Stephen Wolfram Rule 30 cellular automaton emulation in python, with the simplest initial state of exactly one filled cell. (UPDATE: I've published a much more efficient and refined package with builtin visualization: https://github.com/zmwangx/rule30)

rule30.py

import sys

MAX_TIME = int(sys.argv[1])
HALF_SIZE = MAX_TIME
indices = range(-HALF_SIZE, HALF_SIZE+1)

# initial condition
cells = {i: '0' for i in indices}
cells[0] = '1'
# padding on both ends
cells[-HALF_SIZE-1] = '0'
cells[ HALF_SIZE+1] = '0'

# time evolution
#
# rule set: (http://en.wikipedia.org/wiki/Rule_30)
# ------------------------------------------------
# In all of Wolfram's elementary cellular automata, an infinite
# one-dimensional array of cellular automaton cells with only two
# states is considered, with each cell in some initial state. At
# discrete time intervals, every cell spontaneously changes state
# based on its current state and the state of its two neighbors. For
# Rule 30, the rule set which governs the next state of the automaton
# is:
#
# current pattern               111 110 101 100 011 010 001 000
# new state for center cell     0   0   0   1   1   1   1   0
new_state = {"111": '0', "110": '0', "101": '0', "000": '0',
             "100": '1', "011": '1', "010": '1', "001": '1'}
for time in range(0, MAX_TIME):
    # print current state
    for i in indices:
        if cells[i] == '1':
            sys.stdout.write(u'\u2588')
        else:
            sys.stdout.write(' ')
    sys.stdout.write('\n')
    # evolve
    patterns = {i: cells[i-1] + cells[i] + cells[i+1] for i in
                indices}
    cells = {i: new_state[patterns[i]] for i in indices}
    cells[-HALF_SIZE-1] = '0'
    cells[ HALF_SIZE+1] = '0'
    

screenshot-first-100.png

screenshot-first-500.png