def _make_particles(self, nx=20):
x = numpy.linspace(0, 1, nx)
h = numpy.ones_like(x) / (nx - 1)
pa = get_particle_array(name='fluid', x=x, h=h)
nps = nnps.SpatialHashNNPS(dim=1, particles=[pa], sort_gids=True)
return pa, nps
def test_large_number_of_neighbors_spatial_hash():
x = numpy.random.random(1 << 14) * 0.1
y = x.copy()
z = x.copy()
h = numpy.ones_like(x)
pa = get_particle_array(name='fluid', x=x, y=y, z=z, h=h)
nps = nnps.SpatialHashNNPS(dim=3, particles=[pa], cache=False)
nbrs = UIntArray()
nps.get_nearest_particles(0, 0, 0, nbrs)
# print(nbrs.length)
assert nbrs.length == len(x)
def test_spatial_hash_works_for_large_domain(self):
# Given
pa = self._make_particles(20)
# We turn on cache so it computes all the neighbors quickly for us.
nps = nnps.SpatialHashNNPS(dim=3, particles=[pa], cache=True)
nbrs = UIntArray()
direct = UIntArray()
nps.set_context(0, 0)
for i in range(pa.get_number_of_particles()):
nps.get_nearest_particles(0, 0, i, nbrs)
nps.brute_force_neighbors(0, 0, i, direct)
x = nbrs.get_npy_array()
y = direct.get_npy_array()
x.sort(); y.sort()
assert numpy.all(x == y)
def setUp(self):
NNPSTestCase.setUp(self)
self.nps = nnps.SpatialHashNNPS(
dim=3, particles=self.particles, radius_scale=2.0
)
def setUp(self):
"""Default set-up used by all the tests
Particles with the following coordinates (x, y, z) are placed in a box
0 : -1.5 , 0.25 , 0.5
1 : 0.33 , -0.25, 0.25
2 : 1.25 , -1.25, 1.25
3 : 0.05 , 1.25 , -0.5
4 : -0.5 , 0.5 , -1.25
5 : -0.75, 0.75 , -1.25
6 : -1.25, 0.5 , 0.5
7 : 0.5 , 1.5 , -0.5
8 : 0.5 , -0.5 , 0.5
9 : 0.5 , 1.75 , -0.75
The cell size is set to 1. Valid cell indices and the
particles they contain are given below:
(-2, 0, 0) : particle 0, 6
(0, -1, 0) : particle 1, 8
(1, -2, 1) : particle 2
(0, 1, -1) : particle 3, 7, 9
(-1, 0, -2): particle 4, 5
"""
x = numpy.array([
-1.5, 0.33, 1.25, 0.05, -0.5, -0.75, -1.25, 0.5, 0.5, 0.5])
y = numpy.array([
0.25, -0.25, -1.25, 1.25, 0.5, 0.75, 0.5, 1.5, -0.5, 1.75])
z = numpy.array([
0.5, 0.25, 1.25, -0.5, -1.25, -1.25, 0.5, -0.5, 0.5, -0.75])
# using a degenrate (h=0) array will set cell size to 1 for NNPS
h = numpy.zeros_like(x)
pa = get_particle_array(x=x, y=y, z=z, h=h)
self.dict_box_sort_nnps = nnps.DictBoxSortNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
self.box_sort_nnps = nnps.BoxSortNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
self.ll_nnps = nnps.LinkedListNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
self.sp_hash_nnps = nnps.SpatialHashNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
self.ext_sp_hash_nnps = nnps.ExtendedSpatialHashNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
self.strat_radius_nnps = nnps.StratifiedHashNNPS(
dim=3, particles=[pa], radius_scale=1.0
)
# these are the expected cells
self.expected_cells = {
IntPoint(-2, 0, 0): [0, 6],
IntPoint(0, -1, 0): [1, 8],
IntPoint(1, -2, 1): [2],
IntPoint(0, 1, -1): [3, 7, 9],
IntPoint(-1, 0, -2): [4, 5]
}
