Use KDTree instead of cKDTree (#339)

Require scipy>= 1.6.0

Author: daavoo

pyntcloud/filters/kdtree.py

@@ -57,7 +57,7 @@ def __init__(self, *, pyntcloud, kdtree_id, k, r):
         self.r = r
 
     def compute(self):
-        distances = self.kdtree.query(self.points, k=self.k, n_jobs=-1)[0]
+        distances = self.kdtree.query(self.points, k=self.k, workers=-1)[0]
         ror_filter = np.all(distances < self.r, axis=1)
 
         return ror_filter
@@ -96,7 +96,7 @@ def __init__(self, *, pyntcloud, kdtree_id, k, z_max):
         self.z_max = z_max
 
     def compute(self):
-        distances = self.kdtree.query(self.points, k=self.k, n_jobs=-1)[0]
+        distances = self.kdtree.query(self.points, k=self.k, workers=-1)[0]
         z_distances = zscore(np.mean(distances, axis=1), ddof=1)
         sor_filter = abs(z_distances) < self.z_max
 

pyntcloud/neighbors/k_neighbors.py

@@ -17,4 +17,4 @@ def k_neighbors(kdtree, k):
     """
     # [1] to select indices and ignore distances
     # [:,1:] to discard self-neighbor
-    return kdtree.query(kdtree.data, k=k + 1, n_jobs=-1)[1][:, 1:]
+    return kdtree.query(kdtree.data, k=k + 1, workers=-1)[1][:, 1:]

pyntcloud/structures/kdtree.py

@@ -1,9 +1,9 @@
-from scipy.spatial import cKDTree
+from scipy.spatial import KDTree as sKDTree
 
 from .base import Structure
 
 
-class KDTree(cKDTree, Structure):
+class KDTree(sKDTree, Structure):
 
     def __init__(self, *, points, leafsize=16, compact_nodes=False, balanced_tree=False):
         Structure.__init__(self, points=points)
@@ -13,8 +13,7 @@ def __init__(self, *, points, leafsize=16, compact_nodes=False, balanced_tree=Fa
 
     def compute(self):
         self.id = "K({},{},{})".format(self._leafsize, self._compact_nodes, self._balanced_tree)
-        cKDTree.__init__(
-            self,
+        super(sKDTree, self).__init__(
             self._points,
             leafsize=self._leafsize,
             compact_nodes=self._compact_nodes,

pyntcloud/structures/voxelgrid.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from scipy.spatial import cKDTree
+from scipy.spatial import KDTree
 
 from .base import Structure
 from ..plot.voxelgrid import plot_voxelgrid
@@ -202,8 +202,8 @@ def get_feature_vector(self, mode="binary"):
 
         elif mode == "TDF":
             # truncation = np.linalg.norm(self.shape)
-            kdt = cKDTree(self._points)
-            vector, i = kdt.query(self.voxel_centers, n_jobs=-1)
+            kdt = KDTree(self._points)
+            vector, i = kdt.query(self.voxel_centers, workers=-1)
 
         elif mode.endswith("_max"):
             if not is_numba_avaliable:

requirements.txt

@@ -1,5 +1,5 @@
 numpy
-scipy
+scipy>=1.6.0
 pandas
 laspy
 lazrs

setup.py

@@ -20,11 +20,11 @@
     python_requires='>3.7',
     install_requires=[
         "numpy",
-        "scipy",
+        "scipy>=1.6.0",
         "pandas",
     ],
     extras_require={
-        'LAS':  ["laspy", "lazrs"],
+        'LAS': ["laspy", "lazrs"],
         'PLOT': ["ipython", "matplotlib", "pyvista>=0.32.0"],
         'NUMBA': ["numba"]
     },