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 = ancestry._model_factory(model=name.upper())
assert isinstance(model, model_class)
model = ancestry._model_factory(model=name.title())
assert isinstance(model, model_class)
model = ancestry._model_factory(model=name)
assert isinstance(model, model_class)
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,
s=0.01,
dt=0.01,
),
msprime.BetaCoalescent(alpha=2),
msprime.DiracCoalescent(psi=1, c=1),
]
for model in models:
new_model = ancestry._model_factory(model=model)
assert new_model is model
assert new_model.__dict__ == model.__dict__
def test_bad_model_names(self):
for bad_model in ["NOT", "", "MODEL"]:
with pytest.raises(ValueError):
ancestry._model_factory(model=bad_model)
def test_bad_models(self):
for bad_type in [1234, {}]:
with pytest.raises(TypeError):
ancestry._model_factory(model=bad_type)
def test_named_parametric_models_fail(self):
parametric_models = ["beta", "dirac"]
for name in parametric_models:
with pytest.raises(ValueError):
ancestry._model_factory(model=name)
