def test_getParamList(self):
"""testing getting the parameter list"""
model = substitution_model.Nucleotide()
self.assertEqual(model.getParamList(), [])
model = substitution_model.Nucleotide(predicates=['beta:transition'])
self.assertEqual(model.getParamList(), ['beta'])
def test_isIndel(self):
"""testing indel comparison nucleotide model"""
model = substitution_model.Nucleotide(do_scaling=True, model_gaps=True)
isIndel = model.getPredefinedPredicate('indel')
assert isIndel('A', '-')
assert isIndel('-', 'G')
#assert not self.submodel.isIndel('-', '-')
assert not isIndel('a', 't')
def GTR(**kw):
"""General Time Reversible nucleotide substitution model."""
return substitution_model.Nucleotide(
motif_probs = None,
do_scaling = True,
model_gaps = False,
recode_gaps = True,
name = 'GTR',
predicates = _gtr_preds,
**kw)
# specify the 4 taxon tree, and a 'dummy' alignment
t = tree.LoadTree(treestring='(a:0.4,b:0.3,(c:0.15,d:0.2)edge.0:0.1)root;')
#al = alignments.LoadSeqs(data={'a':'a','b':'a','c':'a','d':'a'})
# how long the simulated alignments should be
# at 1000000 the estimates get nice and close
length_of_align = 10000
#########################
#
# For a Jukes Cantor model
#
#########################
sm = substitution_model.Nucleotide()
lf = sm.makeLikelihoodFunction(t)
lf.setConstantLengths()
lf.setName('True JC model')
print lf
simulated = lf.simulateAlignment(sequence_length=length_of_align)
print simulated
new_lf = sm.makeLikelihoodFunction(t)
new_lf = new_lf.setAlignment(simulated)
new_lf.optimise(tolerance=1.0)
new_lf.optimise(local=True)
new_lf.setName('True JC model')
print new_lf
#########################
def _makeModel(self, do_scaling, predicates, scale_rules=[]):
return substitution_model.Nucleotide(do_scaling=do_scaling,
equal_motif_probs=True,
model_gaps=False,
predicates=predicates,
scales=scale_rules)
def getsubmod(self, choice='F81'):
if choice == 'F81':
return substitution_model.Nucleotide(model_gaps=True)
else:
return substitution_model.Nucleotide(
model_gaps=True, predicates={'kappa': 'transition'})
def setUp(self):
self.submodel = substitution_model.Nucleotide(do_scaling=True,
model_gaps=False)
def test_getMotifs(self):
"""testing return of motifs"""
model_motifs = substitution_model.Nucleotide().getMotifs()
def test_includinggaps(self):
"""testing excluding gaps from model"""
model = substitution_model.Nucleotide(model_gaps=True)
assert len(model.getAlphabet()) == 5
def setUp(self):
self.submodel = substitution_model.Nucleotide(motif_length=3,
mprob_model='tuple')
