Merge pull request #3659 from DWesl/ckdtree-rename

src/metpy/calc/tools.py

@@ -17,7 +17,7 @@
 
 import numpy.ma as ma
 from pyproj import CRS, Geod, Proj
-from scipy.spatial import cKDTree
+from scipy.spatial import KDTree
 import xarray as xr
 
 from .. import _warnings
@@ -300,7 +300,7 @@ def reduce_point_density(points, radius, priority=None):
     points = np.where(good_vals, points, 0)
 
     # Make a kd-tree to speed searching of data.
-    tree = cKDTree(points)
+    tree = KDTree(points)
 
     # Need to use sorted indices rather than sorting the position
     # so that the keep mask matches *original* order.

src/metpy/interpolate/geometry.py

@@ -7,7 +7,7 @@
 import math
 
 import numpy as np
-from scipy.spatial import cKDTree
+from scipy.spatial import KDTree
 
 log = logging.getLogger(__name__)
 
@@ -33,7 +33,7 @@ def get_points_within_r(center_points, target_points, r):
         order as, center_points
 
     """
-    tree = cKDTree(target_points)
+    tree = KDTree(target_points)
     indices = tree.query_ball_point(center_points, r)
     return tree.data[indices].T
 
@@ -59,7 +59,7 @@ def get_point_count_within_r(center_points, target_points, r):
         order as, center_points
 
     """
-    tree = cKDTree(target_points)
+    tree = KDTree(target_points)
     indices = tree.query_ball_point(center_points, r)
     return np.array([len(x) for x in indices])
 
@@ -255,7 +255,7 @@ def find_natural_neighbors(tri, grid_points):
 
     """
     # Used for fast identification of points with a radius of another point
-    tree = cKDTree(grid_points)
+    tree = KDTree(grid_points)
 
     # Mask for points that are outside the triangulation
     in_triangulation = tri.find_simplex(tree.data) >= 0

src/metpy/interpolate/points.py

@@ -8,7 +8,7 @@
 
 import numpy as np
 from scipy.interpolate import griddata, Rbf
-from scipy.spatial import cKDTree, ConvexHull, Delaunay, QhullError
+from scipy.spatial import ConvexHull, Delaunay, KDTree, QhullError
 
 from . import geometry, tools
 from ..package_tools import Exporter
@@ -260,7 +260,7 @@ def inverse_distance_to_points(points, values, xi, r, gamma=None, kappa=None, mi
     else:
         raise ValueError(f'{kind} interpolation not supported.')
 
-    obs_tree = cKDTree(points)
+    obs_tree = KDTree(points)
     indices = obs_tree.query_ball_point(xi, r=r)
 
     if hasattr(values, 'units'):