def test_ped_wf_recombination(self):
inds = np.array([1, 2, 3, 4, 5, 6])
parent_indices = np.array([4, 5, 4, 5, 4, 5, 4, 5, -1, -1, -1,
-1]).reshape(-1, 2)
times = np.array([0, 0, 0, 0, 1, 1])
is_sample = np.array([1, 1, 1, 1, 0, 0])
t = max(times)
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(inds,
parent_indices,
times,
is_sample,
sex=None,
ploidy=2)
ts = msprime.simulate(
sample_size=4,
pedigree=ped,
recombination_rate=0.1,
model=[model, (1, "dtwf")],
)
tree = ts.first()
self.assertEqual(tree.num_roots, 1)
all_times = ts.tables.nodes.time
ped_times = all_times[np.logical_and(all_times > 0, all_times <= t)]
self.assertGreater(ped_times.shape[0], 0)
self.assertTrue(np.all(ped_times == np.floor(ped_times)))
wf_times = all_times[all_times > t]
self.assertGreater(wf_times.shape[0], 0)
def test_ped_wf_single_locus(self):
inds = np.array([1, 2, 3, 4, 5, 6])
parent_indices = np.array([4, 5, 4, 5, 4, 5, 4, 5, -1, -1, -1,
-1]).reshape(-1, 2)
times = np.array([0, 0, 0, 0, 1, 1])
is_sample = np.array([1, 1, 1, 1, 0, 0])
t = max(times)
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(inds,
parent_indices,
times,
is_sample,
sex=None,
ploidy=2)
ts = msprime.simulate(
sample_size=4,
pedigree=ped,
demographic_events=[
msprime.SimulationModelChange(
t, msprime.DiscreteTimeWrightFisher(2))
],
model=model,
)
tree = ts.first()
self.assertEqual(tree.num_roots, 1)
all_times = ts.tables.nodes.time
ped_times = all_times[np.logical_and(all_times > 0, all_times <= t)]
self.assertGreater(ped_times.shape[0], 0)
self.assertTrue(np.all(ped_times == np.floor(ped_times)))
wf_times = all_times[all_times > t]
self.assertGreater(wf_times.shape[0], 0)
def test_ped_wf_single_locus(self):
inds = np.array([1, 2, 3, 4, 5, 6])
parent_indices = np.array([4, 5, 4, 5, 4, 5, 4, 5, -1, -1, -1,
-1]).reshape(-1, 2)
times = np.array([0, 0, 0, 0, 1, 1])
is_sample = np.array([1, 1, 1, 1, 0, 0])
t = max(times)
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(inds,
parent_indices,
times,
is_sample,
sex=None,
ploidy=2)
ts = msprime.simulate(sample_size=4,
Ne=2,
pedigree=ped,
model=(model, (t, "dtwf")))
tree = ts.first()
assert tree.num_roots == 1
all_times = ts.tables.nodes.time
ped_times = all_times[np.logical_and(all_times > 0, all_times <= t)]
assert ped_times.shape[0] > 0
assert np.all(ped_times == np.floor(ped_times))
wf_times = all_times[all_times > t]
assert wf_times.shape[0] > 0
def test_ped_wf_recombination(self):
inds = np.array([1, 2, 3, 4, 5, 6])
parent_indices = np.array([4, 5, 4, 5, 4, 5, 4, 5, -1, -1, -1,
-1]).reshape(-1, 2)
times = np.array([0, 0, 0, 0, 1, 1])
is_sample = np.array([1, 1, 1, 1, 0, 0])
t = max(times)
model = msprime.WrightFisherPedigree()
ped = pedigrees.Pedigree(inds,
parent_indices,
times,
is_sample,
sex=None,
ploidy=2)
ts = msprime.simulate(
sample_size=4,
pedigree=ped,
recombination_rate=0.1,
model=model,
demographic_events=[
msprime.SimulationModelChange(time=1, model="dtwf")
],
)
tree = ts.first()
assert tree.num_roots == 1
all_times = ts.tables.nodes.time
ped_times = all_times[np.logical_and(all_times > 0, all_times <= t)]
assert ped_times.shape[0] > 0
assert np.all(ped_times == np.floor(ped_times))
wf_times = all_times[all_times > t]
assert wf_times.shape[0] > 0
def test_wf_ped(self):
inds = np.array([1, 2, 3, 4])
parent_indices = np.array([2, 3, 2, 3, -1, -1, -1, -1]).reshape(-1, 2)
times = np.array([0, 0, 1, 1])
is_sample = np.array([1, 1, 0, 0])
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(
inds, parent_indices, times, is_sample, sex=None, ploidy=2
)
ts = msprime.simulate(2, pedigree=ped, model=model)
assert ts is not None
def test_simulation_models(self):
examples = [
msprime.StandardCoalescent(),
msprime.SmcApproxCoalescent(),
msprime.SmcPrimeApproxCoalescent(),
msprime.DiscreteTimeWrightFisher(),
msprime.WrightFisherPedigree(),
msprime.BetaCoalescent(),
msprime.BetaCoalescent(alpha=1, truncation_point=10),
msprime.DiracCoalescent(),
msprime.DiracCoalescent(psi=1234, c=56),
msprime.SweepGenicSelection(
position=1,
start_frequency=0.5,
end_frequency=0.9,
alpha=1,
dt=1e-4,
),
]
self.assert_repr_round_trip(examples)
def test_pedigree_replicates(self):
individual = np.array([1, 2, 3, 4])
parents = np.array([2, 3, 2, 3, -1, -1, -1, -1]).reshape(-1, 2)
time = np.array([0, 0, 1, 1])
is_sample = np.array([1, 1, 0, 0])
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(individual,
parents,
time,
is_sample,
sex=None,
ploidy=2)
replicates = msprime.simulate(2,
pedigree=ped,
model=model,
recombination_rate=1,
num_replicates=100)
for ts in replicates:
self.assertTrue(ts is not None)
def test_model_instances(self):
models = [
msprime.StandardCoalescent(100),
msprime.SmcApproxCoalescent(30),
msprime.SmcPrimeApproxCoalescent(2132),
msprime.DiscreteTimeWrightFisher(500),
msprime.WrightFisherPedigree(500),
msprime.SweepGenicSelection(reference_size=500,
position=0.5,
start_frequency=0.1,
end_frequency=0.9,
alpha=0.1,
dt=0.01),
msprime.DiracCoalescent(),
msprime.BetaCoalescent(),
]
for model in models:
new_model = msprime.model_factory(model=model)
self.assertFalse(new_model is model)
self.assertEqual(new_model.__dict__, model.__dict__)
def test_model_instances(self):
models = [
msprime.StandardCoalescent(),
msprime.SmcApproxCoalescent(),
msprime.SmcPrimeApproxCoalescent(),
msprime.DiscreteTimeWrightFisher(),
msprime.WrightFisherPedigree(),
msprime.SweepGenicSelection(
position=0.5,
start_frequency=0.1,
end_frequency=0.9,
alpha=0.1,
dt=0.01,
),
msprime.BetaCoalescent(alpha=2),
msprime.DiracCoalescent(psi=1, c=1),
]
for model in models:
new_model = msprime.model_factory(model=model)
self.assertTrue(new_model is model)
self.assertEqual(new_model.__dict__, model.__dict__)
def test_simple_case(self):
# Simple test to check that the current code is still roughly working.
# Should be replace by better tests.
individual = np.array([1, 2, 3, 4])
parents = np.array([-1, -1, -1, -1, 0, 1, 1, 0]).reshape(-1, 2)
time = np.array([1, 1, 0, 0])
is_sample = np.array([0, 0, 1, 1])
model = msprime.WrightFisherPedigree()
ped = pedigrees.Pedigree(individual,
parents,
time,
is_sample,
sex=None,
ploidy=2)
ts = msprime.simulate(2,
pedigree=ped,
model=model,
recombination_rate=1)
table = ts.tables.individuals
assert np.all(table[0].parents == [-1, -1])
assert np.all(table[1].parents == [-1, -1])
def test_reference_size_inherited(self):
for Ne in [1, 10, 100]:
models = [
msprime.DiracCoalescent(psi=0.5, c=0),
msprime.StandardCoalescent(),
msprime.SmcApproxCoalescent(),
msprime.SmcPrimeApproxCoalescent(),
msprime.DiscreteTimeWrightFisher(),
msprime.WrightFisherPedigree(),
msprime.SweepGenicSelection(
position=0.5,
start_frequency=0.1,
end_frequency=0.9,
alpha=0.1,
dt=0.01,
),
msprime.BetaCoalescent(alpha=2),
msprime.DiracCoalescent(psi=1, c=1),
]
for model in models:
new_model = msprime.model_factory(model, reference_size=Ne)
self.assertEqual(new_model.reference_size, Ne)
def test_pedigree(self):
inds = np.array([1, 2, 3, 4, 5, 6])
parent_indices = np.array([4, 5, 4, 5, 4, 5, 4, 5, -1, -1, -1,
-1]).reshape(-1, 2)
times = np.array([0, 0, 0, 0, 1, 1])
is_sample = np.array([1, 1, 1, 1, 0, 0])
t = max(times)
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(inds,
parent_indices,
times,
is_sample,
sex=None,
ploidy=2)
ts = msprime.simulate(
sample_size=4,
pedigree=ped,
demographic_events=[
msprime.SimulationModelChange(
t, msprime.DiscreteTimeWrightFisher())
],
model=model,
)
self.verify(ts)
def test_wf_pedigree(self):
model = msprime.WrightFisherPedigree()
repr_s = "WrightFisherPedigree()"
self.assertEqual(repr(model), repr_s)
self.assertEqual(str(model), repr_s)
def test_wf_pedigree(self):
model = msprime.WrightFisherPedigree()
repr_s = "WrightFisherPedigree()"
assert repr(model) == repr_s
assert str(model) == repr_s