def split_by_confidence_and_add_edges(confidence_threshold, digraphs, rows):
""" dipgrahs is a dictionary of skeleton IDs as keys and DiGraph instances as values,
where the DiGraph does not have any edges yet.
WARNING: side effect on contents of digraph: will add the edges
"""
arbors = {}
# Define edges, which may result in multiple subgraphs for each skeleton
# when splitting at low-confidence edges:
if 0 == confidence_threshold:
# Do not split skeletons
for row in rows:
if row[1]:
digraphs[row[3]].add_edge(row[1], row[0])
for skid, digraph in digraphs.iteritems():
arbors[skid] = [digraph]
else:
# The DiGraph representing the skeleton may be disconnected at a low-confidence edge
to_split = set()
for row in rows:
if row[2] < confidence_threshold:
to_split.add(row[3])
elif row[1]:
digraphs[row[3]].add_edge(row[1], row[0])
for skid, digraph in digraphs.iteritems():
if skid in to_split:
arbors[skid] = weakly_connected_component_subgraphs(digraph)
else:
arbors[skid] = [digraph]
return arbors
def split_by_confidence_and_add_edges(confidence_threshold, digraphs, rows):
""" dipgrahs is a dictionary of skeleton IDs as keys and DiGraph instances as values,
where the DiGraph does not have any edges yet.
WARNING: side effect on contents of digraph: will add the edges
"""
arbors = {}
# Define edges, which may result in multiple subgraphs for each skeleton
# when splitting at low-confidence edges:
if 0 == confidence_threshold:
# Do not split skeletons
for row in rows:
if row[1]:
digraphs[row[3]].add_edge(row[1], row[0])
for skid, digraph in digraphs.iteritems():
arbors[skid] = [digraph]
else:
# The DiGraph representing the skeleton may be disconnected at a low-confidence edge
to_split = set()
for row in rows:
if row[2] < confidence_threshold:
to_split.add(row[3])
elif row[1]:
digraphs[row[3]].add_edge(row[1], row[0])
for skid, digraph in digraphs.iteritems():
if skid in to_split:
arbors[skid] = weakly_connected_component_subgraphs(digraph)
else:
arbors[skid] = [digraph]
return arbors
def subgraphs(digraph, skeleton_id):
chunks = list(weakly_connected_component_subgraphs(digraph))
if 1 == len(chunks):
chunkIDs = (str(skeleton_id),)
else:
chunkIDs = tuple('%s_%s' % (skeleton_id, (i+1)) for i in range(len(chunks)))
return chunks, chunkIDs
def subgraphs(digraph, skeleton_id):
chunks = weakly_connected_component_subgraphs(digraph)
if 1 == len(chunks):
chunkIDs = (str(skeleton_id),)
else:
chunkIDs = tuple('%s_%s' % (skeleton_id, (i+1)) for i in range(len(chunks)))
return chunks, chunkIDs
def subgraphs(digraph, skeleton_id) -> Tuple[List, Tuple]:
chunks = list(weakly_connected_component_subgraphs(digraph))
if 1 == len(chunks):
chunkIDs = (str(skeleton_id),) # type: Tuple
# Note: Here we're loosening the implicit type
else:
chunkIDs = tuple('%s_%s' % (skeleton_id, (i+1)) for i in range(len(chunks)))
return chunks, chunkIDs
def _skeleton_graph(project_id, skeleton_ids, confidence_threshold):
""" Assumes all skeleton_ids belong to project_id. """
skeletons_string = ",".join(str(int(x)) for x in skeleton_ids)
cursor = connection.cursor()
# Fetch all treenodes of all skeletons
cursor.execute('''
SELECT id, parent_id, confidence, skeleton_id
FROM treenode
WHERE skeleton_id IN (%s)
''' % skeletons_string)
rows = tuple(cursor.fetchall())
# Each skeleton is represented with a DiGraph
arbors = defaultdict(nx.DiGraph)
# Create a DiGraph for every skeleton
for row in rows:
arbors[row[3]].add_node(row[0])
# Define edges, which may result in multiple subgraphs for each skeleton
# when splitting at low-confidence edges:
if 0 == confidence_threshold:
# Do not split skeletons
for row in rows:
arbors[row[3]].add_edge(row[1], row[0])
for skid, digraph in arbors.iteritems():
arbors[skid] = [digraph]
else:
# The DiGraph representing the skeleton may be disconnected at a low-confidence edge
to_split = set()
for row in rows:
if row[2] < confidence_threshold:
to_split.add(row[3])
else:
arbors[row[3]].add_edge(row[1], row[0])
for skid, digraph in arbors.iteritems():
if skid in to_split:
arbors[skid] = weakly_connected_component_subgraphs(digraph)
else:
arbors[skid] = [digraph]
# Fetch all synapses
relations = {'presynaptic_to': -1, 'postsynaptic_to': -1}
for r in Relation.objects.filter(relation_name__in=('presynaptic_to', 'postsynaptic_to'), project_id=project_id).values_list('relation_name', 'id'):
relations[r[0]] = r[1]
cursor.execute('''
SELECT connector_id, relation_id, treenode_id, skeleton_id
FROM treenode_connector
WHERE skeleton_id IN (%s)
''' % skeletons_string)
def container():
return defaultdict(list)
connectors = defaultdict(container)
for row in cursor.fetchall():
connectors[row[0]][row[1]].append((row[2], row[3]))
# Obtain neuron names
cursor.execute('''
SELECT cici.class_instance_a, ci.name
FROM class_instance ci,
class_instance_class_instance cici,
relation r
WHERE cici.class_instance_a IN (%s)
AND cici.class_instance_b = ci.id
AND cici.relation_id = r.id
AND r.relation_name = 'model_of'
''' % skeletons_string)
names = {row[0]: row[1] for row in cursor.fetchall()}
# A DiGraph representing the connections between the arbors (every node is an arbor)
circuit = nx.DiGraph()
for skid, digraphs in arbors.iteritems():
for i, g in enumerate(sorted(digraphs, key=len, reverse=True)):
circuit.add_node(g, {'id': "%s_%s" % (skid, i+1),
'label': "%s [%s]" % (names[skid], i+1),
'skeleton_id': skid,
'node_count': len(g)})
# Define edges between arbors, with number of synapses as an edge property
for c in connectors.values():
for pre_treenode, pre_skeleton in c[relations['presynaptic_to']]:
for pre_arbor in arbors.get(pre_skeleton, ()):
if pre_treenode in pre_arbor:
# Found the DiGraph representing an arbor derived from the skeleton to which the presynaptic treenode belongs.
for post_treenode, post_skeleton in c[relations['postsynaptic_to']]:
for post_arbor in arbors.get(post_skeleton, ()):
if post_treenode in post_arbor:
# Found the DiGraph representing an arbor derived from the skeleton to which the postsynaptic treenode belongs.
edge_props = circuit.get_edge_data(pre_arbor, post_arbor)
if edge_props:
edge_props['c'] += 1
else:
circuit.add_edge(pre_arbor, post_arbor, {'c': 1})
break
break
return circuit