Merge branch 'bring-coverage-to-100-percent-#157720119' into rel-5.2.123

src/cr/cube/crunch_cube.py

@@ -512,29 +512,6 @@ def _adjust_axis(self, axis):
 
         return tuple(new_axis)
 
-    def _transform_table(self, table, include_transforms_for_dims):
-        table = self._transform(
-            table,
-            include_transforms_for_dims,
-            inflate=True,
-        )
-        return table
-
-    def _inflate_dim(self, array, axis):
-        # Explicitly check if axis is tuple (which could be the case for doing
-        # cell percentages across 3D cube, when the axis is set to (1, 2)).
-        # Python 3 doesn't support tuple to int conversion, and we have to
-        # check it manually.
-        if (axis and not isinstance(axis, tuple) and
-                axis > 0 and len(array.shape) == 1):
-            # If any of the dimensions has only one element, it's flattened
-            # from the resulting array (as a part of the MR pre-processing).
-            # This can lead to a potential inconsistency between dimensions
-            # and axes, and we need to restore one dimension in this case.
-            array = array[:, np.newaxis]
-
-        return array
-
     @lazyproperty
     def is_univariate_ca(self):
         '''Check if cube is a just the CA ("ca x cat" or "cat x ca" dims)'''
@@ -568,19 +545,6 @@ def dim_types(self):
     def ndim(self):
         return len(self.dimensions)
 
-    @lazyproperty
-    def ind_selected(self):
-        return self._get_mr_slice()
-
-    @lazyproperty
-    def ind_non_selected(self):
-        return self._get_mr_slice(selected=False)
-
-    @lazyproperty
-    def valid_indices(self):
-        '''Valid indices of all dimensions (exclude missing).'''
-        return [dim.valid_indices(False) for dim in self.dimensions]
-
     @property
     def valid_indices_with_selections(self):
         '''Get all valid indices (including MR selections).'''
@@ -631,23 +595,6 @@ def is_double_mr(self):
         '''Check if cube has 2 MR dimensions.'''
         return True if isinstance(self.mr_dim_ind, tuple) else False
 
-    @lazyproperty
-    def double_mr_non_selected_inds(self):
-        '''Gets all combinations of non-selected slices for any double MR cube.
-
-        For double MR cubes, we need combinations of (selected) with
-        (selected + non_selected) for all combinations of MR x MR, but we also
-        need other potential dimensions included
-        '''
-        # This represents an index in between MR dimensions (at this point we
-        # know there are two). It's used subsequently, for creating the
-        # combined conditional slices, where we have to take 'selected' for
-        # one MR, and selected AND non-selected for the other.
-        inflect = self.mr_dim_ind[1] + 1
-        ind_ns_0 = self.ind_selected[:inflect] + self.ind_non_selected[inflect:]
-        ind_ns_1 = self.ind_non_selected[:inflect] + self.ind_selected[inflect:]
-        return self.ind_non_selected, ind_ns_0, ind_ns_1
-
     # Static methods
 
     @staticmethod

src/cr/cube/cube_slice.py

@@ -60,8 +60,11 @@ def _update_args(self, kwargs):
             if self.ca_as_0th:
                 axis = kwargs.get('axis', False)
                 if axis is None:
-                    # TODO: Write detailed explanation here in comments.
-                    # Special case for CA slices (in multitables).
+                    # Special case for CA slices (in multitables). In this case,
+                    # we need to calculate a measurement across CA categories
+                    # dimension (and not across items, because it's not
+                    # allowed). The value for the axis parameter of None, would
+                    # incur the items dimension, and we don't want that.
                     kwargs['axis'] = 1
             return kwargs
 
@@ -118,7 +121,7 @@ def _update_result(self, result):
     def _call_cube_method(self, method, *args, **kwargs):
         kwargs = self._update_args(kwargs)
         result = getattr(self._cube, method)(*args, **kwargs)
-        if method in ('labels', 'inserted_hs_indices'):
+        if method == 'inserted_hs_indices':
             if not self.ca_as_0th:
                 result = result[-2:]
             return result
@@ -156,19 +159,32 @@ def mr_dim_ind(self):
         mr_dim_ind = self._cube.mr_dim_ind
         if self.ndim == 3:
             if isinstance(mr_dim_ind, int):
+                if mr_dim_ind == 0:
+                    # If only the 0th dimension of a 3D is an MR, the sliced
+                    # don't actuall have the MR... Thus return None.
+                    return None
                 return mr_dim_ind - 1
             elif isinstance(mr_dim_ind, tuple):
-                return tuple(i - 1 for i in mr_dim_ind)
+                # If MR dimension index is a tuple, that means that the cube
+                # (only a 3D one if it reached this path) has 2 MR dimensions.
+                # If any of those is 0 ind dimension we don't need to include
+                # in the slice dimension (because the slice doesn't see the tab
+                # that it's on). If it's 1st and 2nd dimension, then subtract 1
+                # from those, and present them as 0th and 1st dimension of the
+                # slice. This can happend e.g. in a CAT x MR x MR cube (which
+                # renders MR x MR slices).
+                mr_dim_ind = tuple(i - 1 for i in mr_dim_ind if i)
+                return mr_dim_ind if len(mr_dim_ind) > 1 else mr_dim_ind[0]
 
         return mr_dim_ind
 
     @property
     def ca_main_axis(self):
         '''For univariate CA, the main axis is the categorical axis'''
-        ca_ind = self.dim_types.index('categorical_array')
-        if ca_ind is not None:
+        try:
+            ca_ind = self.dim_types.index('categorical_array')
             return 1 - ca_ind
-        else:
+        except:
             return None
 
     def labels(self, hs_dims=None, prune=False):

src/cr/cube/dimension.py

@@ -141,11 +141,6 @@ def _transform_anchor(self, subtotal):
             return subtotal.anchor
 
         element_ids = [el['id'] for el in self._elements]
-        if subtotal.anchor not in element_ids:
-            # In case of numeric value which is not a category ID, return
-            # default value 'bottom'
-            return 'bottom'
-
         contiguous_anchors = [
             i for (i, id_) in enumerate(element_ids)
             if id_ == subtotal.anchor

tests/integration/fixtures/__init__.py

@@ -136,3 +136,5 @@ def _load(cube_file):
 FOOD_GROOPS_X_STARTTIME_X_NORDIC_COUNTRIES = _load(
     'food-groops-x-starttime-x-nordic-countries.json',
 )
+MR_X_CAT_X_MR_PRUNE = _load('mr-x-cat-x-mr-prune.json')
+HUFFPOST_ACTIONS_X_HOUSEHOLD = _load('huffpost-actions-x-household.json')