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Game Of Life

Conway's Game of Life implementation using convolution. Based on PyQt5.

Overview

The Game of Life was invented in 1970 by the British mathematician John Horton Conway. Conway developed an interest in a problem which was made evident in the 1940’s by mathematician John von Neumann, who aimed to find a hypothetical machine that had the ability to create copies of itself and was successful when he discovered a mathematical model for such a machine with very complicated rules on a rectangular grid. Thus, the Game of Life was Conway’s way of simplifying von Neumann’s ideas. It is the best-known example of a cellular automaton which is any system in which rules are applied to cells and their neighbors in a regular grid. Martin Gardner popularized the Game of Life by writing two articles for his column “Mathematical Games” in the journal Scientific American in 1970 and 1971. In this programming assignment has been implemented the Game of Life, along with a complete and polished Graphical User Interface to control the simulation.

Rules of the Game

The game is played on a two-dimensional grid (or board). Each grid location is either empty or populated by a single cell. A location’s neighbors are any cells in the surrounding eight adjacent locations. The simulation of starts from an initial state of populated locations and then progresses through time. The evolution of the board state is governed by a few simple rules:

  1. Each populated location with one or zero neighbors dies (from loneliness).
  2. Each populated location with four or more neighbors dies (from overpopulation).
  3. Each populated location with two or three neighbors survives.
  4. Each unpopulated location that becomes populated if it has exactly three populated neighbors.
  5. All updates are performed simultaneously in parallel.

This figure illustrates the rules for cell death, survival, and birth:

Getting started

Requirements

The application has been build and tested with:

  • python 3.8
  • PyQt5 5.9.2
  • scipy 1.7.1
  • numpy 1.21.2
  • qimage2ndarray 1.8.3

Run the Application

  • Clone this repository
  • Navigate inside the root directory of the project
  • Install all the dependencies:
    • with Anaconda: conda install requirements.txt
    • with Pip: pip install -r requirements.txt
    • Use your IDE, it should automatically detect the requirements.txt file
  • Run the application:
    • From command line: python3 AppLauncher.py
    • From IDE: Run AppLauncher.py using your IDE running command

Features

  • Start/pause/clear: The GUI supports controls that allow the user to start and pause the simulation, and clear the current state of the board.
  • Variable framerate: The GUI supports a control that allows the user to select the framerate at which the simulation is run and animated.
  • Drawing/editing of state: The GUI allows the user to draw and edit the state of the board (i.e. fill in or empty oyt occupied locations) with the mouse. Left click allows drawing alive cells, Right click allows delete filled cells. This feature is available to the user whenever the simulation is paused or running, allowing the user to edit the current state in realtime.
  • Loading of initial state: The GUI provides some classic examples of Game of Life Patterns that the user can play with. In addition to this, the GUI supports the loading of a serialized version of the board state. After the loading of the file you can find the new pattern listed in the combo box list. The file format accepted is RLE. You can find many patterns in RLE format here. Maximum size pattern allowed is 64x128. It's also possible to put RLE files in the 'model/patterns' folder of this project to find the respective patterns in the combo box at the starting of the application.
  • Zooming and panning of board: The GUI allows the user to select the grid size (zoom) and which part of the grid is currently viewed (panning). Holding down the scroll button allows the panning of the board according to the movements of the mouse.
  • Cell history: The GUI keeps track of how long each cell has been alive from the activation of the history mode. It's shown visually by changing the color from light blue (newborn) to dark blue (ancient).

Gallery