneral instructions on _how_ to contribute [CONTRIBUTING](https://github.com/Amith225/nPerlinNoise/blob/master/docs/CONTRIBUTING.md)\nand [CODE OF CONDUCT](https://github.com/Amith225/nPerlinNoise/blob/master/CODE_OF_CONDUCT.md)\n\n----\n\n## Open source licensing info\n\n1. [TERMS](https://github.com/Amith225/nPerlinNoise/blob/master/docs/TERMS.md)\n2. [LICENSE](https://github.com/Amith225/nPerlinNoise/blob/master/LICENSE)\n3. [CFPB Source Code Policy](https://github.com/cfpb/source-code-policy/)\n\n----\n\n## Credits and references\n\n1. Inspired from [The Coding Train](https://www.youtube.com/channel/UCvjgXvBlbQiydffZU7m1_aw)\n -> [perlin noise](https://thecodingtrain.com/challenges/24-perlin-noise-flow-field)\n2. hash function by [xxhash](https://github.com/Cyan4973/xxHash)\n inspired the [rand3](https://github.com/Amith225/nPerlinNoise/blob/master/src/nPerlinNoise/tools.py) algo\n and ultimately helped for O(1) time complexity n-dimensional random generator [NPrng](https://github.com/Amith225/nPerlinNoise/blob/master/src/nPerlinNoise/tools.py)\n3. [StackOverflow](https://stackoverflow.com/) for helping on various occasions throughout the development\n4. [vnoise](https://github.com/plottertools/vnoise) and [opensimplex](https://github.com/lmas/opensimplex)\n for ideas for README.md\n5. docs derivative from [open-source-project-template](https://github.com/cfpb/open-source-project-template)\n6. packaging help from [realpython](https://realpython.com/pypi-publish-python-package/)\n\n**Maintainer**:\n\n| <a href=\"https://github.com/Amith225\"><img src=\"https://avatars.githubusercontent.com/u/75326634?v=4\" height=250></a> |\n|:-----------------------------------------------------------------------------------------------------------------------------------:|\n| **[Amith M](https://www.linkedin.com/in/amith-m-17088b246/)** |\n| [](https://instagram.com/amithm3 ) |\n",
"bugtrack_url": null,
"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.",
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