def test_Tajimas_D_per_site(sample_size):
ts = simulate_ts(sample_size, random_seed=1234)
ds = ts_to_dataset(ts)
ds, subsets = add_cohorts(ds, ts, cohort_key_names=None)
ds = Tajimas_D(ds)
d = ds.stat_Tajimas_D.compute().squeeze()
ts_d = ts.Tajimas_D(windows="sites")
np.testing.assert_allclose(d, ts_d)
def test_Tajimas_D(sample_size):
ts = simulate_ts(sample_size)
ds = ts_to_dataset(ts)
ds, subsets = add_cohorts(ds, ts, cohort_key_names=None)
n_variants = ds.dims["variants"]
ds = window_by_variant(ds, size=n_variants) # single window
ds = Tajimas_D(ds)
d = ds.stat_Tajimas_D.compute()
ts_d = ts.Tajimas_D()
np.testing.assert_allclose(d, ts_d)
def test_Tajimas_D(sample_size):
ts = msprime.simulate(sample_size,
length=100,
mutation_rate=0.05,
random_seed=42)
ds = ts_to_dataset(ts) # type: ignore[no-untyped-call]
ds, subsets = add_cohorts(
ds, ts, cohort_key_names=None) # type: ignore[no-untyped-call]
n_variants = ds.dims["variants"]
ds = window(ds, size=n_variants) # single window
ds = Tajimas_D(ds)
d = ds.stat_Tajimas_D.compute()
ts_d = ts.Tajimas_D()
np.testing.assert_allclose(d, ts_d)