def simulation(size, steps):
np.random.seed(None)
grow = ChangeStateRule(from_state=0, to_state=1, p_change=0.0025)
lightning = ChangeStateRule(
from_state=1,
to_state=2,
p_change=1e-5,
)
burn = SlowBurnRule()
forest = CellularAutomaton(
shape=size,
rules=[grow, lightning, burn],
)
recorder = AutomataRecorder(automaton=forest)
forest.start()
for i in range(steps):
forest.step()
palette = np.zeros((256, 3), dtype='uint8')
palette[1] = [0, 255, 0]
palette[2] = [255, 0, 0]
palette[3] = [255, 255, 255]
save_image_sequence(recorder, 'test.gif', palette, 100)
def simulation(p_mold, size, steps, transform=None):
np.random.seed(None)
grow = ChangeStateRule(from_state=0, to_state=1, p_change=0.0025)
# lightning = ChangeStateRule(
# from_state=1,
# to_state=2,
# p_change=1e-5,
# )
# burn = SlowBurnRule()
burn_groves = BurnGrovesRule()
mold = MoldRule(dead_state=3, p_mold=p_mold)
mold_die = ChangeStateRule(from_state=3, to_state=0, p_change=1.0)
fire_out = ChangeStateRule(from_state=2, to_state=0, p_change=1.0)
forest = CellularAutomaton(
shape=size,
rules=[mold_die, fire_out, grow, burn_groves, mold],
)
recorder = AutomataRecorder(automaton=forest, transform=transform)
forest.start()
for i in range(steps):
forest.step()
return recorder.as_array()[:, 1:4].T
def simulation(p_mold, size, steps):
""" Perform a simulation of a moldy forest, returning statistics.
Parameters
----------
p_mold : probability
The probability that a crowded tree dies of mold.
size : size tuple
The number of cells in each direction for the simulation.
steps : int
The number of ticks to run the simulation for.
Returns
-------
counts : array
Array with shape (4, steps) of counts of each state at
each tick.
"""
np.random.seed(None)
# trees grow
grow = ChangeStateRule(from_state=EMPTY, to_state=TREE, p_change=0.0025)
# fires are started, and all connected trees burn
burn_groves = BurnGrovesRule()
# crowded trees have a chance to be infected with mold
mold = MoldRule(dead_state=MOLD, p_mold=p_mold)
# trees which are infected with mold die
mold_die = ChangeStateRule(from_state=MOLD, to_state=EMPTY, p_change=1.0)
# fires are extinguished
fire_out = ChangeStateRule(from_state=FIRE, to_state=EMPTY, p_change=1.0)
forest = CellularAutomaton(
shape=size,
rules=[mold_die, fire_out, grow, burn_groves, mold],
)
# record the number of each state
recorder = AutomataRecorder(automaton=forest, transform=count_states)
forest.start()
for i in range(steps):
forest.step()
return recorder.as_array()
def main(rule, size, pattern, boundary, ticks):
""" Run an elementary 1D cellular automaton. """
pattern = [(0 if char == ' ' else 1) for char in pattern]
# setup simulation
rule = Elementary1DRule(rule_number=rule, boundary=boundary)
pattern_overlay = PatternOverlay(pattern=pattern)
world = CellularAutomaton(shape=(size, ),
initializers=[pattern_overlay],
rules=[rule])
# initialize and display initial state
world.start()
print(automaton_to_text(world))
# run
for i in range(ticks):
world.step()
print(automaton_to_text(world))
def simulation(size, steps):
""" Perform a simulation of a forest fire, outputting a GIF.
Parameters
----------
size : size tuple
The number of cells in each direction for the simulation.
steps : int
The number of ticks to run the simulation for.
"""
np.random.seed(None)
grow = ChangeStateRule(
from_state=EMPTY,
to_state=TREE,
p_change=0.0025
)
lightning = ChangeStateRule(
from_state=TREE,
to_state=FIRE,
p_change=1e-5,
)
burn = SlowBurnRule()
forest = CellularAutomaton(
shape=size,
rules=[grow, lightning, burn],
)
recorder = AutomataRecorder(automaton=forest)
forest.start()
for i in range(steps):
forest.step()
palette = np.zeros((256, 3), dtype='uint8')
palette[1] = GREEN
palette[2] = RED
save_image_sequence(recorder, 'test.gif', palette, 100)