def _create_reversible_ca(self, expected, rule):
rows, _ = expected.shape
cellular_automaton = expected[0]
r = cpl.ReversibleRule(cellular_automaton.tolist()[0], rule)
return cpl.evolve(cellular_automaton,
timesteps=rows,
apply_rule=r.apply_rule)
import numpy as np
import cellpylib as cpl
# NKS page 437 - Rule 214R
# run the CA forward for 32 steps to get the initial condition for the next evolution
cellular_automaton = cpl.init_simple(63)
r = cpl.ReversibleRule(cellular_automaton[0], 214)
cellular_automaton = cpl.evolve(cellular_automaton, timesteps=32,
apply_rule=r.apply_rule)
# use the last state of the CA as the initial, previous state for this evolution
r = cpl.ReversibleRule(cellular_automaton[-1], 214)
cellular_automaton = np.array([cellular_automaton[-2]])
cellular_automaton = cpl.evolve(cellular_automaton, timesteps=62,
apply_rule=r.apply_rule)
cpl.plot(cellular_automaton)
import matplotlib.pyplot as plt
import numpy as np
import cellpylib as cpl
# NKS page 442 - Rule 122R
cellular_automaton = np.array([[0] * 40 + [1] * 20 + [0] * 40])
rule = cpl.ReversibleRule(cellular_automaton[0], 122)
cellular_automaton = cpl.evolve(cellular_automaton,
timesteps=1000,
apply_rule=rule)
timestep = []
bientropies = []
shannon_entropies = []
average_cell_entropies = []
apentropies = []
for i, c in enumerate(cellular_automaton):
timestep.append(i)
bit_string = ''.join([str(x) for x in c])
bientropies.append(cpl.ktbien(bit_string))
shannon_entropies.append(cpl.shannon_entropy(bit_string))
average_cell_entropies.append(
cpl.average_cell_entropy(cellular_automaton[:i + 1]))
apentropies.append(cpl.apen(bit_string, m=1, r=0))
print("%s, %s, %s, %s" %
(i, bientropies[-1], shannon_entropies[-1], apentropies[-1]))
plt.figure(1)
plt.title("KTBiEn")
plt.plot(timestep, bientropies)
# Ajustado especificamente para a regra 90 e sua reversão
# plot the resulting CA evolution
try:
import cellpylib as cpl
except:
!pip install cellpylib
import cellpylib as cpl
from cellpylib import *
#import cellpylib as cpl
# initialize a CA with 200 cells (a random initialization is also available)
cellular_automaton = cpl.init_simple(200)
# evolve the CA for 100 time steps, using Rule 30 as defined in NKS
cellular_automaton = cpl.evolve(cellular_automaton, timesteps=100,
apply_rule=lambda n, c, t: cpl.nks_rule(n, 90))
# plot the resulting CA evolution
cpl.plot(cellular_automaton)
cellular_automaton = cpl.init_random(200)
r = cpl.ReversibleRule(cellular_automaton[0], 90)
cellular_automaton = cpl.evolve(cellular_automaton, timesteps=100,
apply_rule=r.apply_rule)
# plot the resulting reverse CA evolution
cpl.plot(cellular_automaton)