Overview¶
For a given (background-subtracted) image and a corresponding segmentation map indicating the source(s) of interest, statmorph calculates the following morphological statistics for each source:
Gini-M20 statistics (Lotz et al. 2004; Snyder et al. 2015a,b)
Concentration, Asymmetry and Smoothness (CAS) statistics (Bershady et al. 2000; Conselice 2003; Lotz et al. 2004)
Multimode, Intensity and Deviation (MID) statistics (Freeman et al. 2013; Peth et al. 2016)
RMS asymmetry (Sazonova et al. 2024), outer asymmetry (Wen et al. 2014) and shape asymmetry (Pawlik et al. 2016)
Single and double Sérsic indices (Sérsic 1968)
Several shape and size measurements associated to the above statistics (ellipticity, Petrosian radius, half-light radius, etc.)
The current Python implementation is largely based on IDL and Python code originally written by Jennifer Lotz and Greg Snyder.
Authors
Vicente Rodriguez-Gomez (vrodgom.astro@gmail.com)
Jennifer Lotz
Greg Snyder
Acknowledgments
We thank Peter Freeman and Mike Peth for sharing their IDL implementation of the MID statistics.
Citing
If you use this code for a scientific publication, please cite the following article:
Disclaimer
This package is not meant to be the “official” implementation of any of the morphological statistics listed above. Please contact the authors of the original publications for a “reference” implementation.
Licensing
Licensed under a 3-Clause BSD License.