157 lines
6.6 KiB
Markdown
157 lines
6.6 KiB
Markdown
# Neuropecolation
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This package provides an neuropercolation model for [Python 3](https://www.python.org/) forked from Richard Feistenauer's [cellular-automaton](https://gitlab.com/DamKoVosh/cellular_automaton).
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<!---
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A cellular automaton defines a grid of cells and a set of rules.
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All cells then evolve their state depending on their neighbours state simultaneously.
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For further information on cellular automatons consult e.g. [mathworld.wolfram.com](http://mathworld.wolfram.com/CellularAutomaton.html)
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## Yet another cellular automaton module?
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It is not the first python module to provide a cellular automaton,
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but it is to my best knowledge the first that provides all of the following features:
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- easy to use
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- n dimensional
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- speed optimized
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- documented
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- tested
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I originally did not plan to write a new cellular automaton module,
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but when searching for one, I just found some that had little or no documentation with an API that really did not fit my requirements
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and/or Code that was desperately asking for some refactoring.
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So I started to write my own module with the goal to provide an user friendly API
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and acceptable documentation. During the implementation I figured, why not just provide
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n dimensional support and with reading Clean Code from Robert C. Martin the urge
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to have a clean and tested code with a decent coverage added some more requirements.
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The speed optimization and multi process capability was more of challenge for myself.
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IMHO the module now reached an acceptable speed, but there is still room for improvements (e.g. with Numba?).
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## Installation
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This module can be loaded and installed from [pypi](https://pypi.org/project/cellular-automaton/): `pip install cellular-automaton`
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## Usage
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To start and use the automaton you will have to define four things:
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- The neighborhood
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- The dimensions of the grid
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- The evolution rule
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- The initial cell state
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`````python
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class MyCellularAutomaton(CellularAutomaton):
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def init_cell_state(self, coordinate: tuple) -> Sequence:
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return initial_cell_state
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def evolve_rule(self, last_state: tuple, neighbors_last_states: Sequence) -> Sequence:
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return next_cell_state
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neighborhood = MooreNeighborhood(EdgeRule.IGNORE_EDGE_CELLS)
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ca = MyCellularAutomaton(dimension=[100, 100],
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neighborhood=neighborhood)
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``````
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### Neighbourhood
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The Neighborhood defines for a cell neighbours in relative coordinates.
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The evolution of a cell will depend solely on those neighbours.
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The Edge Rule passed as parameter to the Neighborhood defines, how cells on the edge of the grid will be handled.
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There are three options:
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- Ignore edge cells: Edge cells will have no neighbours and thus not evolve.
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- Ignore missing neighbours: Edge cells will add the neighbours that exist. This results in varying count of neighbours on edge cells.
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- First and last cell of each dimension are neighbours: All cells will have the same neighbour count and no edge exists.
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### Dimension
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A list or Tuple which states each dimensions size.
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The example above defines a two dimensional grid with 100 x 100 cells.
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There is no limitation in how many dimensions you choose but your memory and processor power.
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### Evolution and Initial State
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To define the evolution rule and the initial state create a class inheriting from `CellularAutomaton`.
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- The `init_cell_state` method will be called once during the creation process for every cell.
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It will get the coordinate of that cell and is supposed to return a tuple representing that cells state.
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- The `evolve_rule` gets passed the last cell state and the states of all neighbors.
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It is supposed to return a tuple representing the new cell state.
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All new states will be applied simultaneously, so the order of processing the cells is irrelevant.
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## Visualisation
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The package provides a module for visualization of a 2D automaton in a pygame window.
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```
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CAWindow(cellular_automaton=StarFallAutomaton()).run()
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```
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The visual part of this module is fully decoupled and thus should be easily replaceable.
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## Examples
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The package contains three examples:
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- [simple_star_fall](https://gitlab.com/DamKoVosh/cellular_automaton/-/tree/master/examples/simple_star_fall.py)
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- [conways_game_of_life](https://gitlab.com/DamKoVosh/cellular_automaton/-/tree/master/examples/conways_game_of_life.py)
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- [creation_and_process_time_analysis](https://gitlab.com/DamKoVosh/cellular_automaton/-/tree/master/examples/times.py)
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Those example implementations should provide a good start for your own project.
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## Getting Involved
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Feel free to open pull requests, send me feature requests or even join as developer.
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There ist still quite some work to do.
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And for all others, don't hesitate to open issues when you have problems!
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## Changelog
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#### 1.0.8
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- Fixes automaton using edge cells with radius > 1 not working
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- Fixes automaton is not stopping after evolution ended
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#### 1.0.7
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- Fixes automaton not active on reactivation
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#### 1.0.6
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- Fixes reactivation not redrawing all cells
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#### 1.0.5
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- Fixes black lines in the display between some cell lines/columns
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#### 1.0.4
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- Adds active state for automaton
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- Adds reactivation method
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- Fixes cells active state
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#### 1.0.3
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- Fixes init_cell_state called twice the necessary amount
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#### 1.0.2
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- Adds set CellularAutomaton.cells capability to be able to move cell states from one Automaton to another.
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#### 1.0.1
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- Add KeyboardInterrupt suppression to CAWindow
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#### 1.0.0
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- CI was added
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- Coverage was increased
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- Change project structure
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- removed multiprocess capability
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- reduced class count
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- Improved algorithm to increase:
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- creation time by factor of ~2
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- processing speed by factor of ~15
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- Changed API
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- No separate factory anymore: Just create a CellularAutomaton(...)
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- No Rule class anymore: Subclass CellularAutomaton and override `evolve_rule` and `init_cell_state`
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- Cell color is now defined by the CAWindow `state_to_color_cb` parameter.
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- Neighborhood does not need to know the dimension anymore
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--->
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## Dependencies
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There is only a dependency for [recordclass](https://pypi.org/project/recordclass/) at the moment.
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If you want to use the CAWindow or execute the examples you will have to install
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[pygame](https://www.pygame.org/news) for visualisation.
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If you don't want to use this engine for some reason pass another draw_ending to the CAWindow.
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It has to provides the same interface as the [PygameEngine](https://gitlab.com/DamKoVosh/cellular_automaton/-/blob/master/cellular_automaton/display.py)
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## Licence
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This package is distributed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0),
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see [LICENSE.txt](https://gitlab.com/DamKoVosh/cellular_automaton/-/tree/master/LICENSE.txt) |