Merge pull request #415 from Crunch-io/fix-3d-cube-with-odd-order

fix _replaced_order_element_ids to avoid None returns

Author: ernestoarbitrio

src/cr/cube/dimension.py

@@ -63,7 +63,7 @@ class Dimensions(tuple):
     """Collection containing every dimension defined in cube response."""
 
     def __repr__(self) -> str:
-        "str representation of this dimension sequence." ""
+        """str representation of this dimension sequence."""
         dim_lines = "\n".join(str(d) for d in self)
         return f"Dimensions:\n{dim_lines}"
 
@@ -875,7 +875,11 @@ def _replaced_order_element_ids(self, element_ids) -> List[Optional[str]]:
         The explicit order transform includes a list of ids that can be specified in
         many different ways, this translate them to the subvariable aliases.
         """
-        return [self.translate_element_id(_id) for _id in element_ids]
+        return [
+            el
+            for _id in element_ids
+            if (el := self.translate_element_id(_id)) is not None
+        ]
 
     @lazyproperty
     def _subvar_aliases(self) -> Tuple[str, ...]:

src/cr/cube/matrix/measure.py

@@ -1621,7 +1621,7 @@ def _base_values(self):
         in this example (2,3).
         """
         # --- Use "body" reference values for base values
-        (ref_props, ref_bases) = self._reference_values(0)
+        ref_props, ref_bases = self._reference_values(0)
         return self._calculate_t_stats(
             self._proportions[0][0], self._column_bases[0][0], ref_props, ref_bases
         )
@@ -1709,9 +1709,9 @@ def _calculate_t_stats(self, props, bases, ref_props, ref_bases):
 
     @lazyproperty
     def _intersections(self):
-        "2D ndarray np.float64 of the t-stats for the intersections (4th block)" ""
+        """2D ndarray np.float64 of the t-stats for the intersections (4th block)"""
         # --- Use "inserted" reference values for intersections
-        (ref_props, ref_variance) = self._reference_values(1)
+        ref_props, ref_variance = self._reference_values(1)
         return self._calculate_t_stats(
             self._proportions[1][1], self._column_bases[1][1], ref_props, ref_variance
         )
@@ -1725,7 +1725,7 @@ def _proportions(self):
     def _subtotal_columns(self):
         """2D ndarray np.float64 of the values for the subtotal columns (2nd block)"""
         # --- Use "body" reference values for inserted columns
-        (ref_props, ref_variance) = self._reference_values(0)
+        ref_props, ref_variance = self._reference_values(0)
         return self._calculate_t_stats(
             self._proportions[0][1], self._column_bases[0][1], ref_props, ref_variance
         )
@@ -1734,7 +1734,7 @@ def _subtotal_columns(self):
     def _subtotal_rows(self):
         """2D ndarray np.float64 of the t-stats for the subtotal rows (3rd block)"""
         # --- Use "inserted" reference values for inserted rows
-        (ref_props, ref_variance) = self._reference_values(1)
+        ref_props, ref_variance = self._reference_values(1)
         return self._calculate_t_stats(
             self._proportions[1][0], self._column_bases[1][0], ref_props, ref_variance
         )

tests/integration/test_cubepart.py

@@ -22,6 +22,31 @@
 class Test_Slice:
     """Integration-test suite for _Slice object."""
 
+    def test_it_provides_values_for_cat_x_mr_x_mr_with_odd_order(self):
+        transforms = {
+            "columns_dimension": {
+                "elements": {
+                    "2": {"hide": True},
+                    "3": {"hide": True},
+                    "4": {"hide": True},
+                    "foo": {"hide": True},
+                    "bar": {"hide": True},
+                },
+                "order": {
+                    "element_ids": [1, 2, 3, 4, 6, "foo", "bar"],
+                    "type": "explicit",
+                },
+            },
+        }
+        parts = Cube(CR.CAT_X_MR_X_MR, transforms=transforms).partitions
+        assert len(parts) == 2
+        assert parts[0].column_proportions == pytest.approx(
+            np.array([[0.60553814], [0.10292581], [0.10031348]])
+        )
+        assert parts[1].column_proportions == pytest.approx(
+            np.array([[0.08141321], [0.60522273], [0.5847414]])
+        )
+
     def test_it_provides_values_for_cat_x_cat(self):
         slice_ = Cube(CR.CAT_X_CAT, population=9001).partitions[0]
 

tests/integration/test_scale_means.py

@@ -239,7 +239,7 @@ def test_cat_x_cat_pruning_and_hs():
     np.testing.assert_almost_equal(
         slice_.columns_scale_mean,
         [1.57933884, 1.8308135, 2.10618401, 2.30460074, np.nan, 2.34680135],
-    ),
+    )
     np.testing.assert_almost_equal(
         slice_.rows_scale_mean,
         [1.74213625, 2.2364515, 1.97, 2.45356177, 2.11838791, np.nan, 2.0],
@@ -268,7 +268,7 @@ def test_cat_x_cat_pruning_and_hs():
     slice_ = Cube(CR.CAT_HS_MT_X_CAT_HS_MT, transforms=transforms).partitions[0]
     np.testing.assert_almost_equal(
         slice_.columns_scale_mean, [1.57933884, 2.106184, 2.3046007, 2.34680135]
-    ),
+    )
     np.testing.assert_almost_equal(
         slice_.rows_scale_mean, [1.74213625, 1.97, 2.45356177, 2.11838791, 2.0]
     )