Measurements with missing errors about measurements.jl HOT 4 OPEN

Duplicate of #59?

from measurements.jl.

Yes, it is duplicate of #59 and of #62 , and it was decided that measurement(1, missing) should throw error with argumentation as following:

#62 (comment)

This case doesn't seem as clear-cut to me. My reasoning is that just because the uncertainty has gone missing somewhere, doesn't mean the actual value is not known and should be missing. A missing uncertainty might even be interpreted to mean zero uncertainty by some. I think it is better to throw an error here, than just make assumptions for the user.

But why should it be a problem if missing uncertainties just propagate?

x = 1.0 ± 2.0
y = 1.0 ± missing
x + y
>  2.0 ± missing

We do not make any assumptions for the user, they can always decide how to treat the Missings in the final result.

from measurements.jl.

But why should it be a problem if missing uncertainties just propagate?

That'd require changing the layout of the datastructure, with a likely significant impact on performance.

from measurements.jl.

It looks like the missing uncertainty can be emulated by some non-physical value, like NaN.

from measurements.jl.