def test_cache_updates_with_changed_particles(self):
# Given
pa1 = self._make_random_parray('pa1', 5)
particles = [pa1]
nnps = LinkedListNNPS(dim=3, particles=particles)
cache = NeighborCache(nnps, dst_index=0, src_index=0)
cache.update()
# When
pa2 = self._make_random_parray('pa2', 2)
pa1.add_particles(x=pa2.x, y=pa2.y, z=pa2.z)
nnps.update()
cache.update()
nb_cached = UIntArray()
nb_direct = UIntArray()
for i in range(len(particles[0].x)):
nnps.get_nearest_particles_no_cache(0, 0, i, nb_direct, False)
cache.get_neighbors(0, i, nb_cached)
nb_e = nb_direct.get_npy_array()
nb_c = nb_cached.get_npy_array()
self.assertTrue(np.all(nb_e == nb_c))
def test_neighbors_cached_properly(self):
# Given
pa1 = self._make_random_parray('pa1', 5)
pa2 = self._make_random_parray('pa2', 4)
particles = [pa1, pa2]
nnps = LinkedListNNPS(dim=3, particles=particles)
for dst_index in (0, 1):
for src_idx in (0, 1):
# When
cache = NeighborCache(nnps, dst_index, src_idx)
cache.update()
nb_cached = UIntArray()
nb_direct = UIntArray()
# Then.
for i in range(len(particles[dst_index].x)):
nnps.get_nearest_particles_no_cache(
src_idx, dst_index, i, nb_direct, False)
cache.get_neighbors(src_idx, i, nb_cached)
nb_e = nb_direct.get_npy_array()
nb_c = nb_cached.get_npy_array()
self.assertTrue(np.all(nb_e == nb_c))
def test_empty_neigbors_works_correctly(self):
# Given
pa1 = self._make_random_parray('pa1', 5)
pa2 = self._make_random_parray('pa2', 2)
pa2.x += 10.0
particles = [pa1, pa2]
# When
nnps = LinkedListNNPS(dim=3, particles=particles)
# Cache for neighbors of destination 0.
cache = NeighborCache(nnps, dst_index=0, src_index=1)
cache.update()
# Then
nb_cached = UIntArray()
nb_direct = UIntArray()
for i in range(len(particles[0].x)):
nnps.get_nearest_particles_no_cache(1, 0, i, nb_direct, False)
# Get neighbors from source 1 on destination 0.
cache.get_neighbors(src_index=1, d_idx=i, nbrs=nb_cached)
nb_e = nb_direct.get_npy_array()
nb_c = nb_cached.get_npy_array()
self.assertEqual(len(nb_e), 0)
self.assertTrue(np.all(nb_e == nb_c))