SLEPLET: Slepian Scale-Discretised Wavelets in Python (#148)

[paddyroddy]

Submitting Author: Patrick J. Roddy (@paddyroddy)
All current maintainers: (@paddyroddy)
Package Name: SLEPLET
One-Line Description of Package: Slepian Scale-Discretised Wavelets in Python
Repository Link: https://github.com/astro-informatics/sleplet
Version submitted: v1.4.4
EiC: @NickleDave
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): 2024/05/21

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SLEPLET is a Python package for the construction of Slepian wavelets in the spherical and manifold (via meshes) settings. SLEPLET handles 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, SLEPLET comes with two command line interfaces - sphere and mesh - that allow one to easily generate plots on the sphere and a set of meshes using plotly. Whilst these scripts are the primary intended use, SLEPLET may 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. SLEPLET has been used in the development of various papers.

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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.

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(or a similar approach) on the sphere or general manifolds/meshes. SHTools is a Python code used for spherical harmonic transforms, which allows one to compute the Slepian functions of the spherical polar cap. A series of MATLAB scripts exist in slepian_alpha, which permits the calculation of the Slepian functions on the sphere. However, these scripts are very specialised and hard to generalise.

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[NickleDave]

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

[NickleDave]

@paddyroddy related to that to-do item for the docs:
my understanding is that the goal for SLEPLET is to provide general access to the wavelet basis/transform you have developed for incomplete data on manifolds (feel free to correct me if I'm wrong)

Part of what we can do with this review is help you reach a broader audience. But that means we need to know where to look for editors and reviewers.

Can you please help me understand the main domains where you have applied and can apply your methods? I see you are an RSE in an astro group and that you mention cosmology in your thesis; in the papers you apply your methods to topographic data from Earth. Are these methods mainly used by people in cosmology, geophysics, computer imaging, all of the above? Could this work relate to geodesy in earth science?

As examples of places where we might look for editors/reviewers: we work with astropy and software underground. I can also imagine asking people that work on manifolds in general and geometric deep learning, and another place we might look would be users/developers of packages you rely on or mention, e.g. pyssht/py2let. But my sense is that we will want domain experts that can help you translate the methods you've developed for a broader audience.

Any more concrete detail you can give me about domains to look at for editors + reviewers would be helpful. We are looking now, but I want to get your input to make sure this review helps you.

[paddyroddy]

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've added these. Mine isn't particularly lay, but they are a fairly specialised application, so hopefully from the description it's obvious who the target audience are!

[paddyroddy]

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

Annoyingly, I hadn't spotted this, as I cache after the first import. I've had a look and the following are created:

-rw-------    1 paddy staff  74M Nov 24 18:37 EGM2008_Topography_flms_L2190.mat
-rw-------    1 paddy staff 5.6K Nov 24 18:37 meshes_laplacians_basis_functions_bird_b697_eigenvalues.npy
-rw-------    1 paddy staff  15M Nov 24 18:37 meshes_laplacians_basis_functions_bird_b697_eigenvectors.npy
-rw-------    1 paddy staff 5.6K Nov 24 18:37 meshes_laplacians_slepian_functions_bird_b697_N194_eigenvalues.npy
-rw-------    1 paddy staff 1.1M Nov 24 18:37 meshes_laplacians_slepian_functions_bird_b697_N194_eigenvectors.npy
-rw-------    1 paddy staff  132 Nov 24 18:37 slepian_masks_africa_L1.npy
-rw-------    1 paddy staff  132 Nov 24 18:37 slepian_masks_south_america_L1.npy

The problem (in particular) is PyPI doesn't allow over 50MB. I need to work out a way to delay the downloading of EGM2008_Topography_flms_L2190.mat.

[paddyroddy]

The problem (in particular) is PyPI doesn't allow over 50MB. I need to work out a way to delay the downloading of EGM2008_Topography_flms_L2190.mat.

Fixed this in astro-informatics/sleplet#336 (v1.4.5), turns out I had introduced the bug using pydantic.Field(default= values. The data will still be downloaded as and when - but not on initial import. And, as before, it is cached so will only happen once.

[paddyroddy]

@paddyroddy related to that to-do item for the docs: my understanding is that the goal for SLEPLET is to provide general access to the wavelet basis/transform you have developed for incomplete data on manifolds (feel free to correct me if I'm wrong)

Yep that's right!

Can you please help me understand the main domains where you have applied and can apply your methods? I see you are an RSE in an astro group and that you mention cosmology in your thesis; in the papers you apply your methods to topographic data from Earth. Are these methods mainly used by people in cosmology, geophysics, computer imaging, all of the above? Could this work relate to geodesy in earth science?

So I'm actually a general RSE (not strictly working on this), but was formerly a PhD student in an astrophysics group, specialising in cosmology. In reality, these methods could be used by any field in which data is measured only in certain parts of a manifold. Be that: oceanography, geophysics, solar physics, astrophysics, cosmology, computer graphics, machine learning etc. In reality, there are many fields which could use methods such as these.

As examples of places where we might look for editors/reviewers: we work with astropy and software underground. I can also imagine asking people that work on manifolds in general and geometric deep learning, and another place we might look would be users/developers of packages you rely on or mention, e.g. pyssht/py2let. But my sense is that we will want domain experts that can help you translate the methods you've developed for a broader audience.

Those all sound like reasonable places. Would be very interested to have others people's insights. Sorry, I'm not sure how else to help on that front!

[NickleDave]

The problem (in particular) is PyPI doesn't allow over 50MB. I need to work out a way to delay the downloading of EGM2008_Topography_flms_L2190.mat.

Sounds like you fixed the issue overall but jic you're still figuring this out: are you familiar with pooch?
A pattern a lot of core scientific Python packages seem to be converging on is "put the data in the package and use importlib-resources if it fits on PyPI; for everything else use pooch". See for example scikit-image: scikit-image/scikit-image#5146 or librosa: https://github.com/librosa/librosa/tree/main/librosa/util/example_data

[NickleDave]

Thank you @paddyroddy for your replies about the domain, very helpful.
I am very sympathetic to developing methods that are useful across domains (FFT anyone?) but just want to make sure we're helping you reach that broader audience.

We are looking for an editor + reviewers now.

[paddyroddy]

Sounds like you fixed the issue overall but jic you're still figuring this out: are you familiar with pooch?

Yes, I'm using pooch...

[NickleDave]

Whoops, guess I could've just looked 😝 😇 We can say I'm trying to be helpful