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Shapelets is a Python library that implements several shapelet functions and some of their applications in science and engineering. Shapelet functions are a complete and orthogonal set of localized basis functions with mathematical properties convenient for various image analyses. Existing applications from the literature include:
- Astronomy/astrophysics [A. Refregier (2003), R. Massey (2005), J. Berge (2019)]
- Nanostructure characterization [R. Suderman (2015), T. Akdeniz (2018), M. P. Tino (2024)]
- Computational neuroscience [J. D. Victor (2006), T. O. Sharpee (2009)]
- Medical imaging [J. Weissman (2004)]
Shapelets provides implementations of the following shapelet functions from shapelets.functions
- Cartesian shapelets [A. Refregier (2003)]
- Polar shapelets [R. Massey (2005)]
- Exponential shapelets [J. Berge (2019)]
- Orthonormal polar shapelets with no radial symmetry [T. Akdeniz (2018)]
- Orthonormal polar shapelets with one degree of radial extrema [M. P. Tino (2024)]
It also implements several shapelets applications, such as
- The response distance method [R. Suderman (2015)] - see example 1
- The defect identification method [M. P. Tino (2024)] - see example 2
- The local pattern orientation method [M. P. Tino (2024)] - see example 3
- Galaxy decomposition and reconstruction [A. Refregier (2003)] - see example 4
If you have Python 3.10+ installed on your machine, you can install the shapelets library via pip:
pip install shapelets
Otherwise, consult the installation guide.
See the shapelets examples for shapelets applications implemented with this package.
Also checkout our custom commands to see how to use shapelets from the command-line.
The authors of the shapelets library welcome contributions to the source code. Please follow the contribution policy here.
@article{TinoShapelets2024,
author = {Tino, Matthew Peres and Abdulaziz, Abbas Yusuf and Suderman, Robert and Akdeniz, Thomas and Abukhdeir, Nasser Mohieddin},
title = {Shapelets: A Python package implementing shapelet functions and their applications},
doi = {10.21105/joss.06058},
journal = {Journal of Open Source Software},
number = {95},
pages = {6058},
volume = {9},
year = {2024},
url = {https://joss.theoj.org/papers/10.21105/joss.06058}
}