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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).
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()
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.
This software is available under the MIT licence's terms.
FAQs
Python3 library for efficient chess draw-gen functions
We found that chesslib demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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