def getNetworkTransP(s1, s2, graph, endpoints, segmentlengths, pathnodes, decayconstantNet):
"""
Returns transition probability of going from segment s1 to s2, based on network distance of segments, as well as corresponding path
"""
subpath = []
s1_point = None
s2_point = None
if s1 == s2:
dist = 0
else:
#Obtain edges (tuples of endpoints) for segment identifiers
s1_edge = endpoints[s1]
s2_edge = endpoints[s2]
s1_l = segmentlengths[s1]
s2_l = segmentlengths[s2]
#This determines segment endpoints of the two segments that are closest to each other
minpair = [0,0,100000]
for i in range(0,2):
for j in range(0,2):
d = round(pointdistance(s1_edge[i],s2_edge[j]),2)
if d<minpair[2]:
minpair = [i,j,d]
s1_point = s1_edge[minpair[0]]
s2_point = s2_edge[minpair[1]]
## if (s1_point in pathnodes or s2_point in pathnodes): # Avoid paths reusing an old pathnode (to prevent self-crossings)
## dist = 100
## else:
if s1_point == s2_point:
#If segments are touching, use a small network distance
dist = 5
else:
try:
#Compute a shortest path (using segment length) on graph where segment endpoints are nodes and segments are (undirected) edges
if graph.has_node(s1_point) and graph.has_node(s2_point):
dist = nx.shortest_path_length(graph, s1_point, s2_point, weight='length')
path = nx.shortest_path(graph, s1_point, s2_point, weight='length')
#get path edges
path_edges = zip(path,path[1:])
#print "edges: "+str(path_edges)
subpath = []
# get object ids for path edges
for e in path_edges:
oid = graph.edge[e[0]][e[1]]["OBJECTID"]
subpath.append(oid)
#print "oid path:"+str(subpath)
else:
#print "node not in segment graph!"
dist = 3*decayconstantNet #600
except nx.NetworkXNoPath:
#print 'no path available, assume a large distance'
dist = 3*decayconstantNet #700
#print "network distance between "+str(s1) + ' and '+ str(s2) + ' = '+str(dist)
return (getNDProbability(dist,decayconstantNet),subpath,s2_point)
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