Add the original Conway rules.

Use it as the demo.
Add the size as an argument option of the script.
Beautify.
This commit is contained in:
Johann Dreo 2014-12-24 15:42:15 +01:00
commit 1e34699004

99
life.py
View file

@ -6,32 +6,76 @@ import random
def count( node, states, board, graph ):
"""Count the number of neighbours in each given states, in a single pass."""
nb = {s:0 for s in states}
for neighbor in graph[node]:
for state in states:
if board[neighbor] == state:
nb[state] += 1
# This is the max size of the neighborhood on a rhomb Penrose tiling (P2)
assert( all(nb[s] <= 11 for s in states) )
return nb
class Goucher:
class Rule:
"""The template to create a rule for a game of life.
A rule is just a set of states and a function to compute the state of a given node,
given the current board states and a neighborhood represented by an adjacency graph."""
class State:
default = 0
# Available states, the first one should be the default "empty" (or "dead") one.
states = [State.default]
def __call__(self, node, board, graph ):
raise NotImplemented
class Conway(Rule):
"""The original rules for Conway's game of life on square grid."""
class State:
dead = 0
live = 1
states = [State.dead, State.live]
def __call__(self, node, board, graph ):
# "a" is just a shortcut.
a = self.State()
next = a.dead
nb = count( node, [a.live], board, graph )
if board[node] is a.dead:
if nb[a.live] == 3: # reproduction
next = a.live
else:
assert(board[node] is a.live)
if nb[a.live] < 2: # under-population
next = a.dead
elif nb[a.live] > 3: # over-population
next = a.dead
else:
assert( 2 <= nb[a.live] <= 3 )
next = a.live
return next
class Goucher(Rule):
"""This is the Goucher 4-states rule.
It permits gliders on Penrose tiling.
From: Adam P. Goucher, "Gliders in cellular automata on Penrose tilings", J. Cellular Automata, 2012"""
class State: # Should be an Enum in py3k
ground = 0
head = 1
tail = 2
wing = 3
# Available states, the first one is the default "empty" (or "dead") one.
states = [ State.ground, State.head, State.tail, State.wing ]
def __call__(self, node, current, graph ):
"""This is the Goucher 4-states rule.
From: Adam P. Goucher, "Gliders in cellular automata on Penrose tilings", J. Cellular Automata, 2012
Summarized as:
"""Summarized as:
------------------------------------------------------
| Current state | Neighbour condition | Next state |
------------------------------------------------------
@ -51,7 +95,11 @@ class Goucher:
if current[node] is a.ground:
# Count the number of neighbors of each state in one pass.
nb = count( node, [a.head,a.tail,a.wing], current, graph )
stated = [a.head,a.tail,a.wing]
nb = count( node, stated, current, graph )
# This is the max size of the neighborhood on a rhomb Penrose tiling (P2)
assert( all(nb[s] <= 11 for s in stated) )
if nb[a.head] >= 1 and nb[a.tail] >= 1:
next = a.wing
elif nb[a.head] >= 1 and nb[a.wing] >= 3:
@ -60,6 +108,8 @@ class Goucher:
elif current[node] is a.head:
# It is of no use to compute the number of heads and tails if the current state is not ground.
nb = count( node, [a.wing], current, graph )
assert( all(nb[s] <= 11 for s in [a.wing]) )
if nb[a.wing] >= 1:
next = a.tail
else:
@ -103,29 +153,38 @@ def step( current, graph, rule ):
return next
def play( board, graph, nb_gen, rule ):
def play( board, graph, rule, nb_gen ):
for i in range(nb_gen):
board = step( board, graph, rule )
if __name__ == "__main__":
import sys
# Simple demo on a square grid torus.
graph = {}
size = 10
size = 5
if len(sys.argv) >= 2:
size = int(sys.argv[1])
for i in range(size):
for j in range(size):
graph[(i,j)] = []
# All Moore neighborhood around a coordinate.
for di,dj in set(permutations( [0]+[-1,1]*2, 2)):
# Use modulo to avoid limits and create a torus.
graph[ (i,j) ].append( ( (i+di)%size, (j+dj)%size ) )
rule = Goucher()
# All Moore neighborhood around a coordinate.
neighborhood = set(permutations( [0]+[-1,1]*2, 2)) # FIXME ugly
assert( len(neighborhood) == 8 )
graph[(i,j)] = []
for di,dj in neighborhood:
# Use modulo to avoid limits and create a torus.
graph[ (i,j) ].append(( (i+di)%size, (j+dj)%size ))
rule = Conway()
# Fill a board with random states.
board = make_board( graph, lambda x : random.choice(rule.states) )
# Play and print.
for i in range(5):
for i in range(size):
print i
for i in range(size):
for j in range(size):