DataInterpolations.jl

DataInterpolations.jl: Fast Interpolations of 1D data - Published in JOSS (2024)

https://github.com/sciml/datainterpolations.jl

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
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Found .zenodo.json file
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DOI references
Found 7 DOI reference(s) in README and JOSS metadata
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Academic publication links
Links to: joss.theoj.org
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Committers with academic emails
5 of 51 committers (9.8%) from academic institutions
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JOSS paper metadata
Published in Journal of Open Source Software

Keywords from Contributors

graphics fluxes the-human-brain ode differential-equations julialang unconstrained-optimization unconstrained-optimisation optimisation optim

Last synced: 6 months ago · JSON representation

Repository

A library of data interpolation and smoothing functions

Basic Info

Host: GitHub
Owner: SciML
License: mit
Language: Julia
Default Branch: master
Size: 123 MB

Statistics

Stars: 257
Watchers: 11
Forks: 55
Open Issues: 48
Releases: 82

Created over 7 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog License

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.

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.
SmoothedConstantInterpolation(u,t) - An integral preserving continuously differentiable approximation of 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<length(t) which is the number of control points to use, with smaller h indicating more smoothing.
CubicHermiteSpline(du, u, t) - A third order Hermite interpolation, which matches the values and first (du) order derivatives in the data points exactly.
PCHIPInterpolation(u, t) - a type of CubicHermiteSpline where the derivative values du are derived from the input data in such a way that the interpolation never overshoots the data.
QuinticHermiteSpline(ddu, du, u, t) - A fifth order Hermite interpolation, which matches the values and first (du) and second (ddu) order derivatives in the data points exactly.

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 form m(t,p) where p is the vector of parameters. The user's input p 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) via Optim.jl and optimal ps are used in the interpolation. Requires using Optim.
RegularizationSmooth(u,t,d;λ,alg) - A regularization algorithm (ridge regression) which is done by minimizing an objective function (l2 loss + derivatives of order d) integrated in the time span. It is a global method and creates a smooth curve. Requires using 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.

Citing

If you use this software in your work, please cite:

bib @article{Bhagavan2024, doi = {10.21105/joss.06917}, url = {https://doi.org/10.21105/joss.06917}, year = {2024}, publisher = {The Open Journal}, volume = {9}, number = {101}, pages = {6917}, author = {Sathvik Bhagavan and Bart de Koning and Shubham Maddhashiya and Christopher Rackauckas}, title = {DataInterpolations.jl: Fast Interpolations of 1D data}, journal = {Journal of Open Source Software} }

Owner

Name: SciML Open Source Scientific Machine Learning
Login: SciML
Kind: organization
Email: contact@chrisrackauckas.com

Website: https://sciml.ai
Twitter: SciML_Org
Repositories: 170
Profile: https://github.com/SciML

Open source software for scientific machine learning

JOSS Publication

DataInterpolations.jl: Fast Interpolations of 1D data

Published

September 05, 2024

DOI

10.21105/joss.06917

Volume 9, Issue 101, Page 6917

Authors

Sathvik Bhagavan

JuliaHub

Bart de Koning

Deltares

Shubham Maddhashiya
Pumas-AI

Christopher Rackauckas

JuliaHub, Pumas-AI, Massachusetts Institute of Technology

Editor

Arfon Smith

GitHub Events

Total

Create event: 35
Issues event: 54
Release event: 12
Watch event: 31
Delete event: 22
Issue comment event: 267
Push event: 208
Pull request review comment event: 57
Pull request review event: 96
Pull request event: 133
Fork event: 11

Last Year

Create event: 35
Issues event: 54
Release event: 12
Watch event: 31
Delete event: 22
Issue comment event: 267
Push event: 208
Pull request review comment event: 57
Pull request review event: 96
Pull request event: 133
Fork event: 11

Committers

Last synced: 6 months ago

All Time

Total Commits: 787
Total Committers: 51
Avg Commits per committer: 15.431
Development Distribution Score (DDS): 0.813

Past Year

Commits: 195
Committers: 25
Avg Commits per committer: 7.8
Development Distribution Score (DDS): 0.723

Top Committers

Name	Email	Commits
Christopher Rackauckas	a**s@c**m	147
Sathvik Bhagavan	s**n@j**m	136
Bart de Koning	b**g@d**l	123
shubham maddhashiya	s**h@i**n	47
Andreas Noack	a**s@n**k	35
dependabot[bot]	4****]	35
Ashutosh Bharambe	a**3@g**m	28
avik-pal	a**l@i**n	26
Daniel González	d**s@g**m	24
Diogo Netto	d**n@g**m	18
David Widmann	d****n	13
Anshul Singhvi	a**7@s**u	13
Arno Strouwen	a**n@t**e	13
Jonathan Stickel	j**l@g**m	12
Glen Hertz	g**z@g**m	11
github-actions[bot]	4****]	11
Isaac Wheeler	i**r@g**m	9
lexverheem	l**m@d**l	8
user.email	m**e@g**m	7
oscarddssmith	o**h@j**m	6
Venkateshprasad	3****k	6
Tim Kim	t**m@p**u	6
Pepijn de Vos	p**s@g**m	5
Yingbo Ma	m**5@g**m	4
Fredrik Bagge Carlson	b**n@g**m	4
Anant Thazhemadam	a**m@g**m	4
xzackli	x**i@g**m	3
mleseach	1****h	3
contradict	c**t@g**m	3
Sebastian Micluța-Câmpeanu	3****C	3
and 21 more...

Committer Domains (Top 20 + Academic)

chrisrackauckas.com: 2 deltares.nl: 2 juliacomputing.com: 2 uit.no: 1 helgeeichhorn.de: 1 anthropic.com: 1 aluthge.com: 1 princeton.edu: 1 mailbox.org: 1 telenet.be: 1 simons-rock.edu: 1 iitk.ac.in: 1 noack.dk: 1 iitkgp.ac.in: 1 mit.edu: 1 juliahub.com: 1 invenialabs.co.uk: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 93
Total pull requests: 335
Average time to close issues: 6 months
Average time to close pull requests: 5 days
Total issue authors: 50
Total pull request authors: 31
Average comments per issue: 2.44
Average comments per pull request: 1.27
Merged pull requests: 272
Bot issues: 0
Bot pull requests: 54

Past Year

Issues: 43
Pull requests: 149
Average time to close issues: 8 days
Average time to close pull requests: 6 days
Issue authors: 25
Pull request authors: 24
Average comments per issue: 1.28
Average comments per pull request: 1.72
Merged pull requests: 103
Bot issues: 0
Bot pull requests: 24

View more stats

Top Authors

Issue Authors

SouthEndMusic (18)
bclyons12 (5)
sathvikbhagavan (5)
chooron (4)
ChrisRackauckas (4)
DaniGlez (3)
yolhan83 (2)
jjstickel (2)
egavazzi (2)
Ickaser (2)
TorkelE (2)
avik-pal (2)
marcobonici (2)
mleseach (2)
astro-kevin (2)

Pull Request Authors

sathvikbhagavan (102)
SouthEndMusic (62)
dependabot[bot] (43)
ChrisRackauckas (22)
ArnoStrouwen (13)
github-actions[bot] (11)
ashutosh-b-b (10)
oscardssmith (9)
devmotion (9)
DaniGlez (7)
cgarling (5)
SebastianM-C (5)
avik-pal (4)
thazhemadam (3)
Ickaser (3)

Top Labels

Issue Labels

bug (35) question (4) good first issue (2)

Pull Request Labels

dependencies (43) github-actions (4) github_actions (3)

Packages

Total packages: 1
Total downloads:
- julia 1,667 total

Total dependent packages: 25
Total dependent repositories: 0
Total versions: 81

juliahub.com: DataInterpolations

A library of data interpolation and smoothing functions

Documentation: https://docs.juliahub.com/General/DataInterpolations/stable/
License: MIT
Latest release: 8.5.0
published 7 months ago

Versions: 81
Dependent Packages: 25
Dependent Repositories: 0
Downloads: 1,667 Total

Rankings

Dependent packages count: 4.8%

Forks count: 6.3%

Stargazers count: 6.4%

Average: 6.9%

Dependent repos count: 9.9%

Last synced: 6 months ago

Dependencies

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julia-actions/julia-buildpkg v1 composite
julia-actions/julia-invalidations v1 composite
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.github/workflows/Downgrade.yml actions

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cjdoris/julia-downgrade-compat-action v1 composite
julia-actions/julia-buildpkg v1 composite
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.github/workflows/SpellCheck.yml actions

actions/checkout v3 composite
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DataInterpolations.jl

Science Score: 95.0%

Keywords from Contributors

Repository

Basic Info

Statistics

Metadata Files

README.md

DataInterpolations.jl

API

Available Interpolations

Extension Methods

Plotting

Citing

Owner

JOSS Publication

DataInterpolations.jl: Fast Interpolations of 1D data

Authors

Editor

Tags

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

juliahub.com: DataInterpolations

Rankings

Dependencies