fix the use of regrid.most_common in land_cover.py due to changes in …

…version xarray_regrid v0.4.0

src/zampy/datasets/land_cover.py

@@ -134,16 +134,30 @@ def load(
                 )
                 raise ValueError(msg)
         files = list((ingest_dir / self.name).glob(f"{self.name}_*.nc"))
-
         ds = xr.open_mfdataset(files, chunks={"latitude": 200, "longitude": 200})
         ds = ds.sel(time=slice(time_bounds.start, time_bounds.end))
 
         grid = xarray_regrid.create_regridding_dataset(
             utils.make_grid(spatial_bounds, resolution)
         )
-        ds = ds.regrid.most_common(grid)
 
-        return ds
+        ds_regrid = {}
+        for variable in variable_names:
+            # select the variable to be regridded
+            da = ds[variable]
+
+            # get values for most common method
+            if "flag_values" in da.attrs:
+                regrid_values = da.attrs["flag_values"]
+            else:
+                regrid_values = np.unique(da.values)
+
+            da_regrid = da.regrid.most_common(grid, values=regrid_values)
+
+            # make sure dtype is the same
+            ds_regrid[variable] = da_regrid.astype(da.dtype)
+
+        return xr.Dataset(ds_regrid)
 
     def convert(
         self,
@@ -207,7 +221,7 @@ def extract_netcdf_to_zampy(file: Path) -> xr.Dataset:
 
         # only keep land cover class variable
         with xr.open_dataset(unzip_folder / zipped_file_name) as ds:
-            var_list = [var for var in ds.data_vars]
+            var_list = list(ds.data_vars)
             raw_variable = "lccs_class"
             var_list.remove(raw_variable)
             ds = ds.drop_vars(var_list)  # noqa: PLW2901
@@ -225,7 +239,23 @@ def extract_netcdf_to_zampy(file: Path) -> xr.Dataset:
 
             target_dataset = xarray_regrid.create_regridding_dataset(new_grid)
 
-            ds_regrid = ds.regrid.most_common(target_dataset)
+            # select the variable to be regridded
+            da = ds[raw_variable]
+
+            # get values for most common method
+            if "flag_values" in da.attrs:
+                regrid_values = da.attrs["flag_values"]
+            else:
+                regrid_values = np.unique(da.values)
+
+
+            da_regrid = da.regrid.most_common(target_dataset, values=regrid_values)
+
+            # make sure dtype is the same
+            da_regrid = da_regrid.astype(da.dtype)
+
+            # convert dataarray to dataset
+            ds_regrid = da_regrid.to_dataset()
 
         # rename variable to follow the zampy convention
         variable_name = "land_cover"