I am using the measurements.jl package to calculate the error in my dataset. I have an array with voltage values which all have their own uncertainty. But when I want to calculate the mean of the array, it takes 27 seconds and allocates 805.25 MiB to do this. The array consists of around 47000 elements all of the same type. Does anyone know how to improve the performance of this operation? For an array of the same size with only Float64 elements the operation is almost done instantly. Thanks for helping me out.

Welcome! It’s hard to say what’s causing allocations or slowing down your code if you don’t share it with us - please share a MWE (minimal working example) showing the problem.

using Statistics, Measurements
VoltageArray = rand(50000) #Works really fast
VoltageArray = VoltageArray .± 0.1VoltageArray #Works really fast
mean(VoltageArray) #Takes a long time to run

This is a MWE of my problem. I hope I made it clear

Yes, unfortunately that’s exepcted, I made the example of the mean in the issue linked above. As @Sukera pointed out, tracking correlation is hard. It’s pretty easy to write a package to propagate uncertainties super quickly ignoring correlations, this is what Measurements.jl did until v0.02, but that’s also incredibly dumb and useless: almost no identies would hold, for example x + x and 2 * x would give you different results.

Regarding the mean in particular, note that most of the time users want to compute the weighted mean, for which Measurements.jl provides a specific function, which should have much more reasonable performance. Note that it ignores correlation, as warned in the docstring, because you’d usually apply to a sample of independent measurements anyways.

To be clear, Measurements.jl is slow because of an algorithmic limitation: it uses an O(n^2) algorithm to propagate uncertainties, you can understand why mean/sum are particularly bad, and get worse and worse as the size of the vector increases. There may be clever ways to reduce the complexity of the algorithm, but I never had the time to look at it. I believe the Python package uncertainties now has an algorithm which is O(n), or anyways better than O(n^2), but until a few years ago it was using basically the same algorithm as Measurements.jl (well, historically it’s the other way around). If anyone is willing to help, I’d be glad to hear from them.

Then we may be in luck - that license only places restrictions on redistributions in binary or source form (a port is as far as I know neither), as well as not endorsing our port with their name (i.e. we should be fine if we write something like “port inspired by”…).