Data commit

Task/Percolation-Mean-cluster-density/Python/percolation-mean-cluster-density.py

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+from __future__ import division
+from random import random
+import string
+from math import fsum
+
+n_range, p, t = (2**n2 for n2 in range(4, 14, 2)), 0.5, 5
+N = M = 15
+
+NOT_CLUSTERED = 1   # filled but not clustered cell
+cell2char = ' #' + string.ascii_letters
+
+def newgrid(n, p):
+    return [[int(random() < p) for x in range(n)] for y in range(n)]
+
+def pgrid(cell):
+    for n in range(N):
+        print( '%i)  ' % (n % 10)
+               + ' '.join(cell2char[cell[n][m]] for m in range(M)))
+
+
+def cluster_density(n, p):
+    cc = clustercount(newgrid(n, p))
+    return cc / n / n
+
+def clustercount(cell):
+    walk_index = 1
+    for n in range(N):
+        for m in range(M):
+            if cell[n][m] == NOT_CLUSTERED:
+                walk_index += 1
+                walk_maze(m, n, cell, walk_index)
+    return walk_index - 1
+
+
+def walk_maze(m, n, cell, indx):
+    # fill cell
+    cell[n][m] = indx
+    # down
+    if n < N - 1 and cell[n+1][m] == NOT_CLUSTERED:
+        walk_maze(m, n+1, cell, indx)
+    # right
+    if m < M - 1 and cell[n][m + 1] == NOT_CLUSTERED:
+        walk_maze(m+1, n, cell, indx)
+    # left
+    if m and cell[n][m - 1] == NOT_CLUSTERED:
+        walk_maze(m-1, n, cell, indx)
+    # up
+    if n and cell[n-1][m] == NOT_CLUSTERED:
+        walk_maze(m, n-1, cell, indx)
+
+
+if __name__ == '__main__':
+    cell = newgrid(n=N, p=0.5)
+    print('Found %i clusters in this %i by %i grid\n'
+          % (clustercount(cell), N, N))
+    pgrid(cell)
+    print('')
+
+    for n in n_range:
+        N = M = n
+        sim = fsum(cluster_density(n, p) for i in range(t)) / t
+        print('t=%3i p=%4.2f n=%5i sim=%7.5f'
+              % (t, p, n, sim))