https://github.com/hildobby/complex_system
Expansion on the Bak-Sneppen model onto 2 dimensions and 3 dimensions lattices and analysis of the resulting behaviours
https://github.com/hildobby/complex_system
bak-sneppen complex-systems modeling python3
Last synced: over 1 year ago
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Expansion on the Bak-Sneppen model onto 2 dimensions and 3 dimensions lattices and analysis of the resulting behaviours
- Host: GitHub
- URL: https://github.com/hildobby/complex_system
- Owner: hildobby
- Created: 2020-06-13T11:31:35.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-06-27T18:08:45.000Z (almost 6 years ago)
- Last Synced: 2025-01-21T11:14:55.527Z (over 1 year ago)
- Topics: bak-sneppen, complex-systems, modeling, python3
- Language: HTML
- Homepage:
- Size: 18.9 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Expanding and exploring the Bak-Sneppen model written in Python 3+
=========================================
2 dimensions and 3 dimensions Bak-Snappen model, evaluation of the resulting model behaviour and effect of various parameters.
By: [Hildebert Moulié](https://github.com/hildobby), [Philippe Nicolau](https://github.com/PNicolau96), [Louis Weyland](https://github.com/LouisWW) & [Binjie Zhou](https://github.com/binjiezhou).
## Project Plan
1. Bring the 1 dimension Bak-Snappen model to 2 dimensions and 3 dimensions
2. Evaluate different results from the model:
* Avalanche time and compare it between several models
* Mutation distance
* Cluster sizes
* Evolutionary time
3. Test the data with the [powerlaw](https://pypi.org/project/powerlaw/) package
## Overview of some of the results
### Fitness over time
_itr%3D2000_rep%3D10_std%3D0.3.png)
### Impact of percentage of empty node
### Cluster size distribution for different grid sizes
## Working with the repository
In order to locally gather the required packages, the following command can be called:
```
pip3 install -r requirements.txt
```
The code for the model is located in `src/lattice.py` and the graphs can be generated by calling the different functions in `src/main.py`. The file `results_presentation.ipynb` that was used to create the presentation of this project shows how the result figures as shown above can be generated.
Running the `./lattice.py` function in the bash shell will create a Bak-Sneppen model with a grid size of 20X20 and will plot the average fitness, avalanche time, the cluster size distribution and a visualisation of the grid itself.
In `main.py`, various statistical analysis are generated. The various function can be called using the following command where `itr` represent the number of steps in the Bak-Sneppen Model:
```
./main.py -func function_example -itr=200
```
For further details please run the following command which shows which function can be called and what other paramters can be tuned from the command line:
```
./main.py --help
```
Generating the presentation from the `results_presentation.ipynb` Jupyter notebook
```
jupyter nbconvert results_presentation.ipynb --to slides --post serve
```
## References
* Alstott, J., Bullmore, E., & Plenz, D. (2014). powerlaw: a Python package for analysis of heavy-tailed distributions. PloS one, 9(1), e85777.
* Bak, P., & Sneppen, K. (1993). Punctuated equilibrium and criticality in a simple model of evolution. Physical review letters, 71(24), 4083.
* Fraiman, D. (2018). Bak-Sneppen model: Local equilibrium and critical value. Physical Review E, 97(4), 042123.
* Paczuski, M., Maslov, S., & Bak, P. (1996). Avalanche dynamics in evolution, growth, and depinning models. Physical Review E, 53(1), 414.