use "__slots__" for kdtree classes

git-svn-id: https://pycam.svn.sourceforge.net/svnroot/pycam/trunk@1244 bbaffbd6-741e-11dd-a85d-61de82d9cad9

src/pycam/Geometry/PointKdtree.py

@@ -26,6 +26,9 @@ from pycam.Geometry.kdtree import Node, kdtree
 
 
 class PointKdtree(kdtree):
+
+    __slots__ = ["_n", "tolerance"]
+
     def __init__(self, points=None, cutoff=5, cutoff_distance=0.5,
             tolerance=epsilon):
         if points is None:
@@ -34,12 +37,7 @@ class PointKdtree(kdtree):
         self.tolerance = tolerance
         nodes = []
         for p in points:
-            n = Node()
-            n.point = p
-            n.bound = []
-            n.bound.append(p.x)
-            n.bound.append(p.y)
-            n.bound.append(p.z)
+            n = Node(p, (p.x, p.y, p.z))
             nodes.append(n)
         kdtree.__init__(self, nodes, cutoff, cutoff_distance)
 
@@ -53,19 +51,16 @@ class PointKdtree(kdtree):
         #return Point(x,y,z)
         if self._n:
             n = self._n
+            n.bound = (x, y, z)
         else:
-            n = Node()
-        n.bound = []
-        n.bound.append(x)
-        n.bound.append(y)
-        n.bound.append(z)
+            n = Node(None, (x, y, z))
         (nn, dist) = self.nearest_neighbor(n, self.dist)
         if nn and (dist < self.tolerance):
             self._n = n
-            return nn.p
+            return nn.obj
         else:
-            n.p = Point(x, y, z)
+            n.obj = Point(x, y, z)
             self._n = None
             self.insert(n)
-            return n.p
+            return n.obj
 

src/pycam/Geometry/TriangleKdtree.py

@@ -29,13 +29,13 @@ def SearchKdtree2d(tree, minx, maxx, miny, maxy):
         triangles = []
         for n in tree.nodes:
             if not overlaptest:
-                triangles.append(n.triangle)
+                triangles.append(n.obj)
             else:
-                if not (n.bound[0] > maxx
-                        or n.bound[1] < minx
-                        or n.bound[2] > maxy
-                        or n.bound[3] < miny):
-                    triangles.append(n.triangle)
+                if not ((n.bound[0] > maxx) or
+                        (n.bound[1] < minx) or
+                        (n.bound[2] > maxy) or
+                        (n.bound[3] < miny)):
+                    triangles.append(n.obj)
         return triangles
     else:
         if tree.cutdim == 0:
@@ -74,16 +74,15 @@ def SearchKdtree2d(tree, minx, maxx, miny, maxy):
 
 class TriangleKdtree(kdtree):
 
+    __slots__ = []
+
     def __init__(self, triangles, cutoff=3, cutoff_distance=1.0):
         nodes = []
         for t in triangles:
-            n = Node()
-            n.triangle = t
-            n.bound = []
-            n.bound.append(min(t.p1.x, t.p2.x, t.p3.x))
-            n.bound.append(max(t.p1.x, t.p2.x, t.p3.x))
-            n.bound.append(min(t.p1.y, t.p2.y, t.p3.y))
-            n.bound.append(max(t.p1.y, t.p2.y, t.p3.y))
+            n = Node(t, (min(t.p1.x, t.p2.x, t.p3.x),
+                    max(t.p1.x, t.p2.x, t.p3.x),
+                    min(t.p1.y, t.p2.y, t.p3.y),
+                    max(t.p1.y, t.p2.y, t.p3.y)))
             nodes.append(n)
         super(TriangleKdtree, self).__init__(nodes, cutoff, cutoff_distance)
 

src/pycam/Geometry/kdtree.py

@@ -31,6 +31,13 @@ except ImportError:
 
 
 class Node(object):
+
+    __slots__ = ["obj", "bound"]
+
+    def __init__(self, obj, bound):
+        self.obj = obj
+        self.bound = bound
+
     def __repr__(self):
         s = ""
         for bound in self.bound:
@@ -62,6 +69,9 @@ def find_max_spread(nodes):
 
 class kdtree(IDGenerator):
 
+    __slots__ = ["bucket", "dim", "cutoff", "cutoff_distance", "nodes",
+            "cutdim", "minval", "maxval", "cutval", "hi", "lo"]
+
     def __init__(self, nodes, cutoff, cutoff_distance):
         super(kdtree, self).__init__()
         self.bucket = False
@@ -83,8 +93,7 @@ class kdtree(IDGenerator):
             else:
                 self.bucket = False
                 self.cutdim = cutdim
-                nodes.sort(cmp=lambda x, y:
-                        cmp(x.bound[cutdim], y.bound[cutdim]))
+                nodes.sort(key=lambda item: item.bound[cutdim])
                 median = len(nodes) / 2
                 self.minval = nodes[0].bound[cutdim]
                 self.maxval = nodes[-1].bound[cutdim]
@@ -229,4 +238,3 @@ class kdtree(IDGenerator):
             else:
                 self.hi.insert(node)
 
-