Extract list of particles, neighbors, relative positions and distances

Hey, I want to extract the full list of particles, their neighbors, relative positions and distances. So far, I got a small MWE working on `CPU` with the `SerialBackend()` ```julia using PointNeighbors # Generate grid of particles n_particles_per_dimension = (100, 100) n_particles = prod(n_particles_per_dimension) coordinates = Array{Float32}(undef, 2, n_particles) cartesian_indices = CartesianIndices(n_particles_per_dimension) for i in axes(coordinates, 2) coordinates[:, i] .= Tuple(cartesian_indices[i]) end # `FullGridCellList` requires a bounding box min_corner = minimum(coordinates, dims=2) max_corner = maximum(coordinates, dims=2) search_radius = 3.0f0 cell_list = FullGridCellList(; search_radius, min_corner, max_corner) nhs = GridNeighborhoodSearch{2}(; search_radius, cell_list) # Initialize the NHS to find neighbors in `coordinates` of particles in `coordinates` initialize!(nhs, coordinates, coordinates) # Simple example: just count the neighbors of each particle n_neighbors = zeros(Int, n_particles) # Use a function for performance reasons function count_neighbors!(n_neighbors, coordinates, nhs) n_neighbors .= 0 foreach_point_neighbor(coordinates, coordinates, nhs) do i, j, pos_diff, distance n_neighbors[i] += 1 end end function insert_neighbor!(senders, receivers, rel_deplacement, rel_dist_norm, coordinates, nhs) count = 1 foreach_point_neighbor(coordinates, coordinates, nhs; parallelization_backend= SerialBackend()) do i, j, pos_diff, distance senders[count] = i receivers[count] = j rel_deplacement[:,count] = pos_diff rel_dist_norm[count] = distance count += 1 end end count_neighbors!(n_neighbors, coordinates, nhs) n_edges= sum(n_neighbors) senders = zeros(Int, 1, n_edges) receivers = zeros(Int, 1, n_edges) rel_deplacement = zeros(2, n_edges) rel_dist_norm = zeros(1, n_edges) insert_neighbor!(senders, receivers, rel_deplacement, rel_dist_norm, coordinates, nhs) ``` Is there a faster, more straight forward way to achieve this? In the docs, I read about `PrecomputedNeighborhoodSearch` which sounds like it provides the functionality I'm looking for, but I'm unsure of how to use it. Could you provide some assistance to the matter?