The recommended ways to iterate over a whole array are. IteratorSize(itertype::Type) -> IteratorSize.

By default, Julia picks reasonable algorithms and sorts in standard ascending order: julia> sort([2,3,1]) 3-element Array{Int64,1}: 1 2 3. for a in A # Do something with the element a end for i in eachindex(A) # Do something with i and/or A[i] end. Sorting and Related Functions. Arrays are a crucial component of any programming language, particularly for a data-oriented language like Julia. Here Int64 and Float64 are types for the elements inferred by the compiler.. We’ll talk more about types later. Given the type of an iterator, return one of the following values: SizeUnknown() if the length (number of elements) cannot be determined in advance. range(start[, stop]; length, stop, step=1) Given a starting value, construct a range either by length or from start to stop, optionally with a given step (defaults to 1, a UnitRange).One of length or stop is required. HasShape{N}() if there is a known length plus a notion of multidimensional shape (as for an array). You can easily sort in reverse order as well:

HasLength() if there is a fixed, finite length. Arrays store values according to their location: in Julia, given a two-dimensional array A, the expression A[1,3] returns the value stored at a location known as (1,3).If, for example, A stores Float64 numbers, the value returned by this expression will be a single Float64 number. If length, stop, and step are all specified, they must agree..

Julia has an extensive, flexible API for sorting and interacting with already-sorted arrays of values. The hot/cold memory is a non-issue really, since if you need hot memory then you just don't de-allocate it. julia> A = Array{Float64,2}(undef, 2, 3) # N given explicitly 2×3 Array{Float64,2}: 6.90198e-310 6.90198e-310 6.90198e-310 6.90198e-310 6.90198e-310 0.0 julia> B = Array{Float64}(undef, 2) # N determined by the input 2-element Array{Float64,1}: 1.87103e-320 0.0 . So this seems like a GC issue to me, where Julia is using colder memory (since GC not always has time to clear recently used memory - so allocation gives fresh memory) The only fix I think is appropriate is to optimize the range notation [a:inc:b] to use an algorithm like func6 above. The first construct is used … The output tells us that the arrays are of types Array{Int64,1} and Array{Float64,1} respectively..

julia> A = [8,6,7] 3-element Array{Int64,1}: 8 6 7 julia> A[2,1] 6 Iteration.

The 1 in Array{Int64,1} and Array{Any,1} indicates that the array is one dimensional (i.e., a Vector).. source Core.Array — Method. This is the default for many Julia functions that create arrays