SLEPLET

SLEPLET is a Python package for the construction of Slepian wavelets in the spherical and manifold (via meshes) settings. The API of SLEPLET has been designed in an object-orientated manner and is easily extendable. Upon installation, SLEPLET comes with two command line interfaces - sphere and mesh - which allows one to easily generate plots on the sphere and a set of meshes using plotly.

To read more about Slepian wavelets please see the following publications

Installation

The recommended way to install SLEPLET is via pip

pip install sleplet

To install the latest development version of SLEPLET clone this repository and run

pip install -e .

This will install two scripts sphere and mesh which can be used to generate the figures in the associated papers.

Supported Platforms

SLEPLET has been tested with . Windows is not currently supported as SLEPLET relies on pyssht and pys2let which do not work on Windows. These may be replaced with s2fft and s2wav in the future when they are available on PyPI.

Example Usage

SLEPLET may be interacted with via the API or the CLIs.

API Usage

The following demonstrates the first wavelet (ignoring the scaling function) of the South America region on the sphere.

import sleplet

B, J, J_MIN, L = 3, 0, 2, 128

region = sleplet.slepian.Region(mask_name="south_america")
f = sleplet.functions.SlepianWavelets(L, region=region, B=B, j_min=J_MIN, j=J)
f_sphere = sleplet.slepian_methods.slepian_inverse(f.coefficients, f.L, f.slepian)
sleplet.plotting.PlotSphere(
    f_sphere,
    f.L,
    f"slepian_wavelets_south_america_{B}B_{J_MIN}jmin_{J_MIN+J}j_L{L}",
    normalise=False,
    region=f.region,
).execute()

Slepian Wavelet j=2

CLI Usage

The demonstrates the first wavelet (ignoring the scaling function) of the head region of a Homer Simpson mesh for a per-vertex normals field.

mesh homer -e 3 2 0 -m slepian_wavelet_coefficients -u -z

Slepian Mesh Wavelet Coefficients j=2

Documentation

See here for the documentation. This includes demonstrations of the figures from the associated papers along with the API documentation. Further examples are included in the examples folder.

Community Guidelines

We'd love any contributions you may have, please see the contributing guidelines.

Citing

If you use SLEPLET in your research, please cite the paper.

@article{Roddy2023,
  title   = {{SLEPLET: Slepian Scale-Discretised Wavelets in Python}},
  author  = {Roddy, Patrick J.},
  year    = 2023,
  journal = {Journal of Open Source Software},
  volume  = 8,
  number  = 84,
  pages   = 5221,
  doi     = {10.21105/joss.05221},
}

Please also cite S2LET upon which SLEPLET is built, along with SSHT in the spherical setting or libigl in the mesh setting.

Release files for sleplet 1.5.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for sleplet 1.5.2
File	Size	Uploaded
sleplet-1.5.2.tar.gz	349.0 kB	Jul 24, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for sleplet 1.5.2
File	Interpreter	ABI	Platform	Reset
sleplet-1.5.2-py3-none-any.whl 385.2 kB Jul 24, 2025	Python 3	none	any	Details

Total release size: 734.2 kB

Release files / sleplet-1.5.2.tar.gz

Download URL	sleplet-1.5.2.tar.gz
Size	349.0 kB
Tags	Source
SHA-256 checksum How to use checksums	`d344ac876974fd325f8c6ea195d3160cbea459f5149a8ec47f52c2450be18567`
BLAKE2b-256 checksum How to use checksums	`881538d1bf4abf68058dc6783bc167148180c119acc77fa9721a959b5f91c702`
Upload date	Jul 24, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.12.9`

Provenance

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/astro-informatics/sleplet
Publishing commit: github.com/astro-informatics/sleplet/tree/08d5e7b8e42f8d362bd15495fd2bb23359b10925

Workflow

Publishing configuration: github.com/astro-informatics/sleplet/blob/08d5e7b8e42f8d362bd15495fd2bb23359b10925/.github/workflows/deploy.yaml
Publishing logs: github.com/astro-informatics/sleplet/actions/runs/16495042919/attempts/1

Artifact

Subject: sleplet-1.5.2.tar.gz
SHA-256 checksum: d344ac876974fd325f8c6ea195d3160cbea459f5149a8ec47f52c2450be18567 Verifying attestations

Release files / sleplet-1.5.2-py3-none-any.whl

Download URL	sleplet-1.5.2-py3-none-any.whl
Size	385.2 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`76def6b6f065f131e4bd92a6e7dc6fdd0eaabd760e9ed7a386612e8be2644ba3`
BLAKE2b-256 checksum How to use checksums	`dcf03a7a1ce218adc95024ccb11a59c94a21e2d03736ed8eccf30f5cdb9c76eb`
Upload date	Jul 24, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.12.9`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/astro-informatics/sleplet
Publishing commit: github.com/astro-informatics/sleplet/tree/08d5e7b8e42f8d362bd15495fd2bb23359b10925

Workflow

Publishing configuration: github.com/astro-informatics/sleplet/blob/08d5e7b8e42f8d362bd15495fd2bb23359b10925/.github/workflows/deploy.yaml
Publishing logs: github.com/astro-informatics/sleplet/actions/runs/16495042919/attempts/1

Artifact

Subject: sleplet-1.5.2-py3-none-any.whl
SHA-256 checksum: 76def6b6f065f131e4bd92a6e7dc6fdd0eaabd760e9ed7a386612e8be2644ba3 Verifying attestations