store changes

torchgeo/datasets/rioxr.py

@@ -50,6 +50,32 @@ def dtype(self) -> torch.dtype:
             return torch.float32
         else:
             return torch.long
+
+    def harmonize_format(self, ds):
+        """Convert the dataset to the standard format.
+        
+        Args:
+            ds: dataset or array to harmonize
+
+        Returns:
+            the harmonized dataset or array
+        """
+        # rioxarray expects spatial dimensions to be named x and y
+        ds.rio.set_spatial_dims(
+            self.spatial_x_name, self.spatial_y_name, inplace=True
+        )
+
+        # if x coords go from 0 to 360, convert to -180 to 180
+        if ds[self.spatial_x_name].min() > 180:
+            ds = ds.assign_coords({self.spatial_x_name: ds[self.spatial_x_name] % 360 - 180})
+
+        # if y coords go from 0 to 180, convert to -90 to 90
+        if ds[self.spatial_x_name].min() > 90:
+            ds = ds.assign_coords({self.spatial_y_name: ds[self.spatial_y_name] % 180 - 90})
+        # expect asceding coordinate values
+        ds = ds.sortby(self.spatial_x_name, ascending=True)
+        ds = ds.sortby(self.spatial_y_name, ascending=True)
+        return ds
 
     def __init__(
         self,
@@ -94,10 +120,7 @@ def __init__(
             match = re.match(filename_regex, os.path.basename(filepath))
             if match is not None:
                 with xr.open_dataset(filepath, decode_times=True) as ds:
-                    # rioxarray expects spatial dimensions to be named x and y
-                    ds.rio.set_spatial_dims(
-                        self.spatial_x_name, self.spatial_y_name, inplace=True
-                    )
+                    ds = self.harmonize_format(ds)
 
                     if crs is None:
                         crs = ds.rio.crs
@@ -196,44 +219,43 @@ def __getitem__(self, query: BoundingBox) -> dict[str, Any]:
         data_arrays: list["np.typing.NDArray"] = []
         for item in items:
             with xr.open_dataset(item, decode_cf=True) as ds:
-                # rioxarray expects spatial dimensions to be named x and y
-                ds.rio.set_spatial_dims(
-                    self.spatial_x_name, self.spatial_y_name, inplace=True
-                )
-
-                if not ds.rio.crs:
-                    ds.rio.write_crs(self._crs, inplace=True)
-                elif ds.rio.crs != self._crs:
-                    ds = ds.rio.reproject(self._crs)
-
-                # clip box ignores time dimension
-                clipped = ds.rio.clip_box(
-                    minx=query.minx, miny=query.miny, maxx=query.maxx, maxy=query.maxy
-                )
+
+                ds = self.harmonize_format(ds)
                 # select time dimension
                 if hasattr(ds, "time"):
                     try:
-                        clipped["time"] = clipped.indexes["time"].to_datetimeindex()
+                        ds["time"] = ds.indexes["time"].to_datetimeindex()
                     except AttributeError:
-                        clipped["time"] = clipped.indexes["time"]
-                    clipped = clipped.sel(
+                        ds["time"] = ds.indexes["time"]
+                    ds = ds.sel(
                         time=slice(
                             datetime.fromtimestamp(query.mint),
                             datetime.fromtimestamp(query.maxt),
                         )
                     )
 
                 for variable in self.data_variables:
-                    if hasattr(clipped, variable):
+                    if hasattr(ds, variable):
+                        da = ds[variable]
+                        if not da.rio.crs:
+                            da.rio.write_crs(self._crs, inplace=True)
+                        elif da.rio.crs != self._crs:
+                            da = da.rio.reproject(self._crs)
+                        # clip box ignores time dimension
+                        clipped = da.rio.clip_box(
+                            minx=query.minx, miny=query.miny, maxx=query.maxx, maxy=query.maxy
+                        )
                         # rioxarray expects this order
                         clipped = clipped.transpose(
                             "time", self.spatial_y_name, self.spatial_x_name, ...
                         )
 
                         # set proper transform # TODO not working
-                        # clipped = clipped.rio.write_transform(self.transform)
-                        data_arrays.append(clipped[variable].squeeze())
+                        clipped.rio.write_transform(self.transform)
+                        data_arrays.append(clipped.squeeze())
 
+        import pdb
+        pdb.set_trace()
         merged_data = torch.from_numpy(
             merge_arrays(
                 data_arrays, bounds=(query.minx, query.miny, query.maxx, query.maxy)