https://github.com/jw3126/linearinterpolations.jl

Linear interpolate almost anything
https://github.com/jw3126/linearinterpolations.jl

interpolation julia

Last synced: about 1 year ago
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Linear interpolate almost anything

• Host: GitHub
• URL: https://github.com/jw3126/linearinterpolations.jl
• Owner: jw3126
• License: mit
• Created: 2021-03-24T22:49:41.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-10-28T12:46:59.000Z (over 3 years ago)
• Last Synced: 2025-04-10T05:05:28.912Z (about 1 year ago)
• Topics: interpolation, julia
• Language: Julia
• Homepage:
• Size: 51.8 KB
• Stars: 8
• Watchers: 3
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# LinearInterpolations

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://jw3126.github.io/LinearInterpolations.jl/stable)

[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://jw3126.github.io/LinearInterpolations.jl/dev)

[![Build Status](https://github.com/jw3126/LinearInterpolations.jl/workflows/CI/badge.svg)](https://github.com/jw3126/LinearInterpolations.jl/actions)

# Why?

There are many excellent packages for interpolation in Julia. For instance:

* [Interpolations.jl](https://github.com/JuliaMath/Interpolations.jl)

* [Dierckx.jl](https://github.com/kbarbary/Dierckx.jl)

* [GridInterpolations.jl](https://github.com/sisl/GridInterpolations.jl)

All packages I am aware of assume, that the objects being interpolated implement addition and

scalar multiplication. However mathematically only a notion of weighted average is required for linear interpolation.

Examples of objects that support weighted average, but not addition and/or scalar multiplication are:

* Probability distributions

* Rotations and various other Lie groups

This package works with any notion of weighted average.

# Usage

```julia

julia> using LinearInterpolations

julia> xs = 1:3; ys=[10, 100, 1000]; # 1d

julia> interpolate(xs, ys, 1)

10.0

julia> interpolate(xs, ys, 1.5)

55.0

julia> pt = [1.5]; interpolate(xs, ys, pt)

55.0

julia> itp = Interpolate(xs, ys); # construct a callable for convenience

julia> itp(1.5)

55.0

julia> grid=(1:3, [10, 15]); vals = [1 2; 3 4; 5 6]; pt=[1,10]; # multi dimensional

julia> interpolate(grid, vals, pt)

1.0

julia> function winner_takes_it_all(wts, objs)

    # custom notion of weighted average

    I = argmax(wts)

    return objs[I]

end

julia> xs = 1:4; ys=[:no, :addition, :or, :multiplication];

julia> interpolate(xs, ys, 1.1, combine=winner_takes_it_all)

:no

julia> interpolate(xs, ys, 1.9, combine=winner_takes_it_all)

:addition

julia> interpolate(xs, ys, 3.7, combine=winner_takes_it_all)

:multiplication

```

# Design goals

* Lightweight and simple

* Support interpolation of objects that don't define `+,*`

* Reasonable performance