Measurements.jl
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JuliaPhysics
Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical inte...
Currently, `Measurement` is a subtype of, and operates on, `AbstractFloat`, while [`Symbolics.jl`](https://github.com/JuliaSymbolics/Symbolics.jl)'s variables/expressions are subtype of `Real`. Given that `AbstractFloat` is a subtype of `Real`, currently one can not pass...
Hi all! First, thanks for your work. I have a large DataFrame, which I have already combined, enriched, grouped eccetera. Right now, it presents like this: ``` julia> df_grouped =...
`Measurements.jl` implements a sort of layman autodifferentiation engine under the hood. It'd be interesting to base it on a more advanced and general autodifferentiation engine (for example I'm particularly curious...
`weightedmean([1 ± 0, 1 ± 0])` returns `NaN ± 0`, but `1 ± 0` is expected. Could you give me a better solution? Thank you very much.
I was trying out Zygote and was testing how it plays with Measurements, see also a cross post in Zygote's github (https://github.com/FluxML/Zygote.jl/issues/1320). I have encountered a problem when I try...
Measurements.jl implicitly assumes that all errors are statistical errors. In most cases this is correct. However, there are cases when the total error is a combination of statistical and systematic...
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Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical inte...