N-dimensional implementations

General Usage
,
11 
julia> function myfunc(activated::AbstractArray{Bool,N}, gridsize, padsize) where {N} 
           ax = axes(activated)
           for d = 1:N
               axd = ntuple(i -> i == d ? (gridsize[d]+1:padsize[d]) : ax[i], Val{N}())
               activated[CartesianIndices(axd)] .= false
           end
       nothing
       end

julia> function myfunc_ur(activated::AbstractArray{Bool,4}, gridsize, padsize)  
           activated[(gridsize[1]+1:padsize[1]),:,:,:] .= false
           activated[:,(gridsize[2]+1:padsize[2]),:,:] .= false
           activated[:,:,(gridsize[3]+1:padsize[3]),:] .= false
           activated[:,:,:,(gridsize[4]+1:padsize[4])] .= false
nothing
end
julia> function foo(activated::Array{Bool,N}, gridsize, padsize) where {N}
       for k=1:N 
       g=gridsize[k]
       p=padsize[k]
       pref = ntuple(i->Colon(), k-1)
       suff = ntuple(i->Colon(), N-k)
       v=view(activated, pref..., g+1:p, suff...)
       v .= false
       end
       end

julia> activated = rand(Bool, (2,2,2,2)); gridsize=[1,1,1,1]; padsize=[2,2,2,2];

julia> using BenchmarkTools
julia> @btime myfunc_ur($activated, $gridsize, $padsize)
  164.119 ns (4 allocations: 320 bytes)
julia> @btime myfunc($activated, $gridsize, $padsize)
  4.207 μs (36 allocations: 1.25 KiB)
julia> @btime foo($activated, $gridsize, $padsize)
  5.275 μs (37 allocations: 1.31 KiB)

But the fast version needs to be @generated, which is in practice too much of a hassle. Some way of static unrolling would be pretty nice for that.

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