DataInterpolations.jl
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.
Installation
To install DataInterpolations.jl, use the Julia package manager:
using Pkg
+Pkg.add("DataInterpolations")
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 ordern
.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-splinepVec
- Symbol to Parameters Vector,pVec = :Uniform
for uniform spaced parameters andpVec = :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 theBSplineInterpolation
, with the additional parameterh<length(t)
which is the number of control points to use, with smallerh
indicating more smoothing.
Extension Methods
The follow methods require extra dependencies and will be loaded as package extensions.
Curvefit(u,t,m,p,alg)
- An interpolation which is done by fitting a user-given functional formm(t,p)
wherep
is the vector of parameters. The user's inputp
is a an initial value for a least-square fitting,alg
is the algorithm choice to use for optimize the cost function (sum of squared deviations) viaOptim.jl
and optimalp
s are used in the interpolation. Requiresusing RegularizationTools
Plotting
DataInterpolations.jl is tied into the Plots.jl ecosystem, by way of RecipesBase. Any interpolation can be plotted using the plot
command (or any other), since they have type recipes associated with them.
For convenience, and to allow keyword arguments to propagate properly, DataInterpolations.jl also defines several series types, corresponding to different interpolations.
The series types defined are:
:linear_interp
:quadratic_interp
:lagrange_interp
:quadratic_spline
:cubic_spline
By and large, these accept the same keywords as their function counterparts.
Contributing
Please refer to the SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages for guidance on PRs, issues, and other matters relating to contributing to SciML.
See the SciML Style Guide for common coding practices and other style decisions.
There are a few community forums:
- The #diffeq-bridged and #sciml-bridged channels in the Julia Slack
- The #diffeq-bridged and #sciml-bridged channels in the Julia Zulip
- On the Julia Discourse forums
- See also SciML Community page
Reproducibility
The documentation of this SciML package was built using these direct dependencies,
Status `~/work/DataInterpolations.jl/DataInterpolations.jl/docs/Project.toml`
+ [82cc6244] DataInterpolations v4.1.0 `~/work/DataInterpolations.jl/DataInterpolations.jl`
+ [e30172f5] Documenter v1.0.1
+ [429524aa] Optim v1.7.7
+ [91a5bcdd] Plots v1.39.0
+ [29dad682] RegularizationTools v0.6.0
+ [860ef19b] StableRNGs v1.0.0
and using this machine and Julia version.
Julia Version 1.9.3
+Commit bed2cd540a1 (2023-08-24 14:43 UTC)
+Build Info:
+ Official https://julialang.org/ release
+Platform Info:
+ OS: Linux (x86_64-linux-gnu)
+ CPU: 2 × Intel(R) Xeon(R) CPU E5-2673 v4 @ 2.30GHz
+ WORD_SIZE: 64
+ LIBM: libopenlibm
+ LLVM: libLLVM-14.0.6 (ORCJIT, broadwell)
+ Threads: 1 on 2 virtual cores
A more complete overview of all dependencies and their versions is also provided.
Status `~/work/DataInterpolations.jl/DataInterpolations.jl/docs/Manifest.toml`
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+Info Packages marked with ⌃ and ⌅ have new versions available, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`
You can also download the manifest file and the project file.