Since SciPy 1.8 it is recommended to use KDTree instead of cKDTree

grlee77 · Apr 27, 2024 · 5427e35 · 5427e35
1 parent edd1da3
commit 5427e35
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diff --git a/python/cucim/src/cucim/skimage/_shared/coord.py b/python/cucim/src/cucim/skimage/_shared/coord.py
@@ -1,9 +1,9 @@
 import cupy as cp
 import numpy as np
-from scipy.spatial import cKDTree, distance
+from scipy.spatial import KDTree, distance
 
 
-# TODO: avoid host/device transfers (currently needed for cKDTree)
+# TODO: avoid host/device transfers (currently needed for KDTree)
 def _ensure_spacing(coord, spacing, p_norm, max_out):
     """Returns a subset of coord where a minimum spacing is guaranteed.
 
@@ -30,7 +30,7 @@ def _ensure_spacing(coord, spacing, p_norm, max_out):
     """
 
     # Use KDtree to find the peaks that are too close to each other
-    tree = cKDTree(coord)
+    tree = KDTree(coord)
 
     indices = tree.query_ball_point(coord, r=spacing, p=p_norm)
     rejected_peaks_indices = set()

diff --git a/python/cucim/src/cucim/skimage/feature/corner.py b/python/cucim/src/cucim/skimage/feature/corner.py
@@ -4,7 +4,7 @@
 
 import cupy as cp
 import numpy as np
-from scipy import spatial  # TODO: use cuSpatial if cKDTree becomes available
+from scipy import spatial  # TODO: use cuSpatial if KDTree becomes available
 
 import cucim.skimage._vendored.ndimage as ndi
 from cucim.skimage.util import img_as_float
@@ -1370,7 +1370,7 @@ def corner_peaks(
         coords = cp.asnumpy(coords)
 
         # Use KDtree to find the peaks that are too close to each other
-        tree = spatial.cKDTree(coords)
+        tree = spatial.KDTree(coords)
 
         rejected_peaks_indices = set()
         for idx, point in enumerate(coords):