But the wonderful thing about Life is the dazzling complexity that can result from a simple seed. Perhaps you’d expect its behaviour to be vaguely similar to that of the beacon. Now it is up to you to experiment with different seeds! But I can’t resist one more demonstration, using the R-pentomino seed: save ( 'beacon.gif', writer = "imagemagick" ) ArtistAnimation ( fig, ims, interval = 700, repeat_delay = 1000, blit = True ) # Optional to save the animation imshow ( universe, cmap = 'Purples' ),)) generation () # Create the animation axis ( 'off' ) ims = for i in range ( 30 ): # Add a snapshot of the universe, then move to the next generation figure () # Remove the axes for aesthetics Import matplotlib.animation as animation fig = plt. For now, they are all dead, so we initialise with np.zeros In our universe, each cell will either be a 1 (alive) or 0 (dead). Let us start with a small universe consisting of a six-by-six grid. I am using numpy arrays to represent the universe, as they are easy to initialise and manipulate. Iterate steps 3–4 for the desired number of generations.Iterate this survival function over all the cells in the universe.Determine if a given cell survives to the next iteration, based on its neighbours.This is a rough overview of our plan of attack: The massive variety and complexity of the results, often starting from simple seeds, is what makes cellular automata so interesting. Thus, we have to provide the universe with a seed, i.e a set of initial living cells. A dead cell will spring to life if it has exactly three live neighbours ( reproduction).Ĭlearly, no cells can come to life unless there are already some living cells in the universe (Aquinas’ primum movens?). it has more than three live neighbours ( overpopulation).Ģ.it has fewer than two live neighbours ( underpopulation).A living cell will survive into the next generation by default, unless: It is so named because its rules can be interpreted as a very simplistic model of living cells.įirstly, let us define the the eight grid squares around a cell as its neighbours. One of the most widely-studied cellular automata is the Game of Life, sometimes just called ‘Life’, which was discovered (or ‘invented’ if you prefer) by John Conway. The automaton then iterates forward in time, with the new state of each cell being decided by a set of rules. I will refer to this grid as the universe, though this is non-standard terminology. automata) consists of a grid of cells each cell has a state - either alive or dead. I emphasise intuitive code than performance, so it could be a useful starting point for somebody to understand the logic before implementing a more efficient version.Ī cellular automaton (pl. In this short post, I explain how to implement Conway’s Game of Life in python, using numpy arrays and matplotlib animations.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |