_regrid_area_weighted_array: Extra tests for axes ordering

lib/iris/experimental/regrid.py

@@ -498,11 +498,11 @@ def _regrid_area_weighted_array(
         # e.g. a scalar point such as a vertical profile
         pass
     elif x_dim is not None and y_dim is None:
-        # test cross_section along line longitude
+        # test cross_section along line latitude
         src_data = np.moveaxis(src_data, x_dim, -1)
         x_dim = src_data.ndim - 1
     elif y_dim is not None and x_dim is None:
-        # test cross_section along line latitude
+        # test cross_section along line longitude
         src_data = np.moveaxis(src_data, y_dim, -1)
         y_dim = src_data.ndim - 1
     elif x_dim < y_dim:

lib/iris/tests/experimental/regrid/test_regrid_area_weighted_rectilinear_src_and_grid.py

@@ -322,19 +322,36 @@ def test_regrid_latlon_reduced_res(self):
         res = regrid_area_weighted(src, dest)
         self.assertCMLApproxData(res, RESULT_DIR + ("latlonreduced.cml",))
 
-    def test_regrid_transposed(self):
-        src = self.simple_cube.copy()
-        dest = _subsampled_grid(src, 2, 3)
-        # Transpose src so that the coords are not y, x ordered.
-        src.transpose()
+    def test_regrid_reorder_axis(self):
+        src = self.realistic_cube[0, :4, :3, :2]
+        z = src.coord("model_level_number")
+        lat = src.coord("grid_latitude")
+        lon = src.coord("grid_longitude")
+        dest = _resampled_grid(self.realistic_cube[0, 0, :3, :2], 3, 3)
         res = regrid_area_weighted(src, dest)
-        self.assertCMLApproxData(res, RESULT_DIR + ("trasposed.cml",))
-        # Using original and transposing the result should give the
-        # same answer.
-        src = self.simple_cube.copy()
+        self.assertArrayShapeStats(src, (4, 3, 2), 288.08868, 0.008262919)
+        self.assertArrayShapeStats(res, (4, 9, 6), 288.08865, 0.00826281)
+        # Reshape src so that the coords are ordered [x, z, y],
+        # the mean and std statistics should be the same
+        data = np.moveaxis(src.data.copy(), 2, 0)
+        src = iris.cube.Cube(data)
+        src.add_dim_coord(lat, 2)
+        src.add_dim_coord(z, 1)
+        src.add_dim_coord(lon, 0)
         res = regrid_area_weighted(src, dest)
-        res.transpose()
-        self.assertCMLApproxData(res, RESULT_DIR + ("trasposed.cml",))
+        self.assertArrayShapeStats(src, (2, 4, 3), 288.08868, 0.008262919)
+        self.assertArrayShapeStats(res, (6, 4, 9), 288.08865, 0.00826281)
+        # Reshape src so that the coords are ordered [y, x, z],
+        # the mean and std statistics should be the same
+        data = np.moveaxis(src.data.copy(), 2, 0)
+        src = iris.cube.Cube(data)
+        src.add_dim_coord(z, 2)
+        src.add_dim_coord(lon, 1)
+        src.add_dim_coord(lat, 0)
+        dest = _resampled_grid(self.realistic_cube[0, 0, :3, :2], 3, 3)
+        res = regrid_area_weighted(src, dest)
+        self.assertArrayShapeStats(src, (3, 2, 4), 288.08868, 0.008262919)
+        self.assertArrayShapeStats(res, (9, 6, 4), 288.08865, 0.00826281)
 
     def test_regrid_lon_to_half_res(self):
         src = self.simple_cube
@@ -446,6 +463,16 @@ def test_cross_section(self):
         self.assertCMLApproxData(
             res, RESULT_DIR + ("const_lat_cross_section.cml",)
         )
+        # Constant latitude, data order [x, z]
+        # Using original and transposing the result should give the
+        # same answer.
+        src.transpose()
+        dest.transpose()
+        res = regrid_area_weighted(src, dest)
+        res.transpose()
+        self.assertCMLApproxData(
+            res, RESULT_DIR + ("const_lat_cross_section.cml",)
+        )
 
         # Constant longitude
         src = self.realistic_cube[0, :, :, 10]
@@ -460,6 +487,16 @@ def test_cross_section(self):
         self.assertCMLApproxData(
             res, RESULT_DIR + ("const_lon_cross_section.cml",)
         )
+        # Constant longitude, data order [y, z]
+        # Using original and transposing the result should give the
+        # same answer.
+        src.transpose()
+        dest.transpose()
+        res = regrid_area_weighted(src, dest)
+        res.transpose()
+        self.assertCMLApproxData(
+            res, RESULT_DIR + ("const_lon_cross_section.cml",)
+        )
 
     def test_scalar_source_cube(self):
         src = self.simple_cube[1, 2]