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b3ca-plotter.py
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b3ca-plotter.py
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import random
import numpy as np
import matplotlib.pyplot as plt
import array
import itertools
import bases
#plt.plot([1,2,3,4])
#plt.ylabel('some numbers')
#plt.show()
# evenly sampled time at 200ms intervals
#t = np.arange(0., 5., 0.2)
# red dashes, blue squares and green triangles
#plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
#plt.show()
#In python: Black = 0; White = 1
#In ECA: Black = 1; white = 0
#neighborhood radius is number of blocks to either side
#For ECA n_rad = 1
n_rad = 1
#number of states for each site
#for ECA, n_rad = 2 [0,1]
n_k = 3
#Because python arrays are reversed need index limit for applying rule
hood_limit = n_k**(2*n_rad+1)-1
#Rule is integer between 1 and 256 for elementary CA
#For CA, integer may be larger
#Convert rule to bitarray of size 8 (for ECA)
rule = 110112222110112000110112000
rulen = 0
for k in range(len(str(rule))): rulen = rulen + n_k**(hood_limit-k)*int(str(rule)[k])
#rulen = 0b11100011111000001110001111100000
#rulen = 3823231968
#ruleb = bin(rulen)
#rule = ruleb[2:]
#Insert leading zero if necessary
#while len(rule) < n_k**(2*n_rad+1): rule = '0'+rule
#Create initial array
CA_size = 201
#random start
#x = np.random.randint(2,size = CA_size)
#random start with x_on% on
x_on = 0.30
xx = np.random.random_sample(size = CA_size)
x = array.array("i",itertools.repeat(0,CA_size))
for k in range(CA_size):
if xx[k] > 1.-x_on:
x[k] = 2
#seeded start
#x = array.array("i",itertools.repeat(0,CA_size))
#x[CA_size/2] = 1
#Make array "circular" put copy 0th element to end, copy nth element to beginning
#Unnecessary for neighborhood size of 3 --> may need to reinstate for larger neighborhoods
#x_temp = x
#x_temp = np.append(x_temp, x[0])
#x_temp = np.insert(x_temp,0,x[-1])
#Get neighborhood arrangements
nhoods = array.array("i",itertools.repeat(0,CA_size))
steps = 400
for j in range(steps):
for i in range(CA_size):
if i < CA_size-2:
ii = i+1
# iii = i+2
else:
if i == CA_size-1:
ii = 0
# iii = 1
else:
ii = i+1
# iii = 0
if j == 0:
nhoods[i] = x[i-1]*n_k**2+x[i]*n_k**1+x[ii]
else:
nhoods[i] = x[j,i-1]*n_k**2+x[j,i]*n_k**1+x[j,ii]
new_x = array.array("i",itertools.repeat(0,CA_size))
#apply rule
for i in range(CA_size):
new_x[i] = int(str(rule)[hood_limit-nhoods[i]])
if j == 0:
x = np.append([x],[new_x], axis = 0)
else:
x = np.append(x,[new_x], axis = 0)
#write output as portable bitmap (PBM)
#first line is "P1 width height" followed by
#height lines of 0's and 1's of length width
# Lets plot
fig, ax = plt.subplots()
image = x
ax.imshow(image, cmap=plt.cm.gist_yarg, interpolation='nearest')
ax.set_title('ECA Rule #'+str(rulen))
# Move left and bottom spines outward by 10 points
ax.spines['left'].set_position(('outward', 10))
#ax.spines['bottom'].set_position(('outward', 10))
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
#ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.xaxis.set_major_locator(plt.NullLocator())
plt.ylabel('Generation')
# Only show ticks on the left and bottom spines
#ax.yaxis.set_ticks_position('left')
#ax.xaxis.set_ticks_position('bottom')
plt.show()