JuliaApproximation / JuliaApproximation/ApproxFun.jl
Spline basis for data interpolation.
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- Dominant language
- Julia
- Stars
- 559
- Forks
- 71
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Description
Hello! I'm trying to use ApproxFun for some Functional Data Analysis, as it has a lot of quite nice features for the work I'm doing.
From other issues, I see that I can use ApproxFun.transform to fit some data to a certain space.
For example, using one of the samples I'm working with, obtain the following fit.
Fun(Chebyshev(-1..1), ApproxFun.transform(Chebyshev(-1..1), reverse(tecator[:,1])))
However, for my applications I need to be able to use a prioritise closely fitting the data. Looking at Interpolations.jl it seems to be quite helpful for this.
Using a spline, I get:
This is much more useful for my purposes.
Would it be possible for me to use this as a basis?
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
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Research direction
Start with the ApproxFun.transform and Chebyshev examples shown in the issue, then compare the desired spline behavior with the Interpolations.jl example. Define what a supported spline basis would need to provide and how completion would be demonstrated by fitting the sample data more closely.
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Assessment
- Tech stack
- julia
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100