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gridNeighbourhoodPattern <- function(Radius){
# gridNeighbourhoodPattern(Radius)
# returns index for neighbours relative to (0,0) and their distances
#
# INPUT
# Radius Radius in which neighbours should be searched
#
# OUTPUT
# Neighbourhood(1:m,1:3) positions of all weights in the Neighbourhood of c(0,0) together with their distance
# author: Florian Lerch
# gridNeighbourhoodPattern(3)
# the pattern is starting from point 0,0
startPoint = c(0,0)
# get all neighbours
Neighbourhood <- c()
# make sure that Radius is an integer
Radius <- floor(Radius)
# calculate only a quarter of the sphere and get the rest through symmetry
for (y in (startPoint[1] - Radius):(startPoint[1])){
for (x in (startPoint[2] - Radius):(startPoint[2])){
if ((y - startPoint[1])^2 + (x - startPoint[2])^2 <= Radius^2) {
ySym <- startPoint[1] - (y - startPoint[1])
xSym <- startPoint[2] - (x - startPoint[2])
# add the new found neighbours and the points symmetric to them into the Neighbourhood
nn1 <- c(y, x, sqrt(y^2+x^2))
nn2 <- c(y, xSym, sqrt(y^2+xSym^2))
nn3 <- c(ySym , x, sqrt(ySym^2+x^2))
nn4 <- c(ySym, xSym, sqrt(ySym^2+xSym^2))
newNeighbours <- matrix(c(nn1, nn2, nn3, nn4),ncol=3,byrow=TRUE)
Neighbourhood <- rbind(Neighbourhood,newNeighbours)
}
}
}
# remove duplicated entries
Neighbourhood <- unique(Neighbourhood)
# sort by distance, this makes it easier to remove the farther neighbour on a Toroid
Neighbourhood <- Neighbourhood[order(Neighbourhood[,3]),]
Neighbourhood
}
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