Hcat without copy and stack overflow in type inference

General Usage
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1

When using packages like ArrayPartition in GitHub - SciML/RecursiveArrayTools.jl: Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications and Hcat in GitHub - JuliaArrays/LazyArrays.jl: Lazy arrays and linear algebra in Julia . I encounter a lot of errors like:

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What’ s the recommended way to do hcat without copy for now?

2

Can you create a minimal reproducer? It could be useful to report a bug on the Julia Github.

EDIT: that said, I don’t think your issue can be fixed at that end, considering your tuples are truly huge.

3

Can you try to materialize the lazy array every so often, before the tuples become so huge? I think it simply doesn’t make sense to create tuples so large.

4 
using RecursiveArrayTools
using ShiftedArrays
ap = ArrayPartition([randn(3641) for i in 1:1000]...)

function procArr(arr1::AbstractArray)
     s = ShiftedArray(arr1, (1, 0))
     s./arr1
end

Something like this. It seems LazyStack works for this. Would it be better if those packages give an error on long input instead?