The Game of Life is a cellular automaton created by John Horton Conway in 1970. It’s a simulation of a virtual world where cells are either alive or dead, and their states change based on simple rules that determine the next state based on the number of live neighbors. It’s used to demonstrate emergence and self-organization, and how complex behavior can arise from simple rules. Despite its simplicity, it remains a popular subject of study in mathematics, physics, and computer science.
In this example, the
update function takes four arguments:
frame_number is the number of the current frame,
img is a
matplotlib image object that represents the current state of the grid,
grid is a two-dimensional
numpy array that represents the state of the simulation, and
N is the size of the grid. The
update function calculates the next state of the simulation by iterating over each cell in the grid, counting the number of live neighbors, and applying the rules of the Game of Life to determine if the cell should be alive or dead in the next generation. The updated grid is then used to update the
matplotlib image, and the simulation continues until all of the frames have been generated.