#'
# @importFrom rflann Neighbour
# @importFrom hashmap hashmap
#' patch_cluster <- function(bvec, mask, K=500, patch_radius=8, connectivity=27,
#' knn=5, filter=list(lp=0, hp=0)) {
#'
#' mask.idx <- which(mask > 0)
#' grid <- index_to_coord(mask, mask.idx)
#' vgrid <- index_to_grid(mask, mask.idx)
#'
#' valmat <- series(bvec, mask.idx)
#'
#' if (any(filter > 0)) {
#' message("patch_cluster: pre-filtering time series")
#' valmat <- filter_mat(valmat, filter$lp, filter$hp)
#' }
#'
#' slight <- neuroim2::searchlight(mask, radius=patch_radius, eager=TRUE)
#' clist <- lapply(slight, identity)
#'
#' nn <- 50
#' hmap <- hashmap("-1","-1")
#' nabes <- rflann::Neighbour(grid, grid, nn)
#'
#' Rmat <- Matrix(0, nrow = length(clist), ncol = length(clist), sparse = TRUE)
#'
#' lapply(1:length(clist), function(i) {
#' print(i)
#' s1 <- series(bvec, clist[[i]])
#'
#' jind <- nabes$indices[i,2:nn]
#' p1 <- pmin(i, jind)
#' p2 <- pmax(i, jind)
#' knames <- paste(p1, p2, sep="-")
#'
#' for (j in jind) {
#' if (Rmat[i,j] == 0) {
#' Rmat[i,j] <- MatrixCorrelation::RV2(s1, series(bvec, clist[[j]]))
#' }
#' }
#'
#' })
#'
#' }
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