SCONCE-SCMS (Spherical and CONic Cosmic w Eb finder with the extended SCMS algorithms [1] is a Python library for detecting the cosmic web structures (primarily cosmic filaments and the associated cosmic nodes) from a collection of discrete observations with the extended subspace constrained mean shift (SCMS) algorithms ([2], [5], [6]) on the unit (hyper)sphere (in most cases, the 2D (RA,DEC) celestial sphere), and the directional-linear product space (most commonly, the 3D (RA,DEC,redshift) light cone).
(Notes: RA -- Right Ascension, i.e., the celestial longitude; DEC -- Declination, i.e., the celestial latitude.)
- Free software: MIT license
- Documentation: https://sconce-scms.readthedocs.io.
sconce-scms requires Python 3.6+ (earlier version might be applicable), NumPy, SciPy, and Ray (optional and only used for parallel computing). To install the latest version of sconce-scms from this repository, run:
python setup.py install
To pip install a stable release, run:
pip install sconce-scms
[1] Y. Zhang, R. S. de Souza, and Y.-C. Chen (2022). SCONCE: A cosmic web finder for spherical and conic geometries. Monthly Notices of the Royal Astronomical Society, 517 (1): 1197–1217.
[2] U. Ozertem and D. Erdogmus (2011). Locally Defined Principal Curves and Surfaces. Journal of Machine Learning Research, 12, 1249-1286.
[3] Y.-C. Chen, S. Ho, P. E. Freeman, C. R. Genovese, and L. Wasserman (2015). Cosmic web reconstruction through density ridges: method and algorithm. Monthly Notices of the Royal Astronomical Society, 454 (1), 1140-1156.
[4] Y. Zhang and Y.-C. Chen (2021). Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data. Journal of Machine Learning Research, 22 (154), 1-92.
[5] Y. Zhang and Y.-C. Chen (2022). Linear convergence of the subspace constrained mean shift algorithm: from Euclidean to directional data. Information and Inference: A Journal of the IMA, iaac005, https://doi.org/10.1093/imaiai/iaac005.
[6] Y. Zhang and Y.-C. Chen (2021). Mode and ridge estimation in euclidean and directional product spaces: A mean shift approach. arXiv preprint arXiv:2110.08505.