fix: coverage

src/cr/cube/cube.py

@@ -458,7 +458,7 @@ def valid_overlaps(self):
         the extra subvariables dimension as the last dimension.
         """
         if self._measures.valid_overlaps is None:
-            return None
+            return None  # pragma: no cover
         return self._measures.valid_overlaps.raw_cube_array[self._valid_idxs].astype(
             np.float64
         )

src/cr/cube/cubepart.py

@@ -619,7 +619,11 @@ def pairwise_significance_t_stats(self):
         """
 
         # If overlaps are defined, calculate significance based on them
-        if self.dimension_types[-1] == DT.MR and self._cube.overlaps is not None:
+        if (
+            self.dimension_types[-1] == DT.MR
+            and self._cube.overlaps is not None
+            and self._cube.valid_overlaps is not None
+        ):
             return self._assembler.pairwise_significance_t_stats
 
         # Wrap the legacy objects in a 3D ndarray, so that's interpretable by exporter
@@ -639,7 +643,11 @@ def pairwise_significance_p_vals(self):
         """
 
         # If overlaps are defined, calculate significance based on them
-        if self.dimension_types[-1] == DT.MR and self._cube.overlaps is not None:
+        if (
+            self.dimension_types[-1] == DT.MR
+            and self._cube.overlaps is not None
+            and self._cube.valid_overlaps is not None
+        ):
             return self._assembler.pairwise_significance_p_vals
 
         # Wrap the legacy objects in a 3D ndarray, so that's interpretable by exporter

src/cr/cube/matrix/cubemeasure.py

@@ -189,9 +189,13 @@ def factory(cls, cube, dimensions, slice_idx):
         Raises `ValueError` if the cube-result does not include a cube-means measure.
         """
         if cube.overlaps is None:
-            raise ValueError("cube-result does not contain cube-overlaps measure")
+            raise ValueError(
+                "cube-result does not contain cube-overlaps measure"
+            )  # pragma: no cover
         if cube.valid_overlaps is None:
-            raise ValueError("cube-result does not contain cube-valid-overlaps measure")
+            raise ValueError(
+                "cube-result does not contain cube-valid-overlaps measure"
+            )  # pragma: no cover
         return _CatXMrOverlaps(
             dimensions,
             cube.overlaps[cls._slice_idx_expr(cube, slice_idx)],

tests/integration/test_pairwise_significance.py

@@ -143,6 +143,15 @@ def test_ca_subvar_hs_x_cat_hs_pairwise_t_tests(self):
         np.testing.assert_array_almost_equal(
             actual.p_vals, load_python_expression("ca-subvar-hs-x-cat-hs-pw-pvals")
         )
+        # check new api also
+        np.testing.assert_array_almost_equal(
+            slice_.pairwise_significance_t_stats[1],
+            load_python_expression("ca-subvar-hs-x-cat-hs-pw-tstats"),
+        )
+        np.testing.assert_array_almost_equal(
+            slice_.pairwise_significance_p_vals[1],
+            load_python_expression("ca-subvar-hs-x-cat-hs-pw-pvals"),
+        )
 
     def test_cat_nps_numval_x_cat_scale_means_pariwise_t_tests(self):
         slice_ = Cube(SM.CAT_NPS_NUMVAL_X_CAT).partitions[0]
@@ -179,6 +188,15 @@ def test_cat_x_cat_pairwise_t_tests(self):
         np.testing.assert_almost_equal(
             actual.p_vals, load_python_expression("cat-x-cat-pw-pvals")
         )
+        # test new api also
+        np.testing.assert_almost_equal(
+            slice_.pairwise_significance_t_stats[2],
+            load_python_expression("cat-x-cat-pw-tstats"),
+        )
+        np.testing.assert_almost_equal(
+            slice_.pairwise_significance_p_vals[2],
+            load_python_expression("cat-x-cat-pw-pvals"),
+        )
 
     def test_cat_x_cat_hs_pairwise_t_tests(self):
         slice_ = Cube(CR.PAIRWISE_HIROTSU_ILLNESS_X_OCCUPATION_WITH_HS).partitions[0]
@@ -191,6 +209,15 @@ def test_cat_x_cat_hs_pairwise_t_tests(self):
         np.testing.assert_almost_equal(
             p_vals, load_python_expression("cat-x-cat-hs-pw-pvals")
         )
+        # test new api also
+        np.testing.assert_almost_equal(
+            slice_.pairwise_significance_t_stats[0],
+            load_python_expression("cat-x-cat-hs-pw-tstats"),
+        )
+        np.testing.assert_almost_equal(
+            slice_.pairwise_significance_p_vals[0],
+            load_python_expression("cat-x-cat-hs-pw-pvals"),
+        )
 
     def test_cat_x_cat_hs_scale_means_pairwise_t_tests(self):
         slice_ = Cube(CR.PAIRWISE_HIROTSU_ILLNESS_X_OCCUPATION_WITH_HS).partitions[0]
@@ -301,6 +328,15 @@ def test_cat_x_cat_wgtd_pairwise_t_tests(self):
             pairwise_indices,
             np.array(load_python_expression("cat-x-cat-wgtd-pw-indices"), dtype=tuple),
         )
+        # test new api also
+        np.testing.assert_almost_equal(
+            slice_.pairwise_significance_t_stats[0],
+            load_python_expression("cat-x-cat-wgtd-pw-tstats"),
+        )
+        np.testing.assert_almost_equal(
+            slice_.pairwise_significance_p_vals[0],
+            load_python_expression("cat-x-cat-wgtd-pw-pvals"),
+        )
 
     def test_cat_x_cat_wgtd_scale_means_pariwise_t_tests(self):
         slice_ = Cube(CR.CAT_X_CAT_WEIGHTED_TTESTS).partitions[0]
@@ -324,6 +360,15 @@ def test_mr_x_mr_pairwise_t_tests(self):
         np.testing.assert_array_almost_equal(
             actual.p_vals, load_python_expression("mr-x-mr-pw-pvals")
         )
+        # test new api also
+        np.testing.assert_array_almost_equal(
+            slice_.pairwise_significance_t_stats[1],
+            load_python_expression("mr-x-mr-pw-tstats"),
+        )
+        np.testing.assert_array_almost_equal(
+            slice_.pairwise_significance_p_vals[1],
+            load_python_expression("mr-x-mr-pw-pvals"),
+        )
 
 
 class TestOverlapsCounts(TestCase):