Metadata-Version: 2.1
Name: chesslib
Version: 1.0.329340798
Summary: Python3 library for efficient chess draw-gen functions
Home-page: https://github.com/Bonifatius94/ChessLib.Py
Author: Marco Tröster
Author-email: marco@troester-gmbh.de
License: UNKNOWN
Description: 
        # 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.
        
        ```sh
        # 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
        The following sample outlines the usage of the ChessLib:
        ```py
        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.
        
Platform: UNKNOWN
Requires-Python: >=3.0.0
Description-Content-Type: text/markdown
