ChessGame

Library for running chess simulations. Allows customizable AI including random choice and minimax (with variable depth).

Getting Started

Install with pip

$pip install ChessGame

Or clone repo using git.

$git clone https://github.com/pelletier2017/ChessGame.git

Example Usage with 2 players

import chess.player as player
from chess.game import ChessGame

p1 = player.Player()
p2 = player.Player()

# setup game
game = ChessGame(p1, p2, first_move=1)

# useful attributes
curr_player = game.player_turn
player1_pieces = game.p1_pieces
player2_pieces = game.p2_pieces
all_pieces = game.pieces
moves = game.possible_moves

# performs moves in game
game.do_move("b2 b4")

p2_moves = game.possible_moves
game.do_move("c7 c6")

Example with two AI players

import chess.player as player
from chess.game import ChessGame

p1 = player.RandomComputer()
p2 = player.BasicMinimax()
game = ChessGame(p1, p2)
game.play()

Prerequisites

Dependencies are found in requirements.txt

$pip install -r requirements.txt

Running the tests

Tests are discovered using tox. It will take a while the first time to build virtual envs to run tests.

$tox

Travis CI will run tests automatically when changes are pushed to github.

travis-ci.org/pelletier2017/ChessGame

And coding style tests

Landscape evaluates code quality including code style. Project goal is to stay above 95%.

landscape.io/github/pelletier2017/ChessGame

Coveralls evaluates code coverage. Project goal is to stay above 80%.

coveralls.io/github/pelletier2017/ChessGame

Built With

• Travis CI - Continuous Integration
• Coveralls - Test Coverage

Contributing

Contributing guide coming later.

License

This project is licensed under the MIT License - see the LICENSE.md file for details