def test_two_events(self):
expected_events = [
msprime.AncestryModelChange(time=1,
model=msprime.StandardCoalescent()),
msprime.AncestryModelChange(time=2,
model=msprime.SmcApproxCoalescent()),
]
model, events = ancestry._parse_model_arg(
["dtwf", (1, "hudson"), (2, "smc")])
assert model == msprime.DiscreteTimeWrightFisher()
assert events == expected_events
model, events = ancestry._parse_model_arg(
["dtwf", (1, None), (2, msprime.SmcApproxCoalescent())])
assert model == msprime.DiscreteTimeWrightFisher()
assert events == expected_events
model, events = ancestry._parse_model_arg(
["dtwf", expected_events[0], (2, msprime.SmcApproxCoalescent())])
assert model == msprime.DiscreteTimeWrightFisher()
assert events == expected_events
model, events = ancestry._parse_model_arg(
["dtwf", expected_events[0], (2, msprime.SmcApproxCoalescent())])
assert model == msprime.DiscreteTimeWrightFisher()
assert events == expected_events
assert events[0] is not expected_events[0]
model, events = ancestry._parse_model_arg(["dtwf"] + expected_events)
assert model == msprime.DiscreteTimeWrightFisher()
assert events == expected_events
assert events[0] is not expected_events[0]
assert events[1] is not expected_events[1]
def test_two_events(self):
expected_events = [
msprime.SimulationModelChange(time=1,
model=msprime.StandardCoalescent()),
msprime.SimulationModelChange(time=2,
model=msprime.SmcApproxCoalescent()),
]
model, events = msprime.parse_model_arg(
["dtwf", (1, "hudson"), (2, "smc")])
self.assertEqual(model, msprime.DiscreteTimeWrightFisher())
self.assertEqual(events, expected_events)
model, events = msprime.parse_model_arg(
["dtwf", (1, None), (2, msprime.SmcApproxCoalescent())])
self.assertEqual(model, msprime.DiscreteTimeWrightFisher())
self.assertEqual(events, expected_events)
model, events = msprime.parse_model_arg(
["dtwf", expected_events[0], (2, msprime.SmcApproxCoalescent())])
self.assertEqual(model, msprime.DiscreteTimeWrightFisher())
self.assertEqual(events, expected_events)
model, events = msprime.parse_model_arg(
["dtwf", expected_events[0], (2, msprime.SmcApproxCoalescent())])
self.assertEqual(model, msprime.DiscreteTimeWrightFisher())
self.assertEqual(events, expected_events)
self.assertIsNot(events[0], expected_events[0])
model, events = msprime.parse_model_arg(["dtwf"] + expected_events)
self.assertEqual(model, msprime.DiscreteTimeWrightFisher())
self.assertEqual(events, expected_events)
self.assertIsNot(events[0], expected_events[0])
self.assertIsNot(events[1], expected_events[1])
def test_two_changes(self):
models = ancestry._parse_model_arg([
msprime.DiscreteTimeWrightFisher(duration=1),
msprime.StandardCoalescent(duration=2),
msprime.SmcApproxCoalescent(duration=3),
])
assert models == [
msprime.DiscreteTimeWrightFisher(duration=1),
msprime.StandardCoalescent(duration=2),
msprime.SmcApproxCoalescent(duration=3),
]
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, truncation_point=1),
),
msprime.SimulationModelChange(60,
msprime.StandardCoalescent()),
],
random_seed=10,
)
for tree in ts.trees():
self.assertEqual(tree.num_roots, 1)
def test_encode_simulation_models(self):
models = [
msprime.StandardCoalescent(duration=10),
msprime.DiscreteTimeWrightFisher(duration=10),
msprime.SmcApproxCoalescent(duration=10),
msprime.StandardCoalescent(),
]
ts = msprime.sim_ancestry(10, model=models, random_seed=1234)
decoded = self.decode(ts.provenance(0).record)
parameters = decoded.parameters
assert parameters.model[0] == {
"__class__": "msprime.ancestry.StandardCoalescent",
"duration": 10,
}
assert parameters.model[1] == {
"__class__": "msprime.ancestry.DiscreteTimeWrightFisher",
"duration": 10,
}
assert parameters.model[2] == {
"__class__": "msprime.ancestry.SmcApproxCoalescent",
"duration": 10,
}
assert parameters.model[3] == {
"__class__": "msprime.ancestry.StandardCoalescent",
"duration": None,
}
def test_model_change_old_style(self):
main_model = msprime.SmcApproxCoalescent()
sim = msprime.simulator_factory(
Ne=100,
sample_size=2,
model=main_model,
demographic_events=[
msprime.SimulationModelChange(
1, msprime.DiscreteTimeWrightFisher()),
msprime.SimulationModelChange(2, None),
],
)
self.assertEqual(len(sim.model_change_events), 2)
self.assertEqual(sim.model_change_events[0].time, 1)
# When model=None we change to the standard coalescent
self.assertEqual(sim.model_change_events[1].time, 2)
self.assertEqual(sim.model_change_events[1].model.name, "hudson")
# This should be the same in new notation
sim = msprime.simulator_factory(
Ne=100,
sample_size=2,
model=[main_model, (1, "dtwf"), (2, None)],
)
self.assertEqual(len(sim.model_change_events), 2)
self.assertEqual(sim.model_change_events[0].time, 1)
# When model=None we change to the standard coalescent
self.assertEqual(sim.model_change_events[1].time, 2)
self.assertEqual(sim.model_change_events[1].model.name, "hudson")
def test_many_models_simulate(self):
Ne = 10000
ts = msprime.simulate(
Ne=Ne,
sample_size=10,
# Use the old-style SimulationModelChange
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_many_models(self):
# What happens when we have loads of models
models = [
msprime.StandardCoalescent(duration=0.1),
msprime.SmcApproxCoalescent(duration=0.1),
]
ts = msprime.sim_ancestry(10, model=models * 1000, random_seed=2)
assert all(tree.num_roots == 1 for tree in ts.trees())
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()"
assert repr(model) == repr_s
assert str(model) == repr_s
model = msprime.SmcPrimeApproxCoalescent()
repr_s = "SmcPrimeApproxCoalescent()"
assert repr(model) == repr_s
assert str(model) == repr_s
def test_encode_simulation_models(self):
simple_model = ["hudson", [10, "dtwf"], [20, "smc"], [None, None]]
ts = msprime.simulate(10, model=simple_model)
decoded = self.decode(ts.provenance(0).record)
parameters = decoded.parameters
self.assertEqual(parameters.sample_size, 10)
self.assertEqual(list(parameters.model), simple_model)
model_instances = [
msprime.StandardCoalescent(),
msprime.SimulationModelChange(10,
msprime.DiscreteTimeWrightFisher()),
msprime.SimulationModelChange(20, msprime.SmcApproxCoalescent()),
msprime.SimulationModelChange(30,
msprime.BetaCoalescent(alpha=1.1)),
]
ts = msprime.simulate(10, model=model_instances)
decoded = self.decode(ts.provenance(0).record)
parameters = decoded.parameters
self.assertEqual(parameters.sample_size, 10)
self.assertEqual(parameters.model[0],
{"__class__": "msprime.ancestry.StandardCoalescent"})
self.assertDictEqual(
parameters.model[1],
{
"__class__": "msprime.ancestry.SimulationModelChange",
"model": {
"__class__": "msprime.ancestry.DiscreteTimeWrightFisher"
},
"time": 10,
},
)
self.assertDictEqual(
parameters.model[2],
{
"__class__": "msprime.ancestry.SimulationModelChange",
"model": {
"__class__": "msprime.ancestry.SmcApproxCoalescent"
},
"time": 20,
},
)
self.assertDictEqual(
parameters.model[3],
{
"__class__": "msprime.ancestry.SimulationModelChange",
"model": {
"__class__": "msprime.ancestry.BetaCoalescent",
"alpha": 1.1,
"truncation_point": 1.0,
},
"time": 30,
},
)
def test_model_change_inherits_Ne(self):
K = 10
sim = msprime.simulator_factory(sample_size=2,
Ne=10,
demographic_events=[
msprime.SimulationModelChange(
None,
msprime.SmcApproxCoalescent())
for _ in range(K)
])
self.assertEqual(sim.model.reference_size, 10)
self.assertEqual(len(sim.model_change_events), K)
for event in sim.model_change_events:
self.assertEqual(event.model.reference_size, 10)
self.assertEqual(event.time, None)
def test_simulation_models(self):
simple_model = ["hudson", [10, "dtwf"], [20, "smc"]]
ts = msprime.simulate(10, model=simple_model)
self.verify(ts)
model_instances = [
msprime.StandardCoalescent(),
msprime.SimulationModelChange(10,
msprime.DiscreteTimeWrightFisher()),
msprime.SimulationModelChange(20, msprime.SmcApproxCoalescent()),
msprime.SimulationModelChange(30,
msprime.BetaCoalescent(alpha=1.1)),
]
ts = msprime.simulate(10, model=model_instances)
self.verify(ts)
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_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(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 verify_simulation_models(self, sim_func):
simple_model = ["hudson", [10, "dtwf"], [20, "smc"], [None, None]]
ts = sim_func(10, model=simple_model)
decoded = self.decode(ts.provenance(0).record)
parameters = decoded.parameters
assert list(parameters.model) == simple_model
model_instances = [
msprime.StandardCoalescent(),
msprime.SimulationModelChange(10,
msprime.DiscreteTimeWrightFisher()),
msprime.SimulationModelChange(20, msprime.SmcApproxCoalescent()),
msprime.SimulationModelChange(
30, msprime.BetaCoalescent(alpha=1.1, truncation_point=1)),
]
ts = sim_func(10, model=model_instances)
decoded = self.decode(ts.provenance(0).record)
parameters = decoded.parameters
assert parameters.model[0] == {
"__class__": "msprime.ancestry.StandardCoalescent"
}
assert parameters.model[1] == {
"__class__": "msprime.ancestry.SimulationModelChange",
"model": {
"__class__": "msprime.ancestry.DiscreteTimeWrightFisher"
},
"time": 10,
}
assert parameters.model[2] == {
"__class__": "msprime.ancestry.SimulationModelChange",
"model": {
"__class__": "msprime.ancestry.SmcApproxCoalescent"
},
"time": 20,
}
assert parameters.model[3] == {
"__class__": "msprime.ancestry.SimulationModelChange",
"model": {
"__class__": "msprime.ancestry.BetaCoalescent",
"alpha": 1.1,
"truncation_point": 1.0,
},
"time": 30,
}
def test_model_change_no_model_inherits_model_size(self):
main_model = msprime.SmcApproxCoalescent(100)
sim = msprime.simulator_factory(
sample_size=2,
model=main_model,
demographic_events=[
msprime.SimulationModelChange(
1, msprime.DiscreteTimeWrightFisher(500)),
msprime.SimulationModelChange(2, None)
])
self.assertEqual(sim.model.reference_size, 100)
self.assertEqual(len(sim.model_change_events), 2)
self.assertEqual(sim.model_change_events[0].time, 1)
self.assertEqual(sim.model_change_events[0].model.reference_size, 500)
# When model=None we change to the standard coalescent using the
# reference size set by the initial model.
self.assertEqual(sim.model_change_events[1].time, 2)
self.assertEqual(sim.model_change_events[1].model.reference_size, 100)
self.assertEqual(sim.model_change_events[1].model.name, "hudson")
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_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_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)
