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In [1]:



    


import numpy as np
from matplotlib import pyplot as plt
from __future__ import division
from itertools import product
from spacetime.CA_Simulators.CAs import *
from spacetime.Local_Measures.Transducers import *
%matplotlib inline



    











In [2]:



    


print rule_number_conversion(54, 2, 1)



    


















    






00110110

















In [3]:



    


start = np.zeros(20, dtype = int)
example = ECA(54, start)
print 'full lookup table'
example.lookup_table(True)
print 'lookup table value for neighborhood (0,0,1)'
print example.lookup_table()[(0,0,1)]



    


















    






full lookup table
(1, 1, 1) 0
(1, 1, 0) 0
(1, 0, 1) 1
(1, 0, 0) 1
(0, 1, 1) 0
(0, 1, 0) 1
(0, 0, 1) 1
(0, 0, 0) 0
lookup table value for neighborhood (0,0,1)
1

















In [4]:



    


print rule_number_conversion(18, 2, 1)



    


















    






00010010

















In [5]:



    


start = np.zeros(20, dtype = int)
example = ECA(18, start)
print 'full lookup table'
example.lookup_table(True)
print 'lookup table value for neighborhood (0,0,1)'
print example.lookup_table()[(0,0,1)]



    


















    






full lookup table
(1, 1, 1) 0
(1, 1, 0) 0
(1, 0, 1) 0
(1, 0, 0) 1
(0, 1, 1) 0
(0, 1, 0) 0
(0, 0, 1) 1
(0, 0, 0) 0
lookup table value for neighborhood (0,0,1)
1

Test external lookup table (multi-dimensional np array) code for rule 18





In [6]:



    


A = 2
r = 1
table = lookup_table(18, 2, 1)
R = 2*r + 1
scan = tuple(np.arange(0,A)[::-1])
for a in product(scan, repeat = R):
    print a, table[a]



    


















    






(1, 1, 1) 0
(1, 1, 0) 0
(1, 0, 1) 0
(1, 0, 0) 1
(0, 1, 1) 0
(0, 1, 0) 0
(0, 0, 1) 1
(0, 0, 0) 0

















In [7]:



    


x = [0,1,1,0]
print neighborhood(x, 1)
for item in neighborhood(x, 1):
    print item



    


















    






[array([0, 0, 1, 1]), array([0, 1, 1, 0]), array([1, 1, 0, 0])]
[0 0 1 1]
[0 1 1 0]
[1 1 0 0]

















In [8]:



    


print max_rule(2,1)



    


















    






255

















In [9]:



    


print example.current_state()
example.evolve(1)
print example.current_state()
example.reset([0,]*5 + [1] + [0,]*5)
print example.current_state()
example.evolve(1)
print example.current_state()



    


















    






[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 1 0 1 0 0 0 0]

















In [10]:



    


print np.random.choice([0,1], 10 )



    


















    






[1 1 1 0 0 0 1 0 0 0]

















In [11]:



    


start = n_ones(20, 1)
test = ECA(18, start)
test.evolve(10)
print test.get_spacetime()
test.rewind(5)
print 'break'
print test.get_spacetime()



    


















    






[[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0]
 [0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]
 [0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0]
 [1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0]
 [0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 0 0 1]
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0]
 [0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1]
 [1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]]
break
[[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0]
 [0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]
 [0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0]]

















In [12]:



    


domain_state = domain_18(20)
print domain_state
t = transducer(*transducer_18())
print t.scan(domain_state)



    


















    






[1 0 1 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 1 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]

















In [13]:



    


dim = 400
start = random_state(dim, 2)
test = CA(18, 2, 1, start)
test.evolve(dim)



    











In [14]:



    


diagram(test.get_spacetime(), edgecolors='none')

External transduction of CA.get_spacetime()





In [15]:



    


t = transducer(*transducer_18())
transduced = t.spacetime_scan(test.get_spacetime(), direction ='left')



    











In [16]:



    


diagram(transduced, colors = plt.cm.bone, edgecolors = 'none')



    


















    
























In [17]:



    


masked = t.spacetime_mask(test.get_spacetime(), 2)



    











In [18]:



    


diagram(masked, colors = plt.cm.bone, edgecolors = 'none')



    


















    
























In [19]:



    


filtered = t.spacetime_filter(test.get_spacetime(), fill=True)



    











In [20]:



    


diagram(filtered, colors = plt.cm.Blues, edgecolors = 'none')



    


















    
























In [21]:



    


A = 3
R = 2
print max_rule(A, R)



    


















    






87189642485960958202911070585860771696964072404731750085525219437990967093723439943475549906831683116791055225665626

















In [24]:



    


start = random_state(500, A)
test = CA(68513215896546121325651496846315418, A, R, start)
test.evolve(500)



    











In [25]:



    


diagram(test.get_spacetime(), colors = plt.cm.spring, edgecolors = 'none')



    


















    
























In [ ]:

Content source: Saxafras/Spacetime

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