statmorph 0.1.0

Non-parametric morphological diagnostics of galaxy images

Python code for calculating non-parametric morphological diagnostics of galaxy images.

Brief description

For a given 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, by definition, 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

If you do not have Anaconda installed yet, you should have a look at astroconda.

Alternatively, assuming that you already have scikit-image, astropy and photutils installed, statmorph can also be installed via PyPI:

pip install statmorph

Finally, for a manual installation, download the latest release from the GitHub repository, extract the contents of the zipfile, and run:

python setup.py install

Authors

• Vicente Rodriguez-Gomez (vrg [at] jhu.edu)

• Jennifer Lotz

• Greg Snyder

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 describe 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. Also see the LICENSE.

Licensing

Licensed under a 3-Clause BSD License.