Investigate using DataInterpolations.jl

[icweaver]

Follow-up for #54 (comment)

Pros:

• Part of SciML ecosystem
• Highly flexible. Would be useful to have if future dust laws require a range of interpolation schemes

Cons:

• Minor: x, y args are "flipped" relative to other interpolation packages. I guess to keep their interface consistent with the rest of the SciML ecosystem
• Currently failing on one of our Measurements.jl tests, shown below. Could just be holding it wrong
  

  DustExtinction.jl/test/color_laws.jl

  Lines 339 to 343 in af42144

  	# uncertainties
  	noise = rand(length(wave)) .* 0.01
  	wave_unc = wave .± noise
  	reddening = map(w -> @uncertain(law(w)), wave_unc)
  	@test Measurements.value.(reddening) ≈ ref_values[rv] rtol = 1e-3

[cgarling]

Note that FastInterpolations.jl has been getting some positive press and may be interesting for our purposes as well. I've been meaning to try it but haven't found a chance yet

[icweaver]

Thanks for the link. The performance graph looks interesting