def split_by_synapse_domain(bandwidth, locations, arbors, treenode_connector, minis):
""" locations: dictionary of treenode ID vs tuple with x,y,z
arbors: dictionary of skeleton ID vs list of DiGraph (that were, or not, split by confidence)
treenode_connectors: dictionary of treenode ID vs list of tuples of connector_id, string of 'presynaptic_to' or 'postsynaptic_to'
"""
arbors2 = {} # Some arbors will be split further
for skeleton_id, graphs in arbors.items():
subdomains = []
arbors2[skeleton_id] = subdomains
for graph in graphs:
treenode_ids = []
connector_ids =[]
relation_ids = []
for treenode_id in filter(treenode_connector.has_key, graph.nodes_iter()):
for c in treenode_connector.get(treenode_id):
connector_id, relation = c
treenode_ids.append(treenode_id)
connector_ids.append(connector_id)
relation_ids.append(relation)
if not connector_ids:
subdomains.append(graph)
continue
for parent_id, treenode_id in graph.edges_iter():
loc0 = locations[treenode_id]
loc1 = locations[parent_id]
graph[parent_id][treenode_id]['weight'] = norm(subtract(loc0, loc1))
# Invoke Casey's magic
max_density = tree_max_density(graph.to_undirected(), treenode_ids,
connector_ids, relation_ids, [bandwidth])
synapse_group = next(max_density.values())
# The list of nodes of each synapse_group contains only nodes that have connectors
# A local_max is the skeleton node most central to a synapse_group
anchors = {}
for domain in synapse_group.values():
g = nx.DiGraph()
g.add_nodes_from(domain.node_ids) # bogus graph, containing treenodes that point to connectors
subdomains.append(g)
anchors[domain.local_max] = g
# Define edges between domains: create a simplified graph
mini = simplify(graph, anchors.keys())
# Replace each node by the corresponding graph, or a graph of a single node
for node in mini.nodes_iter():
g = anchors.get(node)
if not g:
# A branch node that was not an anchor, i.e. did not represent a synapse group
g = nx.Graph()
g.add_node(node, {'branch': True})
subdomains.append(g)
# Associate the Graph with treenodes that have connectors
# with the node in the minified tree
mini.node[node]['g'] = g
# Put the mini into a map of skeleton_id and list of minis,
# to be used later for defining intra-neuron edges in the circuit graph
minis[skeleton_id].append(mini)
return arbors2, minis
def split_by_both(skeleton_id, digraph, locations, bandwidth, cs, connectors, intraedges):
""" Split by confidence and synapse domain. Populates connectors and intraedges (side effects). """
nodes = []
branch_nodes = []
chunks, chunkIDs = subgraphs(digraph, skeleton_id)
for i, chunkID, chunk in zip(count(start=1), chunkIDs, chunks):
# Populate edge properties with the weight
for parent, child in chunk.edges_iter():
chunk[parent][child]['weight'] = norm(subtract(locations[child], locations[parent]))
# Check if need to expand at all
blob = tuple(c for c in cs if c[0] in chunk)
if 0 == len(blob):
nodes.append(chunkID)
continue
treenode_ids, connector_ids, relation_ids, confidences = list(zip(*blob))
if 0 == len(connector_ids):
nodes.append(chunkID)
continue
# Invoke Casey's magic: split by synapse domain
max_density = tree_max_density(chunk.to_undirected(), treenode_ids,
connector_ids, relation_ids, [bandwidth])
# Get first element of max_density
domains = next(iter(max_density.values()))
# domains is a dictionary of index vs SynapseGroup instance
if 1 == len(domains):
for connector_id, relation_id, confidence in zip(connector_ids, relation_ids, confidences):
connectors[connector_id][relation_id].append((chunkID, confidence))
nodes.append(chunkID)
continue
# Create edges between domains
# Pick one treenode from each domain to act as anchor
anchors = {d.node_ids[0]: (i+k, d) for k, d in domains.items()}
# Create new Graph where the edges are the edges among synapse domains
mini = simplify(chunk, anchors.keys())
# Many side effects:
# * add internal edges to intraedges
# * add each domain to nodes
# * custom-apply populate_connectors with the known synapses of each domain
# (rather than having to sift through all in cs)
mini_nodes = {}
for node in mini.nodes_iter():
blob = anchors.get(node, None)
if blob:
index, domain = blob
domainID = '%s_%s' % (chunkID, index)
nodes.append(domainID)
for connector_id, relation_id in zip(domain.connector_ids, domain.relations):
confidence = confidences[connector_ids.index(connector_id)]
connectors[connector_id][relation_id].append((domainID, confidence))
else:
domainID = '%s_%s' % (chunkID, node)
branch_nodes.append(domainID)
mini_nodes[node] = domainID
for a1, a2 in mini.edges_iter():
intraedges.append((mini_nodes[a1], mini_nodes[a2]))
return nodes, branch_nodes
def split_by_both(skeleton_id, digraph, locations, bandwidth, cs, connectors, intraedges) -> Tuple[List, List]:
""" Split by confidence and synapse domain. Populates connectors and intraedges (side effects). """
nodes = []
branch_nodes = []
chunks, chunkIDs = subgraphs(digraph, skeleton_id)
for i, chunkID, chunk in zip(count(start=1), chunkIDs, chunks):
# Populate edge properties with the weight
for parent, child in chunk.edges_iter():
chunk[parent][child]['weight'] = norm(subtract(locations[child], locations[parent]))
# Check if need to expand at all
blob = tuple(c for c in cs if c[0] in chunk) # type: Optional[Tuple]
# the two different uses of blob in this context
# (other being in for node in mini.nodes_iter() below )
# makes typing difficult and may be a maintenance concern
if 0 == len(blob): # type: ignore
nodes.append(chunkID)
continue
treenode_ids, connector_ids, relation_ids, confidences = list(zip(*blob)) # type: ignore
if 0 == len(connector_ids):
nodes.append(chunkID)
continue
# Invoke Casey's magic: split by synapse domain
max_density = tree_max_density(chunk.to_undirected(), treenode_ids,
connector_ids, relation_ids, [bandwidth])
# Get first element of max_density
domains = next(iter(max_density.values()))
# domains is a dictionary of index vs SynapseGroup instance
if 1 == len(domains):
for connector_id, relation_id, confidence in zip(connector_ids, relation_ids, confidences):
connectors[connector_id][relation_id].append((chunkID, confidence))
nodes.append(chunkID)
continue
# Create edges between domains
# Pick one treenode from each domain to act as anchor
anchors = {d.node_ids[0]: (i+k, d) for k, d in domains.items()}
# Create new Graph where the edges are the edges among synapse domains
mini = simplify(chunk, anchors.keys())
# Many side effects:
# * add internal edges to intraedges
# * add each domain to nodes
# * custom-apply populate_connectors with the known synapses of each domain
# (rather than having to sift through all in cs)
mini_nodes = {}
for node in mini.nodes_iter():
blob = anchors.get(node)
if blob:
index, domain = blob
domainID = '%s_%s' % (chunkID, index)
nodes.append(domainID)
for connector_id, relation_id in zip(domain.connector_ids, domain.relations):
confidence = confidences[connector_ids.index(connector_id)]
connectors[connector_id][relation_id].append((domainID, confidence))
else:
domainID = '%s_%s' % (chunkID, node)
branch_nodes.append(domainID)
mini_nodes[node] = domainID
for a1, a2 in mini.edges_iter():
intraedges.append((mini_nodes[a1], mini_nodes[a2]))
return nodes, branch_nodes
def split_by_synapse_domain(bandwidth, locations, arbors, treenode_connector,
minis):
""" locations: dictionary of treenode ID vs tuple with x,y,z
arbors: dictionary of skeleton ID vs list of DiGraph (that were, or not, split by confidence)
treenode_connectors: dictionary of treenode ID vs list of tuples of connector_id, string of 'presynaptic_to' or 'postsynaptic_to'
"""
arbors2 = {} # Some arbors will be split further
for skeleton_id, graphs in six.iteritems(arbors):
subdomains = []
arbors2[skeleton_id] = subdomains
for graph in graphs:
treenode_ids = []
connector_ids = []
relation_ids = []
for treenode_id in filter(treenode_connector.has_key,
graph.nodes_iter()):
for c in treenode_connector.get(treenode_id):
connector_id, relation = c
treenode_ids.append(treenode_id)
connector_ids.append(connector_id)
relation_ids.append(relation)
if not connector_ids:
subdomains.append(graph)
continue
for parent_id, treenode_id in graph.edges_iter():
loc0 = locations[treenode_id]
loc1 = locations[parent_id]
graph[parent_id][treenode_id]['weight'] = norm(
subtract(loc0, loc1))
# Invoke Casey's magic
max_density = tree_max_density(graph.to_undirected(), treenode_ids,
connector_ids, relation_ids,
[bandwidth])
synapse_group = next(six.itervalues(max_density))
# The list of nodes of each synapse_group contains only nodes that have connectors
# A local_max is the skeleton node most central to a synapse_group
anchors = {}
for domain in six.itervalues(synapse_group):
g = nx.DiGraph()
g.add_nodes_from(
domain.node_ids
) # bogus graph, containing treenodes that point to connectors
subdomains.append(g)
anchors[domain.local_max] = g
# Define edges between domains: create a simplified graph
mini = simplify(graph, anchors.keys())
# Replace each node by the corresponding graph, or a graph of a single node
for node in mini.nodes_iter():
g = anchors.get(node)
if not g:
# A branch node that was not an anchor, i.e. did not represent a synapse group
g = nx.Graph()
g.add_node(node, {'branch': True})
subdomains.append(g)
# Associate the Graph with treenodes that have connectors
# with the node in the minified tree
mini.node[node]['g'] = g
# Put the mini into a map of skeleton_id and list of minis,
# to be used later for defining intra-neuron edges in the circuit graph
minis[skeleton_id].append(mini)
return arbors2, minis