from python import util
dataset = []
while True:
s = input()
if not s:
break
dataset.append(s)
pairs = set()
corr = []
for x in dataset:
if not x in pairs:
cx = util.reverseComplement(x)
if not cx in pairs:
pairs.add(x)
else:
pairs.remove(cx)
corr.append(x)
corr.append(cx)
else:
pairs.remove(x)
corr.append(x)
err_list = list(pairs)
for i in err_list:
for j in corr:
if util.getHammingDistance(i, j) == 1 or util.getHammingDistance(i, util.reverseComplement(j)) == 1:
print("%s->%s" % (i, j))
break
queue.append(e)
elif len(e.path) < len(node.path) + 1:
e.path = node.path + [e]
if e.value == start_node and len(node.path) >= data_len - k + 1:
if not minPath or len(minPath) > len(node.path):
minPath = node.path
return minPath
if __name__ == '__main__':
s = []
while True:
l = input()
if not l: break
s.append(l)
un = set(s).union({reverseComplement(x) for x in s})
data_len = len(s[0])
k = data_len - 1
while k >= 1:
edges = build_deBruijin_graph(un, k)
g_visited_nodes = set()
start_node = first(edges.keys())
path = None
while True:
visited_nodes = set()
path = traverseGraph(start_node, edges, visited_nodes, k)
if path:
break
g_visited_nodes |= visited_nodes
def build_deBruijin_graph(s):
un = s.union({util.reverseComplement(x) for x in s})
return [(r[:-1], r[1:]) for r in un]
