def NJ(thatdm):
# Reconstruct tree
treehat = DistanceTreeConstructor().nj(thatdm)
xtreehat = XTree(
treehat,
dict((clade, set([clade.name])) for clade in treehat.get_terminals()))
return (xtreehat)
def reconstruct_tree_NJ(kmer_distance_matrices, ignore_coalescent=False):
candidates = []
for k in kmer_distance_matrices.keys():
thatdm = kmer_distance_matrices[k]
treehat = DistanceTreeConstructor().nj(thatdm)
xtreehat = XTree(
treehat,
dict((clade, set([clade.name]))
for clade in treehat.get_terminals()))
# treehat = DistanceTreeConstructor().nj(kmer_distance_matrices.values()[0])
candidates.append(xtreehat)
return (most_common_xtree(candidates))
def reconstruct_tree_CoalescentJCNJ(kmer_distance_matrices):
candidates = []
k_pairs = combinations(kmer_distance_matrices.keys(), 2)
for k1, k2 in k_pairs:
thatdm, thetahat = estimate_parameters3(
{
k1: kmer_distance_matrices[k1],
k2: kmer_distance_matrices[k2]
},
mu=1.0)
treehat = DistanceTreeConstructor().nj(thatdm)
xtreehat = XTree(
treehat,
dict((clade, set([clade.name]))
for clade in treehat.get_terminals()))
candidates.append(xtreehat)
return (most_common_xtree(candidates))
def NJArgMinSumOfDistancesFromCoalescentJCExpectedKmerPairDistanceParameterizationMap(
kmer_distance_matrices):
candidates = []
k_pairs = combinations(kmer_distance_matrices.keys(), 2)
for k1, k2 in k_pairs:
thatdm, thetahat = ArgMinSumOfDistancesFromCoalescentJCExpectedKmerPairDistanceParameterizationMap(
{
k1: kmer_distance_matrices[k1],
k2: kmer_distance_matrices[k2]
},
mu=1.0)
treehat = DistanceTreeConstructor().nj(thatdm)
xtreehat = XTree(
treehat,
dict((clade, set([clade.name]))
for clade in treehat.get_terminals()))
candidates.append(xtreehat)
return (most_common_xtree(candidates))
