| Name | piegy JSON |
| Version |
2.3.10
JSON |
| download |
| home_page | None |
| Summary | Payoff-Driven Stochastic Spatial Model for Evolutionary Game Theory |
| upload_time | 2025-07-18 18:27:03 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | BSD 3-Clause License
Copyright (c) 2025, Chenning Xu
Redistribution and use in source and binary forms, with or without
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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|
| keywords |
game theory
evolutionary game theory
spatial model
stochastic model
payoff driven
|
| VCS |
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# piegy
The package full name is: Payoff-Driven Stochastic Spatial Model for Evolutionary Game Theory. "pi" refers to "payoff, and "egy" is taken from "Evolutionary Game Theory".
Provides a stochastic spatial model for simulating the interaction and evolution of two species in either 1D or 2D space, as well as analytic tools.
## Installation
To install *piegy*, run the following in terminal:
```bash
pip install piegy
```
## Documentation and Source
See source code at: [piegy GitHub-repo](https://github.com/Chenning04/piegy.git).
The *piegy* documentation at: [piegy Documentation](https://piegy.readthedocs.io/en/).
## How the Model Works
Our model can be summarized as "classical evolutionary game theory endowed with spatial structure and payoff-driven migration rules". Consider two species, predators and preys (denoted by *U* and *V*), in a rectangular region. We divide the region into N by M patches and simulate their interaction within a patch by classical game theory (i.e., payoff matrices and carrying capacity). Interactions across patches are simulated by payoff-driven migration rules. An individual migrates to a neighboring patch with probability weighted by payoff in the neighbors.
We use the Gillepie algorithm as the fundamental event-selection algorithm. At each time step, one event is selected and let happen; and the step size is continuous, dependent on the current state in the space. Data are recorded every some specified time interval.
## Analytic Tools
The *piegy* package also provides a wide range of analytic and supportive tools alongside the main model, such as plotting, numerical tools, data saving & reading, etc. We also provide the *piegy.videos* module for more direct visualizations such as how population distribution change over time.
## C Core
From version 2 on, the *piegy* simulations are now equipped with a C core, which makes it significantly faster than previous versions.
## Examples
To get started, simply get our demo model and run simulation:
```python
from piegy import simulation, figures
import matplotlib.pyplot as plt
mod = simulation.demo_model()
simulation.run(mod)
fig1, ax1 = plt.subplots()
figures.UV_dyna(mod, ax1)
fig2, ax2 = plt.subplots(1, 2, figsize = (12.8, 4.8))
figures.UV_heatmap(mod, ax2[0], ax2[1])
```
The figures reveal population dynamics and steady state population distribution.
## Acknowledgments
- Thanks Professor Daniel Cooney at University of Illinois Urbana-Champaign. This package is developed alongside a project with Prof. Cooney and received enormous help from him.
- Special thanks to the open-source community for making this package possible.
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"description": "# piegy\n\nThe package full name is: Payoff-Driven Stochastic Spatial Model for Evolutionary Game Theory. \"pi\" refers to \"payoff, and \"egy\" is taken from \"Evolutionary Game Theory\".\n\nProvides a stochastic spatial model for simulating the interaction and evolution of two species in either 1D or 2D space, as well as analytic tools.\n\n## Installation\n\nTo install *piegy*, run the following in terminal:\n\n```bash\npip install piegy\n```\n\n## Documentation and Source\n\nSee source code at: [piegy GitHub-repo](https://github.com/Chenning04/piegy.git). \nThe *piegy* documentation at: [piegy Documentation](https://piegy.readthedocs.io/en/). \n\n## How the Model Works\n\nOur model can be summarized as \"classical evolutionary game theory endowed with spatial structure and payoff-driven migration rules\". Consider two species, predators and preys (denoted by *U* and *V*), in a rectangular region. We divide the region into N by M patches and simulate their interaction within a patch by classical game theory (i.e., payoff matrices and carrying capacity). Interactions across patches are simulated by payoff-driven migration rules. An individual migrates to a neighboring patch with probability weighted by payoff in the neighbors.\n\nWe use the Gillepie algorithm as the fundamental event-selection algorithm. At each time step, one event is selected and let happen; and the step size is continuous, dependent on the current state in the space. Data are recorded every some specified time interval.\n\n## Analytic Tools\n\nThe *piegy* package also provides a wide range of analytic and supportive tools alongside the main model, such as plotting, numerical tools, data saving & reading, etc. We also provide the *piegy.videos* module for more direct visualizations such as how population distribution change over time.\n\n## C Core\n\nFrom version 2 on, the *piegy* simulations are now equipped with a C core, which makes it significantly faster than previous versions.\n\n## Examples\n\nTo get started, simply get our demo model and run simulation:\n\n```python\nfrom piegy import simulation, figures\nimport matplotlib.pyplot as plt\n\nmod = simulation.demo_model()\nsimulation.run(mod)\n\nfig1, ax1 = plt.subplots()\nfigures.UV_dyna(mod, ax1)\nfig2, ax2 = plt.subplots(1, 2, figsize = (12.8, 4.8))\nfigures.UV_heatmap(mod, ax2[0], ax2[1])\n```\n\nThe figures reveal population dynamics and steady state population distribution.\n\n\n## Acknowledgments\n\n- Thanks Professor Daniel Cooney at University of Illinois Urbana-Champaign. This package is developed alongside a project with Prof. Cooney and received enormous help from him.\n- Special thanks to the open-source community for making this package possible.\n\n",
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