A python package for simulating battles and visualizing them in animation
battlesim: Modelling and animating simulated battles between units in Python.
The aim of this side project is to become familiar with Python classes and with primitive forms of animation and simulating environments. We map units onto a 2D plane and run simple simulations that involve them moving towards an enemy unit and attacking it. Rounds finish when one team has completely wiped out the other side, or we have reached the maximum number of timesteps.
Users familiar with Totally Accurate Battle Simulator will hopefully love this package as a lot of the basic ideas are derived from this.
Current version: 0.3.6
The code for the primary engine is found in
battlesim/, and implementations/examples are found in the Jupyter notebooks. Animations should display properly in these notebooks.
battlesim requires the following dependencies:
- python (>=3.5)
- numpy (>=1.11.0)
- scipy (>=1.3)
- pandas (>=0.25.1)
- matplotlib (>=3.1.1)
- numba (>=0.45)
With the following for exporting the animation as a gif:
- ffmpeg (>=4.2)
The following packages are not required but significantly improve the usage of this package. If you are unfamiliar with the Jupyter project see here:
- jupyter (1.0.0)
If you have working versions of the dependencies, similarly install using pip (version 0.3.5):
pip install battlesim
We recommend updating the dependencies yourself using conda rather than through pip because conda manages the dependencies better, but pip will do it for you. See the
environment.yml file for dependencies.
From Cloning the GitHub Repository
Alternatively if you are cloning this GitHub repository, use:
git clone https://github.com/gregparkes/BattleSimulator conda env create -f environment.yml conda activate bsm
Now within the
bsm environment run your Jupyter notebook:
You will need the following for testing (soft requirement):
- PyTest (5.1.2)
Then perform the following within a console:
cd tests/ pytest -v
How to use: The Basics
Firstly, check the requirements for using this simulator, of which most come with the Anaconda distribution. In addition you will need the ffmpeg video conversion package to generate the simulations as animations.
Secondly, you will need to import the package as:
>>> import battlesim as bsm
We recommend using
bsm as a shorthand to reduce the amount of writing out you have to do. If you're using Jupyter notebook we also recommend:
>>> import matplotlib.pyplot as plt >>> plt.rcParams["animation.html"] = "html5" >>> %matplotlib inline
The second line is important when you come to plotting the animations, as there are a number of issues with using it. All of the heavy lifting comes in the
bsm.Battle object that provides a neat interface for all of the operations you would like to conduct:
>>> import battlesim as bsm >>> battle = bsm.Battle("datasets/starwars-clonewars.csv") >>> battle bsm.Battle(init=False)
You can see that we have specified a 'dataset' from which all of the unit roster can be drawn from; for specifics of how this file should be oriented, see the documentation. We then need to specify units to create to form an army. For example, in this Star Wars example, we could specify a play-off between Clone troopers and B1 battledroids:
>>> battle.create_army([("B1 battledroid", 70), ("Clone Trooper", 50)]) bsm.Battle(init=True, n_armies=2, simulated=False)
Here we call the
create_army function, which internally creates an efficient
numpy matrix, ready to perform the simulation. This is stored in the
battle.M_ object, a heterogenous
ndarray element. From here, we might also want to specify the locations of our different blobs, as by default they will be sitting on top of each other at (0, 0).
>>> battle.apply_position([dict(name="normal", x_loc=10), dict(name="normal", loc=0)]) bsm.Battle(init=True, n_armies=2, simulated=False)
Here the first element of each tuple represents the mean of the gaussian distribution, and the second element refers to the variance (or spread). From here, all we need to do now is simulate this:
>>> F = battle.simulate()
By default, the simulation function will make a record of important parameters at each step and then return these parameters as a
pandas.DataFrame at the end in long form (with a cached element called
sim_). In addition, because you want to see what's going on - we can animate the frames using this convenience method within the battle object:
sim_jupyter treats each unit object as a quiver arrow in 2-d space (position and direction facing it's enemy). The targets should move towards each other and attempt to kill each other. Dead units are represented as crosses 'x' on the map.
The rest is for you to explore, tweak and enjoy watching arrows move towards each other and kill each other. We have extensive examples to look at within this repository.
One step further: Repeated runs
If you're interested in seeing how each team fare over multiple runs (to eliminate random biases), then
bsm.Battle objects once defined, contain a
simulate_k() method, where
k specifies the number of runs you wish to complete. Unlike
simulate() by itself, it does not return a
pandas.DataFrame of frames, but rather the number of units from each team left standing at each iteration.
>>> runs = battle.simulate_k(k=40)
This is the beginning of creating an interface similar to Machine Learning, whereby the outcome can be a classification (team) or regression (number of units surviving) target, and the unit compositions, aspects of the engine etc., can be inputs.
New in 0.3.6
There are a number of exciting changes in this current update, including:
- Introduction of Terrains. This is a major expansion giving 3D pseudodepth to animated battles. Depth now influences movement speed of units, with terrain penalties applied (up to 50%) on higher hills. They also increase range for units on hills and increase damage when firing downhill on an enemy unit.
- Introduction of armor. Armor acts as another health buffer to protect units from harm.
Further changes can be found in the Changelog.
As well as a fully-fledged package simulator, you can find teaching material in Jupyter notebook form within the
teaching/ subfolder, that takes users through the development process of this package, compares and contrasts Object-Oriented (OO) implementations to numpy-esque implementations, their performance, plotting, animations and more. We hope you find this material interesting and will aid as you use the package and possibly develop packages of your own in the future.
Material covered so far:
- Basics, including importing the dataset, the
Unitclass, basic simulation
- Improving the
Unitclass and simulation early-stopping for performance.
- Plotting simulations and performance-driven development
This is still in active development retracing the steps of the project. All legacy functions associated with this can be found in the
- Include AI-based behavior that makes use of height (to occupy hills)
- Develop 'defensive' AI.
- Build objects in the terrain.
Ensure that any use of this material is appropriately referenced and in compliance with the license.
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