Esempio n. 1
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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)
Esempio n. 2
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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))
Esempio n. 3
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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))
Esempio n. 4
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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))