def _get_nearest_particles_nocahe(self, i, output_array, exclude_index=-1):
""" Use the linked list to get nearest neighbors.
The functions defined in `linked_list_functions.pyx` are used to
find the nearest neighbors.
Parameters:
-----------
i : (in) int
The destination particle index
output_array : (in/out) LongArray
Neighbor indices are stored in this array.
exclude_index : int
Optional index to exclude from the neighbor list
NOTIMPLEMENTED!
"""
manager = self.manager
src = self.source
dst = self.dest
# Enqueue a copy if the binning is done with OpenCL
manager.enqueue_copy()
# get the bin structure parameters
ncx = manager.ncx
ncy = manager.ncy
ncells = manager.ncells
#CHANGE
# cell_counts and indices for the source
cellc = manager.cell_counts[ src.name ]
s_indices = manager.indices[ src.name ]
# destination indices
d_indices = manager.indices[ dst.name ]
# cellid for the destination particle
cellid = manager.cellids[dst.name][i]
# get all neighbors from the 27 neighboring cells
nbrs = util.rs_get_neighbors(cellid, ncx, ncy, ncells, cellc, s_indices)
xs = src.x.astype(numpy.float32)
ys = src.y.astype(numpy.float32)
zs = src.z.astype(numpy.float32)
xi = numpy.float32( dst.x[d_indices[i]] )
yi = numpy.float32( dst.y[d_indices[i]] )
zi = numpy.float32( dst.z[d_indices[i]] )
radius = numpy.float32( self.scale_fac * dst.h[d_indices[i]] )
# filter the neighbors to within a cutoff radius
nbrs = util.filter_neighbors(xi, yi, zi, radius, xs, ys, zs, nbrs)
output_array.resize( len(nbrs) )
output_array.set_data( nbrs )
def _get_nearest_particles_nocahe(self, i, output_array, exclude_index=-1):
""" Use the linked list to get nearest neighbors.
The functions defined in `linked_list_functions.pyx` are used to
find the nearest neighbors.
Parameters:
-----------
i : (in) int
The destination particle index
output_array : (in/out) LongArray
Neighbor indices are stored in this array.
exclude_index : int
Optional index to exclude from the neighbor list
NOTIMPLEMENTED!
"""
manager = self.manager
src = self.source
dst = self.dest
# Enqueue a copy if the binning is done with OpenCL
manager.enqueue_copy()
# get the bin structure parameters
ncx = manager.ncx
ncy = manager.ncy
ncells = manager.ncells
# linked list for the source
head = manager.head[src.name]
next = manager.Next[src.name]
# cellid for the destination
cellid = manager.cellids[dst.name][i]
ix = manager.ix[dst.name][i]
iy = manager.iy[dst.name][i]
iz = manager.iz[dst.name][i]
# get all neighbors from the 27 neighboring cells
nbrs = util.ll_get_neighbors(cellid, ix, iy, iz,
ncx, ncy, ncells, head, next)
x = src.x.astype(numpy.float32)
y = src.y.astype(numpy.float32)
z = src.z.astype(numpy.float32)
xi = numpy.float32( dst.x[i] )
yi = numpy.float32( dst.y[i] )
zi = numpy.float32( dst.z[i] )
h = dst.h.astype(numpy.float32)
radius = self.scale_fac * h[i]
# filter the neighbors to within a cutoff radius
nbrs = util.filter_neighbors(xi, yi, zi, radius, x, y, z, nbrs)
output_array.resize( len(nbrs) )
output_array.set_data( nbrs )
