ChessLib Python Extension

About

This project provides an efficient chess draw generation extension for Python3. The main purpose of this project is enabling further TensorFlow AI projects and learning how to write an efficient Python3 extension (using good old C).

Usage

Install the official Python package using pip:

pip install chesslib

For implementing a chess AI, see following example applying randomly selected actions until the chess game is over. You might use this code snippet and make your AI choose more sophisticated actions instead of random ones, that's already it - as simple as that.

import chesslib
import numpy as np


def choose_action(board, drawing_side, last_draw):
    poss_actions = chesslib.GenerateDraws(board, drawing_side, last_draw, True)
    return np.random.choice(poss_actions)


def main():

    # init cache variables
    last_draw = chesslib.ChessDraw_Null
    board = chesslib.ChessBoard_StartFormation()
    drawing_side = chesslib.ChessColor_White

    # init game state
    state = chesslib.GameState_None
    game_over_states = [chesslib.GameState_Checkmate, chesslib.GameState_Tie]

    # log the start formation to console
    print('start of game')
    print(chesslib.VisualizeBoard(board))

    # continue until the game is over
    while state not in game_over_states:

        # choose an action and apply it to the board
        next_action = choose_action(board, drawing_side, last_draw)
        board = chesslib.ApplyDraw(board, next_action)

        # update the cache variables
        last_draw = next_action
        drawing_side = 1 - drawing_side # alternate between white and black side
        state = chesslib.GameState(board, last_draw)

        # log the game progress to console
        print()
        print(chesslib.VisualizeDraw(next_action), f'state={state}')
        print(chesslib.VisualizeBoard(board))

    # log the game's outcome to console
    winning_side = "black" if drawing_side == chesslib.ChessColor_White else "white"
    print('tie' if state == chesslib.GameState_Tie else f'{winning_side} player won')


if __name__ == '__main__':
    main()

Following example outlines some of the chesslib API functionality like applying / reverting draws, creating board hashes or visualizing boards / draws properly as human-readable texts:

import chesslib
import numpy as np


def main():

    # create a new chess board in start formation
    board = chesslib.ChessBoard_StartFormation()
    
    # generate all possible draws
    draws = chesslib.GenerateDraws(board, chesslib.ChessColor_White, chesslib.ChessDraw_Null, True)
    
    # apply one of the possible draws
    draw_to_apply = draws[random.randint(0, len(draws) - 1)]
    new_board = chesslib.ApplyDraw(board, draw_to_apply)
    
    # write the draw's name
    print(chesslib.VisualizeDraw(draw_to_apply))
    
    # visualize the board before / after applying the draw
    print(chesslib.VisualizeBoard(board))
    print(chesslib.VisualizeBoard(new_board))
    
    # revert the draw (just call ApplyDraw again with the new board)
    rev_board = chesslib.ApplyDraw(new_board, draw_to_apply)
    
    # get the board's 40-byte-hash and create a new board instance from the hash
    board_hash = chesslib.Board_ToHash(board)
    board_reloaded = chesslib.Board_FromHash(board_hash)
    
    # see tests/ folder for more examples


if __name__ == '__main__':
    main()

How to Develop

For a quickstart, set up your dev machine as a VM (e.g. Ubuntu 20.04 hosted by VirtualBox). After successfully creating the VM, use following commands to install all essential dev tools (git, docker, good text editor).

# install docker (e.g. Ubuntu 20.04)
sudo apt-get update && sudo apt-get install -y git docker.io
sudo usermod -aG docker $USER && reboot

# download the project's source code (if you haven't done before)
git clone https://github.com/Bonifatius94/ChessLib.Py
cd ChessLib.Py

# install the 'Visual Studio Code' text editor (optional)
sudo snap install code --classic

The commands for dev-testing the chesslib are wrapped within a Docker environment. Therefore build the 'Dockerfile-dev' file which takes your source code and performs all required CI steps (build + install + unit tests). Afterwards you may attach to the Docker image with the bash console interactively and run commands, etc.

# make a dev build using the 'Dockerfile-dev' build environment
# this runs all CI steps (build + install + unit tests)
docker build . --file Dockerfile-dev -t "chesslib-dev"

# attach to the build environment's interactive bash console (leave the session with 'exit')
docker run -it "chesslib-dev" bash

# mount a Python test script into the build environment and run it
docker run -v $PWD:/scripts "chesslib-dev" python3 /scripts/test.py

# debug the GitHub release pipeline
docker run -v $PWD/dist:/output -v $PWD:/build -it "chesslib-dev" bash

See the GitHub wiki for more details.

Copyright

This software is available under the MIT licence's terms.