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Want to watch arrows move and attack each other? Then look no further than this
BattleSimulator we provide! Users familiar with
Totally Accurate Battle Simulator <https://steamcommunity.com/app/508440>__
will hopefully love this package as a lot of the basic ideas are derived from this.
.. image:: simulations/main.gif :alt: Image not found :align: center
Here are just a few things that battlesim aims to do well:
- Formulate your simulation in a few lines of code from scratch.
- Scales up to thousands (and 10s of thousands) of units
- Flexibility: unit values are taken from a data file with flexible AI options
- Performance: Just-in-time compiling (JIT) can manage thousands of units
- Visualisation: Animations can be customized to change look-and-feelbattlesim requires the following dependencies <environment.yml>__:
With the following for exporting the animation as a gif:
The following packages are not required but significantly improve the usage of this package.
If you are unfamiliar with the Jupyter project see here <https://jupyter.org/>__:
From PyPI (Legacy)
If you have working versions of the dependencies, similarly install
using `pip <https://pypi.org/project/battlesim/>`__ (version 0.3.7)::
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 RepositoryAlternatively if you are cloning this
GitHub repository <https://github.com/gregparkes/BattleSimulator>__, use::
git clone https://github.com/gregparkes/BattleSimulator
conda env create -f environment.yml
conda activate bsmNow within the bsm environment run your Jupyter notebook::
jupyter notebookRunning Tests
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 <https://www.anaconda.com/>`__.
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:
.. code-block:: python
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:
.. code-block:: python
import matplotlib.pyplot as plt
plt.rcParams["animation.html"] = "html5"
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:
.. code-block:: python
bat = bsm.Battle("datasets/starwars-clonewars.csv")
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.
This is achieved using a meta-information object called a ``Composite``, which holds
a group of units of a given type:
.. code-block:: python
armies = [
bsm.Composite("B1 battledroid", 70),
bsm.Composite("Clone Trooper", 50)
]
bat.create_army(armies)
which internally creates an efficient numpy matrix,
ready to perform the simulation. This is stored in the ``battle.M_``
object, a heterogenous ``ndarray`` element. By default, each Composite spawns
on top of each other using a gaussian distribution at (0, 0). When initialising
the Composite we can specify a new sampling using the ``Sampling`` class or
override directly:
.. code-block:: python
bat.composition_[1].pos = bsm.Sampling("normal", 10., 2.)
And now to simulate (note that the first time this executes will be painfully slow
as JIT compiles a lot of code):
.. code-block:: python
F = bat.simulate()
By default, the simulation function will make a record of
important parameters at each step and then return these
parameters as a heterogenous ``ndarray`` 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:
.. code-block:: python
bat.sim_jupyter()
The result is as follows.
.. image:: simulations/sim2.gif
:alt: Image not Found
:align: center
Here ``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.
.. image:: images/quiver1.svg
:alt: Image not found
:align: center
The rest is for you to explore, tweak and enjoy watching arrows
move towards each other and kill each other. We have
extensive `examples <https://github.com/gregparkes/BattleSimulator/tree/master/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 ``ndarray`` of frames, but rather the
number of units from each team left standing at each iteration:
.. code-block:: python
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 v0.3.6
-------------
There are a number of exciting changes in this `current update <CHANGELOG.md>`__, 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.
Teaching series
---------------
As well as a fully-fledged package simulator, you can find
`teaching material <https://github.com/gregparkes/BattleSimulator/tree/master/teaching>`__ 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:
1. Basics, including importing the dataset, the ``Unit`` class, basic simulation
2. Improving the ``Unit`` class and simulation early-stopping for performance.
3. 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 *battlesim/legacy.py* document.
Future plans
------------
* Include AI-based behavior that makes use of height (to occupy hills)
* Develop 'defensive' AI.
Ensure that any use of this material is appropriately referenced
and in compliance with the `license <LICENSE.txt>`__.