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)