Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/SciML/DataInterpolations.jl
A library of data interpolation and smoothing functions
https://github.com/SciML/DataInterpolations.jl
Last synced: 3 months ago
JSON representation
A library of data interpolation and smoothing functions
- Host: GitHub
- URL: https://github.com/SciML/DataInterpolations.jl
- Owner: SciML
- License: mit
- Created: 2018-08-14T21:17:46.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-08-02T16:17:38.000Z (3 months ago)
- Last Synced: 2024-08-02T19:01:34.641Z (3 months ago)
- Language: Julia
- Size: 34.6 MB
- Stars: 207
- Watchers: 15
- Forks: 44
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-sciml - PumasAI/DataInterpolations.jl: A library of data interpolation and smoothing functions
README
# DataInterpolations.jl
[](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
[](https://docs.sciml.ai/DataInterpolations/stable/)
[](https://codecov.io/gh/SciML/DataInterpolations.jl)
[](https://github.com/SciML/DataInterpolations.jl/actions/workflows/Tests.yml)
[](https://github.com/SciML/ColPrac)
[](https://github.com/SciML/SciMLStyle)
DataInterpolations.jl is a library for performing interpolations of one-dimensional data. By
"data interpolations" we mean techniques for interpolating possibly noisy data, and thus
some methods are mixtures of regressions with interpolations (i.e. do not hit the data
points exactly, smoothing out the lines). This library can be used to fill in intermediate
data points in applications like timeseries data.
## API
All interpolation objects act as functions. Thus for example, using an interpolation looks like:
```julia
u = rand(5)
t = 0:4
interp = LinearInterpolation(u, t)
interp(3.5) # Gives the linear interpolation value at t=3.5
```
We can efficiently interpolate onto a vector of new `t` values:
```julia
t′ = 0.5:1.0:3.5
interp(t′)
```
In-place interpolation also works:
```julia
u′ = similar(u, length(t′))
interp(u′, t′)
```
## Available Interpolations
In all cases, `u` an `AbstractVector` of values and `t` is an `AbstractVector` of timepoints
corresponding to `(u,t)` pairs.
- `ConstantInterpolation(u,t)` - A piecewise constant interpolation.
- `LinearInterpolation(u,t)` - A linear interpolation.
- `QuadraticInterpolation(u,t)` - A quadratic interpolation.
- `LagrangeInterpolation(u,t,n)` - A Lagrange interpolation of order `n`.
- `QuadraticSpline(u,t)` - A quadratic spline interpolation.
- `CubicSpline(u,t)` - A cubic spline interpolation.
- `AkimaInterpolation(u, t)` - Akima spline interpolation provides a smoothing effect and is computationally efficient.
- `BSplineInterpolation(u,t,d,pVec,knotVec)` - An interpolation B-spline. This is a B-spline which hits each of the data points. The argument choices are:
+ `d` - degree of B-spline
+ `pVec` - Symbol to Parameters Vector, `pVec = :Uniform` for uniform spaced parameters and `pVec = :ArcLen` for parameters generated by chord length method.
+ `knotVec` - Symbol to Knot Vector, `knotVec = :Uniform` for uniform knot vector, `knotVec = :Average` for average spaced knot vector.
- `BSplineApprox(u,t,d,h,pVec,knotVec)` - A regression B-spline which smooths the fitting curve. The argument choices are the same as the `BSplineInterpolation`, with the additional parameter `h