def test_convert_q_to_p(self):
q = models.goldman_Q()
p = models.convert_q_to_p(q, t=0)
self.assertEqual(p.shape, (61, 61))
self.assertTrue((p==identity(61)).all())
p = models.convert_q_to_p(q, t=1)
self.assertTrue((p.max() <= 1.0))
def test_make_sub_from_p(self):
q = models.goldman_Q(scale_q=False)
p = models.convert_q_to_p(q, t=10)
p_cumsum, p_codons, p_cumsum_dict = models.get_cumulative_p(p, return_dict=True)
old_codon_seq = 'aaa'
old_codon = models.Codon(seq=old_codon_seq)
models.make_sub_from_p(old_codon, p_cumsum_dict)
self.assertEqual(len(old_codon.seq), 3)
allowed_letters = 'atgc'
for i in old_codon.seq:
self.assertIn(i, allowed_letters)
def test_make_subs_in_locus(self):
q = models.goldman_Q(scale_q=False)
p = models.convert_q_to_p(q, t=10)
p_cumsum, p_codons, p_cumsum_dict = models.get_cumulative_p(p, return_dict=True)
old_codon_seq = 'aaa'
old_codons = [models.Codon(seq=old_codon_seq)]*2
locus = models.Locus(codons=old_codons)
old_seq = locus.sequence
while locus.sequence == old_seq:
models.make_subs_in_locus(locus, p_cumsum_dict)
self.assertNotEqual(locus.history, [])
def evolve_sequence_with_q(sequence, q, t=1e-2, lmbda=1e-4, ti_td=0.1, indel_codon_freq=None):
if not 0 <= lmbda <= 1:
raise ValueError('lmbda must be in range 0-1')
if not ti_td >= 0:
raise ValueError('ti_td must be positive')
new_sequence = deepcopy(sequence)
p = models.convert_q_to_p(q, t=t)
p_cumsum, p_codons, p_cumsum_dict = models.get_cumulative_p(p, return_dict=True)
for locus in new_sequence.loci:
models.make_subs_in_locus(locus, p_cumsum_dict)
for i in range(len(locus.codons)):
if uniform(0, 1) <= lmbda:
models.make_indel(locus, index=i, ti_td=ti_td, codon_freq=indel_codon_freq)
return new_sequence
def evolve_sequence_with_q(sequence,
q,
t=1e-2,
lmbda=1e-4,
ti_td=0.1,
indel_codon_freq=None):
if not 0 <= lmbda <= 1:
raise ValueError('lmbda must be in range 0-1')
if not ti_td >= 0:
raise ValueError('ti_td must be positive')
new_sequence = deepcopy(sequence)
p = models.convert_q_to_p(q, t=t)
p_cumsum, p_codons, p_cumsum_dict = models.get_cumulative_p(
p, return_dict=True)
for locus in new_sequence.loci:
models.make_subs_in_locus(locus, p_cumsum_dict)
for i in range(len(locus.codons)):
if uniform(0, 1) <= lmbda:
models.make_indel(locus,
index=i,
ti_td=ti_td,
codon_freq=indel_codon_freq)
return new_sequence
def test_get_mutation_from_cumulative_p(self):
q = models.goldman_Q(scale_q=False)
p = models.convert_q_to_p(q, t=10)
pc, pcod, pcdict = models.get_cumulative_p(p, return_dict=True)
old_codon = 'aaa'
new_codon = models.get_mutation_from_cumulative_p(old_codon, pcdict)
def test_get_cumulative_p(self):
q = models.goldman_Q(scale_q=False)
p = models.convert_q_to_p(q, t=10)
pc, pcod = models.get_cumulative_p(p)
for i in pc:
self.assertTrue(isclose(i[-1], 1))