def test_EstimateDistances_other_model_params(self):
"""test getting other model params from EstimateDistances"""
d = EstimateDistances(self.al, HKY85(), est_params=['kappa'])
d.run()
# this will be a Number object with Mean, Median etc ..
kappa = d.getParamValues('kappa')
self.assertAlmostEqual(kappa.Mean, 0.8939, 4)
# this will be a dict with pairwise instances, it's called by the above
# method, so the correctness of it's values is already checked
kappa = d.getPairwiseParam('kappa')
def test_EstimateDistances_other_model_params(self):
"""test getting other model params from EstimateDistances"""
d = EstimateDistances(self.al, HKY85(), est_params=['kappa'])
d.run()
# this will be a Number object with Mean, Median etc ..
kappa = d.getParamValues('kappa')
self.assertAlmostEqual(kappa.Mean, 0.8939, 4)
# this will be a dict with pairwise instances, it's called by the above
# method, so the correctness of it's values is already checked
kappa = d.getPairwiseParam('kappa')
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 = list(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 list(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
align = edge.getViterbiPath().getAlignment()
info = Info()
info["AlignParams"] = param_vals
info["AlignParams"].update(dict(indel_length=indel_length, indel_rate=indel_rate))
align.Info = info
return align, tree
