def test_point_types(self):
emptyTree = kdtree.createKDTree(dimensions=3)
point1 = (2, 3, 4)
point2 = [4, 5, 6]
Point = collections.namedtuple('Point', 'x y z')
point3 = Point(5, 3, 2)
tree = kdtree.createKDTree([point1, point2, point3])
res, dist = tree.search_nn((1, 2, 3))
self.assertEqual(res, kdtree.KDNode((2, 3, 4)))
def test_invalid_child(self):
""" Children on wrong subtree invalidate Tree """
child = kdtree.KDNode((3, 2))
child.axis = 2
tree = kdtree.createKDTree([(2, 3)])
tree.left = child
self.assertFalse(tree.is_valid())
tree = kdtree.createKDTree([(4, 1)])
tree.right = child
self.assertFalse(tree.is_valid())
def test_search_nn3(self):
points = [(0, 25, 73), (1, 91, 85), (1, 47, 12), (2, 90, 20),
(2, 66, 79), (2, 46, 27), (4, 48, 99), (5, 73, 64),
(7, 42, 70), (7, 34, 60), (8, 86, 80), (10, 27, 14),
(15, 64, 39), (17, 74, 24), (18, 58, 12), (18, 58, 5),
(19, 14, 2), (20, 88, 11), (20, 28, 58), (20, 79, 48),
(21, 32, 8), (21, 46, 41), (22, 6, 4), (22, 42, 68),
(22, 62, 42), (24, 70, 96), (27, 77, 57), (27, 47, 39),
(28, 61, 19), (30, 28, 22), (34, 13, 85), (34, 39, 96),
(34, 90, 32), (39, 7, 45), (40, 61, 53), (40, 69, 50),
(41, 45, 16), (41, 15, 44), (42, 40, 19), (45, 6, 68),
(46, 79, 91), (47, 91, 86), (47, 50, 24), (48, 57, 64),
(49, 21, 72), (49, 87, 21), (49, 41, 62), (54, 94, 32),
(56, 14, 54), (56, 93, 2), (58, 34, 44), (58, 27, 42),
(59, 62, 80), (60, 69, 69), (61, 67, 35), (62, 31, 50),
(63, 9, 93), (63, 46, 95), (64, 31, 2), (64, 2, 36),
(65, 23, 96), (66, 94, 69), (67, 98, 10), (67, 40, 88),
(68, 4, 15), (68, 1, 6), (68, 88, 72), (70, 24, 53),
(70, 31, 87), (71, 95, 26), (74, 80, 34), (75, 59, 99),
(75, 15, 25), (76, 90, 99), (77, 75, 19), (77, 68, 26),
(80, 19, 98), (82, 90, 50), (82, 87, 37), (84, 88, 59),
(85, 76, 61), (85, 89, 20), (85, 64, 64), (86, 55, 92),
(86, 15, 69), (87, 48, 46), (87, 67, 47), (89, 81, 65),
(89, 87, 39), (89, 87, 3), (91, 65, 87), (94, 37, 74),
(94, 20, 92), (95, 95, 49), (96, 15, 80), (96, 27, 39),
(97, 87, 32), (97, 43, 7), (98, 78, 10), (99, 64, 55)]
tree = kdtree.createKDTree(points)
point = (66, 54, 29)
nn, dist = tree.search_nn(point)
best, best_dist = self.find_best(tree, point)
self.assertEqual(best_dist, dist)
def test_search_nn2(self):
points = [(1, 2, 3), (5, 1, 2), (9, 3, 4), (3, 9, 1), (4, 8, 3),
(9, 1, 1), (5, 0, 0), (1, 1, 1), (7, 2, 2), (5, 9, 1),
(1, 1, 9), (9, 8, 7), (2, 3, 4), (4, 5, 4.01)]
tree = kdtree.createKDTree(points)
point = (2, 5, 6)
nn, dist = tree.search_nn(point)
best, best_dist = self.find_best(tree, point)
self.assertEqual(best_dist, dist)
def test_search_nn(self, nodes=100):
points = list(islice(random_points(), 0, nodes))
tree = kdtree.createKDTree(points)
point = random_point()
nn, dist = tree.search_nn(point)
best, best_dist = self.find_best(tree, point)
self.assertEqual(best_dist,
dist,
msg=', '.join(repr(p)
for p in points) + ' / ' + repr(point))
def test_search_nn_dist(self):
""" tests search_nn_dist() according to bug #8 """
points = [(x, y) for x in range(10) for y in range(10)]
tree = kdtree.createKDTree(points)
nn = tree.search_nn_dist((5, 5), 2.5)
self.assertEquals(len(nn), 4)
self.assertTrue((6, 6) in nn)
self.assertTrue((5, 5) in nn)
self.assertTrue((5, 6) in nn)
self.assertTrue((6, 5) in nn)
def do_random_add(self, num_points=100):
points = list(set(islice(random_points(), 0, num_points)))
tree = kdtree.createKDTree(dimensions=len(points[0]))
for n, point in enumerate(points, 1):
tree.add(point)
self.assertTrue(tree.is_valid())
self.assertTrue(point in [node.data for node in tree.inorder()])
nodes_in_tree = len(list(tree.inorder()))
self.assertEqual(nodes_in_tree, n)
def test_remove_duplicates(self):
""" creates a tree with only duplicate points, and removes them all """
points = [(1, 1)] * 100
tree = kdtree.createKDTree(points)
self.assertTrue(tree.is_valid())
random.shuffle(points)
while points:
point = points.pop(0)
tree = tree.remove(point)
# Check if the Tree is valid after the removal
self.assertTrue(tree.is_valid())
# Check if the removal reduced the number of nodes by 1 (not more, not less)
remaining_points = len(points)
nodes_in_tree = len(list(tree.inorder()))
self.assertEqual(nodes_in_tree, remaining_points)
def do_random_remove(self):
""" Creates a random tree, removes all points in random order """
points = list(set(islice(random_points(), 0, 20)))
tree = kdtree.createKDTree(points)
self.assertTrue(tree.is_valid())
random.shuffle(points)
while points:
point = points.pop(0)
tree = tree.remove(point)
# Check if the Tree is valid after the removal
self.assertTrue(tree.is_valid())
# Check if the point has actually been removed
self.assertTrue(point not in [n.data for n in tree.inorder()])
# Check if the removal reduced the number of nodes by 1 (not more, not less)
remaining_points = len(points)
nodes_in_tree = len(list(tree.inorder()))
self.assertEqual(nodes_in_tree, remaining_points)
def test_remove_empty_tree(self):
tree = kdtree.createKDTree(dimensions=2)
tree.remove((1, 2))
self.assertFalse(bool(tree))
def random_tree(nodes=20, dimensions=3, minval=0, maxval=100):
points = list(islice(random_points(), 0, nodes))
tree = kdtree.createKDTree(points)
return tree