def find_subgraph(self, start=None, end=None):
"""Find subgraph by provided start and end endpoints
:param end: task name
:param start: task name
:param include: iterable with task names
:returns: DeploymentGraph instance (subgraph from original)
"""
working_graph = self
if start:
# simply traverse starting from root,
# A->B, B->C, B->D, C->E
working_graph = self.subgraph(
nx.dfs_postorder_nodes(working_graph, start))
if end:
# nx.dfs_postorder_nodes traverses graph from specified point
# to the end by following successors, here is example:
# A->B, C->D, B->D , and we want to traverse up to the D
# for this we need to reverse graph and make it
# B->A, D->C, D->B and use dfs_postorder
working_graph = self.subgraph(nx.dfs_postorder_nodes(
working_graph.reverse(), end))
return working_graph
def _analyze(self):
region = self._region.recursive_copy()
# visit the region in post-order DFS
parent_map = { }
stack = [ region ]
while stack:
current_region = stack[-1]
has_region = False
for node in networkx.dfs_postorder_nodes(current_region.graph, current_region.head):
if type(node) is GraphRegion:
stack.append(node)
parent_map[node] = current_region
has_region = True
if not has_region:
# pop this region from the stack
stack.pop()
# Get the parent region
parent_region = parent_map.get(current_region, None)
# structure this region
st = self.project.analyses.Structurer(current_region, parent_region=parent_region)
# replace this region with the resulting node in its parent region... if it's not an orphan
if not parent_region:
# this is the top-level region. we are done!
self.result = st.result
break
else:
self._replace_region(parent_region, current_region, st.result)
def labelWithSubnodes( T, root, lostNodes ):
"""
For each node in the tree T, we add two vertex properties:
1) leaves -- A set containing all of the leaves rooted at this subtree
2) subnodes -- A set containing all of the nodes (internal & leaves) in this subtree
3) enets -- A set containing network identifiers for all extant networks which exist as some leaf in this subtree
"""
lost = 'LOST'
def enet(nodes):
r = set([])
for n in nodes:
if n not in lostNodes:
# if n.find("LOST") != -1:
# r.add(n[:2])
#else:
r.add(n[-2:])
else:
r.add(lost)
return r
for n in nx.dfs_postorder_nodes(T, root) :
successors = T.successors(n)
if len(successors) == 0 :
T.node[n]['subnodes'] = set([n])
T.node[n]['leaves'] = T.node[n]['leaves'].union( set([n]) )
T.node[n]['enets'] = enet([n])
else :
subnodes = [ set([n]) ] + [ set([s]) for s in successors ] + [ T.node[s]['subnodes'] for s in successors ]
for sn in subnodes:
T.node[n]['subnodes'] = T.node[n]['subnodes'].union( sn )
leaves = [ T.node[s]['leaves'] for s in successors ]
T.node[n]['leaves'] = T.node[n]['leaves'].union( *leaves )
T.node[n]['enets'] = enet( T.node[n]['leaves'] )
def dfs_edges(G):
"""
(source,target) for edges in directed spanning tree resulting from depth
first search
"""
DG = nx.dfs_tree(G)
return [(src,targ) for targ in nx.dfs_postorder_nodes(DG) for src in DG.predecessors(targ)]
def extract_genes(self):
"""
Returns all genes located in the matchtree
:return: List of gene names
"""
genes = []
# iterate through the graph
for node_id in list(nx.dfs_postorder_nodes(self.g, source=1)):
node = self.g.node[node_id]
if node['type'] == 'genomic':
if 'hugo_symbol' in node['value']:
gene = node['value']['hugo_symbol']
if 'wildtype' in node['value'] and node['value']['wildtype'] is True:
continue
variant = None
for k in ['protein_change', 'wildcard_protein_change']:
if k in node['value']:
variant = node['value'][k].replace('p.', '')
if variant and variant.startswith('!'):
continue
if 'variant_category' in node['value'] and node['value']['variant_category'].startswith('!'):
continue
if node['value']['hugo_symbol'] not in genes:
genes.append(gene)
return genes
def extract_cancer_types(self):
"""
Returns all cancer types located in the match tree
:param g: DiGraph match tree
:return: List of cancer types
"""
diagnoses = []
cancer_types_expanded = []
primary_cancer_types = []
excluded_cancer_types = []
onco_tree = oncotreenx.build_oncotree(file_path=TUMOR_TREE)
liquid_children_txt, solid_children_txt = expand_liquid_oncotree(onco_tree)
# iterate through the graph
for node_id in list(nx.dfs_postorder_nodes(self.g, source=1)):
node = self.g.node[node_id]
if node['type'] == 'clinical':
if 'oncotree_primary_diagnosis' in node['value']:
diagnosis = node['value']['oncotree_primary_diagnosis']
n = oncotreenx.lookup_text(onco_tree, diagnosis.replace('!', ''))
children = list(nx.dfs_tree(onco_tree, n))
if diagnosis == '_SOLID_':
children_txt = solid_children_txt
primary_parent = 'All Solid Tumors'
parents_txt = ['All Solid Tumors']
elif diagnosis == '_LIQUID_':
children_txt = liquid_children_txt
primary_parent = 'All Liquid Tumors'
parents_txt = ['All Liquid Tumors']
else:
children_txt = [onco_tree.node[nn]['text'] for nn in children]
if n is not None:
parents, parents_txt, primary_parent = get_parents(onco_tree, n)
else:
parents_txt = []
primary_parent = ''
diagnoses.append(diagnosis)
if diagnosis.startswith('!'):
excluded_cancer_types.append(diagnosis.replace('!', ''))
excluded_cancer_types.extend(children_txt)
else:
primary_tumors = get_primary_tumors()
cancer_types_expanded.append(parse_diagnosis(diagnosis))
cancer_types_expanded.extend(children_txt)
cancer_types_expanded.extend([i for i in parents_txt if i.split()[0] not in primary_tumors])
primary_cancer_types.append(primary_parent)
return {
'diagnoses': list(set(i for i in diagnoses if i.strip() != 'root')),
'cancer_types_expanded': list(set(i for i in cancer_types_expanded if i.strip() != 'root')),
'primary_cancer_types': list(set(i for i in primary_cancer_types if i.strip() != 'root')),
'excluded_cancer_types': list(set(i for i in excluded_cancer_types if i.strip() != 'root'))
}
def reduce_node_expr( n, network):
"""reduce_node_expr Reduce the expression on network node. Replace local
:param n: The node of network currently being processed.
:param network: Whole network.
"""
global keys_with_expressions_
global globals_
attr = network.node[n]
# Create the expression graph.
expG = nx.DiGraph( )
for k, v in attr.items():
expG.add_node(k, expr = v )
for k, v in attr.items():
st = build_ast( v )
if st is None:
continue
if reduce_expr(v)[1]:
expG.node[k]['reduced'] = reduce_expr(v)[0]
for d in get_identifiers( st ):
expG.add_edge( k, d )
# Once the dependencies graph is build, we need to reduce it.
for x in nx.dfs_postorder_nodes( expG ):
# If its expr is not available locally, look-up in global graph. If we
# don't find it in global also, then it must be another node.
if 'expr' not in expG.node[x]:
if x in globals_:
# Copy its value from globals.
expG.node[x]['expr'] = globals_[x]
elif x in network.nodes():
# x is another node in network. Its expression is itself.
expG.node[x]['expr'] = x
else: pass
# By now we might have an expression on this node. If not, then we just
# continue.
if 'expr' in expG.node[x]:
expr = expG.node[x]['expr']
for xx in expG.successors(x):
if 'expr' in expG.node[xx]:
expr = expr.replace( xx, expG.node[xx]['expr'] )
else: pass
expG.node[x]['expr'] = reduce_expr( expr )[0]
# Now only if this key is in keys_with_expressions_ list, then only
# reduce it. Put the reduced expression into node attribute.
if x in keys_with_expressions_:
newExpr = reduce_expr( expr )[0]
attr[x] = newExpr
logger_.debug( '@node %s, reduced expr = %s' % (x, newExpr) )
else:
logger_.debug( "No expression found for node %s, %s" % (x,
expG.node[x]))
def induceTreeOnLeaves(nxtree, leaves):
leaves = set(leaves)
dg = nxtree.nxDg
nodesToKeep = []
for node in dg.nodes():
succ = set([nxtree.getName(i) for i in nx.dfs_postorder_nodes(dg, node) if nxtree.hasName(i)])
if len(succ.intersection(leaves)) != 0:
nodesToKeep.append(node)
return NXTree(dg.subgraph(nodesToKeep))
def kosaraju_strongly_connected_components(G, source=None):
"""Generate nodes in strongly connected components of graph.
Parameters
----------
G : NetworkX Graph
An directed graph.
Returns
-------
comp : generator of sets
A genrator of sets of nodes, one for each strongly connected
component of G.
Raises
------
NetworkXNotImplemented:
If G is undirected.
Examples
--------
Generate a sorted list of strongly connected components, largest first.
>>> G = nx.cycle_graph(4, create_using=nx.DiGraph())
>>> nx.add_cycle(G, [10, 11, 12])
>>> [len(c) for c in sorted(nx.kosaraju_strongly_connected_components(G),
... key=len, reverse=True)]
[4, 3]
If you only want the largest component, it's more efficient to
use max instead of sort.
>>> largest = max(nx.kosaraju_strongly_connected_components(G), key=len)
See Also
--------
connected_components
weakly_connected_components
Notes
-----
Uses Kosaraju's algorithm.
"""
with nx.utils.reversed(G):
post = list(nx.dfs_postorder_nodes(G, source=source))
seen = set()
while post:
r = post.pop()
if r in seen:
continue
c = nx.dfs_preorder_nodes(G, r)
new = {v for v in c if v not in seen}
yield new
seen.update(new)
def get_node_to_subtree_thickness(T, root):
thickness = {}
for node in nx.dfs_postorder_nodes(T, root):
successors = T.successors(node)
if not successors:
thickness[node] = 1
else:
w = sorted((thickness[n] for n in successors), reverse=True)
thickness[node] = max(w + i for i, w in enumerate(w))
return thickness
def describe(self, data_type):
G = self.hierarchy
df = self.get_data(data_type)
for n in nx.dfs_postorder_nodes(G, "all"):
G.node[n]["cnt"] = len(df[df["area"] == n].index) + pl.sum([G.node[c]["cnt"] for c in G.successors(n)])
G.node[n]["depth"] = nx.shortest_path_length(G, "all", n)
for n in nx.dfs_preorder_nodes(G, "all"):
if G.node[n]["cnt"] > 0:
print " *" * G.node[n]["depth"], n, int(G.node[n]["cnt"])
def hdag(N):
for G in N:
lst = list(nx.dfs_postorder_nodes(G))[::-1]
mk = True
for v in lst[:-1]:
if not nx.has_path(G, v, lst[lst.index(v) + 1]): # much faster
print -1
mk = False
break
if mk:
print 1, " ".join(map(str, lst))
def describe(self, data_type):
G = self.hierarchy
df = self.get_data(data_type)
for n in nx.dfs_postorder_nodes(G, 'all'):
G.node[n]['cnt'] = len(df[df['area']==n].index) + pl.sum([G.node[c]['cnt'] for c in G.successors(n)])
G.node[n]['depth'] = nx.shortest_path_length(G, 'all', n)
for n in nx.dfs_preorder_nodes(G, 'all'):
if G.node[n]['cnt'] > 0:
print ' *'*G.node[n]['depth'], n, int(G.node[n]['cnt'])
def main():
g = networkx.DiGraph()
with Timer('Tree Generation'):
root = generateTree(g, 4, 127)
print 'Number of nodes: ', nodeNumber
with Timer('Tree Traversal'):
nNodes = sum(1 for n in networkx.dfs_postorder_nodes(g, root))
print 'Number of nodes traversed: ', nNodes
def max_repeated(self, k):
'''Return the node of maximum prefix length with #leaves >= k under it.'''
g = self._g
num_leaves = [0] * g.number_of_nodes()
for node in nx.dfs_postorder_nodes(g): num_leaves[node] = 1 if g.out_degree(node) == 0 else sum(num_leaves[child] for child in g.successors_iter(node))
prefix_len = np.zeros((g.number_of_nodes(),), dtype=int)
for node in nx.dfs_preorder_nodes(g):
node_prefix_len = prefix_len[node]
for child, e_attr in g[node].iteritems(): prefix_len[child] = node_prefix_len + e_attr['weight'][1]
try: return max(it.ifilter(lambda x: x[0][1] >= k, ((v, k) for k, v in enumerate(zip(prefix_len, num_leaves)))))[1]
except ValueError: return None
def get_dag(dag):
sorted_dag = dag.copy()
# The children of a node are traversed in a random order. Therefore,
# copy the graph, sort the children and then traverse it
for adj in sorted_dag.adj:
sorted_dag.adj[adj] = OrderedDict(
sorted(sorted_dag.adj[adj].items(), key=lambda item: item[0]))
nodes = nx.dfs_postorder_nodes(sorted_dag,
source='__HPOlib_configuration_space_root__')
nodes = [node for node in nodes if node !=
'__HPOlib_configuration_space_root__']
return nodes
def detailed_connection_graph(start_with=None, end_with=None):
g = nx.MultiDiGraph()
clients = Connections.read_clients()
for emitter_name, destination_values in clients.items():
for emitter_input, receivers in destination_values.items():
for receiver_name, receiver_input in receivers:
label = '{}:{}'.format(emitter_input, receiver_input)
g.add_edge(emitter_name, receiver_name, label=label)
ret = g
if start_with is not None:
ret = g.subgraph(
nx.dfs_postorder_nodes(ret, start_with)
)
if end_with is not None:
ret = g.subgraph(
nx.dfs_postorder_nodes(ret.reverse(), end_with)
)
return ret
def dendrogram_to_ultrametric(dendrogram):
"""Returns the ultrametric corresponding to the dendrogram.
Args:
dendrogram (networkx.DiGraph): The dendrogram to be converted. Each
node should have a 'distance' attribute, which denotes the threshold
at which the children are merged.
Returns:
(ndarray): The ultrametric as a condensed distance matrix.
"""
t = dendrogram.copy()
leaf_nodes = [x for x in t if t.out_degree(x) == 0]
n = sum(1 for _ in leaf_nodes)
ultrametric = _np.zeros((n,n))
for u in leaf_nodes:
t.node[u]['leafy_ancestors'] = set([u])
for u in list(_nx.dfs_postorder_nodes(t)):
if t.out_degree(u) == 0:
continue
leafy_ancestors = []
for child in t.successors(u):
leafy_ancestors.append(t.node[child]['leafy_ancestors'])
t.remove_node(child)
d = t.node[u]['distance']
for i in range(len(leafy_ancestors)):
for j in range(len(leafy_ancestors)):
if i == j:
continue
left_set = leafy_ancestors[i]
right_set = leafy_ancestors[j]
for x,y in _itertools.product(left_set, right_set):
ultrametric[x][y] = ultrametric[y][x] = d
ancestors = set()
for x in leafy_ancestors:
ancestors |= x
t.node[u]['leafy_ancestors'] = ancestors
return _distance.squareform(ultrametric)
def flatten_global_expressions( network ):
global global_expr_g_
g = global_expr_g_
for x in nx.dfs_postorder_nodes( g ):
logger_.debug("Trying to reduce expression on node %s" % x)
if 'expr' not in g.node[x]:
logger_.debug("Node %s does not have any expr" % x)
logger_.debug("Not doing anything.")
else:
expr = flatten_expression_on_node(x, g )
# update the value global variables.
if x in network.graph['graph']:
network.graph['graph'][x] = expr
return True
def buildSceneGraph(self, atNode):
"""
Evaluate the DAG in a postorder fashion to create a list of the data
packets in the scene graph sorted by execution order.
"""
dagPathList = list()
nodeEvalOrder = networkx.dfs_postorder_nodes(self.network, atNode)
for dagNode in nodeEvalOrder:
# Retrieve specialized output types
specializationDict = dict()
#for output in dagNode.outputs():
# specializationDict[output.name] = self.nodeOutputType(dagNode, output)
dagPathList.extend(dagNode.scene_graph_handle(specializationDict))
return dagPathList
def execute_graph(self, node_eval=None):
"""
Executes the full graph in order or the nodes given by `node_eval` in the order of the input.
Note: it is recommended to ensure `node_eval` is topologically sorted (in reverse).
"""
# Get dag processing order
if node_eval is None:
node_eval = networkx.dfs_postorder_nodes(self.network)
# TODO: Wherever the graph starts a new empty Context must be created (or possibly predefined?)
# TODO: When graph branches a copy must be made of the Context so each branch operates on its own Context.
# Execute each node
for node in node_eval:
self.execute_node(node)
def _merge_single_entry_node(self, graph):
r = False
while True:
for node in networkx.dfs_postorder_nodes(graph):
preds = graph.predecessors(node)
if len(preds) == 1:
# merge the two nodes
self._absorb_node(graph, preds[0], node)
r = True
break
else:
break
return r
def digraph_eliminate(cpts,evidence,query_list):
"""
Use elimination algorithm to find joint distribution over variables in
query_list, given evidence.
Parameters
------------
cpts : a list of DataArray with variable names for axis names
evidence : a dictionary of observed variables (strings) -> values
query_list : a list of variables (strings)
Returns
--------
marginals : dictionary of variable -> prob_table
likelihood : likelihood of observations in the model
"""
# find the directed graphical model
DG = cpts2digraph(cpts)
# use postorder (leaves to root) from depth-first search as elimination order
rvs = nx.dfs_postorder_nodes(DG)
# modify elimination list so query nodes are at the end
rvs_elim = [rv for rv in rvs if rv not in query_list] + query_list
for rv in rvs_elim:
# find potentials that reference that node
pots_here = filter(lambda cpt: rv in cpt.names, cpts)
# remove them from cpts
cpts = filter(lambda cpt: rv not in cpt.names, cpts)
# Find joint probability distribution of this variable and the ones coupled to it
product_pot = multiply_potentials(*pots_here)
# if node is in query set, we don't sum over it
if rv not in query_list:
# if node is in evidence set, take slice
if rv in evidence: product_pot = product_pot.axis[rv][evidence[rv]]
# otherwise, sum over it
else: product_pot = product_pot.sum(axis=rv)
# add resulting product potential to cpts
cpts.append(product_pot)
assert len(cpts) == 1
unnormed_prob = cpts[0]
likelihood = unnormed_prob.sum()
return unnormed_prob/likelihood, likelihood
def taint():
#get_s
lines = [
"15 | ------ IMark(0x80495b8, 2, 0) ------",
"16 | t2 = GET:I8(eax)",
"17 | t1 = GET:I8(eax)",
"18 | t0 = And8(t2,t1)",
"19 | PUT(cc_op) = 0x0000000d",
"20 | t3 = 8Uto32(t0)",
"21 | PUT(cc_dep1) = t3",
"22 | PUT(cc_dep2) = 0x00000000",
"23 | PUT(cc_ndep) = 0x00000000",
"24 | PUT(eip) = 0x080495ba",
"25 | ------ IMark(0x80495ba, 6, 0) ------",
"26 | t5 = GET:I32(cc_op)",
"27 | t6 = GET:I32(cc_dep1)",
"28 | t7 = GET:I32(cc_dep2)",
"29 | t8 = GET:I32(cc_ndep)",
"30 | t9 = x86g_calculate_condition(0x00000004,t5,t6,t7,t8):Ity_I32",
"31 | t4 = 32to1(t9)",
"32 | if (t4) { PUT(eip) = 0x8049735L; Ijk_Boring }",
"33 | PUT(eip) = 0x080495c0",
"34 | t10 = GET:I32(eip)"
]
queue = []
cfg = nx.DiGraph()
for line in lines:
if "if" in line:
pass
elif "=" in line:
ls = line.split('=',1)
rhs = re.findall('t[0-9]+|cc_[a-z]+[0-9]?|eax|ebx|ecx|edx|esi|edi|esp|ebp', ls[0])
lhs = re.findall('t[0-9]+|cc_[a-z]+[0-9]?|eax|ebx|ecx|edx|esi|edi|esp|ebp', ls[1])
if rhs and lhs:
r = rhs[0]
#print lhs.captures(1)
for item in lhs:
cfg.add_edge(r, item)
lst = list(nx.dfs_postorder_nodes(cfg, "t4"))
print lst
def kosaraju_strongly_connected_components(G,source=None):
"""Return nodes in strongly connected components of graph.
Parameters
----------
G : NetworkX Graph
An directed graph.
Returns
-------
comp : list of lists
A list of nodes for each component of G.
The list is ordered from largest connected component to smallest.
Raises
------
NetworkXError: If G is undirected
See Also
--------
connected_components
Notes
-----
Uses Kosaraju's algorithm.
"""
if not G.is_directed():
raise nx.NetworkXError("""Not allowed for undirected graph G.
Use connected_components() """)
components=[]
G=G.reverse(copy=False)
post=list(nx.dfs_postorder_nodes(G,source=source))
G=G.reverse(copy=False)
seen={}
while post:
r=post.pop()
if r in seen:
continue
c=nx.dfs_preorder_nodes(G,r)
new=[v for v in c if v not in seen]
seen.update([(u,True) for u in new])
components.append(new)
components.sort(key=len,reverse=True)
return components
def reverse_post_order_sort_nodes(graph, nodes=None):
"""
Sort a given set of nodes in reverse post ordering.
:param networkx.DiGraph graph: A local transition graph of a function.
:param iterable nodes: A collection of nodes to sort.
:return: A list of sorted nodes.
:rtype: list
"""
post_order = networkx.dfs_postorder_nodes(graph)
if nodes is None:
return reversed(list(post_order))
else:
addrs_to_index = {}
for i, n in enumerate(post_order):
addrs_to_index[n.addr] = i
return sorted(nodes, key=lambda n: addrs_to_index[n.addr], reverse=True)
def compute_dominance_frontier(graph, domtree):
"""
Compute a dominance frontier based on the given post-dominator tree.
This implementation is based on figure 2 of paper An Efficient Method of Computing Static Single Assignment
Form by Ron Cytron, etc.
:param graph: The graph where we want to compute the dominance frontier.
:param domtree: The dominator tree
:returns: A dict of dominance frontier
"""
df = {}
# Perform a post-order search on the dominator tree
for x in networkx.dfs_postorder_nodes(domtree):
if x not in graph:
# Skip nodes that are not in the graph
continue
df[x] = set()
# local set
for y in graph.successors(x):
if x not in domtree.predecessors(y):
df[x].add(y)
# up set
if x is None:
continue
for z in domtree.successors(x):
if z is x:
continue
if z not in df:
continue
for y in df[z]:
if x not in list(domtree.predecessors(y)):
df[x].add(y)
return df
def induce_total_volume(volumeless_tree, root=None):
"""
Given a tree with edges having the 'volume' metadata, computes the
'total_volume' edge and node property which is the sum of all edge volumes
between successors. If the 'root' parameter is not supplied, it will be
determined by performing a topological sort.
"""
tree = volumeless_tree.copy()
if root is None:
root = nx.topological_sort(tree)[0]
# compute the depth-first search traversal, returning nodes in post-order
traversal = nx.dfs_postorder_nodes(tree, root)
for u in traversal:
tree.node[u]['total_volume'] = 0
for v in tree.successors(u):
tree.node[u]['total_volume'] += (tree.node[v]['total_volume'] +
tree.edge[u][v]['volume'])
tree.edge[u][v]['total_volume'] = (tree.edge[u][v]['volume'] +
tree.node[v]['total_volume'])
return tree
def extract_signatures(self):
"""
Returns all mutational signatures located in the trial match tree g
:return: List of mutational signatures
"""
mmr = []
ms = []
# iterate through the graph
for node_id in list(nx.dfs_postorder_nodes(self.g, source=1)):
node = self.g.node[node_id]
if node['type'] == 'genomic':
if 'mmr_status' in node['value']:
mmr.append(node['value']['mmr_status'])
if 'ms_status' in node['value']:
ms.append(node['value']['ms_status'])
return mmr, ms
def StrahlerOrder(G, root):
# initialize all nodes to 1
for p in G.nodes_iter():
G.node[p]['Strahler'] = 1
G.node[p]['level'] = 0 # longest path from this node to a leaf - leaves have level 1.
nodes = nx.dfs_postorder_nodes(G, root)
for p in nodes:
neigh = nx.all_neighbors(G, p)
delta = 0
mx = 0
level = 0
for q in neigh:
if G.node[q]['Strahler'] == mx: # we met the max value again
delta = 1
if G.node[q]['Strahler'] > mx:
mx = G.node[q]['Strahler']
delta = 0
level = max(level, G.node[q]['level'])
G.node[p]['Strahler'] = mx + delta
G.node[p]['level'] = level + 1
# assign Strahler orders to edges too
for e in G.edges_iter():
s = min(G.node[e[0]]['Strahler'], G.node[e[1]]['Strahler'])
G.edge[e[0]] [e[1]] ['Strahler'] = s
s = min(G.node[e[0]]['level'], G.node[e[1]]['level'])
G.edge[e[0]] [e[1]] ['level'] = s
# determine the parent of each node, based on the node 'level'
for n in G.nodes_iter():
m = 0
p = None
for n2 in G[n]:
if G.node[n2]['level'] > m and G.node[n2]['level'] > G.node[n]['level']:
m = G.node[n2]['level']
p = n2
G.node[n]['parent'] = p
return
def dfs_postorder(self, root):
G = nx.Graph(self.E)
tree_graph = nx.dfs_tree(G, root)
clique_ordering = list(nx.dfs_postorder_nodes(tree_graph, root))
return clique_ordering