<gh_stars>1-10
#!/usr/bin/env python

from __future__ import with_statement
from cogent import LoadTree
from cogent.phylo import nj as NJ
from cogent.phylo.distance import EstimateDistances
from cogent.core.info import Info
from cogent.util import progress_display as UI

__author__ = "<NAME>"
__copyright__ = "Copyright 2007-2012, The Cogent Project"
__credits__ = ["<NAME>", "<NAME>"]
__license__ = "GPL"
__version__ = "1.5.3"
__maintainer__ = "<NAME>"
__email__ = "<EMAIL>"
__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 alignment and tree.
    
    Uses the provided substitution model and a tree for determining the
    progressive order. If a tree is not provided a Neighbour Joining tree is
    constructed from pairwise distances estimated from pairwise aligning the
    sequences. If running in parallel, only the distance estimation is
    parallelised and only the master CPU returns the alignment and tree, other
    CPU's return None, None.
    
    Arguments:
        - model: a substitution model
        - 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.
    """
    _exclude_params = ['mprobs', 'rate', 'bin_switch']
    if param_vals:
        param_vals = dict(param_vals)
    else:
        param_vals = {}
    if isinstance(seqs, dict):
        seq_names = seqs.keys()
    else:
        seq_names = seqs.getSeqNames()
    
    two_seqs = len(seq_names) == 2
    
    if tree:
        tip_names = tree.getTipNames()
        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 = LoadTree(tip_names=seqs.getSeqNames())
        ests_from_pairwise = False
    else:
        if ests_from_pairwise:
            est_params = [param for param in model.getParamList() \
                                    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.getPairwiseDistances()
        tree = NJ.nj(dists)
    
    LF = model.makeLikelihoodFunction(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.getParamValues(param)
            print "Param Estimate Summary Stats: %s" % param
            print numbers.summarize()
            param_vals[param] = numbers.Median
    
    ui.display("Doing %s alignment" % ["progressive", "pairwise"][two_seqs])
    with LF.updatesPostponed():
        for param, val in param_vals.items():
            LF.setParamRule(param, value=val, is_constant=True)
        LF.setParamRule('indel_rate', value=indel_rate, is_constant=True)
        LF.setParamRule('indel_length', value=indel_length, is_constant=True)
        LF.setSequences(seqs)
    edge = LF.getLogLikelihood().edge
    (vtLnL, align) = edge.getViterbiScoreAndAlignment(0.5)
    info = Info()
    info["AlignParams"] = param_vals
    info["AlignParams"].update(dict(indel_length=indel_length, indel_rate=indel_rate))
    align.Info = info
    return align, tree

#!/usr/bin/env python

from __future__ import with_statement
from cogent import LoadTree
from cogent.phylo import nj as NJ
from cogent.phylo.distance import EstimateDistances
from cogent.core.info import Info
from cogent.util import progress_display as UI

__author__ = "<NAME>"
__copyright__ = "Copyright 2007-2012, The Cogent Project"
__credits__ = ["<NAME>", "<NAME>"]
__license__ = "GPL"
__version__ = "1.5.3"
__maintainer__ = "<NAME>"
__email__ = "<EMAIL>"
__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 alignment and tree.
    
    Uses the provided substitution model and a tree for determining the
    progressive order. If a tree is not provided a Neighbour Joining tree is
    constructed from pairwise distances estimated from pairwise aligning the
    sequences. If running in parallel, only the distance estimation is
    parallelised and only the master CPU returns the alignment and tree, other
    CPU's return None, None.
    
    Arguments:
        - model: a substitution model
        - 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.
    """
    _exclude_params = ['mprobs', 'rate', 'bin_switch']
    if param_vals:
        param_vals = dict(param_vals)
    else:
        param_vals = {}
    if isinstance(seqs, dict):
        seq_names = seqs.keys()
    else:
        seq_names = seqs.getSeqNames()
    
    two_seqs = len(seq_names) == 2
    
    if tree:
        tip_names = tree.getTipNames()
        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 = LoadTree(tip_names=seqs.getSeqNames())
        ests_from_pairwise = False
    else:
        if ests_from_pairwise:
            est_params = [param for param in model.getParamList() \
                                    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.getPairwiseDistances()
        tree = NJ.nj(dists)
    
    LF = model.makeLikelihoodFunction(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.getParamValues(param)
            print "Param Estimate Summary Stats: %s" % param
            print numbers.summarize()
            param_vals[param] = numbers.Median
    
    ui.display("Doing %s alignment" % ["progressive", "pairwise"][two_seqs])
    with LF.updatesPostponed():
        for param, val in param_vals.items():
            LF.setParamRule(param, value=val, is_constant=True)
        LF.setParamRule('indel_rate', value=indel_rate, is_constant=True)
        LF.setParamRule('indel_length', value=indel_length, is_constant=True)
        LF.setSequences(seqs)
    edge = LF.getLogLikelihood().edge
    (vtLnL, align) = edge.getViterbiScoreAndAlignment(0.5)
    info = Info()
    info["AlignParams"] = param_vals
    info["AlignParams"].update(dict(indel_length=indel_length, indel_rate=indel_rate))
    align.Info = info
    return align, tree
#!/usr/bin/env python Returns a multiple alignment and tree.          Uses the provided substitution model and a tree for determining the     progressive order. If a tree is not provided a Neighbour Joining tree is     constructed from pairwise distances estimated from pairwise aligning the     sequences. If running in parallel, only the distance estimation is     parallelised and only the master CPU returns the alignment and tree, other     CPU's return None, None.          Arguments:         - model: a substitution model         - 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. # we use the Median to avoid the influence of outlier pairs