def test_divergence__missing_calls():
ds = get_dataset([
[[0, 0], [-1, -1], [-1, -1]], # all of cohort 1 calls are missing
])
ds["sample_cohort"] = xr.DataArray(np.array([0, 1, 1]), dims="samples")
ds = divergence(ds)
np.testing.assert_equal(ds["stat_divergence"].values[0, 1], np.nan)
def test_divergence__windowed(sample_size, n_cohorts, chunks):
ts = msprime.simulate(sample_size,
length=200,
mutation_rate=0.05,
random_seed=42)
ds = ts_to_dataset(ts, chunks) # type: ignore[no-untyped-call]
ds, subsets = add_cohorts(ds, ts,
n_cohorts) # type: ignore[no-untyped-call]
ds = window(ds, size=25)
ds = divergence(ds)
div = ds["stat_divergence"].values
# test off-diagonal entries, by replacing diagonal with NaNs
div[:, np.arange(2), np.arange(2)] = np.nan
# Calculate diversity using tskit windows
# Find the variant positions so we can have windows with a fixed number of variants
positions = ts.tables.sites.position
windows = np.concatenate(([0], positions[::25][1:], [ts.sequence_length]))
n_windows = len(windows) - 1
ts_div = np.full([n_windows, n_cohorts, n_cohorts], np.nan)
for i, j in itertools.combinations(range(n_cohorts), 2):
ts_div[:, i, j] = ts.divergence([subsets[i], subsets[j]],
windows=windows,
span_normalise=False)
ts_div[:, j, i] = ts_div[:, i, j]
np.testing.assert_allclose(div, ts_div)
def test_divergence__windowed_scikit_allel_comparison(sample_size, n_cohorts,
chunks):
ts = simulate_ts(sample_size, length=200)
ds = ts_to_dataset(ts, chunks) # type: ignore[no-untyped-call]
ds, subsets = add_cohorts(ds, ts,
n_cohorts) # type: ignore[no-untyped-call]
ds = window(ds, size=25)
ds = divergence(ds)
div = ds["stat_divergence"].values
# test off-diagonal entries, by replacing diagonal with NaNs
div[:, np.arange(2), np.arange(2)] = np.nan
# Calculate divergence using scikit-allel moving_statistic
# (Don't use windowed_divergence, since it treats the last window differently)
ds1 = count_variant_alleles(ts_to_dataset(
ts, samples=ts.samples()[:1])) # type: ignore[no-untyped-call]
ds2 = count_variant_alleles(ts_to_dataset(
ts, samples=ts.samples()[1:])) # type: ignore[no-untyped-call]
ac1 = ds1["variant_allele_count"].values
ac2 = ds2["variant_allele_count"].values
mpd = allel.mean_pairwise_difference_between(ac1, ac2, fill=0)
ska_div = allel.moving_statistic(mpd, np.sum, size=25) # noqa: F841
# TODO: investigate why numbers are different
np.testing.assert_allclose(
div[:-1], ska_div) # scikit-allel has final window missing
def test_divergence(sample_size, n_cohorts, chunks):
ts = msprime.simulate(sample_size,
length=100,
mutation_rate=0.05,
random_seed=42)
ds = ts_to_dataset(ts, chunks) # type: ignore[no-untyped-call]
ds, subsets = add_cohorts(ds, ts,
n_cohorts) # type: ignore[no-untyped-call]
ds = divergence(ds)
div = ds.stat_divergence.sum(axis=0, skipna=False).values
# entries on the diagonal are diversity values
for i in range(n_cohorts):
ts_div = ts.diversity([subsets[i]], span_normalise=False)
np.testing.assert_allclose(div[i, i], ts_div)
# test off-diagonal entries, by replacing diagonal with NaNs
np.fill_diagonal(div, np.nan)
ts_div = np.full([n_cohorts, n_cohorts], np.nan)
for i, j in itertools.combinations(range(n_cohorts), 2):
ts_div[i, j] = ts.divergence([subsets[i], subsets[j]],
span_normalise=False)
ts_div[j, i] = ts.divergence([subsets[j], subsets[i]],
span_normalise=False)
np.testing.assert_allclose(div, ts_div)