Some tests

c/tskit/trees.c

@@ -6377,7 +6377,16 @@ count_mutations_on_path(tsk_id_t u, tsk_id_t v, const tsk_id_t *restrict parent,
             tv = time[v];
         }
     }
-    tsk_bug_assert((u == TSK_NULL) == (v == TSK_NULL));
+    if (u != v) {
+        while (u != TSK_NULL) {
+            count += mutations_per_node[u];
+            u = parent[u];
+        }
+        while (v != TSK_NULL) {
+            count += mutations_per_node[v];
+            v = parent[v];
+        }
+    }
     return count;
 }
 

python/tests/test_divmat.py

@@ -32,6 +32,8 @@
 from tests import tsutil
 from tests.test_highlevel import get_example_tree_sequences
 
+DIVMAT_MODES = ["branch", "site"]
+
 # ↑ See https://github.com/tskit-dev/tskit/issues/1804 for when
 # we can remove this.
 
@@ -712,25 +714,39 @@ class TestSuiteExamples:
     Python code above on.
     """
 
-    def check(self, ts, windows=None, samples=None, num_threads=0):
-        D1 = stats_api_divergence_matrix(ts, windows=windows, samples=samples)
-        D2 = ts.divergence_matrix(
-            windows=windows, samples=samples, num_threads=num_threads
+    def check(self, ts, windows=None, samples=None, num_threads=0, mode="branch"):
+        D1 = ts.divergence_matrix(
+            windows=windows,
+            samples=samples,
+            num_threads=num_threads,
+            mode=mode,
         )
-        # If we have missing data then parts of the divmat are defined to be zero,
-        # so relative tolerances aren't useful. Because the stats API
-        # method necessarily involves subtracting away all of the previous
-        # values for an empty tree, there is a degree of numerical imprecision
-        # here. This value for atol is what is needed to get the tests to
-        # pass in practise.
-        has_missing_data = any(tree._has_isolated_samples() for tree in ts.trees())
-        assert D1.shape == D2.shape
-        atol = 1e-12 if has_missing_data else 0
-        np.testing.assert_allclose(D1, D2, atol=atol)
+        if mode == "branch":
+            D2 = stats_api_divergence_matrix(ts, windows=windows, samples=samples)
+            # If we have missing data then parts of the divmat are defined to be zero,
+            # so relative tolerances aren't useful. Because the stats API
+            # method necessarily involves subtracting away all of the previous
+            # values for an empty tree, there is a degree of numerical imprecision
+            # here. This value for atol is what is needed to get the tests to
+            # pass in practise.
+            has_missing_data = any(tree._has_isolated_samples() for tree in ts.trees())
+            assert D1.shape == D2.shape
+            atol = 1e-12 if has_missing_data else 0
+            np.testing.assert_allclose(D1, D2, atol=atol)
+        else:
+            assert mode == "site"
+            D2 = site_divergence_matrix_naive(ts, windows=windows, samples=samples)
+            # print("D1 = ")
+            # print(D1)
+            # print("D2 = ")
+            # print(D2)
+            assert D1.shape == D2.shape
+            np.testing.assert_array_equal(D1, D2)
 
     @pytest.mark.parametrize("ts", get_example_tree_sequences())
-    def test_defaults(self, ts):
-        self.check(ts)
+    @pytest.mark.parametrize("mode", DIVMAT_MODES)
+    def test_defaults(self, ts, mode):
+        self.check(ts, mode=mode)
 
     @pytest.mark.parametrize("ts", get_example_tree_sequences())
     def test_subset_samples(self, ts):

python/tests/test_highlevel.py

@@ -228,7 +228,7 @@ def get_gap_examples():
                 assert len(t.parent_dict) == 0
                 found = True
         assert found
-        ret.append((f"gap {x}", ts))
+        ret.append((f"gap_{x}", ts))
     # Give an example with a gap at the end.
     ts = msprime.simulate(10, random_seed=5, recombination_rate=1)
     tables = get_table_collection_copy(ts.dump_tables(), 2)