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