def test_smc_models(self):
model = msprime.SmcApproxCoalescent()
repr_s = "SmcApproxCoalescent()"
self.assertEqual(repr(model), repr_s)
self.assertEqual(str(model), repr_s)
model = msprime.SmcPrimeApproxCoalescent()
repr_s = "SmcPrimeApproxCoalescent()"
self.assertEqual(repr(model), repr_s)
self.assertEqual(str(model), repr_s)
def test_smc_models(self):
model = msprime.SmcApproxCoalescent()
repr_s = "SmcApproxCoalescent(duration=None)"
assert repr(model) == repr_s
assert str(model) == repr_s
model = msprime.SmcPrimeApproxCoalescent()
repr_s = "SmcPrimeApproxCoalescent(duration=None)"
assert repr(model) == repr_s
assert str(model) == repr_s
def test_model_instances(self):
models = [
msprime.StandardCoalescent(100),
msprime.SmcApproxCoalescent(30),
msprime.SmcPrimeApproxCoalescent(2132),
msprime.DiscreteTimeWrightFisher(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):
for bad_type in [1234, {}]:
self.assertRaises(TypeError,
msprime.simulator_factory,
sample_size=2,
model=bad_type)
models = [
msprime.StandardCoalescent(),
msprime.SmcApproxCoalescent(),
msprime.SmcPrimeApproxCoalescent(),
msprime.BetaCoalescent(),
msprime.DiracCoalescent(),
]
for model in models:
sim = msprime.simulator_factory(sample_size=10, model=model)
self.assertEqual(sim.get_model(), model)
def test_many_models_sim_ancestry(self):
ts = msprime.sim_ancestry(
samples=10,
population_size=10_000,
model=[
msprime.StandardCoalescent(duration=10),
msprime.SmcApproxCoalescent(duration=10),
msprime.SmcPrimeApproxCoalescent(duration=10),
msprime.DiscreteTimeWrightFisher(duration=10),
msprime.BetaCoalescent(alpha=1.1, duration=10),
msprime.StandardCoalescent(),
],
random_seed=10,
)
for tree in ts.trees():
assert tree.num_roots == 1
def test_many_models(self):
Ne = 10000
ts = msprime.simulate(
Ne=Ne,
sample_size=10,
recombination_rate=0.1,
demographic_events=[
msprime.SimulationModelChange(10, msprime.StandardCoalescent(Ne)),
msprime.SimulationModelChange(20, msprime.SmcApproxCoalescent(Ne)),
msprime.SimulationModelChange(30, msprime.SmcPrimeApproxCoalescent(Ne)),
msprime.SimulationModelChange(
40, msprime.DiscreteTimeWrightFisher(100)),
msprime.SimulationModelChange(
50, msprime.BetaCoalescent(reference_size=10)),
msprime.SimulationModelChange(60, msprime.StandardCoalescent(0.1))],
random_seed=10)
for tree in ts.trees():
self.assertEqual(tree.num_roots, 1)
def test_many_models(self):
Ne = 10000
ts = msprime.simulate(
Ne=Ne,
sample_size=10,
recombination_rate=0.1,
model=[
"hudson",
msprime.SimulationModelChange(10, msprime.StandardCoalescent()),
msprime.SimulationModelChange(20, msprime.SmcApproxCoalescent()),
msprime.SimulationModelChange(30, msprime.SmcPrimeApproxCoalescent()),
msprime.SimulationModelChange(40, msprime.DiscreteTimeWrightFisher()),
msprime.SimulationModelChange(50, msprime.BetaCoalescent(alpha=1.1)),
msprime.SimulationModelChange(60, msprime.StandardCoalescent()),
],
random_seed=10,
)
for tree in ts.trees():
assert tree.num_roots == 1
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_many_models_sim_ancestry(self):
ts = msprime.sim_ancestry(
samples=10,
population_size=10_000,
model=[
"hudson",
msprime.AncestryModelChange(10, msprime.StandardCoalescent()),
msprime.AncestryModelChange(20, msprime.SmcApproxCoalescent()),
msprime.AncestryModelChange(
30, msprime.SmcPrimeApproxCoalescent()),
msprime.AncestryModelChange(
40, msprime.DiscreteTimeWrightFisher()),
msprime.AncestryModelChange(50,
msprime.BetaCoalescent(alpha=1.1)),
msprime.AncestryModelChange(60, msprime.StandardCoalescent()),
],
random_seed=10,
)
for tree in ts.trees():
assert tree.num_roots == 1
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_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)
