def test_init(self):
tree = KdTree([(1, 2)])
self.assertTupleEqual(tree.root.point, (1, 2))
self.assertIsNone(tree.root.left_child)
self.assertIsNone(tree.root.right_child)
tree = KdTree([(1, 2), (2, 1)])
self.assertTupleEqual(tree.root.point, (2, 1))
self.assertTupleEqual(tree.root.left_child.point, (1, 2))
self.assertIsNone(tree.root.right_child)
tree = KdTree([(1, ), (2, ), (3, )])
self.assertTupleEqual(tree.root.point, (2, ))
self.assertTupleEqual(tree.root.left_child.point, (1, ))
self.assertTupleEqual(tree.root.right_child.point, (3, ))
def test_nearest_neighbour_big(self):
# creating tree - 36 s
# finding nn - 0.3 ms
# nth element - 1.37 ms
# sort - 1.2 ms
random.seed(42)
points = [(random.randint(-100000,
100000), random.randint(-100000, 100000),
random.randint(-100000, 100000)) for _ in range(25000)]
point = (random.randint(-100000,
100000), random.randint(-100000, 100000),
random.randint(-100000, 100000))
closest = (-10000000, -10000000, -10000000)
for p in points:
if distance(point, p) < distance(point, closest):
closest = p
start = timeit.default_timer()
tree = KdTree(points)
print('Creating tree', timeit.default_timer() - start)
start = timeit.default_timer()
nearest = tree.nearest_neighbour(point, 100)
print('Searching NN', timeit.default_timer() - start)
self.assertTupleEqual(nearest[0].point, closest)
def test_nearest_neighbour(self):
tree = KdTree([(1, )])
nearest = tree.nearest_neighbour((-3, ), 1)
self.assertTupleEqual(nearest[0].point, (1, ))
tree = KdTree([(1, 3), (2, 4)])
nearest = tree.nearest_neighbour((0, 2), 1)
self.assertTupleEqual(nearest[0].point, (1, 3))
nearest = tree.nearest_neighbour((3, 5), 1)
self.assertTupleEqual(nearest[0].point, (2, 4))
tree = KdTree([(1, 3, 3), (4, 8, 7), (2, -1, 2), (3, 4, 1)])
nearest = tree.nearest_neighbour((1, 2, 0), 1)
self.assertTupleEqual(nearest[0].point, (3, 4, 1))
tree = KdTree([(-1, 1), (0, -2), (1, -2)])
nearest = tree.nearest_neighbour((1, 2), 1)
self.assertTupleEqual(nearest[0].point, (-1, 1))
def test_nearest_neighbour_random(self):
for seed in range(1, 10):
random.seed(seed)
points = [self.random_point(100) for _ in range(100)]
point = self.random_point(100)
closest = sorted(points, key=lambda x: distance(point, x))
tree = KdTree(points)
self.assertTupleEqual(
tree.nearest_neighbour(point, 1)[0].point, closest[0])
nearest = tree.nearest_neighbour(point, 10)
for n, c in zip(nearest, closest):
self.assertTupleEqual(n.point, c)
def _build_tree(self):
# build tree
self._log.info(u"Building kd-tree...")
self.tree = KdTree(
point_list=openOff(self._model_var.get()).get_vertices())
self._log.info(u"Building kd-tree done.")
# create vertex visualization
self._log.info(u"Building kd-tree vizualization...")
GLTargetDistantLight(pos=(0.3, -0.5, 1))
mat = GLMaterial(diffuse=(1, 0, 0))
vertices = list(self.tree.iter_vertices())
t_vertices, t_faces = self._generate_trimesh_params(vertices)
TriMesh(verts=t_vertices, faces=t_faces,
material=mat) #, dynamics=False, static=True)
#for vertex in list(tree.iter_vertices()):
#Box(pos=vertex, lx=0.05, ly=0.05, lz=0.05, dynamics=False, static=True, material=mat)
#Sphere()
# create bbox visualization
pass
self._log.info(u"Building kd-tree vizualization done.")
milestones = []
milestones.append(np.array([5, 5]))
for coord in coordinates:
circle = Circle(coord, robot.b / 2)
if len(m.get_intersecting_rects(circle)) == 0:
milestones.append(coord)
milestones.append(np.array([70, 15]))
milestones.append(np.array([90, 50]))
milestones.append(np.array([30, 95]))
milestones.append(np.array([5, 50]))
milestones = np.array(milestones)
# Generate kd-tree
tree = KdTree(milestones)
tree.init_build()
# Create connection/adjacency matrix for nodes
# Also remove invalid connections
connections = np.zeros((tree.nodes.shape[0], tree.nodes.shape[0]),
dtype=int)
for milestone in milestones:
best = tree.get_nn(milestone, 5)
for b in best:
line_l = Line(best[0].val - robot.b / 2, b.val - robot.b / 2)
line_r = Line(best[0].val + robot.b / 2, b.val + robot.b / 2)
if m.intersects_line(line_l) or m.intersects_line(line_r):
continue
nearest = [node.data for node in nodes]
distances = [position.distance(system) for system in nearest]
return render_template('closest.html',
position=position,
rows=zip(nearest, distances))
else:
return render_template('closest.html')
def load_systems(filename):
logging.info('Reading systems data')
systems = []
coords = []
with open(filename, 'r') as file:
systems_json = json.load(file)
systems = [
System(row['name'], sectors=sectors) for row in systems_json
]
coords = [system.coordinates for system in systems]
logging.info('Number of systems read: %d', len(systems))
return systems, coords
logging.basicConfig(level=logging.INFO)
sectors = Sectors()
systems, coordinates = load_systems('resources/systemsWithoutCoordinates.json')
logging.info('Creating tree')
kd_tree = KdTree(coordinates, systems)
logging.info('Tree created')
def render():
pygame.display.flip()
def handel_events(self, click_callback):
for event in pygame.event.get():
if event.type == pygame.QUIT:
exit()
if event.type == pygame.MOUSEBUTTONDOWN:
click_callback(event.pos[0] // self.scale, event.pos[1] // self.scale)
if __name__ == '__main__':
renderer = Renderer(100, 100, 5)
tree = KdTree(AaBb(2, Vec(2, 0, 0), Vec(2, 100, 100)), 2)
def draw_callback(values, box: AaBb):
for item in values:
renderer.draw_box(item.top_left, item.down_right, 0x0000ff)
renderer.draw_box(box.top_left, box.down_right)
while True:
renderer.cls()
ray_point = Vec(2, random.randint(0, 50), random.randint(0, 100))
ray_direct = Vec(2, 100 - ray_point[0], random.randint(0, 100) - ray_point[1])
renderer.draw_line(ray_point[0], ray_point[1],
ray_point[0] + ray_direct[0], ray_point[1] + ray_direct[1], 0xff0000)
