add future annotations to improve docs in tdigest

ml_tools/eolearn/ml_tools/tdigest.py

@@ -7,9 +7,11 @@
 
 This source code is licensed under the MIT license, see the LICENSE file in the root directory of this source tree.
 """
+from __future__ import annotations
+
 from functools import partial
 from itertools import product
-from typing import Any, Callable, Dict, Generator, Iterable, List, Literal, Tuple, Union
+from typing import Any, Callable, Generator, Iterable, Literal, Union
 
 import numpy as np
 import tdigest as td
@@ -34,7 +36,7 @@ def __init__(
         self,
         in_feature: FeaturesSpecification,
         out_feature: FeaturesSpecification,
-        mode: Union[Literal["standard", "timewise", "monthly", "total"], Callable] = "standard",
+        mode: Literal["standard", "timewise", "monthly", "total"] | Callable = "standard",
         pixelwise: bool = False,
         filternan: bool = False,
     ):
@@ -119,8 +121,8 @@ def _is_output_ftype(feature_type: FeatureType, mode: ModeTypes, pixelwise: bool
 
 
 def _looper(
-    in_feature: List[FeatureSpec], out_feature: List[FeatureSpec], eopatch: EOPatch
-) -> Generator[Tuple[FeatureSpec, FeatureSpec, np.ndarray], None, None]:
+    in_feature: list[FeatureSpec], out_feature: list[FeatureSpec], eopatch: EOPatch
+) -> Generator[tuple[FeatureSpec, FeatureSpec, np.ndarray], None, None]:
     for in_feature_, out_feature_ in zip(in_feature, out_feature):
         shape = np.array(eopatch[in_feature_].shape)
         yield in_feature_, out_feature_, shape
@@ -182,7 +184,7 @@ def _process_total(input_array: np.ndarray, filternan: bool, **_: Any) -> np.nda
     return _get_tdigest(input_array, filternan)
 
 
-_processing_function: Dict[str, Callable] = {
+_processing_function: dict[str, Callable] = {
     "standard": _process_standard,
     "timewise": _process_timewise,
     "monthly": _process_monthly,