edge case in getitem. spatial only feature caching / regridding

sup3r/preprocessing/accessor.py

@@ -138,15 +138,17 @@ def __getitem__(self, keys) -> Union[T_Array, Self]:
         if no_slices:
             return out
 
+        if not just_coords and not is_fancy:
+            out = out.isel(**slices)
+
+        out = out.data if single_feat else out.as_array()
         if just_coords:
-            return out.as_array()[tuple(slices.values())]
+            return out[tuple(slices.values())]
 
         if is_fancy:
-            out = out.data if single_feat else out.as_array()
             return out.vindex[tuple(slices.values())]
 
-        out = out.isel(**slices)
-        return out.data if single_feat else out.as_array()
+        return out
 
     def __getattr__(self, attr):
         """Get attribute and cast to ``type(self)`` if a ``xr.Dataset`` is

sup3r/preprocessing/rasterizers/dual.py

@@ -24,14 +24,14 @@ class DualRasterizer(Container):
     (Usually ERA5 and WTK, respectively). This essentially just regrids the
     low-res data to the coarsened high-res grid.  This is useful for caching
     prepping data which then can go directly to a
-    :class:`~sup3r.preprocessing.samplers.DualSampler` object for a
-    :class:`~sup3r.preprocessing.batch_queues.DualBatchQueue`.
+    :class:`~sup3r.preprocessing.samplers.dual.DualSampler`
+    :class:`~sup3r.preprocessing.batch_queues.dual.DualBatchQueue`.
 
     Note
     ----
     When first extracting the low_res data make sure to extract a region that
     completely overlaps the high_res region. It is easiest to load the full
-    low_res domain and let :class:`DualRasterizer` select the appropriate
+    low_res domain and let :class:`.DualRasterizer` select the appropriate
     region through regridding.
     """
 
@@ -154,8 +154,9 @@ def update_hr_data(self):
 
             hr_data_new = {}
             for f in self.hr_data.features:
-                hr_slices = [f, *[slice(sh) for sh in self.hr_required_shape]]
-                hr_data_new[f] = self.hr_data[tuple(hr_slices)]
+                hr_slices = [slice(sh) for sh in self.hr_required_shape]
+                hr = self.hr_data.to_dataarray().sel(variable=f).data
+                hr_data_new[f] = hr[tuple(hr_slices)]
 
             hr_coords_new = {
                 Dimension.LATITUDE: self.hr_lat_lon[..., 0],