NickleDave
commented
6 months ago Hi @paddyroddy I'm adding the initial Editor-in-Chief checks.
The tl;dr is that we are about ready to start but I need you to do two things first:
- [ ] Add a code of conduct
- [ ] Add a "layperson" summary of the package to the docs page
- The first thing someone should read on the docs is a statement of what the package does--I wouldn't assume someone will bother to click again to go to PyPI. Provide enough background that someone outside your domain will have some understanding of how the package fits into a bigger picture, kind of like the first couple of sentences of the abstract of your thesis.
I will add one more comment below but it is not required that you address it before we start the review
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Editor comments
- when I first tested that I could import the package, it downloaded a lot of data. Many people might find this surprising. I would suggest adding data directly to the package if possible--if the files are small enough--and/or deferring download of large files
paddyroddy
SimonMolinsky
magsol
gnypit
Submitting Author: Patrick J. Roddy (@paddyroddy) All current maintainers: (@paddyroddy) Package Name:
SLEPLETOne-Line Description of Package: Slepian Scale-Discretised Wavelets in Python Repository Link: https://github.com/astro-informatics/sleplet Version submitted: v1.4.4 Editor: Szymon Moliński (@SimonMolinsky ) Reviewer 1: Shannon Quinn (@magsol ) Reviewer 2: Jakub Tomasz Gnyp (@gnypit ) Archive: 10.5281/zenodo.7268074 JOSS DOI: 10.21105/joss.05221 Version accepted: 1.4.7 Date accepted (month/day/year): 2024/05/21Code of Conduct & Commitment to Maintain Package
Description
SLEPLETis a Python package for the construction of Slepian wavelets in the spherical and manifold (via meshes) settings.SLEPLEThandles any spherical region as well as the general manifold setting. The API is documented and easily extendible, designed in an object-orientated manner. Upon installation,SLEPLETcomes with two command line interfaces -sphereandmesh- that allow one to easily generate plots on the sphere and a set of meshes usingplotly. Whilst these scripts are the primary intended use,SLEPLETmay be used directly to generate the Slepian coefficients in the spherical/manifold setting and use methods to convert these into real space for visualisation or other intended purposes. The construction of the sifting convolution was required to create Slepian wavelets. As a result, there are also many examples of functions on the sphere in harmonic space (rather than Slepian) that were used to demonstrate its effectiveness.SLEPLEThas been used in the development of various papers.Scope
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Many fields in science and engineering measure data that inherently live on non-Euclidean geometries, such as the sphere. Techniques developed in the Euclidean setting must be extended to other geometries. Due to recent interest in geometric deep learning, analogues of Euclidean techniques must also handle general manifolds or graphs. Often, data are only observed over partial regions of manifolds, and thus standard whole-manifold techniques may not yield accurate predictions. Slepian wavelets are designed for datasets like these. Slepian wavelets are built upon the eigenfunctions of the Slepian concentration problem of the manifold: a set of bandlimited functions that are maximally concentrated within a given region. Wavelets are constructed through a tiling of the Slepian harmonic line by leveraging the existing scale-discretised framework. Whilst these wavelets were inspired by spherical datasets, like in cosmology, the wavelet construction may be utilised for manifold or graph data.
To the author's knowledge, there is no public software that allows one to compute Slepian wavelets (or a similar approach) on the sphere or general manifolds/meshes.
SHToolsis aPythoncode used for spherical harmonic transforms, which allows one to compute the Slepian functions of the spherical polar cap. A series ofMATLABscripts exist inslepian_alpha, which permits the calculation of the Slepian functions on the sphere. However, these scripts are very specialised and hard to generalise.@tagthe editor you contacted: #148Technical checks
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