Metadata-Version: 1.1
Name: statmorph
Version: 0.3.1
Summary: Non-parametric morphological diagnostics of galaxy images
Home-page: https://github.com/vrodgom/statmorph
Author: Vicente Rodriguez-Gomez
Author-email: vrg@jhu.edu
License: BSD
Description: statmorph
        =========
        
        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) <http://adsabs.harvard.edu/abs/2004AJ....128..163L>`_,
        but a more complete list can be found in the *Citing* section.
        
        Documentation
        -------------
        
        The documentation can be found on
        `ReadTheDocs <http://statmorph.readthedocs.io/en/latest/>`_.
        
        Tutorial / How to use
        ---------------------
        
        Please see the
        `statmorph tutorial <http://nbviewer.jupyter.org/github/vrodgom/statmorph/blob/master/notebooks/tutorial.ipynb>`_.
        
        Installation
        ------------
        
        The easiest way to install this package is within the Anaconda environment:
        
        .. code:: bash
        
            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:
        
        .. code:: bash
        
            pip install statmorph
        
        Finally, if you prefer a manual installation, download the latest release
        from the `GitHub repository <https://github.com/vrodgom/statmorph>`_,
        extract the contents of the zipfile, and run:
        
        .. code:: bash
        
            python setup.py install
        
        Running the built-in tests
        --------------------------
        
        To test that the installation was successful, run:
        
        .. code:: bash
        
            python -c "import statmorph.tests; statmorph.tests.runall()"
        
        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:
        
        .. image:: https://zenodo.org/badge/95412529.svg
           :target: https://zenodo.org/badge/latestdoi/95412529
        
        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.
        
Keywords: astronomy galaxies galaxy-morphology non-parametric
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
