nashpy 0.0.43
A library with algorithms on 2 player games.
Requires Python: >=3.10

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# Nashpy: a python library for 2 player games.

Nashpy is:

- An [excellently documented library](https://nashpy.readthedocs.io/en/stable/):
  - The [Nashpy game theory text book](https://nashpy.readthedocs.io/en/stable/text-book/index.html) aims
    to be a course text on the background theory.
  - The [contributor
    documentation](https://nashpy.readthedocs.io/en/stable/contributing/index.html)
    aims to be a text on research software development and help **first time** open
    source software contributions.
- A [state of the art developed code
  base](https://nashpy.readthedocs.io/en/stable/contributing/index.html) which
  aims to use the best of available tools to ensure the code is correct,
  readable and robust.
- Feature rich, the following are implemented:
  - Support enumeration [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/solve-with-support-enumeration.html) - [Theory docs 📘](https://nashpy.readthedocs.io/en/stable/text-book/support-enumeration.html)
  - Vertex enumeration [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/solve-with-vertex-enumeration.html) - [Theory docs 📘](https://nashpy.readthedocs.io/en/stable/text-book/vertex-enumeration.html)
  - Lemke-Howson algorithm [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/solve-with-lemke-howson.html) - [Theory docs 📘](https://nashpy.readthedocs.io/en/stable/text-book/lemke-howson.html)
  - Fictitious play [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/use-fictitious-play.html) - [Theory docs 📘](https://nashpy.readthedocs.io/en/stable/text-book/fictitious-play.html)
  - Stochastic fictitious play [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/use-stochastic-fictitious-play.html) - [Theory docs 📘](https://nashpy.readthedocs.io/en/stable/text-book/stochastic-fictitious-play.html)
  - Replicator dynamics [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/use-replicator-dynamics.html) - [Theory docs 📘](https://nashpy.readthedocs.io/en/stable/text-book/replicator-dynamics.html)
  - Replicator-mutation dynamics [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/use-replicator-dynamics-with-mutation.html) - [Theory docs 📘](https://nashpy.readthedocs.io/en/stable/text-book/replicator-dynamics.html#the-replicator-mutation-dynamics-equation)
  - Asymmetric replicator dynamics [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/use-asymmetric-replicator-dynamics.html) - [Theory docs 📘](https://nashpy.readthedocs.io/en/stable/text-book/asymmetric-replicator-dynamics.html)
  - Moran processes [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/use-moran-processes.html)
  - Generate games from repeated games [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/obtain-a-repeated-game.html) - [Theory docs 📘](https://nashpy.readthedocs.io/en/stable/text-book/repeated-games.html)
  - Moran processes on interaction graphs [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/use-moran-process-on-interaction-graph.html)
  - Moran processes on replication graphs [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/use-moran-process-on-replication-graph.html)
  - Introspection dynamics [How to docs 🐍](https://nashpy.readthedocs.io/en/stable/how-to/use-introspection-dynamics.html) - [Theory docs 📘](https://nashpy.readthedocs.io/en/stable/text-book/introspection-dynamics.html)

## Documentation

Full documentation is available here: http://nashpy.readthedocs.io/

## Installation

```bash
$ python -m pip install nashpy
```

To install Nashpy on Fedora, use:

```sh
$ dnf install python3-nashpy
```

## Usage

<!--alex ignore bi-->

Create bi matrix games by passing two 2 dimensional arrays/lists:

```python
>>> import nashpy as nash
>>> A = [[1, 2], [3, 0]]
>>> B = [[0, 2], [3, 1]]
>>> game = nash.Game(A, B)
>>> for eq in game.support_enumeration():
...     print(eq)
(array([1., 0.]), array([0., 1.]))
(array([0., 1.]), array([1., 0.]))
(array([0.5, 0.5]), array([0.5, 0.5]))
>>> game[[0, 1], [1, 0]]
array([3, 3])

```

## Other game theoretic software

- [Gambit](http://www.gambit-project.org/) is a library with a python api and
  support for more algorithms and more than 2 player games.
- [Game theory explorer](http://gte.csc.liv.ac.uk/index/) a web interface to
  gambit useful for teaching.
- [Axelrod](http://axelrod.readthedocs.io/en/stable/) a research library aimed
  at the study of the Iterated Prisoners dilemma

## Development

Clone the repository and create a virtual environment:

```bash
$ git clone https://github.com/drvinceknight/nashpy.git
$ cd nashpy
$ python -m venv env

```

Activate the virtual environment and install [`tox`](https://tox.readthedocs.io/en/latest/):

```bash
$ source env/bin/activate
$ python -m pip install tox

```

Make modifications.

To run the tests:

```bash
$ python -m tox

```

To build the documentation. First install the software which also installs the
documentation build requirements.

```bash
$ python -m pip install flit
$ python -m flit install --symlink
```

Then:

```bash
$ cd docs
$ make html
```

Full contribution documentation is available at
https://nashpy.readthedocs.io/en/latest/contributing/index.html

Pull requests are welcome.

## Code of conduct

In the interest of fostering an open and welcoming environment, all
contributors, maintainers and users are expected to abide by the Python code of
conduct: https://www.python.org/psf/codeofconduct/