def getsubmod(self, choice="F81"):
if choice == "F81":
return substitution_model.TimeReversibleNucleotide(model_gaps=True)
else:
return substitution_model.TimeReversibleNucleotide(
model_gaps=True, predicates={"kappa": "transition"}
)
def test_get_param_list(self):
"""testing getting the parameter list"""
model = substitution_model.TimeReversibleNucleotide()
self.assertEqual(model.get_param_list(), [])
model = substitution_model.TimeReversibleNucleotide(
predicates=["beta:transition"])
self.assertEqual(model.get_param_list(), ["beta"])
def _est_simulations():
# specify the 4 taxon tree, and a 'dummy' alignment
t = make_tree(treestring="(a:0.4,b:0.3,(c:0.15,d:0.2)edge.0:0.1)root;")
# 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.TimeReversibleNucleotide()
lf = sm.make_likelihood_function(t)
lf.set_constant_lengths()
lf.set_name("True JC model")
print(lf)
simulated = lf.simulate_alignment(sequence_length=length_of_align)
print(simulated)
new_lf = sm.make_likelihood_function(t)
new_lf = new_lf.set_alignment(simulated)
new_lf.optimise(tolerance=1.0)
new_lf.optimise(local=True)
new_lf.set_name("True JC model")
print(new_lf)
#########################
#
# a Kimura model
#
#########################
# has a ts/tv term, different values for every edge
sm = substitution_model.TimeReversibleNucleotide(
predicates={"kappa": "transition"})
lf = sm.make_likelihood_function(t)
lf.set_constant_lengths()
lf.set_param_rule("kappa", is_constant=True, value=4.0, edge_name="a")
lf.set_param_rule("kappa", is_constant=True, value=0.5, edge_name="b")
lf.set_param_rule("kappa", is_constant=True, value=0.2, edge_name="c")
lf.set_param_rule("kappa", is_constant=True, value=3.0, edge_name="d")
lf.set_param_rule("kappa", is_constant=True, value=2.0, edge_name="edge.0")
lf.set_name("True Kappa model")
print(lf)
simulated = lf.simulate_alignment(sequence_length=length_of_align)
print(simulated)
new_lf = sm.make_likelihood_function(t)
new_lf.set_param_rule("kappa", is_independent=True)
new_lf.set_alignment(simulated)
new_lf.optimise(tolerance=1.0)
new_lf.optimise(local=True)
new_lf.set_name("Estimated Kappa model")
print(new_lf)
def test_isIndel(self):
"""testing indel comparison nucleotide model"""
model = substitution_model.TimeReversibleNucleotide(model_gaps=True)
isIndel = model.get_predefined_predicate("indel")
assert isIndel("A", "-")
assert isIndel("-", "G")
# assert not self.submodel.isIndel('-', '-')
assert not isIndel("a", "t")
def _makeModel(self, predicates, scale_rules=None):
scale_rules = scale_rules or []
return substitution_model.TimeReversibleNucleotide(
equal_motif_probs=True,
model_gaps=False,
predicates=predicates,
scales=scale_rules,
)
def test_nonrev_exception(self):
"""constructing a Nucleotide model with non-reversible preds raises exception"""
preds = predicate.MotifChange("A", "G", forward_only=True)
with self.assertRaises(ValueError):
sm = substitution_model.TimeReversibleNucleotide(
predicates=[preds])
def test_getMotifs(self):
"""testing return of motifs"""
model_motifs = substitution_model.TimeReversibleNucleotide(
).get_motifs()
def test_includinggaps(self):
"""testing excluding gaps from model"""
model = substitution_model.TimeReversibleNucleotide(model_gaps=True)
assert len(model.get_alphabet()) == 5
def setUp(self):
self.submodel = substitution_model.TimeReversibleNucleotide(
motif_length=3, mprob_model="tuple")
def setUp(self):
self.submodel = substitution_model.TimeReversibleNucleotide(
model_gaps=False)
