Fix mypy drift: cast xarray returns, drop deprecated numpy plugin

pyproject.toml

@@ -176,7 +176,6 @@ exclude_lines = [
 
 [tool.mypy]
 python_version = "3.10"
-plugins = "numpy.typing.mypy_plugin"
 disallow_untyped_defs = true
 disallow_any_unimported = true
 no_implicit_optional = true

src/xarray_regrid/methods/_shared.py

@@ -2,7 +2,7 @@
 
 import warnings
 from collections.abc import Hashable
-from typing import Any, overload
+from typing import Any, cast, overload
 
 import numpy as np
 import pandas as pd
@@ -70,7 +70,7 @@ def restore_properties(
             else:
                 result = result.where(covered, fill_value)
 
-    return result.transpose(*original_data.dims)
+    return cast("xr.DataArray | xr.Dataset", result.transpose(*original_data.dims))
 
 
 @overload

src/xarray_regrid/methods/conservative.py

@@ -1,7 +1,7 @@
 """Conservative regridding implementation."""
 
 from collections.abc import Hashable
-from typing import overload
+from typing import cast, overload
 
 import numpy as np
 import xarray as xr
@@ -98,7 +98,7 @@ def conservative_regrid(
 
     regridded_data = regridded_data.reindex_like(target_ds, copy=False)
 
-    return regridded_data
+    return cast("xr.DataArray | xr.Dataset", regridded_data)
 
 
 def conservative_regrid_dataset(
@@ -241,7 +241,7 @@ def apply_spherical_correction(
     latitude_res = np.median(np.diff(dot_array[latitude_coord].to_numpy(), 1))
     lat_weights = lat_weight(dot_array[latitude_coord].to_numpy(), latitude_res)
     da.values = utils.normalize_overlap(dot_array.values * lat_weights[:, np.newaxis])
-    return da
+    return cast("xr.DataArray", da)
 
 
 def lat_weight(latitude: np.ndarray, latitude_res: float) -> np.ndarray:
@@ -299,4 +299,4 @@ def format_weights(
     elif sparse is not None:
         new_weights.data = sparse.COO(weights.data)
 
-    return new_weights
+    return cast("xr.DataArray", new_weights)

src/xarray_regrid/methods/interp.py

@@ -1,6 +1,6 @@
 """Methods based on xr.interp."""
 
-from typing import Literal, overload
+from typing import Literal, cast, overload
 
 import xarray as xr
 
@@ -49,4 +49,4 @@ def interp_regrid(
     for coord in coord_names:
         interped[coord].attrs = coord_attrs[coord]
 
-    return interped
+    return cast("xr.DataArray | xr.Dataset", interped)

src/xarray_regrid/utils.py

@@ -1,6 +1,6 @@
 from collections.abc import Callable, Hashable
 from dataclasses import dataclass
-from typing import Any, TypedDict, overload
+from typing import Any, TypedDict, cast, overload
 
 import numpy as np
 import pandas as pd
@@ -219,9 +219,12 @@ def call_on_dataset(
         msg = "Trying to convert Dataset with more than one data variable to DataArray"
         if len(result.data_vars) > 1:
             raise TypeError(msg)
-        return next(iter(result.data_vars.values())).rename(obj.name)
+        return cast(
+            "xr.DataArray",
+            next(iter(result.data_vars.values())).rename(obj.name),
+        )
 
-    return result
+    return cast("xr.DataArray | xr.Dataset", result)
 
 
 @overload
@@ -283,7 +286,7 @@ def format_for_regrid(
                 if len(obj[var].chunksizes.get(coord, ())) == 1:
                     result[var] = result[var].chunk({coord: -1})
 
-    return result
+    return cast("xr.DataArray | xr.Dataset", result)
 
 
 def format_lat(
@@ -322,15 +325,15 @@ def format_lat(
         south_pole = obj.isel({lat_coord: 0})
         if lon_coord is not None:
             south_pole = south_pole.mean(lon_coord, keep_attrs=True)
-        obj = xr.concat([south_pole, obj], dim=lat_coord)  # type: ignore
+        obj = xr.concat([south_pole, obj], dim=lat_coord)
         lat_vals = np.concatenate([[-polar_lat], lat_vals])
 
     # North pole
     if polar_lat - dy <= obj.coords[lat_coord].values[-1] < polar_lat:
         north_pole = obj.isel({lat_coord: -1})
         if lon_coord is not None:
             north_pole = north_pole.mean(lon_coord, keep_attrs=True)
-        obj = xr.concat([obj, north_pole], dim=lat_coord)  # type: ignore
+        obj = xr.concat([obj, north_pole], dim=lat_coord)
         lat_vals = np.concatenate([lat_vals, [polar_lat]])
 
     obj = update_coord(obj, lat_coord, lat_vals)