make mr_selections aware they have an items dim

src/cr/cube/crunch_cube.py

@@ -107,7 +107,7 @@ def _fix_shape(self, array):
         # selections dimension (and subsequently purge the dimension itself).
 
         display_ind = [
-            0 if dim.is_selections else slice(None)
+            0 if dim.is_mr_selections(self.all_dimensions) else slice(None)
             for dim in self.all_dimensions
         ]
         array = array[display_ind]
@@ -309,6 +309,7 @@ def _prune_indices(self, transforms):
         row_indices = self._margin_pruned_indices(
             row_margin, self._inserted_dim_inds(transforms, 0), 0
         )
+
         if row_indices.ndim > 1:
             # In case of MR, we'd have 2D prune indices
             row_indices = row_indices.all(axis=1)
@@ -909,7 +910,7 @@ def hs_dims_for_den(hs_dims, axis):
         )
         if isinstance(arr, np.ma.core.MaskedArray):
             inflate_ind = [
-                None if d.is_selections else slice(None)
+                None if d.is_mr_selections(self.all_dimensions) else slice(None)
                 for i, d in enumerate(self.all_dimensions)
             ]
             mask = np.logical_or(

src/cr/cube/dimension.py

@@ -307,3 +307,17 @@ def is_selections(self):
         if category_ids == [1, 0, -1]:
             return True
         return False
+
+    def is_mr_selections(self, others):
+        '''Check whether a selections dimension has a corresponding items dim
+
+        Sometimes selections are used to in conjunction with another dim,
+        that knows where to find them (following it). Other times, they behave
+        as a normal categorical dimension. This checks against the aliases of
+        all other dims to see which is the case.
+        '''
+        if self.is_selections:
+            for dim in others:
+                if dim.alias == self.alias and not dim.is_selections:
+                    return True
+        return False

tests/integration/fixtures/__init__.py

@@ -22,13 +22,15 @@ def _load(cube_file):
 # Bivariate Cubes
 CAT_X_CAT = _load('cat-x-cat.json')
 CAT_X_DATETIME = _load('cat-x-datetime.json')
+LOGICAL_X_CAT = _load('logical-x-cat.json')
 
 # Univariate Cubes
 UNIVARIATE_CATEGORICAL = _load('univariate-categorical.json')
 VOTER_REGISTRATION = _load('voter-registration.json')
 SIMPLE_DATETIME = _load('simple-datetime.json')
 SIMPLE_TEXT = _load('simple-text.json')
 SIMPLE_CAT_ARRAY = _load('simple-cat-array.json')
+LOGICAL_UNIVARIATE = _load('logical-univariate.json')
 
 # Various other Cubes
 CAT_X_NUM_X_DATETIME = _load('cat-x-num-x-datetime.json')

tests/integration/fixtures/cubes/logical-univariate.json

@@ -0,0 +1 @@
+{"element": "shoji:view", "self": "/api/datasets/123/cube/?filter=%5B%5D&query=%7B%22dimensions%22:%5B%7B%22variable%22:%22/api/datasets/123%2Fvariables%2Fd2350ebd19384952856371f3ebfdded5%2F%22%7D%5D,%22measures%22:%7B%22count%22:%7B%22function%22:%22cube_count%22,%22args%22:%5B%5D%7D%7D,%22weight%22:%22/api/datasets/123%2Fvariables%2F000057%2F%22%7D", "value": {"query": {"measures": {"count": {"function": "cube_count", "args": []}}, "dimensions": [{"variable": "/api/datasets/123/variables/d2350ebd19384952856371f3ebfdded5/"}], "weight": "/api/datasets/123/variables/000057/"}, "query_environment": {"filter": []}, "result": {"dimensions": [{"derived": true, "references": {"alias": "bar", "name": "bar"}, "type": {"ordinal": false, "class": "categorical", "categories": [{"numeric_value": 1, "selected": true, "id": 1, "missing": false, "name": "Selected"}, {"numeric_value": 0, "missing": false, "id": 0, "name": "Other"}, {"numeric_value": null, "missing": true, "id": -1, "name": "No Data"}]}}], "missing": 15, "measures": {"count": {"data": [653.5771596093624, 815.5660185795266, 30.856821811109807], "n_missing": 15, "metadata": {"references": {}, "derived": true, "type": {"integer": false, "missing_rules": {}, "missing_reasons": {"No Data": -1}, "class": "numeric"}}}}, "element": "crunch:cube", "counts": [734, 751, 15], "n": 1500}}}

tests/integration/fixtures/cubes/logical-x-cat.json

@@ -0,0 +1 @@
+{"element": "shoji:view", "self": "/api/datasets/123/cube/?filter=%5B%5D&query=%7B%22dimensions%22:%5B%7B%22variable%22:%22/api/datasets/123%2Fvariables%2F000026%2F%22%7D,%7B%22variable%22:%22/api/datasets/123%2Fvariables%2F5a2969d0f6d440bfb42d3bb4540eee27%2F%22%7D%5D,%22measures%22:%7B%22count%22:%7B%22function%22:%22cube_count%22,%22args%22:%5B%5D%7D%7D,%22weight%22:null%7D", "value": {"query": {"measures": {"count": {"function": "cube_count", "args": []}}, "dimensions": [{"variable": "/api/datasets/123/variables/000026/"}, {"variable": "/api/datasets/123/variables/5a2969d0f6d440bfb42d3bb4540eee27/"}], "weight": null}, "query_environment": {"filter": []}, "result": {"dimensions": [{"references": {"alias": "pasta", "notes": "A categorical variable", "name": "Shapes of pasta", "description": "The geometry of pasta"}, "derived": false, "type": {"ordinal": false, "class": "categorical", "categories": [{"numeric_value": 1, "missing": false, "id": 1, "name": "Bucatini"}, {"numeric_value": 2, "missing": false, "id": 2, "name": "Chitarra"}, {"numeric_value": 0, "missing": false, "id": 0, "name": "Boccoli"}, {"numeric_value": 4, "missing": false, "id": 4, "name": "Orecchiette"}, {"numeric_value": 5, "missing": false, "id": 5, "name": "Quadrefiore"}, {"numeric_value": 6, "missing": false, "id": 6, "name": "Fileja"}, {"numeric_value": 32766, "missing": true, "id": 32766, "name": "Skipped"}, {"numeric_value": 32767, "missing": true, "id": 32767, "name": "Not asked"}, {"numeric_value": null, "missing": true, "id": -1, "name": "No Data"}]}}, {"references": {"alias": "long pastas", "api_derivation": null, "name": "long pastas"}, "derived": true, "type": {"ordinal": false, "class": "categorical", "categories": [{"numeric_value": 1, "selected": true, "id": 1, "name": "Selected", "missing": false}, {"numeric_value": 0, "id": 0, "name": "Other", "missing": false}, {"numeric_value": null, "id": -1, "name": "No Data", "missing": true}]}}], "missing": 4, "measures": {"count": {"data": [55, 0, 0, 126, 0, 0, 0, 616, 0, 0, 310, 0, 0, 401, 0, 0, 150, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0], "n_missing": 4, "metadata": {"references": {}, "derived": true, "type": {"integer": true, "missing_rules": {}, "missing_reasons": {"No Data": -1}, "class": "numeric"}}}}, "element": "crunch:cube", "counts": [55, 0, 0, 126, 0, 0, 0, 616, 0, 0, 310, 0, 0, 401, 0, 0, 150, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0], "n": 1662}}}

tests/integration/test_dimension.py

@@ -4,6 +4,7 @@
 from .fixtures import ECON_BLAME_WITH_HS_MISSING
 from .fixtures import ECON_BLAME_X_IDEOLOGY_ROW_HS
 from .fixtures import CA_WITH_NETS
+from .fixtures import LOGICAL_UNIVARIATE, LOGICAL_X_CAT
 
 from cr.cube.crunch_cube import CrunchCube
 
@@ -73,3 +74,20 @@ def test_hs_indices_with_bad_data(self):
         cat_dim = cube.dimensions[1]
         actual = [entry['anchor_ind'] for entry in cat_dim.hs_indices]
         assert actual == expected
+
+    def test_logical_univariate_dim(self):
+        cube = CrunchCube(LOGICAL_UNIVARIATE)
+        dimension = cube.dimensions[0]
+        expected = 'categorical'
+        actual = dimension.type
+        self.assertEqual(expected, actual)
+        self.assertFalse(dimension.is_mr_selections(cube.all_dimensions))
+
+    def test_logical_x_cat_dims(self):
+        cube = CrunchCube(LOGICAL_X_CAT)
+        logical_dim = cube.dimensions[1]
+        self.assertEqual(cube.dimensions[0].type, 'categorical')
+        self.assertEqual(logical_dim.type, 'categorical')
+
+        self.assertTrue(logical_dim.is_selections)
+        self.assertFalse(logical_dim.is_mr_selections(cube.all_dimensions))