def space_invader_query(self):
from boxtree.area_query import SpaceInvaderQueryBuilder
return SpaceInvaderQueryBuilder(self.cl_context)
def test_space_invader_query(ctx_factory, dims, dtype, do_plot=False):
logging.basicConfig(level=logging.INFO)
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
dtype = np.dtype(dtype)
nparticles = 10**5
particles = make_normal_particle_array(queue, nparticles, dims, dtype)
if do_plot:
import matplotlib.pyplot as pt
pt.plot(particles[0].get(), particles[1].get(), "x")
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
queue.finish()
tree, _ = tb(queue, particles, max_particles_in_box=30, debug=True)
nballs = 10**4
ball_centers = make_normal_particle_array(queue, nballs, dims, dtype)
ball_radii = cl.array.empty(queue, nballs, dtype).fill(0.1)
from boxtree.area_query import (LeavesToBallsLookupBuilder,
SpaceInvaderQueryBuilder)
siqb = SpaceInvaderQueryBuilder(ctx)
# We can use leaves-to-balls lookup to get the set of overlapping balls for
# each box, and from there to compute the outer space invader distance.
lblb = LeavesToBallsLookupBuilder(ctx)
siq, _ = siqb(queue, tree, ball_centers, ball_radii)
lbl, _ = lblb(queue, tree, ball_centers, ball_radii)
# get data to host for test
tree = tree.get(queue=queue)
siq = siq.get(queue=queue)
lbl = lbl.get(queue=queue)
ball_centers = np.array([x.get() for x in ball_centers])
ball_radii = ball_radii.get()
# Find leaf boxes.
from boxtree import box_flags_enum
outer_space_invader_dist = np.zeros(tree.nboxes)
for ibox in range(tree.nboxes):
# We only want leaves here.
if tree.box_flags[ibox] & box_flags_enum.HAS_CHILDREN:
continue
start, end = lbl.balls_near_box_starts[ibox:ibox + 2]
space_invaders = lbl.balls_near_box_lists[start:end]
if len(space_invaders) > 0:
outer_space_invader_dist[ibox] = np.max(
np.abs(tree.box_centers[:, ibox].reshape((-1, 1)) -
ball_centers[:, space_invaders]))
assert np.allclose(siq, outer_space_invader_dist)