I have a matrix of vectors, the indices of the matrix serve as (row, column) and the Vector is the z coordinate. Is there some way to convert this?

I read data from disk in as Array{Float64, 3} and then try to do a cumulative trapezoidal integration on it like this:

Example code generating the data I need converted:

function cumtrapz(x::AbstractVector, y::AbstractVector)
@assert length(x) == length(y)
N = length(x)
g = similar(y)
g[1] = zero(eltype(y))
for k = 1:N - 1
g[k + 1] = g[k] + (x[k + 1] - x[k]) * (y[k + 1] + y[k]) / 2
end
return g
end
function integrate_data(x, y)
xx = eachslice(x, dims=(1, 2))
yy = eachslice(y, dims=(1, 2))
copd = cumtrapz.(xx, yy)
end
# Junk data, representing real data.
U = Array{Float64, 3}(reshape(1:27, (3, 3, 3)))
V = sqrt.(U)
cumulative_v = integrate_data(U, V)
# typeof(cumulative_v) is Matrix{Vector{Float64}} ... and I need it to be Array{Float64, 3}

permutedims (or PermutedDimsArray) is necessary because you want to have the vectors splayed along the final dimension, rather than first. Alternatively, you could write few loops to do the whole concatenation. It wouldn’t be intrinsically slower than using these built-ins.

Ha! mere seconds after I figured it out.
I know and have tested that loops are not a bad thing, but so many years of Matlab and Python have me thinking that loops are bad, hard to get out of the desire to not use them.