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).

How to Build / Test

The commands for building the Python3 extension module and testing it properly are wrapped as a Docker image. Therefore just build the Dockerfile and use the image as base for your Python3 application importing the module.

Alternatively you could run the commands from the Dockerfile onto an Ubuntu-like machine and build the binaries on your own. I'm using the default distutils tools, so making your own builds should not be too hard to achieve.

# install docker (e.g. Ubuntu 18.04)
sudo apt-get update && sudo apt-get install -y git docker.io
sudo usermod -aG docker $USER && reboot

# download the project's source code
git clone https://github.com/Bonifatius94/ChessLib.Py
cd ChessLib.Py

# build the chesslib Python3 module using the commands from the Dockerfile
# this also includes running the unit tests (Docker build fails if tests don't pass)
docker build . -t "chesslib-python3:1.0"

# run a test command using the chesslib
docker run "chesslib-python3:1.0" python3 test.py

Usage

Install the official Python package using pip:

pip install chesslib

The following sample outlines the usage of the ChessLib:

import chesslib
import numpy as np
import random


test():

    # 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 ChessLib/test.py file for more examples

Roadmap

Following features are planned for the near future:

change Board_ToHash() / Board_FromHash() exchange format to Python type 'bytes' or 'bytearray' for better compatibility
improve code coverage of unit tests
implement CI/CD GitHub pipelines for DockerHub and PyPi releases
fix all memory leaks of the lib
think of performence testing / performance improvements (especially draw-gen)

Following optional / fancy improvements are to be considered:

add fancy travis build labels, beautify README
add API documentation compatible with common Python linters

Copyright

You may use this project under the MIT licence's conditions.

Metadata

Release files for chesslib 1.0.329484926

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for chesslib 1.0.329484926
File	Interpreter	ABI	Platform	Reset
chesslib-1.0.329484926-cp36-cp36m-manylinux1_x86_64.whl 50.2 kB Nov 17, 2020	CPython 3.6	CPython 3.6 pymalloc	Linux glibc 2.5+ x86-64	Details

Release files / chesslib-1.0.329484926-cp36-cp36m-manylinux1_x86_64.whl

Download URL	chesslib-1.0.329484926-cp36-cp36m-manylinux1_x86_64.whl
Size	50.2 kB
Tags	CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`73442a0e956f8f6fd13259b42cb852b7dda8658529b7367c62538321d730f661`
BLAKE2b-256 checksum How to use checksums	`88e21d7701c3ac48cebc15d81bbc71888ca56f1262f345d8754459acd467c1ae`
Upload date	Nov 17, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/49.2.1 requests-toolbelt/0.9.1 tqdm/4.52.0 CPython/3.8.6`