add msprime and use np.testing

Author: daletovar

requirements-dev.txt

@@ -6,5 +6,6 @@ pytest-datadir
 hypothesis
 statsmodels
 zarr
+msprime
 sphinx
 sphinx_rtd_theme

sgkit/tests/test_popgen.py

@@ -39,7 +39,7 @@ def test_diversity(size):
     ds = ts_to_dataset(ts)  # type: ignore[no-untyped-call]
     div = diversity(ds).compute()
     ts_div = ts.diversity(span_normalise=False)
-    assert np.allclose(div, ts_div)
+    np.testing.assert_allclose(div, ts_div)
 
 
 @pytest.mark.parametrize("size", [2, 3, 10, 100])
@@ -51,7 +51,7 @@ def test_divergence(size):
     ds2 = ts_to_dataset(ts, subset_2)  # type: ignore[no-untyped-call]
     div = divergence(ds1, ds2).compute()
     ts_div = ts.divergence([subset_1, subset_2], span_normalise=False)
-    assert np.allclose(div, ts_div)
+    np.testing.assert_allclose(div, ts_div)
 
 
 @pytest.mark.parametrize("size", [2, 3, 10, 100])
@@ -63,7 +63,7 @@ def test_Fst(size):
     ds2 = ts_to_dataset(ts, subset_2)  # type: ignore[no-untyped-call]
     fst = Fst(ds1, ds2).compute()
     ts_fst = ts.Fst([subset_1, subset_2])
-    assert np.allclose(fst, ts_fst)
+    np.testing.assert_allclose(fst, ts_fst)
 
 
 @pytest.mark.parametrize("size", [2, 3, 10, 100])
@@ -72,4 +72,4 @@ def test_Tajimas_D(size):
     ds = ts_to_dataset(ts)  # type: ignore[no-untyped-call]
     ts_d = ts.Tajimas_D()
     d = Tajimas_D(ds).compute()
-    assert np.allclose(d, ts_d)
+    np.testing.assert_allclose(d, ts_d)