Esempio n. 1
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def BTC_networks_generator(taxa):
    """
    Generator that yields all the BTC networks over taxa.
    """
    if len(taxa) == 1:
        yield phylonetwork.PhyloNetwork(eNewick=taxa[0] + ';')
        return
    ell = taxa[-1]
    parent_generator = BTC_networks_generator(taxa[:-1])
    for net in parent_generator:
        for augmented in BTC_offspring_generator(net, ell):
            yield augmented
Esempio n. 2
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def random_BTC_network(taxa):
    """
    Returns a random BTC network over taxa. It uses a recursive implementation, where
    at each stage the generated network is chosen uniformly between its offspring
    (which does not imply that the overall procedure is uniform).
    """
    if len(taxa) == 1:
        return phylonetwork.PhyloNetwork(eNewick=taxa[0] + ';')
    net_previous = random_BTC_network(taxa[:-1])
    feasibles = feasible_pairs(net_previous)
    chosen = random.choice(feasibles)
    f = chosen[0]
    args = chosen[1:]
    return f(net_previous, taxa[-1], *args)
def read_enewick(s):
    return ph.PhyloNetwork(eNewick=s)
Esempio n. 4
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def treat_line(line):
    line = line.strip()
    net = phylonetwork.PhyloNetwork(eNewick=line)
    return count_feasible_pairs(net)
def treat_line(line):
    line = line.strip()
    net = phylonetwork.PhyloNetwork(eNewick=line)
    new_nets = BTC_offspring_generator(net, newlabel)
    return [net.eNewick() for net in new_nets]