def test_init_random_dtype(self):
arr = cpl.init_random(3, dtype=np.float32)
self.assertEqual(len(arr), 1)
self.assertEqual(len(arr[0]), 3)
self.assertTrue(0.0 <= arr[0][0] < 1.0)
self.assertTrue(0.0 <= arr[0][1] < 1.0)
self.assertTrue(0.0 <= arr[0][2] < 1.0)
def test_init_random_3_k3_n2(self):
arr = cpl.init_random(3, k=3, n_randomized=2, empty_value=9)
self.assertEqual(len(arr), 1)
self.assertEqual(len(arr[0]), 3)
self.assertTrue(0 <= arr[0][0] <= 2)
self.assertTrue(0 <= arr[0][1] <= 2)
self.assertEqual(arr[0][2], 9)
def test_init_random_3_k3_n0(self):
arr = cpl.init_random(3, k=3, n_randomized=0, empty_value=9)
self.assertEqual(len(arr), 1)
self.assertEqual(len(arr[0]), 3)
self.assertEqual(arr[0][0], 9)
self.assertEqual(arr[0][1], 9)
self.assertEqual(arr[0][2], 9)
def test_init_random_3(self):
arr = cpl.init_random(3, empty_value=9)
self.assertEqual(len(arr), 1)
self.assertEqual(len(arr[0]), 3)
self.assertTrue(0 <= arr[0][0] <= 1)
self.assertTrue(0 <= arr[0][1] <= 1)
self.assertTrue(0 <= arr[0][2] <= 1)
# In[ ]:
for carule in range(5000):
print(carule)
k = 2 + np.random.randint(6)
rn = np.random.randint(int(k**(3 * k - 2)))
f = open("data5k/%.06d/rule.txt" % carule, "w")
# k, r, type, rule
# type: 0 = General, 1 = Totalistic
f.write("%d 1 1 %d" % (k, rn))
f.close()
for i in range(50):
ca = cpl.init_random(256)
ca = cpl.evolve(
ca,
timesteps=256,
apply_rule=lambda n, c, t: cpl.totalistic_rule(n, k=k, rule=rn),
r=1)
im = render(ca, k)
im.save("data5k/%.06d/%.06d.png" % (carule, i))
# In[16]:
counts = [np.sum(ca == i) for i in range(k)]
# In[17]:
counts
import cellpylib as cpl
# Glider
cellular_automaton = cpl.init_simple2d(60, 60)
cellular_automaton[:, [28, 29, 30, 30], [30, 31, 29, 31]] = 1
# Blinker
cellular_automaton[:, [40, 40, 40], [15, 16, 17]] = 1
# Light Weight Space Ship (LWSS)
cellular_automaton[:, [18, 18, 19, 20, 21, 21, 21, 21, 20],
[45, 48, 44, 44, 44, 45, 46, 47, 48]] = 1
# evolve the cellular automaton for 60 time steps
cellular_automaton = cpl.evolve2d(cellular_automaton,
timesteps=60,
neighbourhood='Moore',
apply_rule=cpl.game_of_life_rule)
cpl.plot2d_animate(cellular_automaton)
#%% rule table
import cellpylib as cpl
rule_table, actual_lambda, quiescent_state = cpl.random_rule_table(
lambda_val=0.45, k=4, r=2, strong_quiescence=True, isotropic=True)
cellular_automaton = cpl.init_random(128, k=4)
# use the built-in table_rule to use the generated rule table
cellular_automaton = cpl.evolve(
cellular_automaton,
timesteps=200,
apply_rule=lambda n, c, t: cpl.table_rule(n, rule_table),
r=2)
def test_init_random_1_k3_n0(self):
arr = cpl.init_random(1, k=3, n_randomized=0, empty_value=9)
self.assertEqual(len(arr), 1)
arr = arr[0]
self.assertEqual(len(arr), 1)
self.assertEqual(arr[0], 9)
def test_init_random_1_k3_n1(self):
arr = cpl.init_random(1, k=3, n_randomized=1, empty_value=9)
self.assertEqual(len(arr), 1)
self.assertEqual(len(arr[0]), 1)
self.assertTrue(0 <= arr[0][0] <= 2)
import cellpylib as cpl
rule_table, actual_lambda, quiescent_state = cpl.random_rule_table(
lambda_val=0.37, k=4, r=2, strong_quiescence=True, isotropic=True)
# cellular_automaton = cpl.init_simple(128, val=1)
cellular_automaton = cpl.init_random(128, k=4, n_randomized=20)
# evolve the cellular automaton for 200 time steps
cellular_automaton = cpl.evolve(
cellular_automaton,
timesteps=200,
apply_rule=lambda n, c, t: cpl.table_rule(n, rule_table),
r=2)
cpl.plot(cellular_automaton)
# 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)