docs: changes in joss paper according to reviews #299
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joss/paper.md
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| # Summary | |||
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| Interpolations are used to estimate values between known data points using an approximate continuous function.DataInterpolations.jl is a Julia [@Bezanson2017] package containing 1D implementations of some of the most commonly used interpolation functions. These include Constant Interpolation, Linear Interpolation, Quadratic Interpolation, Lagrange Interpolation [@lagrange], Quadratic Splines, Cubic Splines [@Schoenberg1988], Akima Splines [@10.1145/321607.321609], Cubic Hermite Splines, Quintic Hermite Splines, B-Splines [@Curry1988] [@DEBOOR197250] and Regression based B-Splines. Along with these, the package also has methods to fit parameterized curves with the data points and Tikhonov regularization [@Tikhonov1943OnTS] [@amt-14-7909-2021] for obtaining smooth curves. The package also provides functionality to compute integrals and derivatives upto second order for those interpolations methods. | |||
| Interpolations are used to estimate values between known data points using an approximate continuous function.DataInterpolations.jl is a Julia [@Bezanson2017] package containing 1D implementations of some of the most commonly used interpolation functions. These include Constant Interpolation, Linear Interpolation, Quadratic Interpolation, Lagrange Interpolation [@lagrange], Quadratic Splines, Cubic Splines [@Schoenberg1988], Akima Splines [@10.1145/321607.321609], Cubic Hermite Splines, Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), Quintic Hermite Splines, B-Splines [@Curry1988] [@DEBOOR197250] and Regression based B-Splines. Along with these, the package also has methods to fit parameterized curves with the data points and Tikhonov regularization [@Tikhonov1943OnTS] [@amt-14-7909-2021] for obtaining smooth curves. The package also provides functionality to compute integrals and derivatives upto second order for those interpolations methods. It is also automatic differentiation friendly. It can also be used symbolically with Symbolics.jl [@gowda2021high] and plugged into models defined using ModelingToolkit.jl [@ma2021modelingtoolkit]. | |||
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| Interpolations are used to estimate values between known data points using an approximate continuous function.DataInterpolations.jl is a Julia [@Bezanson2017] package containing 1D implementations of some of the most commonly used interpolation functions. These include Constant Interpolation, Linear Interpolation, Quadratic Interpolation, Lagrange Interpolation [@lagrange], Quadratic Splines, Cubic Splines [@Schoenberg1988], Akima Splines [@10.1145/321607.321609], Cubic Hermite Splines, Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), Quintic Hermite Splines, B-Splines [@Curry1988] [@DEBOOR197250] and Regression based B-Splines. Along with these, the package also has methods to fit parameterized curves with the data points and Tikhonov regularization [@Tikhonov1943OnTS] [@amt-14-7909-2021] for obtaining smooth curves. The package also provides functionality to compute integrals and derivatives upto second order for those interpolations methods. It is also automatic differentiation friendly. It can also be used symbolically with Symbolics.jl [@gowda2021high] and plugged into models defined using ModelingToolkit.jl [@ma2021modelingtoolkit]. | |
| Interpolations are used to estimate values between known data points using an approximate continuous function.DataInterpolations.jl is a Julia [@Bezanson2017] package containing 1D implementations of some of the most commonly used interpolation functions. These include: | |
| - Constant Interpolation | |
| - Linear Interpolation | |
| - Quadratic Interpolation | |
| - Lagrange Interpolation [@lagrange] | |
| - Quadratic Splines | |
| - Cubic Splines [@Schoenberg1988] | |
| - Regression Splines | |
| - Akima Splines [@10.1145/321607.321609] | |
| - Cubic Hermite Splines | |
| - Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) | |
| - Quintic Hermite Splines, B-Splines [@Curry1988] [@DEBOOR197250] | |
| - Regression based B-Splines | |
| and a continually growing list. Along with these, the package also has methods to fit parameterized curves with the data points and Tikhonov regularization [@Tikhonov1943OnTS] [@amt-14-7909-2021] for obtaining smooth curves. The package also provides functionality to compute integrals and derivatives upto second order for those interpolations methods. It is also automatic differentiation friendly. It can also be used symbolically with Symbolics.jl [@gowda2021high] and plugged into models defined using ModelingToolkit.jl [@ma2021modelingtoolkit]. | |
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joss/paper.md
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| # Statement of need | ||
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| Interpolations are a very important component of many modeling workflows. In many models, inputs which are sampled or measured need to be represented as a continuous function or a smooth curve for simulation. In many scientific machine learning workflows, we need interpolations of data to learn continuous models. There already have been a few interpolation packages in Julia like Interpolations.jl but it has a limitation of assuming uniformly spaced data which is not usually the case with data collected from real world. DataInterpolations.jl provides fast interpolation methods for arbitrary spaced 1D data with a consistent and simple interface. It is also automatic differentiation friendly. It can also be used symbolically with Symbolics.jl [@gowda2021high] and plugged into models defined using ModelingToolkit.jl [@ma2021modelingtoolkit]. | ||
| Interpolations are a very important component of many modeling workflows. Often, sampled or measured inputs need to be transformed into continuous functions or smooth curves for simulation purposes. In many scientific machine learning workflows, interpolating data is essential to learn continuous models. DataInterpolations.jl can be used for facilitating these types of workflows. Several interpolation packages already exist in Julia, such as [Interpolations.jl](https://juliamath.github.io/Interpolations.jl/stable/), which primarily specializes in B-Splines and uniformly spaced data with some support for irregularly spaced data. In contrast, DataInterpolations.jl does not assume any specific structure in the data, offering greater flexibility for diverse datasets. [Interpolations.jl](https://juliamath.github.io/Interpolations.jl/stable/) also doesn't offer methods like Quadratic Interpolation, Lagrange Interpolation, Hermite Splines etc. [BasicInterpolators.jl](https://github.com/markmbaum/BasicInterpolators.jl) is more similar to DataInterpolations.jl, although it doesn't offer methods like B-Splines. Rest of the interpolation packages focus on particular methods like [BSplineKit.jl](https://github.com/jipolanco/BSplineKit.jl) for B-Splines, [FastChebInterp.jl](https://github.com/JuliaMath/FastChebInterp.jl) for Chebyshev interpolation, [PCHIPInterpolation](https://github.com/gerlero/PCHIPInterpolation.jl) for PCHIP interpolation etc. In summary, DataInterpolations.jl is more generic from other packages and offers many fast interpolation methods for arbitrarily spaced 1D data, all within a consistent and simple interface. |
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| Interpolations are a very important component of many modeling workflows. Often, sampled or measured inputs need to be transformed into continuous functions or smooth curves for simulation purposes. In many scientific machine learning workflows, interpolating data is essential to learn continuous models. DataInterpolations.jl can be used for facilitating these types of workflows. Several interpolation packages already exist in Julia, such as [Interpolations.jl](https://juliamath.github.io/Interpolations.jl/stable/), which primarily specializes in B-Splines and uniformly spaced data with some support for irregularly spaced data. In contrast, DataInterpolations.jl does not assume any specific structure in the data, offering greater flexibility for diverse datasets. [Interpolations.jl](https://juliamath.github.io/Interpolations.jl/stable/) also doesn't offer methods like Quadratic Interpolation, Lagrange Interpolation, Hermite Splines etc. [BasicInterpolators.jl](https://github.com/markmbaum/BasicInterpolators.jl) is more similar to DataInterpolations.jl, although it doesn't offer methods like B-Splines. Rest of the interpolation packages focus on particular methods like [BSplineKit.jl](https://github.com/jipolanco/BSplineKit.jl) for B-Splines, [FastChebInterp.jl](https://github.com/JuliaMath/FastChebInterp.jl) for Chebyshev interpolation, [PCHIPInterpolation](https://github.com/gerlero/PCHIPInterpolation.jl) for PCHIP interpolation etc. In summary, DataInterpolations.jl is more generic from other packages and offers many fast interpolation methods for arbitrarily spaced 1D data, all within a consistent and simple interface. | |
| Interpolations are a very important component of many modeling workflows. Often, sampled or measured inputs need to be transformed into continuous functions or smooth curves for simulation purposes. In many scientific machine learning workflows, interpolating data is essential to learn continuous models. DataInterpolations.jl can be used for facilitating these types of workflows. Several interpolation packages already exist in Julia, such as [Interpolations.jl](https://juliamath.github.io/Interpolations.jl/stable/), which primarily specializes in B-Splines and uniformly spaced data with some support for irregularly spaced data. In contrast, DataInterpolations.jl does not assume any specific structure in the data, offering greater flexibility for diverse datasets. [Interpolations.jl](https://juliamath.github.io/Interpolations.jl/stable/) also doesn't offer methods like Quadratic Interpolation, Lagrange Interpolation, Hermite Splines etc. [BasicInterpolators.jl](https://github.com/markmbaum/BasicInterpolators.jl) is more similar to DataInterpolations.jl, although it doesn't offer methods like B-Splines. Rest of the interpolation packages focus on particular methods like [BSplineKit.jl](https://github.com/jipolanco/BSplineKit.jl) for B-Splines, [FastChebInterp.jl](https://github.com/JuliaMath/FastChebInterp.jl) for Chebyshev interpolation, [PCHIPInterpolation](https://github.com/gerlero/PCHIPInterpolation.jl) for PCHIP interpolation etc. Additionally, DataInterpolations.jl includes many novel techniques for accelerating the interpolation searches, with specialized caching, quasi-linear guessing, and more to improve the performance algorithmically, beyond the simple computational optimizations. In summary, DataInterpolations.jl is more generic from other packages and offers many fast interpolation methods for arbitrarily spaced 1D data, all within a consistent and simple interface. |
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Addressed in 601491d
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@ChrisRackauckas, can this be merged? |
ChrisRackauckas
approved these changes
Jul 23, 2024
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Ref: openjournals/joss-reviews#6917 (comment)