finished seed

README.md

@@ -1,6 +1,6 @@
 ![Alt text](http://i.imgur.com/6B84SNI.png "Screenshot")
 
-Fast Python implementation of Conway's game of life and other cellular automata.
+Fast Python implementation of Conway's game of life [and other cellular automata](http://www.conwaylife.com/wiki/Cellular_automaton#Well-known_Life-like_cellular_automata).
 
 Requires Python 2.7 (will check Python 3 soon), with Numpy and Matplotlib.
 
@@ -14,6 +14,8 @@ To run [Replicator](http://www.conwaylife.com/wiki/Replicator_(CA)):
 $ python replicator.py
 ```
 
+Other cellular automata implementations are run the same way.
+
 End each program using a keyboard interrupt (ctrl-c).
 
 The game grid is encoded as a simple `m` by `n` array (default 100x100 in the code) of zeros and ones.

conway.py

@@ -6,7 +6,7 @@
 
 def conway(state, k):
     """
-    Iterates one step using the rules for CGOL.
+    Conway's game of life state transition
     """
     # computes sums around each pixel
     b = fft_convolve2d(state,k).round()

replicator.py

@@ -7,7 +7,7 @@
 
 def replicator(state, k):
     """
-    'Replicator' cellular automaton iteration
+    'Replicator' cellular automaton state transition
     """
     b = fft_convolve2d(state,k).round()
     c = np.zeros(b.shape)

seed.py

@@ -0,0 +1,39 @@
+import numpy as np
+import time
+from lib import fft_convolve2d
+import matplotlib.pyplot as plt
+plt.ion()
+
+
+def seed(state, k):
+    """
+    'Seed' cellular automaton state transition
+    """
+    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
+
+    # return new state
+    return c
+
+if __name__ == "__main__":
+    # set up board
+    m,n = 100,100
+    A = np.zeros((m,n))
+    A[20,20] = 1
+    A[20,21] = 1
+
+    # 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()
+    for i in xrange(1,5000):
+        A = seed(A, k)
+        img_plot.set_data(A)
+        plt.draw()
+        time.sleep(0.05)