def _node_centrality_by_synapse(tree, nodes: Dict, totalOutputs: int,
totalInputs: int) -> None:
""" tree: a DiGraph
nodes: a dictionary of treenode ID vs Counts instance
totalOutputs: the total number of output synapses of the tree
totalInputs: the total number of input synapses of the tree
Returns nothing, the results are an update to the Counts instance of each treenode entry in nodes, namely the nPossibleIOPaths. """
# 1. Ensure the root is an end by checking that it has only one child; otherwise reroot at the first end node found
if 0 == totalOutputs:
# Not computable
for counts in nodes.values():
counts.synapse_centrality = -1
return
if len(list(tree.successors(find_root(tree)))) > 1:
# Reroot at the first end node found
tree = tree.copy()
endNode = next(nodeID for nodeID in nodes.keys()
if not list(tree.successors(nodeID)))
reroot(tree, endNode)
# 2. Partition into sequences, sorted from small to large
sequences = sorted(partition(tree), key=len)
# 3. Traverse all partitions counting synapses seen
for seq in sequences:
# Each seq runs from an end node towards the root or a branch node
seenI = 0
seenO = 0
for nodeID in seq:
counts = nodes[nodeID]
seenI += counts.inputs + counts.seenInputs
seenO += counts.outputs + counts.seenOutputs
counts.seenInputs = seenI
counts.seenOutputs = seenO
counts.nPossibleIOPaths = counts.seenInputs * (
totalOutputs - counts.seenOutputs) + counts.seenOutputs * (
totalInputs - counts.seenInputs)
counts.synapse_centrality = counts.nPossibleIOPaths / float(
totalOutputs)
def _node_centrality_by_synapse(tree, nodes, totalOutputs, totalInputs):
""" tree: a DiGraph
nodes: a dictionary of treenode ID vs Counts instance
totalOutputs: the total number of output synapses of the tree
totalInputs: the total number of input synapses of the tree
Returns nothing, the results are an update to the Counts instance of each treenode entry in nodes, namely the nPossibleIOPaths. """
# 1. Ensure the root is an end by checking that it has only one child; otherwise reroot at the first end node found
if 0 == totalOutputs:
# Not computable
for counts in nodes.values():
counts.synapse_centrality = -1
return
if len(tree.successors(find_root(tree))) > 1:
# Reroot at the first end node found
tree = tree.copy()
endNode = next(nodeID for nodeID in nodes.keys() if not tree.successors(nodeID))
reroot(tree, endNode)
# 2. Partition into sequences, sorted from small to large
sequences = sorted(partition(tree), key=len)
# 3. Traverse all partitions counting synapses seen
for seq in sequences:
# Each seq runs from an end node towards the root or a branch node
seenI = 0
seenO = 0
for nodeID in seq:
counts = nodes[nodeID]
seenI += counts.inputs + counts.seenInputs
seenO += counts.outputs + counts.seenOutputs
counts.seenInputs = seenI
counts.seenOutputs = seenO
counts.nPossibleIOPaths = counts.seenInputs * (totalOutputs - counts.seenOutputs) + counts.seenOutputs * (totalInputs - counts.seenInputs)
counts.synapse_centrality = counts.nPossibleIOPaths / float(totalOutputs)