def test_pedigree_sanity_checks(self):
individual = np.array([1, 2, 3, 4])
parents = np.array([2, 3, 2, 3, -1, -1, -1, -1]).reshape(-1, 2)
time = np.array([0, 0, 1, 1])
is_sample = np.array([1, 1, 0, 0])
self.assertRaises(
NotImplementedError,
msprime.Pedigree,
individual=individual,
parents=parents,
time=time,
ploidy=1,
)
bad_parents = np.array([2, 3, 2, 3, -1, -1, -1, -1]).reshape(-1, 4)
self.assertRaises(
ValueError,
msprime.Pedigree,
individual=individual,
parents=bad_parents,
time=time,
)
bad_individual = np.array([-1, 2, 3, 4])
self.assertRaises(
ValueError,
msprime.Pedigree,
individual=bad_individual,
parents=parents,
time=time,
)
bad_times = np.array([1, 1, 1, 1])
ped = msprime.Pedigree(individual,
parents,
bad_times,
is_sample,
sex=None,
ploidy=2)
self.assertRaises(
ValueError,
ped.check_times,
individual=ped.individual,
parents=ped.parents,
time=ped.time,
)
ped = msprime.Pedigree(individual, parents, time, sex=None, ploidy=2)
self.assertRaises(ValueError, ped.set_samples)
self.assertRaises(ValueError, ped.set_samples, num_samples=10)
self.assertRaises(ValueError,
ped.set_samples,
num_samples=2,
sample_IDs=[1, 2])
self.assertRaises(ValueError, ped.get_times, individual=ped.individual)
def test_ped_wf_recombination(self):
inds = np.array([1, 2, 3, 4, 5, 6])
parent_indices = np.array([4, 5, 4, 5, 4, 5, 4, 5, -1, -1, -1,
-1]).reshape(-1, 2)
times = np.array([0, 0, 0, 0, 1, 1])
is_sample = np.array([1, 1, 1, 1, 0, 0])
t = max(times)
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(inds,
parent_indices,
times,
is_sample,
sex=None,
ploidy=2)
ts = msprime.simulate(
sample_size=4,
pedigree=ped,
recombination_rate=0.1,
model=[model, (1, "dtwf")],
)
tree = ts.first()
self.assertEqual(tree.num_roots, 1)
all_times = ts.tables.nodes.time
ped_times = all_times[np.logical_and(all_times > 0, all_times <= t)]
self.assertGreater(ped_times.shape[0], 0)
self.assertTrue(np.all(ped_times == np.floor(ped_times)))
wf_times = all_times[all_times > t]
self.assertGreater(wf_times.shape[0], 0)
def test_pedigree_unsupported_events(self):
inds = np.array([1, 2, 3, 4, 5, 6])
parent_indices = np.array([4, 5, 4, 5, 4, 5, 4, 5, -1, -1, -1,
-1]).reshape(-1, 2)
times = np.array([0, 0, 0, 0, 1, 1])
is_sample = np.array([1, 1, 1, 1, 0, 0])
t = max(times)
ped = msprime.Pedigree(inds,
parent_indices,
times,
is_sample,
sex=None,
ploidy=2)
bad_model_change = msprime.SimulationModelChange(
0.5, msprime.DiscreteTimeWrightFisher())
self.assertRaises(
NotImplementedError,
msprime.simulate,
4,
pedigree=ped,
demographic_events=[bad_model_change],
model="wf_ped",
)
bad_demographic_event = msprime.PopulationParametersChange(
t, initial_size=2)
self.assertRaises(
NotImplementedError,
msprime.simulate,
4,
pedigree=ped,
demographic_events=[bad_demographic_event],
model="wf_ped",
)
def test_ped_wf_single_locus(self):
inds = np.array([1, 2, 3, 4, 5, 6])
parent_indices = np.array([4, 5, 4, 5, 4, 5, 4, 5, -1, -1, -1,
-1]).reshape(-1, 2)
times = np.array([0, 0, 0, 0, 1, 1])
is_sample = np.array([1, 1, 1, 1, 0, 0])
t = max(times)
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(inds,
parent_indices,
times,
is_sample,
sex=None,
ploidy=2)
ts = msprime.simulate(sample_size=4,
Ne=2,
pedigree=ped,
model=(model, (t, "dtwf")))
tree = ts.first()
assert tree.num_roots == 1
all_times = ts.tables.nodes.time
ped_times = all_times[np.logical_and(all_times > 0, all_times <= t)]
assert ped_times.shape[0] > 0
assert np.all(ped_times == np.floor(ped_times))
wf_times = all_times[all_times > t]
assert wf_times.shape[0] > 0
def test_pedigree_samples(self):
individual = np.array([1, 2, 3, 4])
parents = np.array([2, 3, 2, 3, -1, -1, -1, -1]).reshape(-1, 2)
time = np.array([0, 0, 1, 1])
ped = msprime.Pedigree(individual, parents, time)
# This interface is pretty poor - what we want is for the
# samples argument to simulate to be interpreted as individual
# IDs. For now, let's just leave it as it is, though.
ped.set_samples(2)
ts = msprime.simulate(2, pedigree=ped, model="wf_ped")
self.assertGreater(ts.num_edges, 0)
ped.set_samples(sample_IDs=[1, 2])
ts = msprime.simulate(2, pedigree=ped, model="wf_ped")
self.assertTrue(ts is not None)
self.assertRaises(ValueError, ped.set_samples, sample_IDs=[1, 1])
self.assertRaises(ValueError, ped.set_samples, sample_IDs=[1, 3])
self.assertRaises(NotImplementedError,
ped.set_samples,
sample_IDs=[1, 3],
probands_only=False)
ped.set_samples(sample_IDs=[1, 2])
self.assertEqual(ped.get_proband_indices(), [0, 1])
def test_pedigree_read_write(self):
individual = np.array([1, 2, 3, 4])
parents = np.array([2, 3, 2, 3, -1, -1, -1, -1]).reshape(-1, 2)
time = np.array([0, 0, 1, 1])
is_sample = np.array([1, 1, 0, 0])
ped = msprime.Pedigree(individual,
parents,
time,
is_sample,
sex=None,
ploidy=2)
ped.save_txt(self.temp_pedigree_text_file)
self.assertRaises(NotImplementedError,
msprime.Pedigree.read_txt,
self.temp_pedigree_text_file,
sex_col=4)
ped_from_txt = msprime.Pedigree.read_txt(self.temp_pedigree_text_file,
time_col=None)
ts = msprime.simulate(2, pedigree=ped_from_txt, model="wf_ped")
self.assertTrue(ts is not None)
ped_from_txt = msprime.Pedigree.read_txt(self.temp_pedigree_text_file,
time_col=3)
ts = msprime.simulate(2, pedigree=ped_from_txt, model="wf_ped")
self.assertTrue(ts is not None)
ped.save_npy(self.temp_pedigree_array_file)
ped_from_npy = msprime.Pedigree.read_npy(self.temp_pedigree_array_file)
ts = msprime.simulate(2, pedigree=ped_from_npy, model="wf_ped")
self.assertTrue(ts is not None)
def test_pedigree_samples(self):
individual = np.array([1, 2, 3, 4])
parents = np.array([2, 3, 2, 3, -1, -1, -1, -1]).reshape(-1, 2)
time = np.array([0, 0, 1, 1])
ped = msprime.Pedigree(individual, parents, time)
ped.set_samples(2)
ts = msprime.simulate(2, pedigree=ped, model="wf_ped")
self.assertTrue(ts is not None)
ped.set_samples(1)
self.assertRaises(ValueError,
msprime.simulate,
2,
pedigree=ped,
model="wf_ped")
ped.set_samples(sample_IDs=[1, 2])
ts = msprime.simulate(2, pedigree=ped, model="wf_ped")
self.assertTrue(ts is not None)
self.assertRaises(ValueError, ped.set_samples, sample_IDs=[1, 1])
self.assertRaises(ValueError, ped.set_samples, sample_IDs=[1, 3])
self.assertRaises(NotImplementedError,
ped.set_samples,
sample_IDs=[1, 3],
probands_only=False)
ped.set_samples(sample_IDs=[1])
self.assertRaises(ValueError,
msprime.simulate,
2,
pedigree=ped,
model="wf_ped")
ped.set_samples(sample_IDs=[1, 2])
self.assertEqual(ped.get_proband_indices(), [0, 1])
def test_ped_wf_single_locus(self):
inds = np.array([1, 2, 3, 4, 5, 6])
parent_indices = np.array([4, 5, 4, 5, 4, 5, 4, 5, -1, -1, -1,
-1]).reshape(-1, 2)
times = np.array([0, 0, 0, 0, 1, 1])
is_sample = np.array([1, 1, 1, 1, 0, 0])
t = max(times)
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(inds,
parent_indices,
times,
is_sample,
sex=None,
ploidy=2)
ts = msprime.simulate(
sample_size=4,
pedigree=ped,
demographic_events=[
msprime.SimulationModelChange(
t, msprime.DiscreteTimeWrightFisher(2))
],
model=model,
)
tree = ts.first()
self.assertEqual(tree.num_roots, 1)
all_times = ts.tables.nodes.time
ped_times = all_times[np.logical_and(all_times > 0, all_times <= t)]
self.assertGreater(ped_times.shape[0], 0)
self.assertTrue(np.all(ped_times == np.floor(ped_times)))
wf_times = all_times[all_times > t]
self.assertGreater(wf_times.shape[0], 0)
def test_wf_ped(self):
inds = np.array([1, 2, 3, 4])
parent_indices = np.array([2, 3, 2, 3, -1, -1, -1, -1]).reshape(-1, 2)
times = np.array([0, 0, 1, 1])
is_sample = np.array([1, 1, 0, 0])
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(
inds, parent_indices, times, is_sample, sex=None, ploidy=2
)
ts = msprime.simulate(2, pedigree=ped, model=model)
assert ts is not None
def test_pedigree(self):
with tempfile.TemporaryDirectory() as tmpdir:
ped_path = pathlib.Path(tmpdir) / "tmp.ped"
individual = np.array([1, 2, 3, 4])
parents = np.array([2, 3, 2, 3, -1, -1, -1, -1]).reshape(-1, 2)
time = np.array([0, 0, 1, 1])
is_sample = np.array([1, 1, 0, 0])
ped = msprime.Pedigree(individual,
parents,
time,
is_sample,
sex=None,
ploidy=2)
ped.save_txt(ped_path)
ts = self.run_script(
f"2 --pedigree-file {ped_path} --model=wf_ped")
for tree in ts.trees():
self.assertGreater(tree.num_roots, 1)
def to_msprime_pedigree(self):
"""Create an ``msprime`` representation of the pedigree
This converts a :class:`Pedigree` into an ``msprime.Pedigree``
Returns:
msprime.Pedigree: an msprime Pedigree
"""
tbl = self.to_table()
individual = tbl.individual.values
parent_IDs = np.vstack((tbl.father, tbl.mother)).transpose()
parent_idx = msp.Pedigree.parent_ID_to_index(individual, parent_IDs)
msp_ped = msp.Pedigree(individual, parent_idx, tbl.time.values)
msp_ped.set_samples(sample_IDs=self.probands(), probands_only=True)
return msp_ped
def test_pedigree_replicates(self):
individual = np.array([1, 2, 3, 4])
parents = np.array([2, 3, 2, 3, -1, -1, -1, -1]).reshape(-1, 2)
time = np.array([0, 0, 1, 1])
is_sample = np.array([1, 1, 0, 0])
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(individual,
parents,
time,
is_sample,
sex=None,
ploidy=2)
replicates = msprime.simulate(2,
pedigree=ped,
model=model,
recombination_rate=1,
num_replicates=100)
for ts in replicates:
self.assertTrue(ts is not None)
def test_pedigree_read_write(self):
individual = np.array([1, 2, 3, 4])
parents = np.array([2, 3, 2, 3, -1, -1, -1, -1]).reshape(-1, 2)
time = np.array([0, 0, 1, 1])
is_sample = np.array([1, 1, 0, 0])
ped = msprime.Pedigree(individual,
parents,
time,
is_sample,
sex=None,
ploidy=2)
ped.save_txt(self.temp_pedigree_text_file)
self.assertRaises(
NotImplementedError,
msprime.Pedigree.read_txt,
self.temp_pedigree_text_file,
sex_col=4,
)
# FIXME
# The compute_times should be done automatically in this case .
# ped_from_txt = msprime.Pedigree.read_txt(
# self.temp_pedigree_text_file, time_col=None
# )
# ts = msprime.simulate(2, pedigree=ped_from_txt, model="wf_ped")
# self.assertTrue(ts is not None)
# ped_from_txt = msprime.Pedigree.read_txt(
# self.temp_pedigree_text_file, time_col=3
# )
# ts = msprime.simulate(2, pedigree=ped_from_txt, model="wf_ped")
# self.assertTrue(ts is not None)
ped.save_npy(self.temp_pedigree_array_file)
ped_from_npy = msprime.Pedigree.read_npy(self.temp_pedigree_array_file)
# TODO compre this to the file above.
self.assertIsInstance(ped_from_npy, msprime.Pedigree)
def test_pedigree(self):
inds = np.array([1, 2, 3, 4, 5, 6])
parent_indices = np.array([4, 5, 4, 5, 4, 5, 4, 5, -1, -1, -1,
-1]).reshape(-1, 2)
times = np.array([0, 0, 0, 0, 1, 1])
is_sample = np.array([1, 1, 1, 1, 0, 0])
t = max(times)
model = msprime.WrightFisherPedigree()
ped = msprime.Pedigree(inds,
parent_indices,
times,
is_sample,
sex=None,
ploidy=2)
ts = msprime.simulate(
sample_size=4,
pedigree=ped,
demographic_events=[
msprime.SimulationModelChange(
t, msprime.DiscreteTimeWrightFisher())
],
model=model,
)
self.verify(ts)
