fixes for python3

conway.py

@@ -35,7 +35,7 @@ def conway(state, k):
     plt.figure()
     img_plot = plt.imshow(A, interpolation="nearest", cmap = plt.cm.gray)
     plt.show()
-    for i in xrange(1,5000):
+    while True:
         A = conway(A, k)
         img_plot.set_data(A)
         plt.draw()

lib/__init__.py

@@ -1 +1 @@
-from lib import fft_convolve2d
+from .lib import fft_convolve2d

lib/lib.py

@@ -9,6 +9,6 @@ def fft_convolve2d(x,y):
     fr2 = fft2(np.flipud(np.fliplr(y)))
     m,n = fr.shape
     cc = np.real(ifft2(fr*fr2))
-    cc = np.roll(cc, -m/2+1,axis=0)
-    cc = np.roll(cc, -n/2+1,axis=1)
+    cc = np.roll(cc, - int(m / 2) + 1, axis=0)
+    cc = np.roll(cc, - int(n / 2) + 1, axis=1)
     return cc

replicator.py

@@ -32,7 +32,7 @@ def replicator(state, k):
     plt.figure()
     img_plot = plt.imshow(A, interpolation="nearest", cmap = plt.cm.gray)
     plt.show()
-    for i in xrange(1,5000):
+    while True
         A = replicator(A, k)
         img_plot.set_data(A)
         plt.draw()

seed.py

@@ -12,7 +12,7 @@ def seed(state, k):
     b = fft_convolve2d(state,k).round()
     c = np.zeros(b.shape)
     # checks the values, and sets alive vs. dead state
-    c[np.where(b==2)] = 1
+    c[np.where(b == 2)] = 1
 
     # return new state
     return c
@@ -32,8 +32,8 @@ def seed(state, k):
     plt.figure()
     img_plot = plt.imshow(A, interpolation="nearest", cmap = plt.cm.gray)
     plt.show()
-    for i in xrange(1,5000):
+    while True:
         A = seed(A, k)
         img_plot.set_data(A)
         plt.draw()
-        time.sleep(0.05)
+        time.sleep(0.02)

two_by_two.py

@@ -0,0 +1,43 @@
+import numpy as np
+import time
+from lib import fft_convolve2d
+import matplotlib.pyplot as plt
+plt.ion()
+
+def two_by_two(state, k):
+    """
+    '2x2' automata state transition
+    """
+    # computes sums around each pixel
+    b = fft_convolve2d(state,k).round()
+    c = np.zeros(b.shape)
+    # checks the values, and sets alive vs. dead state
+
+    c[np.where((b == 1) & (state == 1))] = 1
+    c[np.where((b == 2) & (state == 1))] = 1
+    c[np.where((b == 5) & (state == 1))] = 1
+    c[np.where((b == 3) & (state == 0))] = 1
+    c[np.where((b == 6) & (state == 0))] = 1
+
+    # return new state
+    return c
+
+if __name__ == "__main__":
+    # set up board
+    m,n = 100,100
+    A = np.zeros((m,n))
+    A = np.random.random(m*n).reshape((m, n)).round()
+
+    # construct convolution kernel
+    k = np.zeros((m, n))
+    k[m/2-1 : m/2+2, n/2-1 : n/2+2] = np.array([[1,1,1],[1,0,1],[1,1,1]])
+
+    # plot each frame
+    plt.figure()
+    img_plot = plt.imshow(A, interpolation="nearest", cmap = plt.cm.gray)
+    plt.show()
+    while True:
+        A = two_by_two(A, k)
+        img_plot.set_data(A)
+        plt.draw()
+        time.sleep(0.1)