def hapinitial(allele):
#Edge counts are used. Count of edge/Total count for all edges.
for i in G.out_edges(1, data=True):
if i[2]['allele'] == allele:
return sum(G.node[i[1]]['frequency'])/sum(G.node[1]['frequency'])
else:
x = sum(G.node[e[1]]['frequency'])
#Edge count/parent node count is returned
return x/sum(G.node[e[0]]['frequency'])
else:
return 0.0
#Diploid transition probabilities
def diptrans(a,b):
return haptrans(a)*haptrans(b)
#Initiation. Iterate through pairs of outgoing edges from node 1.
for a, b in itertools.product([(i, j) for i, j in enumerate(G.out_edges(1, keys=True, data=True))], repeat=2):
#If emmsion probability does not equal 0
if emission(GT[0],(a[1][3]['allele'],b[1][3]['allele'])) != 0:
if a[1] == b[1]:
t = hapinitial(a[1][3]['allele'])
var = t*t
else:
var = dipinitial(a[1][3]['allele'], b[1][3]['allele'])
#Matrix element is set to calculated diploid initial probability
m[0][a[0]][b[0]] = var
#Induction. Iterate through each level
for i in range(1,ll):
