Non-parametric morphological diagnostics of galaxy images
Project description
Python code for calculating non-parametric morphological diagnostics of galaxy images.
Brief description
For a given (background-subtracted) image and a corresponding segmentation map indicating the source(s) of interest, this code calculates the following morphological statistics for each source:
Gini-M20 statistics (Lotz et al. 2004)
Concentration, Asymmetry and Smoothness (CAS) statistics (Conselice 2003; Lotz et al. 2004)
Multimode, Intensity and Deviation (MID) statistics (Freeman et al. 2013; Peth et al. 2016)
Outer asymmetry and shape asymmetry (Wen et al. 2014; Pawlik et al. 2016)
Sersic index (Sersic 1968)
Some properties associated to the above statistics (Petrosian radii, half-light radii, etc.)
Although the Sersic index is the opposite of a non-parametric morphological quantity, it is included anyway due to its popularity.
This Python implementation is largely based on IDL code originally written by Jennifer Lotz, Peter Freeman and Mike Peth, as well as Python code by Greg Snyder. The main scientific reference is Lotz et al. (2004), but a more complete list can be found in the Citing section.
Documentation
The documentation can be found on ReadTheDocs.
Tutorial / How to use
Please see the statmorph tutorial.
Installation
The easiest way to install this package is within the Anaconda environment:
conda install -c conda-forge statmorph
Alternatively, assuming that you already have recent versions of scipy, scikit-image, astropy and photutils installed, statmorph can also be installed via PyPI:
pip install statmorph
Finally, if you prefer a manual installation, download the latest release from the GitHub repository, extract the contents of the zipfile, and run:
python setup.py install
Running the built-in tests
To test that the installation was successful, run:
python -c "import statmorph.tests; statmorph.tests.runall()"
Acknowledgments
We thank Peter Freeman and Mike Peth for their IDL implementation of the MID statistics.
Citing
If you use this code for a scientific publication, please cite the following article:
Rodriguez-Gomez et al. (in prep.)
In addition, the Python package can also be cited using its Zenodo record:
Finally, below we provide some of the main references that introduce the morphological parameters implemented in this code. The following list is provided as a starting point and is not meant to be exhaustive. Please see the references within each publication for more information.
Gini–M20 statistics:
Lotz J. M., Primack J., Madau P., 2004, AJ, 128, 163
Snyder G. F. et al., 2015, MNRAS, 454, 1886
Concentration, asymmetry and clumpiness (CAS) statistics:
Bershady M. A., Jangren A., Conselice C. J., 2000, AJ, 119, 2645
Conselice C. J., 2003, ApJS, 147, 1
Lotz J. M., Primack J., Madau P., 2004, AJ, 128, 163
Multimode, intensity and deviation (MID) statistics:
Freeman P. E., Izbicki R., Lee A. B., Newman J. A., Conselice C. J., Koekemoer A. M., Lotz J. M., Mozena M., 2013, MNRAS, 434, 282
Peth M. A. et al., 2016, MNRAS, 458, 963
Outer asymmetry and shape asymmetry:
Wen Z. Z., Zheng X. Z., Xia An F., 2014, ApJ, 787, 130
Pawlik M. M., Wild V., Walcher C. J., Johansson P. H., Villforth C., Rowlands K., Mendez-Abreu J., Hewlett T., 2016, MNRAS, 456, 3032
Sersic index:
Sersic J. L., 1968, Atlas de Galaxias Australes, Observatorio Astronomico de Cordoba, Cordoba
Any textbook about galaxies
Disclaimer
This package is not meant to be the “official” implementation of any of the morphological statistics described above. Please contact the authors of the original publications for a “reference” implementation.
Licensing
Licensed under a 3-Clause BSD License.
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