gistfile1.py

def find_largest_contiguous_region(countsarray):
	"""
	Find the contiguous regions in countsarray using the modified
	flood count algorithm described in get_flood_count
	"""

	Q = countsarray.copy()
	points_checked = []
	rows, cols = Q.shape

	best_score = 0
	best_ind = -1
	best_point = (0, 0)

	for i in range(rows):
		for j in range(cols):

			if Q[i][j] >= 0:
				score = get_flood_count(Q, i, j, Q[i][j])
				if score > best_score:
					best_score = score
					best_ind = countsarray[i][j]
					best_point = (i, j)


	# Generate a nice little display
	print countsarray
	print "Best score: {0:d} ({1:d}, {2:d})".format(best_score, best_point[0], best_point[1])

	if best_score < 3:
		raise NoPointFoundException("No point found.")

	return best_point

def get_flood_count(Q, i, j, target, replacement=-1):
	"""
	A modified flood count algorithm.
	
	Makes some assumptions about the input, namely:

	1. Input array is small enough not to overflow the stack
	2. Input array is numeric, and
	3. Input array is wholly positive (or at least, has no -1's)
	
	>>> Q = numpy.array([[1,1,2,4], [1, 3, 5, 7]])
	>>> get_flood_count(Q, 0, 0, Q[0][0])
	3
	"""
	s = Q.shape
	if i < 0 or j < 0 or i >= s[0] or j >= s[1] or Q[i][j] != target:
		 return 0

	# Q[i][j] == target_ind
	Q[i][j] = replacement

	return 1 + (get_flood_count(Q, i+1, j, target, replacement) + 
			get_flood_count(Q, i, j+1, target, replacement) +
			get_flood_count(Q, i-1, j, target, replacement) +
			get_flood_count(Q, i, j-1, target, replacement))