from cogent3 import make_tree
from cogent3.evolve.distance import EstimateDistances
from cogent3.phylo import nj as NJ
from cogent3.util import progress_display as UI


__author__ = "Peter Maxwell"
__copyright__ = "Copyright 2007-2022, The Cogent Project"
__credits__ = ["Peter Maxwell", "Gavin Huttley"]
__license__ = "BSD-3"
__version__ = "2022.8.24a1"
__maintainer__ = "Peter Maxwell"
__email__ = "pm67nz@gmail.com"
__status__ = "Production"


@UI.display_wrap
def TreeAlign(
    model,
    seqs,
    tree=None,
    indel_rate=0.01,
    indel_length=0.01,
    ui=None,
    ests_from_pairwise=True,
    param_vals=None,
):
    """Returns a multiple sequence alignment and tree.

    Parameters
    ----------
    model
        a substitution model or the name of one, see available_models()
    seqs
        a sequence collection
    indel_rate, indel_length
        parameters for the progressive pair-HMM
    ests_from_pairwise
        if no tree provided and True, the median value
        of the substitution model parameters are used
    param_vals
        named key, value pairs for model parameters. These
        override ests_from_pairwise.

    Notes
    -----
    Uses a tree for determining the progressive order. If a tree is not
    provided, a Neighbour Joining tree is constructed from pairwise
    distances estimated (using the provided substitution model) from pairwise
    aligning the sequences.

    Parameters and tree are added to ``<align>.info["align_params"]``.
    """
    from cogent3 import get_model

    _exclude_params = ["mprobs", "rate", "bin_switch"]
    param_vals = dict(param_vals) if param_vals else {}
    seq_names = list(seqs.keys()) if isinstance(seqs, dict) else seqs.names
    two_seqs = len(seq_names) == 2

    model = get_model(model)
    if tree:
        tip_names = tree.get_tip_names()
        tip_names.sort()
        seq_names.sort()
        assert (
            tip_names == seq_names
        ), "names don't match between seqs and tree: tree=%s; seqs=%s" % (
            tip_names,
            seq_names,
        )
        ests_from_pairwise = False
    elif two_seqs:
        tree = make_tree(tip_names=seqs.names)
        ests_from_pairwise = False
    else:
        if ests_from_pairwise:
            est_params = [
                param
                for param in model.get_param_list()
                if param not in _exclude_params
            ]
        else:
            est_params = None

        dcalc = EstimateDistances(
            seqs, model, do_pair_align=True, est_params=est_params
        )
        dcalc.run()
        dists = dcalc.get_pairwise_distances().to_dict()
        tree = NJ.nj(dists)

    LF = model.make_likelihood_function(
        tree.bifurcating(name_unnamed=True), aligned=False
    )
    if ests_from_pairwise and not param_vals:
        # we use the median to avoid the influence of outlier pairs
        param_vals = {}
        for param in est_params:
            numbers = dcalc.get_param_values(param)
            param_vals[param] = numbers.median

    ui.display(f"Doing {['progressive', 'pairwise'][two_seqs]} alignment")
    with LF.updates_postponed():
        for param, val in list(param_vals.items()):
            LF.set_param_rule(param, value=val, is_constant=True)
        LF.set_param_rule("indel_rate", value=indel_rate, is_constant=True)
        LF.set_param_rule("indel_length", value=indel_length, is_constant=True)
        LF.set_sequences(seqs)
    lnL = LF.get_log_likelihood()
    edge = lnL.edge
    align = edge.get_viterbi_path().get_alignment()
    align = align.to_moltype(model.moltype)
    param_vals.update(
        dict(
            indel_length=indel_length,
            indel_rate=indel_rate,
            guide_tree=tree.get_newick(with_distances=True),
            model=model.name,
            lnL=lnL,
        )
    )
    align.info["align_params"] = param_vals
    return align, tree
