Remove any use of legacy NumPy random number generator

examples/analysis/point_extraction/interpolation.py

@@ -24,9 +24,9 @@
 import numpy as np
 
 # Replace by Raster function once done (valid coords)
-np.random.seed(42)
-x_coords = np.random.uniform(rast.bounds.left + 50, rast.bounds.right - 50, 50)
-y_coords = np.random.uniform(rast.bounds.bottom + 50, rast.bounds.top - 50, 50)
+rng = np.random.default_rng(42)
+x_coords = rng.uniform(rast.bounds.left + 50, rast.bounds.right - 50, 50)
+y_coords = rng.uniform(rast.bounds.bottom + 50, rast.bounds.top - 50, 50)
 
 vals = rast.interp_points(points=list(zip(x_coords, y_coords)))
 

examples/analysis/point_extraction/reduction.py

@@ -24,9 +24,9 @@
 import numpy as np
 
 # Replace by Raster function once done
-np.random.seed(42)
-x_coords = np.random.uniform(rast.bounds.left + 50, rast.bounds.right - 50, 50)
-y_coords = np.random.uniform(rast.bounds.bottom + 50, rast.bounds.top - 50, 50)
+rng = np.random.default_rng(42)
+x_coords = rng.uniform(rast.bounds.left + 50, rast.bounds.right - 50, 50)
+y_coords = rng.uniform(rast.bounds.bottom + 50, rast.bounds.top - 50, 50)
 
 vals = rast.value_at_coords(x=x_coords, y=y_coords)
 

examples/io/import_export/from_array.py

@@ -14,8 +14,8 @@
 import geoutils as gu
 
 # A random 3 x 3 masked array
-np.random.seed(42)
-arr = np.random.normal(size=(5, 5))
+rng = np.random.default_rng(42)
+arr = rng.normal(size=(5, 5))
 # Introduce a NaN value
 arr[2, 2] = np.nan
 # A transform with 3 x 3 pixels in a [0-1, 0-1] bound square
@@ -39,7 +39,7 @@
 # We could also have created directly from a :class:`~numpy.ma.MaskedArray`.
 
 # A random mask, that will mask one out of two values on average
-mask = np.random.randint(0, 2, size=(5, 5), dtype="bool")
+mask = rng.integers(0, 2, size=(5, 5), dtype="bool")
 ma = np.ma.masked_array(data=arr, mask=mask)
 
 # This time, we pass directly the masked array

geoutils/raster/raster.py

@@ -3781,7 +3781,7 @@ def to_pointcloud(
         subsample: float | int = 1,
         *,
         as_array: Literal[False] = False,
-        random_state: np.random.RandomState | int | None = None,
+        random_state: int | np.random.Generator | None = None,
         force_pixel_offset: Literal["center", "ul", "ur", "ll", "lr"] = "ul",
     ) -> NDArrayNum:
         ...
@@ -3796,7 +3796,7 @@ def to_pointcloud(
         subsample: float | int = 1,
         *,
         as_array: Literal[True],
-        random_state: np.random.RandomState | int | None = None,
+        random_state: int | np.random.Generator | None = None,
         force_pixel_offset: Literal["center", "ul", "ur", "ll", "lr"] = "ul",
     ) -> Vector:
         ...
@@ -3811,7 +3811,7 @@ def to_pointcloud(
         subsample: float | int = 1,
         *,
         as_array: bool = False,
-        random_state: np.random.RandomState | int | None = None,
+        random_state: int | np.random.Generator | None = None,
         force_pixel_offset: Literal["center", "ul", "ur", "ll", "lr"] = "ul",
     ) -> NDArrayNum | Vector:
         ...
@@ -3824,7 +3824,7 @@ def to_pointcloud(
         auxiliary_column_names: list[str] | None = None,
         subsample: float | int = 1,
         as_array: bool = False,
-        random_state: np.random.RandomState | int | None = None,
+        random_state: int | np.random.Generator | None = None,
         force_pixel_offset: Literal["center", "ul", "ur", "ll", "lr"] = "ul",
     ) -> NDArrayNum | Vector:
         """
@@ -4106,7 +4106,7 @@ def subsample(
         subsample: int | float,
         return_indices: Literal[False] = False,
         *,
-        random_state: np.random.RandomState | int | None = None,
+        random_state: int | np.random.Generator | None = None,
     ) -> NDArrayNum:
         ...
 
@@ -4116,7 +4116,7 @@ def subsample(
         subsample: int | float,
         return_indices: Literal[True],
         *,
-        random_state: np.random.RandomState | int | None = None,
+        random_state: int | np.random.Generator | None = None,
     ) -> tuple[NDArrayNum, ...]:
         ...
 
@@ -4125,15 +4125,15 @@ def subsample(
         self,
         subsample: float | int,
         return_indices: bool = False,
-        random_state: np.random.RandomState | int | None = None,
+        random_state: int | np.random.Generator | None = None,
     ) -> NDArrayNum | tuple[NDArrayNum, ...]:
         ...
 
     def subsample(
         self,
         subsample: float | int,
         return_indices: bool = False,
-        random_state: np.random.RandomState | int | None = None,
+        random_state: int | np.random.Generator | None = None,
     ) -> NDArrayNum | tuple[NDArrayNum, ...]:
         """
         Randomly sample the raster. Only valid values are considered.

geoutils/raster/sampling.py

@@ -16,7 +16,7 @@ def subsample_array(
     subsample: float | int,
     return_indices: Literal[False] = False,
     *,
-    random_state: np.random.RandomState | int | None = None,
+    random_state: int | np.random.Generator | None = None,
 ) -> NDArrayNum:
     ...
 
@@ -27,7 +27,7 @@ def subsample_array(
     subsample: float | int,
     return_indices: Literal[True],
     *,
-    random_state: np.random.RandomState | int | None = None,
+    random_state: int | np.random.Generator | None = None,
 ) -> tuple[NDArrayNum, ...]:
     ...
 
@@ -37,7 +37,7 @@ def subsample_array(
     array: NDArrayNum | MArrayNum,
     subsample: float | int,
     return_indices: bool = False,
-    random_state: np.random.RandomState | int | None = None,
+    random_state: int | np.random.Generator | None = None,
 ) -> NDArrayNum | tuple[NDArrayNum, ...]:
     ...
 
@@ -46,7 +46,7 @@ def subsample_array(
     array: NDArrayNum | MArrayNum,
     subsample: float | int,
     return_indices: bool = False,
-    random_state: np.random.RandomState | int | None = None,
+    random_state: int | np.random.Generator | None = None,
 ) -> NDArrayNum | tuple[NDArrayNum, ...]:
     """
     Randomly subsample a 1D or 2D array by a sampling factor, taking only non NaN/masked values.
@@ -60,12 +60,7 @@ def subsample_array(
     :returns: The subsampled array (1D) or the indices to extract (same shape as input array)
     """
     # Define state for random sampling (to fix results during testing)
-    if random_state is None:
-        rnd: np.random.RandomState | np.random.Generator = np.random.default_rng()
-    elif isinstance(random_state, np.random.RandomState):
-        rnd = random_state
-    else:
-        rnd = np.random.RandomState(np.random.MT19937(np.random.SeedSequence(random_state)))
+    rng = np.random.default_rng(random_state)
 
     # Remove invalid values and flatten array
     mask = get_mask_from_array(array)  # -> need to remove .squeeze in get_mask
@@ -92,7 +87,7 @@ def subsample_array(
         npoints = np.size(valids)
 
     # Randomly extract npoints without replacement
-    indices = rnd.choice(valids, npoints, replace=False)
+    indices = rng.choice(valids, npoints, replace=False)
     unraveled_indices = np.unravel_index(indices, array.shape)
 
     if return_indices:

tests/test_array.py

@@ -169,11 +169,10 @@ def test_get_xy_rotated(self) -> None:
         """Check the function to rotate array."""
 
         # Create an artificial raster
+        rng = np.random.default_rng(42)
         width = height = 5
         transform = rio.transform.from_bounds(0, 0, 1, 1, width, height)
-        r1 = gu.Raster.from_array(
-            np.random.randint(1, 255, (height, width), dtype="uint8"), transform=transform, crs=None
-        )
+        r1 = gu.Raster.from_array(rng.integers(1, 255, (height, width), dtype="uint8"), transform=transform, crs=None)
 
         # First, we get initial coords
         xx, yy = r1.coords(grid=True, force_offset="ll")

tests/test_projtools.py

@@ -127,8 +127,9 @@ def test_latlon_reproject(self, example: str) -> None:
 
         # Test on random points
         nsample = 100
-        randx = np.random.randint(low=img.bounds.left, high=img.bounds.right, size=(nsample,))
-        randy = np.random.randint(low=img.bounds.bottom, high=img.bounds.top, size=(nsample,))
+        rng = np.random.default_rng(42)
+        randx = rng.integers(low=img.bounds.left, high=img.bounds.right, size=(nsample,))
+        randy = rng.integers(low=img.bounds.bottom, high=img.bounds.top, size=(nsample,))
 
         lat, lon = pt.reproject_to_latlon([list(randx), list(randy)], img.crs)
         x, y = pt.reproject_from_latlon([lat, lon], img.crs)