{"id":24738386,"url":"https://github.com/astro-informatics/sleplet","last_synced_at":"2025-07-06T23:33:58.604Z","repository":{"id":65219573,"uuid":"306052936","full_name":"astro-informatics/sleplet","owner":"astro-informatics","description":"Slepian Scale-Discretised Wavelets in 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SLEPLET\n\n[![PyPI](https://badge.fury.io/py/sleplet.svg)](https://pypi.org/project/sleplet)\n[![Zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.7268074.svg)](https://doi.org/10.5281/zenodo.7268074)\n[![Documentation](https://img.shields.io/badge/Documentation-SLEPLET-blueviolet.svg)](https://astro-informatics.github.io/sleplet)\n[![Licence](https://img.shields.io/github/license/astro-informatics/sleplet)](https://github.com/astro-informatics/sleplet?tab=BSD-3-Clause-1-ov-file#readme)\n\n[![Python](https://img.shields.io/pypi/pyversions/sleplet)](https://www.python.org)\n[![repostatus](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)\n[![Test](https://github.com/astro-informatics/sleplet/actions/workflows/test.yaml/badge.svg)](https://github.com/astro-informatics/sleplet/actions/workflows/test.yaml)\n[![Coverage Status](https://coveralls.io/repos/github/astro-informatics/sleplet/badge.svg?branch=main)](https://coveralls.io/github/astro-informatics/sleplet?branch=main)\n[![CodeFactor](https://www.codefactor.io/repository/github/astro-informatics/sleplet/badge)](https://www.codefactor.io/repository/github/astro-informatics/sleplet)\n\n[![JOSS](https://joss.theoj.org/papers/55d9cf16a27bf2d3141f0f66c676b7f2/status.svg)](https://joss.theoj.org/papers/55d9cf16a27bf2d3141f0f66c676b7f2)\n[![PyOpenSci](https://img.shields.io/badge/PyOpenSci-Peer%20Reviewed-success.svg)](https://github.com/pyOpenSci/software-submission/issues/149)\n[![Citation](https://img.shields.io/badge/cite-SLEPLET-yellow)](https://github.com/astro-informatics/sleplet#citing)\n\n[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)\n[![Renovate](https://img.shields.io/badge/renovate-enabled-orange?logo=renovatebot)](https://docs.renovatebot.com)\n\n`SLEPLET` is a Python package for the construction of Slepian wavelets in the\nspherical and manifold (via meshes) settings. The API of `SLEPLET` has been\ndesigned in an object-orientated manner and is easily extendable. Upon\ninstallation, `SLEPLET` comes with two command line interfaces - `sphere` and\n`mesh` - which allows one to easily generate plots on the sphere and a set of\nmeshes using `plotly`.\n\nTo read more about Slepian wavelets please see the following publications\n\n[![Sifting Convolution on the Sphere](https://img.shields.io/badge/DOI-10.1109/LSP.2021.3050961-pink.svg)](https://dx.doi.org/10.1109/LSP.2021.3050961)\n[![Slepian Scale-Discretised Wavelets on the Sphere](https://img.shields.io/badge/DOI-10.1109/TSP.2022.3233309-pink.svg)](https://dx.doi.org/10.1109/TSP.2022.3233309)\n[![Slepian Scale-Discretised Wavelets on Manifolds](https://img.shields.io/badge/DOI-10.48550/arXiv.2302.06006-pink.svg)](https://doi.org/10.48550/arXiv.2302.06006)\n[![Slepian Wavelets for the Analysis of Incomplete Data on Manifolds](https://img.shields.io/badge/PhD%20Thesis-Patrick%20J.%20Roddy-pink.svg)](https://paddyroddy.github.io/thesis)\n\n## Installation\n\nThe recommended way to install `SLEPLET` is via\n[pip](https://pypi.org/project/pip)\n\n```sh\npip install sleplet\n```\n\nTo install the latest development version of `SLEPLET` clone this repository and\nrun\n\n```sh\npip install -e .\n```\n\nThis will install two scripts `sphere` and `mesh` which can be used to generate\nthe figures in\n[the associated papers](https://astro-informatics.github.io/sleplet#paper-figures).\n\n### Supported Platforms\n\n`SLEPLET` has been tested with\n[![Python](https://img.shields.io/pypi/pyversions/sleplet)](https://www.python.org).\nWindows is not currently supported as `SLEPLET` relies on\n[pyssht](https://pypi.org/project/pyssht) and\n[pys2let](https://pypi.org/project/pys2let) which do not work on Windows. These\nmay be replaced with [s2fft](https://github.com/astro-informatics/s2fft) and\n[s2wav](https://github.com/astro-informatics/s2wav) in the future when they are\navailable on [PyPI](https://pypi.org).\n\n## Example Usage\n\n`SLEPLET` may be interacted with via the API or the CLIs.\n\n### API Usage\n\nThe following demonstrates the first wavelet (ignoring the scaling function) of\nthe South America region on the sphere.\n\n```python\nimport sleplet\n\nB, J, J_MIN, L = 3, 0, 2, 128\n\nregion = sleplet.slepian.Region(mask_name=\"south_america\")\nf = sleplet.functions.SlepianWavelets(L, region=region, B=B, j_min=J_MIN, j=J)\nf_sphere = sleplet.slepian_methods.slepian_inverse(f.coefficients, f.L, f.slepian)\nsleplet.plotting.PlotSphere(\n    f_sphere,\n    f.L,\n    f\"slepian_wavelets_south_america_{B}B_{J_MIN}jmin_{J_MIN+J}j_L{L}\",\n    normalise=False,\n    region=f.region,\n).execute()\n```\n\n![Slepian Wavelet j=2](https://github.com/astro-informatics/sleplet/blob/main/documentation/slepian_wavelets_south_america_3B_2jmin_2j_L128_res512_real.png?raw=true)\n\n### CLI Usage\n\nThe demonstrates the first wavelet (ignoring the scaling function) of the head\nregion of a Homer Simpson mesh for a per-vertex normals field.\n\n```sh\nmesh homer -e 3 2 0 -m slepian_wavelet_coefficients -u -z\n```\n\n![Slepian Mesh Wavelet Coefficients j=2](https://github.com/astro-informatics/sleplet/blob/main/documentation/slepian_wavelet_coefficients_homer_3B_2jmin_2j_zoom.png?raw=true)\n\n## Documentation\n\nSee here for the [documentation](https://astro-informatics.github.io/sleplet).\nThis includes demonstrations of the figures from the associated papers along\nwith the API documentation. Further examples are included in the\n[examples folder](https://github.com/astro-informatics/sleplet/tree/main/examples).\n\n## Community Guidelines\n\nWe'd love any contributions you may have, please see the\n[contributing guidelines](https://github.com/astro-informatics/sleplet/blob/main/CONTRIBUTING.md).\n\n## Citing\n\nIf you use `SLEPLET` in your research, please cite the paper.\n\n```bibtex\n@article{Roddy2023,\n  title   = {{SLEPLET: Slepian Scale-Discretised Wavelets in Python}},\n  author  = {Roddy, Patrick J.},\n  year    = 2023,\n  journal = {Journal of Open Source Software},\n  volume  = 8,\n  number  = 84,\n  pages   = 5221,\n  doi     = {10.21105/joss.05221},\n}\n```\n\nPlease also cite [S2LET](https://doi.org/10.1051/0004-6361/201220729) upon which\n`SLEPLET` is built, along with [SSHT](https://doi.org/10.1109/TSP.2011.2166394)\nin the spherical setting or [libigl](https://doi.org/10.1145/3134472.3134497) in\nthe mesh setting.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fastro-informatics%2Fsleplet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fastro-informatics%2Fsleplet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fastro-informatics%2Fsleplet/lists"}