def test_get_bbox():
cell_size = 0.1
cell = nnps.Cell(IntPoint(0, 0, 0), cell_size=cell_size, narrays=1)
centroid = Point()
boxmin = Point()
boxmax = Point()
cell.get_centroid(centroid)
cell.get_bounding_box(boxmin, boxmax)
assert (abs(boxmin.x - (centroid.x - 1.5 * cell_size)) < 1e-10)
assert (abs(boxmin.y - (centroid.y - 1.5 * cell_size)) < 1e-10)
assert (abs(boxmin.z - (centroid.z - 1.5 * cell_size)) < 1e-10)
assert (abs(boxmax.x - (centroid.x + 1.5 * cell_size)) < 1e-10)
assert (abs(boxmax.y - (centroid.y + 1.5 * cell_size)) < 1e-10)
assert (abs(boxmax.z - (centroid.z + 1.5 * cell_size)) < 1e-10)
cell_size = 0.5
cell = nnps.Cell(IntPoint(1, 2, 0), cell_size=cell_size, narrays=1)
cell.get_centroid(centroid)
cell.get_bounding_box(boxmin, boxmax)
assert (abs(boxmin.x - (centroid.x - 1.5 * cell_size)) < 1e-10)
assert (abs(boxmin.y - (centroid.y - 1.5 * cell_size)) < 1e-10)
assert (abs(boxmin.z - (centroid.z - 1.5 * cell_size)) < 1e-10)
assert (abs(boxmax.x - (centroid.x + 1.5 * cell_size)) < 1e-10)
assert (abs(boxmax.y - (centroid.y + 1.5 * cell_size)) < 1e-10)
assert (abs(boxmax.z - (centroid.z + 1.5 * cell_size)) < 1e-10)
def test_flatten_unflatten():
# first consider the 2D case where we assume a 4 X 5 grid of cells
dim = 2
ncells_per_dim = IntArray(3)
ncells_per_dim[0] = 4
ncells_per_dim[1] = 5
ncells_per_dim[2] = 0
# valid un-flattened cell indices
cids = [[i, j] for i in range(4) for j in range(5)]
for _cid in cids:
cid = IntPoint(_cid[0], _cid[1], 0)
flattened = nnps.py_flatten(cid, ncells_per_dim, dim)
unflattened = nnps.py_unflatten(flattened, ncells_per_dim, dim)
# the unflattened index should match with cid
assert (cid == unflattened)
# 3D
dim = 3
ncells_per_dim = IntArray(3)
ncells_per_dim[0] = 4
ncells_per_dim[1] = 5
ncells_per_dim[2] = 2
# valid un-flattened indices
cids = [[i, j, k] for i in range(4) for j in range(5) for k in range(2)]
for _cid in cids:
cid = IntPoint(_cid[0], _cid[1], _cid[2])
flattened = nnps.py_flatten(cid, ncells_per_dim, dim)
unflattened = nnps.py_unflatten(flattened, ncells_per_dim, dim)
# the unflattened index should match with cid
assert (cid == unflattened)
def test_1D_get_valid_cell_index():
dim = 1
# simulate a dummy distribution such that 10 cells are along the
# 'x' direction
n_cells = 10
ncells_per_dim = IntArray(3)
ncells_per_dim[0] = n_cells
ncells_per_dim[1] = 0
ncells_per_dim[2] = 0
# target cell
cx = 1
cy = cz = 0
# as long as cy and cz are 0, the function should return the valid
# flattened cell index for the cell
for i in [-1, 0, 1]:
index = nnps.py_get_valid_cell_index(IntPoint(cx + i, cy, cz),
ncells_per_dim, dim, n_cells)
assert index != -1
# index should be -1 whenever cy and cz are > 1. This is
# specifically the case that was failing earlier.
for j in [-1, 1]:
for k in [-1, 1]:
index = nnps.py_get_valid_cell_index(IntPoint(cx, cy + j, cz + k),
ncells_per_dim, dim, n_cells)
assert index == -1
def test_get_centroid():
cell = nnps.Cell(IntPoint(0, 0, 0), cell_size=0.1, narrays=1)
centroid = Point()
cell.get_centroid(centroid)
assert (abs(centroid.x - 0.05) < 1e-10)
assert (abs(centroid.y - 0.05) < 1e-10)
assert (abs(centroid.z - 0.05) < 1e-10)
cell = nnps.Cell(IntPoint(1, 2, 3), cell_size=0.5, narrays=1)
cell.get_centroid(centroid)
assert (abs(centroid.x - 0.75) < 1e-10)
assert (abs(centroid.y - 1.25) < 1e-10)
assert (abs(centroid.z - 1.75) < 1e-10)
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]
}