review changes

Author: rcomer

lib/iris/analysis/__init__.py

@@ -1841,8 +1841,7 @@ def __init__(self, groupby_coords, shared_coords=None):
 
         Kwargs:
 
-        * shared_coords (list of tuples containing :class:`iris.coords.Coord`
-            instances with `int`):
+        * shared_coords (list of (:class:`iris.coords.Coord`, `int`) pairs):
             One or more coordinates (including multidimensional coordinates)
             that share the same group-by coordinate axis.  The `int` identifies
             which dimension of the coord is on the group-by coordinate axis.
@@ -2022,13 +2021,10 @@ def _compute_shared_coords(self):
                     # of interest to trailing dim and flatten the others.
                     work_arr = np.moveaxis(coord.points, dim, -1)
                     shape = work_arr.shape
-                    if coord.ndim == 1:
-                        work_shape = (1, ) + work_arr.shape
-                        new_shape = (len(self),)
-                    else:
-                        work_shape = (np.product(work_arr.shape[:-1]),
-                                      work_arr.shape[-1])
-                        new_shape = (len(self),) + shape[:-1]
+                    work_shape = (-1, shape[-1])
+                    new_shape = (len(self),)
+                    if coord.ndim > 1:
+                        new_shape += shape[:-1]
                     work_arr = work_arr.reshape(work_shape)
 
                     for key_slice in six.itervalues(self._slices_by_key):

lib/iris/cube.py

@@ -3387,13 +3387,16 @@ def aggregated_by(self, coords, aggregator, **kwargs):
             lambda coord_: coord_ not in groupby_coords,
             self.coords(contains_dimension=dimension_to_groupby)))
 
-        match_dims = [
-            index for coord_ in shared_coords for (index, dim) in
-            enumerate(self.coord_dims(coord_)) if dim == dimension_to_groupby]
+        # Determine which of each shared coord's dimensions will be aggregated.
+        shared_coords_and_dims = [
+            (coord_, index)
+            for coord_ in shared_coords
+            for (index, dim) in enumerate(self.coord_dims(coord_))
+            if dim == dimension_to_groupby]
 
         # Create the aggregation group-by instance.
         groupby = iris.analysis._Groupby(groupby_coords,
-                                         zip(shared_coords, match_dims))
+                                         shared_coords_and_dims)
 
         # Create the resulting aggregate-by cube and remove the original
         # coordinates that are going to be groupedby.

lib/iris/tests/unit/cube/test_Cube.py

@@ -503,6 +503,9 @@ def test_agg_by_label(self):
                                long_name='label', units='no_unit')
         self.assertEqual(res_cube.coord('val'), val_coord)
         self.assertEqual(res_cube.coord('label'), label_coord)
+
+    def test_2d_agg_by_label(self):
+        res_cube = self.cube.aggregated_by('label', self.mock_agg)
         # For 2d coord, slices of aggregated coord should be the same as
         # aggregated slices.
         for res_slice, cube_slice in zip(res_cube.slices('val'),
@@ -524,6 +527,9 @@ def test_agg_by_val(self):
                                long_name='label', units='no_unit')
         self.assertEqual(res_cube.coord('val'), val_coord)
         self.assertEqual(res_cube.coord('label'), label_coord)
+
+    def test_2d_agg_by_val(self):
+        res_cube = self.cube.aggregated_by('val', self.mock_agg)
         # For 2d coord, slices of aggregated coord should be the same as
         # aggregated slices.
         for res_slice, cube_slice in zip(res_cube.slices('val'),