| Name | pyscail JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | A package that helps build cellular automata |
| upload_time | 2024-05-06 06:15:19 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | None |
| keywords |
cellular automata
game of life
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# pyscail
**pyscail** is a package for creating cellular automata. It allows you to specify the
allowed states, and the transitions between them, and does everything else for you
## Installation
You need to have python (>= 3.11) installed on your system. Once you have that,
you can:
```bash
pip install pyscail
```
(or `pip3`, depending on your system)
The project's link on PyPI: https://pypi.org/project/pyscail/
## Usage
See this video for a demonstration of how to use the library:
https://youtu.be/BjPNs30i2TQ
A brief summary - provide a class with a `next` method and a
`display` method. Provide a initialization function that returns
an instance of your class given some coordinates. Provide a settings
object that you can make using method provided by the library.
Optionally, provide a function that will be called once each generation.
Then, simply call `scail.run` to run the ruleset.
## Demos
See the following video for a demonstration on how to write code using this
library.
See the demos folder for a lot more (and more varied) examples on how to use the
library. Written demo automata include:
- Conway's game of life
- The modified game of life with extinction
- Langton's ant
- Wireworld
- Rock-Paper-Scissors automata
- Belusov-Zhabotinsky reaction (https://www.hermetic.ch/pca/bz.htm)
- the Mandelbrot and Julia sets
- A simulation of grain growth in metals (https://www.sciencedirect.com/science/article/pii/1359646296000553)
- And lastly, this demo of many of the above rulesets combined into one
ruleset and run all together, then displayed as a weighted average
## Other
### Kernel Caching
Kernel caching is an option you can enable through `kernels.initialize_kernel_caching`
If this is set, then instead of being calculated every frame, the neighborhoods
of a given point will be calculated once then cached. This will probably
not cause any issues unless you change your neighborhood strategy mid-simulation.
Still, I didn't notice a significant speedup upon implementing caching, so for now
it is just an option that is turned off by default.
Raw data
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