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wolf-sheep-grass

https://github.com/knappa/wolf-sheep-grass

Science Score: 44.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: knappa
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 45.9 KB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created about 2 years ago · Last pushed 12 months ago
Metadata Files
Readme License Citation Authors

README.md

The Wolf-Sheep-Grass model

This is a Python reimplementation of NetLogo's Wolf-Sheep-Grass model https://ccl.northwestern.edu/netlogo/models/WolfSheepPredation

Install via commandline pip install -e . within this directory. (The "editable" -e flag may be omitted if you do not plan on changing the model.)

Typical usage can be viewed in wolves-sheep-grass.py. e.g. the model can be instantiated with parameters: ```python from wolfsheepgrass import WolfSheepGrassModel

model = WolfSheepGrassModel( GRIDWIDTH=..., GRIDHEIGHT=..., INITWOLVES=..., WOLFGAINFROMFOOD=..., WOLFREPRODUCE=..., INITSHEEP=..., SHEEPGAINFROMFOOD=..., SHEEPREPRODUCE=..., INITGRASSPROPORTION=..., GRASSREGROWTHTIME=..., ) `` The model is advanced forward in time usingmodel.timestep(). Classic usage will findmodel.numwolves,model.numsheep, andsum(model.grass)interesting. More advanced usage might look atmodel.grassdirectly which is a 2d boolean array indicating grass presence or one of the agent arrays: *model.wolfpos/model.sheeppos *model.wolfdir/model.sheepdir *model.wolfenergy/model.sheep_energy`

each of which should be masked by the boolean arrays model.wolf_alive or model.sheep_alive. That is, arrays such as model.wolf_pos are fixed length, (mostly) independent of the number of wolves and contain meaningless entries. To get only the meaningful entries, use model.wolf_pos[model.wolf_alive].

Owner

  • Name: Adam Knapp
  • Login: knappa
  • Kind: user
  • Location: Washington, DC
  • Company: University of Florida

A topologist

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: wolf-sheep-grass
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: "A. C."
    family-names: Knapp
    email: adam.knapp@medicine.ufl.edu
    affiliation: University of Florida
    orcid: 'https://orcid.org/0000-0002-5719-6003'
identifiers:
  - type: url
    value: 'https://github.com/knappa/wolf-sheep-grass'
    description: github repository for the WSG model
repository-code: 'https://github.com/knappa/wolf-sheep-grass'
abstract: >-
  Python reïmplementation of Netlogo's Wolf-sheep-grass
  model of predator-prey dynamics.
license: MIT

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Dependencies

pyproject.toml pypi
  • attrs *
  • numpy *