def test_negative_rec_rate(self):
ts = msprime.simulate(5,
recombination_rate=1,
random_seed=12,
record_full_arg=True)
with pytest.raises(ValueError):
msprime.log_arg_likelihood(ts, recombination_rate=-1)
with pytest.raises(ValueError):
log_arg_likelihood(ts, recombination_rate=-1)
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 verify(self, ts, recombination_rate):
l1 = msprime.log_arg_likelihood(ts,
recombination_rate=recombination_rate)
l2 = log_arg_likelihood(ts, recombination_rate)
self.assertAlmostEqual(l1, l2)
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)))
def test_log_likelihoods(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.nodes.add_row(flags=tskit.NODE_IS_SAMPLE,
population=0,
individual=-1,
time=0)
tables.edges.add_row(left=0, right=0.5, parent=3, child=2)
tables.edges.add_row(left=0.5, right=1, parent=4, child=2)
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=1, parent=5, child=1)
tables.edges.add_row(left=0, right=0.5, parent=5, child=3)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=0.25)
tables.edges.add_row(left=0, right=1, parent=6, child=0)
tables.edges.add_row(left=0.5, right=1, parent=6, child=4)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=0.5)
tables.edges.add_row(left=0, right=1, parent=7, child=5)
tables.edges.add_row(left=0, right=1, parent=7, child=6)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=1)
tables.mutations.add_row(site=0, node=0, derived_state="1")
tables.mutations.add_row(site=1, node=1, derived_state="1")
tables.mutations.add_row(site=2, node=3, derived_state="1")
tables.mutations.add_row(site=3, node=0, derived_state="1")
tables.mutations.add_row(site=4, node=5, derived_state="1")
tables.sites.add_row(0.1, "0")
tables.sites.add_row(0.2, "0")
tables.sites.add_row(0.3, "0")
tables.sites.add_row(0.4, "0")
tables.sites.add_row(0.45, "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) - (3 + 3 * r) * 0.1
log_arg_likelihood_exact -= (6 + 3 * r) * 0.15
log_arg_likelihood_exact -= (3 + 2.5 * r) * 0.25
log_arg_likelihood_exact -= (1 + 2 * r) * 0.5
assert math.isclose(log_arg_likelihood_exact,
msprime.log_arg_likelihood(arg, r, 0.5))
theta = np.arange(0.1, 10, 0.1)
tree_length = 19 / 8
for t in theta:
mutation_ll_exact = 5 * math.log(tree_length * t) - tree_length * t
mutation_ll_exact -= 2 * math.log(4 * tree_length)
mutation_ll_exact -= 2 * math.log(tree_length)
mutation_ll_exact += math.log(3 / (4 * tree_length))
assert math.isclose(
mutation_ll_exact,
msprime.log_mutation_likelihood(arg, t),
)
def verify(self, ts):
for r in [0.001, 1]:
l1 = msprime.log_arg_likelihood(ts, r)
l2 = log_arg_likelihood(ts, r)
self.assertAlmostEqual(l1, l2)
def test_gap_in_ancestral_material(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=1, parent=7, child=1)
tables.edges.add_row(left=0, right=0.3, parent=7, child=6)
tables.edges.add_row(left=0.5, right=1, parent=7, child=6)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=0.4)
tables.edges.add_row(left=0.3, right=0.5, parent=8, child=4)
tables.edges.add_row(left=0.3, right=0.5, parent=8, child=7)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=0.5)
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=7, derived_state="1")
tables.mutations.add_row(site=3, node=6, derived_state="1")
tables.mutations.add_row(site=4, node=5, derived_state="1")
tables.sites.add_row(0.1, "0")
tables.sites.add_row(0.35, "0")
tables.sites.add_row(0.4, "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 -= (3 + 2.2 * r) * 0.1
log_arg_likelihood_exact -= (1 + 0.4 * r) * 0.1
assert math.isclose(log_arg_likelihood_exact,
msprime.log_arg_likelihood(arg, r, 0.5))
theta = np.arange(0.1, 10, 0.1)
tree_length = 0.84
for t in theta:
mutation_ll_exact = 5 * math.log(tree_length * t) - tree_length * t
mutation_ll_exact += 3 * math.log(0.4 / tree_length)
mutation_ll_exact += 2 * math.log(0.5 / tree_length)
assert math.isclose(
mutation_ll_exact,
msprime.log_mutation_likelihood(arg, t),
)
def test_merger_with_overhang(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=0.7, parent=4, child=0)
tables.edges.add_row(left=0.7, 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=0.7, 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=0.7, right=1, parent=7, child=5)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=1)
tables.edges.add_row(left=0.5, right=0.7, parent=8, child=6)
tables.edges.add_row(left=0.5, right=0.7, parent=8, child=7)
tables.nodes.add_row(flags=0, population=0, individual=-1, time=1.3)
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(0.6, "0")
tables.sites.add_row(0.75, "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 -= (3 + r) * 0.5
log_arg_likelihood_exact -= (1 + 0.4 * r) * 0.3
assert math.isclose(log_arg_likelihood_exact,
msprime.log_arg_likelihood(arg, r, 0.5))
theta = np.arange(0.1, 10, 0.1)
tree_length = 81 / 50
for t in theta:
mutation_ll_exact = 3 * math.log(tree_length * t) - tree_length * t
mutation_ll_exact -= math.log(2 * tree_length)
mutation_ll_exact += math.log(1.3 / tree_length)
mutation_ll_exact -= math.log(tree_length)
assert math.isclose(
mutation_ll_exact,
msprime.log_mutation_likelihood(arg, t),
)
