def test_named_model_variants(self):
simulation_models = [("hudson", msprime.StandardCoalescent),
("smc", msprime.SmcApproxCoalescent),
("smc_prime", msprime.SmcPrimeApproxCoalescent),
("dtwf", msprime.DiscreteTimeWrightFisher),
("wf_ped", msprime.WrightFisherPedigree)]
for name, model_class in simulation_models:
model = msprime.model_factory(model=name.upper())
self.assertIsInstance(model, model_class)
model = msprime.model_factory(model=name.title())
self.assertIsInstance(model, model_class)
model = msprime.model_factory(model=name)
self.assertIsInstance(model, model_class)
def test_reference_size_instance(self):
for size in range(1, 10):
existing_model = msprime.StandardCoalescent()
model = msprime.model_factory(existing_model, reference_size=size)
self.assertEqual(model.reference_size, size)
existing_model = msprime.StandardCoalescent(None)
model = msprime.model_factory(existing_model, reference_size=size)
self.assertEqual(model.reference_size, size)
# If the size is already set, the model isn't changed.
existing_model = msprime.StandardCoalescent(10**4)
model = msprime.model_factory(existing_model, reference_size=size)
self.assertEqual(model.reference_size, 10**4)
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):
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)
def test_named_parametric_models_fail(self):
parametric_models = ["beta", "dirac"]
for name in parametric_models:
with self.assertRaises(ValueError):
msprime.model_factory(model=name)
def test_reference_size_string(self):
for size in range(1, 10):
model = msprime.model_factory("hudson", reference_size=size)
self.assertEqual(model.reference_size, size)
