def test_natsel_sitehet(self):
"""site-het natsel hypothesis test"""
opt = dict(max_evaluations=2, limit_action="ignore")
aln = load_aligned_seqs("data/primate_brca1.fasta", moltype="dna")
# default, not optimising root probs
natsel = evo_app.natsel_sitehet("MG94HKY",
tree="data/primate_brca1.tree",
opt_args=opt)
result = natsel(aln)
# one free param for each edge, 1 for kappa, 1 for omega, 1 for bprobs
self.assertEqual(result.null.lf.nfp, 14)
# plus one extra bprob and one extra omega
self.assertEqual(result.alt.lf.nfp, 16)
# fails if not a codon model
with self.assertRaises(ValueError):
_ = evo_app.natsel_sitehet("F81", tree="data/primate_brca1.tree")
def test_natsel_sitehet_mprob(self):
"""natsel_sitehet correctly applies genetic code and optimise_motif_probs args"""
opt = dict(max_evaluations=2, limit_action="ignore")
aln = load_aligned_seqs("data/ENSG00000198712.fa", moltype="dna")
# optimising root probs
natsel = evo_app.natsel_sitehet(
"MG94HKY", opt_args=opt, gc=2, optimise_motif_probs=True
)
# test of genetic code is implicit, if not correct, the following
# call would return NotCompleted (for this mtDNA gene), which does not
# have a .null attribute
result = natsel(aln)
# 3 edges, 1 kappa, 1 omega, 1 bprob, 3 mprob
self.assertEqual(result.null.lf.nfp, 9)