account for non-gridded data correctly

deepsensor/data/loader.py

@@ -821,41 +821,53 @@ def _compute_global_coordinate_bounds(self) -> List[float]:
 
         return [x1_min, x1_max, x2_min, x2_max]
 
-    def _compute_x1x2_direction(self) -> str:
+    def _compute_x1x2_direction(self) -> dict:
         """Compute whether the x1 and x2 coords are ascending or descending.
 
         Returns:
-        -------
-        coord_directions: dict(str)
-            Dictionary containing two keys: x1 and x2, with boolean values
-            defining if these coordings increase or decrease from top left corner.
+            dict(bool)
+                Dictionary containing two keys: x1 and x2, with boolean values
+                defining if these coordings increase or decrease from top left corner.
+
+        Raises:
+            ValueError:
+                If all datasets are non-gridded or if direction of ascending
+                coordinates does not match across non-gridded datasets.
 
         """
+        non_gridded = {"x1": None, "x2": None}  # value to use for non-gridded data
+        ascending = []
         for var in itertools.chain(self.context, self.target):
             if isinstance(var, (xr.Dataset, xr.DataArray)):
                 coord_x1_left = var.x1[0]
                 coord_x1_right = var.x1[-1]
                 coord_x2_top = var.x2[0]
                 coord_x2_bottom = var.x2[-1]
 
-                x1_ascend = True if coord_x1_left <= coord_x1_right else False
-                x2_ascend = True if coord_x2_top <= coord_x2_bottom else False
-
-                coord_directions = {
-                    "x1": x1_ascend,
-                    "x2": x2_ascend,
-                }
+                ascending.append(
+                    {
+                        "x1": True if coord_x1_left <= coord_x1_right else False,
+                        "x2": True if coord_x2_top <= coord_x2_bottom else False,
+                    }
+                )
 
-            # TODO- what to input for pd.dataframe
             elif isinstance(var, (pd.DataFrame, pd.Series)):
-                # var_x1_min = var.index.get_level_values("x1").min()
-                # var_x1_max = var.index.get_level_values("x1").max()
-                # var_x2_min = var.index.get_level_values("x2").min()
-                # var_x2_max = var.index.get_level_values("x2").max()
+                ascending.append(non_gridded)
 
-                coord_directions = {"x1": None, "x2": None}
+        if len(list(filter(lambda x: x != non_gridded, ascending))) == 0:
+            raise ValueError(
+                "All data is non gridded, can not proceed with sliding window sampling."
+            )
+
+        # get the directions for only the gridded data
+        gridded = list(filter(lambda x: x != non_gridded, ascending))
+        # raise error if directions don't match across gridded data
+        if gridded.count(gridded[0]) != len(gridded):
+            raise ValueError(
+                "Direction of ascending coordinates does not match across all gridded datasets."
+            )
 
-        return coord_directions
+        return gridded[0]
 
     def sample_random_window(self, patch_size: Tuple[float]) -> Sequence[float]:
         """Sample random window uniformly from global coordinates to slice data.