def test_multiple_mrcas(self):
tables = tskit.TableCollection(sequence_length=1)
tables.nodes.add_row(flags=tskit.NODE_IS_SAMPLE, population=0,
individual=-1, time=0)
tables.nodes.add_row(flags=tskit.NODE_IS_SAMPLE, population=0,
individual=-1, time=0)
tables.edges.add_row(left=0, right=0.5, parent=2, child=1)
tables.edges.add_row(left=0.5, right=1, parent=3, child=1)
tables.nodes.add_row(flags=msprime.NODE_IS_RE_EVENT, population=0,
individual=-1, time=0.1)
tables.nodes.add_row(flags=msprime.NODE_IS_RE_EVENT, population=0,
individual=-1, time=0.1)
tables.edges.add_row(left=0, right=0.5, parent=4, child=0)
tables.edges.add_row(left=0.5, right=1, parent=5, child=0)
tables.nodes.add_row(flags=msprime.NODE_IS_RE_EVENT, population=0,
individual=-1, time=0.15)
tables.nodes.add_row(flags=msprime.NODE_IS_RE_EVENT, population=0,
individual=-1, time=0.15)
tables.edges.add_row(left=0, right=0.5, parent=6, child=2)
tables.edges.add_row(left=0, right=0.5, parent=6, child=4)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=0.5)
tables.edges.add_row(left=0.5, right=1, parent=7, child=3)
tables.edges.add_row(left=0.5, right=1, parent=7, child=5)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=1)
tables.mutations.add_row(site=0, node=1, derived_state="1")
tables.mutations.add_row(site=1, node=4, derived_state="1")
tables.mutations.add_row(site=2, node=3, derived_state="1")
tables.sites.add_row(0.1, "0")
tables.sites.add_row(0.2, "0")
tables.sites.add_row(0.7, "0")
tables.populations.add_row()
arg = tables.tree_sequence()
rho = np.arange(0.1, 10, 0.1)
for r in rho:
log_arg_likelihood_exact = math.log(r) - (1 + 2 * r) * 0.1
log_arg_likelihood_exact += math.log(r) - (3 + 2 * r) * 0.05
log_arg_likelihood_exact -= (6 + 2 * r) * 0.35
log_arg_likelihood_exact -= (1 + r) * 0.5
self.assertTrue(math.isclose(log_arg_likelihood_exact,
msprime.log_arg_likelihood(arg, r)))
theta = np.arange(0.1, 10, 0.1)
tree_length = 1.5
for t in theta:
unnormalised_mutation_ll_exact = (3 * math.log(tree_length * t) -
tree_length * t)
unnormalised_mutation_ll_exact -= math.log(tree_length)
unnormalised_mutation_ll_exact -= 2 * math.log(2 * tree_length)
self.assertTrue(math.isclose(
unnormalised_mutation_ll_exact,
msprime.unnormalised_log_mutation_likelihood(arg, t)))
def test_zero_mut_rate(self):
# No mutations
ts = msprime.simulate(5,
recombination_rate=1,
random_seed=12,
record_full_arg=True)
lik = msprime.unnormalised_log_mutation_likelihood(ts, 0)
self.assertEqual(lik, 0)
# With mutations
ts = msprime.simulate(
5,
recombination_rate=1,
mutation_rate=1,
random_seed=12,
record_full_arg=True,
)
lik = msprime.unnormalised_log_mutation_likelihood(ts, 0)
self.assertEqual(lik, float("-inf"))
def test_recombination_in_material_gap(self):
tables = tskit.TableCollection(sequence_length=1)
tables.nodes.add_row(flags=tskit.NODE_IS_SAMPLE,
population=0,
individual=-1,
time=0)
tables.nodes.add_row(flags=tskit.NODE_IS_SAMPLE,
population=0,
individual=-1,
time=0)
tables.edges.add_row(left=0, right=0.3, parent=2, child=0)
tables.edges.add_row(left=0.3, right=1, parent=3, child=0)
tables.nodes.add_row(flags=msprime.NODE_IS_RE_EVENT,
population=0,
individual=-1,
time=0.1)
tables.nodes.add_row(flags=msprime.NODE_IS_RE_EVENT,
population=0,
individual=-1,
time=0.1)
tables.edges.add_row(left=0.3, right=0.5, parent=4, child=3)
tables.edges.add_row(left=0.5, right=1, parent=5, child=3)
tables.nodes.add_row(flags=msprime.NODE_IS_RE_EVENT,
population=0,
individual=-1,
time=0.2)
tables.nodes.add_row(flags=msprime.NODE_IS_RE_EVENT,
population=0,
individual=-1,
time=0.2)
tables.edges.add_row(left=0, right=0.3, parent=6, child=2)
tables.edges.add_row(left=0.5, right=1, parent=6, child=5)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=0.3)
tables.edges.add_row(left=0, right=0.3, parent=7, child=6)
tables.edges.add_row(left=0.5, right=1, parent=8, child=6)
tables.nodes.add_row(flags=msprime.NODE_IS_RE_EVENT,
population=0,
individual=-1,
time=0.4)
tables.nodes.add_row(flags=msprime.NODE_IS_RE_EVENT,
population=0,
individual=-1,
time=0.4)
tables.edges.add_row(left=0.3, right=0.5, parent=9, child=4)
tables.edges.add_row(left=0.5, right=1, parent=9, child=8)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=0.5)
tables.edges.add_row(left=0, right=1, parent=10, child=1)
tables.edges.add_row(left=0, right=0.3, parent=10, child=7)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=0.6)
tables.edges.add_row(left=0.3, right=1, parent=11, child=9)
tables.edges.add_row(left=0.3, right=1, parent=11, child=10)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=0.7)
tables.mutations.add_row(site=0, node=2, derived_state="1")
tables.mutations.add_row(site=1, node=3, derived_state="1")
tables.mutations.add_row(site=2, node=6, derived_state="1")
tables.mutations.add_row(site=3, node=5, derived_state="1")
tables.sites.add_row(0.1, "0")
tables.sites.add_row(0.35, "0")
tables.sites.add_row(0.6, "0")
tables.sites.add_row(0.8, "0")
tables.populations.add_row()
arg = tables.tree_sequence()
rho = np.arange(0.1, 10, 0.1)
for r in rho:
log_arg_likelihood_exact = math.log(r) - (1 + 2 * r) * 0.1
log_arg_likelihood_exact += math.log(r) - (3 + 2 * r) * 0.1
log_arg_likelihood_exact -= (6 + 2 * r) * 0.1
log_arg_likelihood_exact += math.log(0.2 * r) - (3 + 2.2 * r) * 0.1
log_arg_likelihood_exact -= (6 + 2 * r) * 0.1
log_arg_likelihood_exact -= (3 + 2 * r) * 0.1
log_arg_likelihood_exact -= (1 + 1.4 * r) * 0.1
self.assertTrue(
math.isclose(log_arg_likelihood_exact,
msprime.log_arg_likelihood(arg, r)))
theta = np.arange(0.1, 10, 0.1)
tree_length = 1.34
for t in theta:
unnormalised_mutation_ll_exact = (4 * math.log(tree_length * t) -
tree_length * t)
unnormalised_mutation_ll_exact += math.log(0.6 / tree_length)
unnormalised_mutation_ll_exact += 3 * math.log(0.7 / tree_length)
self.assertTrue(
math.isclose(
unnormalised_mutation_ll_exact,
msprime.unnormalised_log_mutation_likelihood(arg, t)))