I've been playing around with DS9.jl for a while now and figured a couple things could be addressed.

In particular the way array data is transferred between XPA and Julia permutes each dimension, since Julia is column-ordered. Using PyDS9 through PyCall will correctly orient the data (something in PyCall makes sure arrays show up in python knowing they are column-oriented), but the same image put through DS9.jl will be transposed.

At a low-level, I'm not sure if there is an appropriate way to write things in XPA.jl to make sure when the array pointers are given they are column ordered, if that's even a thing. At a higher level, within this library when set encounters an array, it should permute it appropriately to become row-ordered.

I also work with 3d data frequently, and it seems pretty trivial to add the feature here. I've coded up a version that seems to work-

function set(arr::DenseArray{T,3};
             endian::Symbol=:native,
             mask::Bool=false,
             new::Bool=false) where {T<:PixelTypes}
    !new && set("frame delete all"); set("frame new")
    args = String[]
    push!(args, "array")
    if mask; push!(args, "mask"); end
    parr = permutedims(arr, (3, 2, 1))
    set(args..., _arraydescriptor(parr; endian=endian); data=parr)
end

function _arraydescriptor(arr::DenseArray{T,3};
                          endian::Symbol=:native) where {T}
    bp = bitpix_of(T)
    bp != 0 || error("unsupported data type")
    return string("[xdim=",size(arr,1),",ydim=",size(arr,2),",zdim=",size(arr,3),
                  ",bitpix=",bp,",endian=",endian,"]")
end

I'm happy to wrap this up in a PR, but I wanted to discuss the transposition issue first.