Historical battle simulation package for Python

Related tags

Game Developmentsimulationmodelmodelingtacticsstrategymodellingmilitarycombatwarwargamingstrategy-gameoperations-researchhistoricalbattleattritionmilitary-historywar-gamesstrategy-gameslanchester
Overview

Jomini v0.1.4

Jomini GitHub license Maintenance

ForTheBadge built-with-love

Jomini creates military simulations by using mathematical combat models. Designed to be helpful for game developers, students, history enthusiasts and -to some extent- scientists. You can mail me at [email protected] if you want to contribute.

To download: https://pypi.org/project/Jomini

Documentation will be available in the next update.

Lanchester Models 101

This package uses the combat models developed by Frederick William Lanchester, a.k.a one of the founding fathers of Operations Research.

  • Lanchester Models are deterministic, which means the model will always yield the same result for the same input parameters.
  • Lanchester Models view battles as an attrition model, therefore manuevers and sudden changes during the battle can not be represented.
  • You might need to do some manual fine-tuning if you are not able to get quality parameters (rho, beta, engagement_width) from a data set.
  • Despite the downsides, even the primitive models developed by Lanchester himself works wonders with the right parameters.

Code Sample: Lanchester's Linear Law

  • The Linear Law is based on force concentration
  • Good for modelling melee battles and unaimed fire (artillery, arquebus, handcannon etc.)
from jomini.Lanchester import Battle, LinearLaw

# Re-creating the Battle of Cerignola (AD 1503)
# In the actual battle, Spanish(red) lost 500 men while the French(blue) lost 4000 men
# Parameters rho, beta, engagement_width and time are manually fine_tuned
b = Battle(red=6_300, blue=9_000, rho=0.0800, beta=0.0100)
L = LinearLaw(b, engagement_width=100)
print(L.get_casualty_rates()) # Returns casualty rates 
print(L.get_casualties(time=7))
print(L.get_remaining(time=7))
print(L.simulate_battle(time=7))

Linear Law

Code Sample: Visiting models used in Jomini

  • Square Law: Given equal power coefficients, the fighting power is proportional to the square of army size.
    • Good for modelling aimed fire (e.g Napoleonic line-battles)
  • Logarithmic Law: Basically square law at a larger scale, used by Weiss to model the American Civil War
    • Good for modelling tank combat as well
from jomini.Lanchester import Battle, LinearLaw, SquareLaw, LogarithmicLaw, GeneralLaw

# Simulating a fictitious battle with each of the laws
b = Battle(red=20_000, blue=30_000, rho=0.0150, beta=0.0120)
Linear = LinearLaw(b, engagement_width=500)
Square = SquareLaw(b)
Log = LogarithmicLaw(b)
Generalized = GeneralLaw(b, engagement_width=500, p=0.450, q=0.600)

# If time is not specified, the battle goes on until one side is annihilated.
print(Linear.simulate_battle() + "\n")
print(Square.simulate_battle() + "\n")
print(Log.simulate_battle() + "\n")
print(Generalized.simulate_battle())

Model-1 Model-2

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Comments
  • Added more errors to fix issues

    Added more errors to fix issues

    Added TypeErrors where a side has 0 soldiers in battle Added ValueError for the following case in pseudocode: (rate_red > size_red and rate_blue > size_blue) Closes #3 Closes #4

    opened by umitkaanusta 0
Releases(v0.1.4)
Owner
Ümit Kaan Usta
Marmara University - CSE
Ümit Kaan Usta
GitHub Repository
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