def updateEDMSTI(d, seq, tree, num, clusteringType):
newD = {}
if clusteringType == 'NJ':
maxSimilarity = NJUtils.getMaxSimilarityForNJ(d, seq)
elif clusteringType == 'UPGMA':
maxSimilarity = UPGMAUtils.getMaxSimilarityForUPGMA(d)
elif clusteringType == 'WPGMA':
maxSimilarity = WPGMAUtils.getMaxSimilarityForWPGMA(d)
f = maxSimilarity['key'][0]
iF = seq.index(f)
numF = num[iF]
g = maxSimilarity['key'][1]
if f == g:
iG = iF + 1 + seq[iF + 1:].index(g)
else:
iG = seq.index(g)
numG = num[iG]
u = '(' + f + ':' + str(d[maxSimilarity['key']]) + ':' + g + ')'
seq.remove(f)
seq.remove(g)
if clusteringType == 'NJ':
delta = NJUtils.getDeltaDistForNJ(d, f, g, u, seq)
elif clusteringType == 'UPGMA':
delta = UPGMAUtils.getDeltaDistForUPGMA(d, f, g, u)
elif clusteringType == 'WPGMA':
delta = WPGMAUtils.getDeltaDistForWPGMA(d, f, g, u)
tree.append({'1': u, '2': f, 'dist': delta[u, f]})
tree.append({'1': u, '2': g, 'dist': delta[u, g]})
for i in range(len(seq)):
for j in range(len(seq)):
if i != j:
newD[seq[i], seq[j]] = d[seq[i], seq[j]]
if clusteringType == 'NJ':
newD = NJUtils.getDistUForNJ(d, newD, f, g, u, seq)
elif clusteringType == 'UPGMA':
newD = UPGMAUtils.getDistUForUPGMA(d, newD, f, g, u, seq, numF, numG)
elif clusteringType == 'WPGMA':
newD = WPGMAUtils.getDistUForWPGMA(d, newD, f, g, u, seq)
seq.append(u)
num.append(numF + numG + 1)
return newD, seq, num, tree, iF, iG
