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Use of Measurements.jl values works in some cases and fails in others.

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
35/100
Issue type
Bug
Clarity
Needs clarification
Activity status
Stale
Tech stack
julia
Domain
tooling

Research direction

Reproduce the two Polynomials.fit examples with Measurement values and compare the successful degree-2 path with the failing degree-4 path. Start at the fit/vander path named in the stack trace and determine where Measurement values are converted to Float64. Done should establish whether fitting can support these values or clearly define the limitation and its handling.

Written by the indexing model from the issue text.

Description

Hi!

When using Measurement types, Polynomials plays nicely with Measurements.jl until the polynomials get large enough / complicated enough in fitting that Vandermonde is used. Is there any way to skip this and use a nieve fitting method when I'm needing to use these types to propagate error?

Working minimal example: You will find that the following works

julia> Polynomials.fit([1, 2, 3] .± 0.3, [4, 5, 6.3].±0.2, 2)
> Polynomials.Polynomial(3.3 ± 1.6 + 0.55 ± 1.9*x + 0.15 ± 0.49*x^2)

and this will not

julia> Polynomials.fit([1, 2, 3] .± 0.3, [4, 5, 6.3].±0.2, 4)
ERROR: MethodError: no method matching Float64(::Measurement{Float64})
Closest candidates are:
  (::Type{T})(::Real, ::RoundingMode) where T<:AbstractFloat at rounding.jl:200
  (::Type{T})(::T) where T<:Number at boot.jl:772
  (::Type{T})(::AbstractChar) where T<:Union{AbstractChar, Number} at char.jl:50
  ...
Stacktrace:
 [1] convert(#unused#::Type{Float64}, x::Measurement{Float64})
   @ Base .\number.jl:7
 [2] setindex!(A::Vector{Float64}, x::Measurement{Float64}, i1::Int64)
   @ Base .\array.jl:966
 [3] vander(P::Type{Polynomials.Polynomial}, x::Vector{Measurement{Float64}}, degs::UnitRange{Int64})
Dominant language
Julia
Stars
321
Forks
81
Avg merge
10h 1m
Merged PRs (30d)
5

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