def four_chain(three_chain_list, DAG_TC): #Uses the transitive completion of the DAG to find all of the three chains in the DAG
four_chain_list = []
for three_chain in three_chain_list: #Iterates over every 3 chain
[node1, node2, node3] = three_chain
for node in DAG_TC.nodes(): #Iterates over every node in the DAG
if dl.age_check(DAG_TC, node1, node): #If a node has birthdays between two of the nodes in the 3 chain, it could be possible to find a 4 chain that has this node added in to the 3 chain
if dl.age_check(DAG_TC, node, node2):
if nx.has_path(DAG_TC, node1, node):
if nx.has_path(DAG_TC, node, node2):
four_chain_list.append([node1, node, node2, node3]) #If a three chain can be formed, add it to the list
return four_chain_list
def three_chain(DAG_TC): #Uses the transitive completion of the DAG to find all of the three chains in the DAG
three_chain_list = []
for edge in DAG_TC.edges(): #Iterates over every edge in the TC, which is also every 2 chain
[node1, node2] = edge
for node in DAG_TC.nodes():
if dl.age_check(DAG_TC, node1, node): #If a node has birthdays between each end of the 2 chain, it could be possible to find a 3 chain that has this node inbetween the the ends of the 2 chain
if dl.age_check(DAG_TC, node, node2):
if nx.has_path(DAG_TC, node1, node): #check if there is a path to the middle node from the 1st
if nx.has_path(DAG_TC, node, node2): #check if there is a path from the middle node to the 2nd
three_chain_list.append([node1, node, node2]) #If a three chain can be formed, add it to the list
return three_chain_list
def trans_comp(DAG):
for node1 in DAG.nodes():
for node2 in DAG.nodes():
if dl.age_check(DAG, node1, node2):
if not node1 == node2:
if nx.has_path(DAG, node1, node2):
DAG.add_edge(node1, node2)
return DAG
def test_TR(DAG, TR):
missing_edges = []
for node1 in DAG.nodes():
for node2 in DAG.nodes(): #iterates over all pairs of nodes in the DAG
if dl.age_check(DAG, node1, node2): #ensure that there could possibly be a path from node1 to node2
if nx.has_path(DAG, node1, node2): #tests whether there is a path between these two nodes in the original DAG
if not nx.has_path(TR, node1, node2):
missing_edges.append([node1, node2]) #if there is no longer a path between these two pairs of nodes in the transitive reduction...
return missing_edges #...then these two edges are stored and printed
def path_check(DAG, start, end):
if end in DAG.successors(start):
return True
for child in DAG.successors(start):
if dl.age_check(DAG, child, end):
# We can possibly go from this child to the end
if path_check(DAG, child, end):
return True
else:
return False
def trans_comp_nx(DAG):
N = DAG.number_of_nodes()
i = 0
print_limit = 0.01
for node1 in DAG.nodes():
for node2 in DAG.nodes(): #iterates over all pairs of edges
if dl.age_check(DAG, node1, node2): #ensures that the time ordering of the pair is such that an edge could exist from node1 to node2
if not node1 == node2: #prevents loops
if nx.has_path(DAG, node1, node2):
DAG.add_edge(node1, node2) #if there is a path from node1 to node2, add an edge from node1 to node2
i += 1.
pc = (i/float(N))*100. #convert to percentage
if pc > print_limit:
#print 'Finished %f percent' %pc
print_limit += 0.01
return DAG
