update type hint

rio_tiler/models.py

@@ -8,6 +8,7 @@
 import numpy
 from affine import Affine
 from color_operations import parse_operations, scale_dtype, to_math_type
+from numpy.typing import NDArray
 from pydantic import BaseModel
 from rasterio import windows
 from rasterio.coords import BoundingBox
@@ -796,7 +797,7 @@ def get_coverage_array(
         shape: Dict,
         shape_crs: CRS = WGS84_CRS,
         cover_scale: int = 10,
-    ) -> numpy.array:
+    ) -> NDArray[numpy.floating]:
 
         """
         cover_scale: int, optional

rio_tiler/utils.py

@@ -7,6 +7,7 @@
 import numpy
 import rasterio
 from affine import Affine
+from numpy.typing import NDArray
 from rasterio import windows
 from rasterio.crs import CRS
 from rasterio.dtypes import _gdal_typename
@@ -36,7 +37,7 @@ def get_array_statistics(
     categorical: bool = False,
     categories: Optional[List[float]] = None,
     percentiles: Optional[List[int]] = None,
-    coverage: Optional[numpy.ndarray] = None,
+    coverage: Optional[NDArray[numpy.floating]] = None,
     **kwargs: Any,
 ) -> List[Dict[Any, Any]]:
     """Calculate per band array statistics.
@@ -46,6 +47,7 @@ def get_array_statistics(
         categorical (bool): treat input data as categorical data. Defaults to `False`.
         categories (list of numbers, optional): list of categories to return value for.
         percentiles (list of numbers, optional): list of percentile values to calculate. Defaults to `[2, 98]`.
+        coverage (numpy.array, optional): Data coverage fraction.
         kwargs (optional): options to forward to `numpy.histogram` function (only applies for non-categorical data).
 
     Returns: