Add missing type annotations

io/eolearn/io/geometry_io.py

@@ -46,7 +46,7 @@ def __init__(
         self.clip = clip
 
     @abc.abstractmethod
-    def _load_vector_data(self, bbox: Optional[BBox]):
+    def _load_vector_data(self, bbox: Optional[BBox]) -> gpd.GeoDataFrame:
         """Loads vector data given a bounding box"""
 
     def _reproject_and_clip(self, vectors: gpd.GeoDataFrame, bbox: Optional[BBox]) -> gpd.GeoDataFrame:
@@ -122,7 +122,7 @@ def __init__(
 
         self.fiona_kwargs = kwargs
         self._aws_session = None
-        self._dataset_crs = None
+        self._dataset_crs: Optional[CRS] = None
 
         super().__init__(feature=feature, reproject=reproject, clip=clip, config=config)
 
@@ -163,7 +163,7 @@ def dataset_crs(self) -> Optional[CRS]:
 
         return self._dataset_crs
 
-    def _read_crs(self):
+    def _read_crs(self) -> None:
         """Reads information about CRS from a dataset"""
         with fiona.open(self.full_path, **self.fiona_kwargs) as features:
             self._dataset_crs = CRS(features.crs)

mask/eolearn/mask/snow_mask.py

@@ -50,7 +50,7 @@ def __init__(
         self.undefined_value = undefined_value
         self.mask_feature = (FeatureType.MASK, mask_name)
 
-    def _apply_dilation(self, snow_masks):
+    def _apply_dilation(self, snow_masks: np.ndarray) -> np.ndarray:
         """Apply binary dilation for each mask in the series"""
         if self.dilation_size:
             snow_masks = np.array([binary_dilation(mask, disk(self.dilation_size)) for mask in snow_masks])
@@ -148,7 +148,7 @@ def __init__(
         red_params: Tuple[float, float, float, float, float] = (12, 0.3, 0.1, 0.2, 0.040),
         ndsi_params: Tuple[float, float, float] = (0.4, 0.15, 0.001),
         b10_index: Optional[int] = None,
-        **kwargs,
+        **kwargs: Any,
     ):
         """
         :param data_feature: EOPatch feature represented by a tuple in the form of `(FeatureType, 'feature_name')`
@@ -187,7 +187,7 @@ def __init__(
         self.b10_index = b10_index
         self._validate_params()
 
-    def _validate_params(self):
+    def _validate_params(self) -> None:
         """Check length of parameters defining threshold values"""
         for params, n_params in [(self.dem_params, 2), (self.red_params, 5), (self.ndsi_params, 3)]:
             if not isinstance(params, (tuple, list)) or len(params) != n_params:
@@ -229,7 +229,7 @@ def _adjust_cloud_mask(
         ).astype(np.uint8)
 
     def _apply_first_pass(
-        self, bands: np.ndarray, ndsi: np.ndarray, clm: np.ndarray, dem, clm_temp: np.ndarray
+        self, bands: np.ndarray, ndsi: np.ndarray, clm: np.ndarray, dem: np.ndarray, clm_temp: np.ndarray
     ) -> Tuple[np.ndarray, Optional[np.ndarray], np.ndarray]:
         """Apply first pass of snow detection"""
         snow_mask_pass1 = np.where(