Rolls back some of my complicated changes and goes with the simpler a…

…pproach instead.

improver/nbhood/nbhood.py

@@ -356,10 +356,12 @@ def _do_nbhood_sum(
         size = data.size
         extreme = 0
         when_all_extremes = 0
-        half_nb_size = self.nb_size // 2
+        half_nb_size = (self.nb_size // 2)
         for _extreme, _when_all_extremes in ((0, 0), (1, max_extreme)):
-            if _when_all_extremes is None:
-                # We can't take this shortcut
+            if _when_all_extremes is None or issubclass(data.dtype.type, np.complexfloating):
+                # We can't take this shortcut if we don't have either a default value/array,
+                # or the data values are complex, as comparisons with non-complex values are
+                # tricky.
                 continue
             nonextreme_indices = np.argwhere(data != _extreme)
             if nonextreme_indices.size == 0:
@@ -370,14 +372,6 @@ def _do_nbhood_sum(
                     nonextreme_indices.min(0),
                     nonextreme_indices.max(0) + 1,
                 )
-                if (
-                    (_ystart - self.nb_size < 0)
-                    or (_ystop + self.nb_size > data_shape[-2])
-                    or (_xstart - self.nb_size < 0)
-                    or (_xstop + self.nb_size > data_shape[-1])
-                ):
-                    #  Cannot safely crop this domain with enough buffer to preserve the result.
-                    continue
                 _ystart = max(0, _ystart - half_nb_size)
                 _ystop = min(data_shape[0], _ystop + half_nb_size)
                 _xstart = max(0, _xstart - half_nb_size)
@@ -393,34 +387,21 @@ def _do_nbhood_sum(
                     _xstart,
                     _xstop,
                 )
-        square_buffer_kwargs = {"mode": "constant", "constant_value": extreme}
         if size != data.size:
             # Determine default array for the extremes around the edges, or everywhere
             if isinstance(when_all_extremes, np.ndarray):
                 untrimmed = when_all_extremes.astype(data.dtype)
             else:
                 untrimmed = np.full(data_shape, when_all_extremes, dtype=data.dtype)
-            if (
-                self.neighbourhood_method == "square"
-                and min(ystart, xstart, data_shape[-2] - ystop, data_shape[-1] - xstop)
-                >= half_nb_size + 1
-            ):
-                # Trim to the calculated box plus padding
-                # and rely on boxsum to return the right sized array
-                data = data[
-                    ystart - half_nb_size - 1 : ystop + half_nb_size,
-                    xstart - half_nb_size - 1 : xstop + half_nb_size,
-                ]
-                square_buffer_kwargs = {}
-            else:
-                # Trim to the calculated box without padding and let boxsum do its padding.
-                # Circular neighbourhoods will ignore the square_buffer_kwargs.
-                data = data[ystart:ystop, xstart:xstop]
-
         if size:
-            # Calculate neighbourhood totals for input data if it isn't all constant.
+            # Trim to the calculated box
+            data = data[ystart:ystop, xstart:xstop]
+
+            # Calculate neighbourhood totals for input data.
             if self.neighbourhood_method == "square":
-                data = boxsum(data, self.nb_size, **square_buffer_kwargs)
+                data = boxsum(
+                    data, self.nb_size, mode="constant", constant_values=extreme
+                )
             elif self.neighbourhood_method == "circular":
                 data = correlate(data, self.kernel, mode="nearest")
         else: