In [1]:

    

import numpy as np
from matplotlib import pyplot as plt
from __future__ import division
from CAs import *
from Local_Complexity import *
from Spacetime_Information import *
%matplotlib inline
import pickle


    

In [2]:

    

test = np.random.rand(20)


    

In [3]:

    

print test


    

    




[ 0.96633351  0.87893885  0.92223609  0.53667781  0.28450923  0.43779711
  0.92328181  0.4987719   0.5477519   0.7059378   0.52194894  0.60273117
  0.9018477   0.90354623  0.71241708  0.65064048  0.22332363  0.90138463
  0.20956276  0.51116738]

In [4]:

    

pickle.dump( test, open( "pickle_test.p", "wb" ) )


    

In [5]:

    

next_test = pickle.load(open( "pickle_test.p", "rb" ) )


    

In [6]:

    

print next_test


    

    




[ 0.96633351  0.87893885  0.92223609  0.53667781  0.28450923  0.43779711
  0.92328181  0.4987719   0.5477519   0.7059378   0.52194894  0.60273117
  0.9018477   0.90354623  0.71241708  0.65064048  0.22332363  0.90138463
  0.20956276  0.51116738]

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In [2]:

    

test_CA = ECA(110, random_state(500,2))
test_CA.evolve(500)
test_CA.diagram()


    

In [3]:

    

ee_test = local_excess_entropy(test_CA.get_spacetime(),5)
ee_test.estimate_states(3,1)


    

In [4]:

    

print ee_test.C_plus()
print np.mean(ee_test.C_plus_field())


    

    




4.57031451772
4.57031451772

In [5]:

    

print ee_test.C_minus()
print np.mean(ee_test.C_minus_field())


    

    




5.47857811089
5.47857811089

In [7]:

    

diagram(ee_test.C_plus_field(), size = 20, colorbar = True)


    

In [6]:

    

diagram(ee_test.C_minus_field(), size = 20, colorbar = True)


    

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