Metadata-Version: 1.1
Name: ugali
Version: 1.6.1
Summary: Ultra-faint galaxy likelihood toolkit.
Home-page: https://github.com/DarkEnergySurvey/ugali
Author: Keith Bechtol & Alex Drlica-Wagner
Author-email: bechtol@kicp.uchicago.edu, kadrlica@fnal.gov
License: UNKNOWN
Description: 
        [![Build](https://img.shields.io/travis/DarkEnergySurvey/ugali.svg)](https://travis-ci.org/DarkEnergySurvey/ugali)
        [![Release](https://img.shields.io/github/tag/DarkEnergySurvey/ugali.svg)](../../releases)
        [![License](https://img.shields.io/badge/license-MIT-blue.svg)](../../)
        
        OVERVIEW
        --------
        
        The ultra-faint galaxy likelihood (UGaLi) toolkit provides a set of python classes and functions developed for maximum-likelihood-based studies of Milky Way satellite galaxies. The primary inputs are stellar object catalogs derived from optical photometric surveys and the coverage masks of those surveys.
        
        [Keith Bechtol](https://github.com/bechtol) & [Alex Drlica-Wagner](https://github.com/kadrlica)
        
        INSTALLATION
        ------------
        
        The ugali codebase can be installed by downloading from github and
        using the `setup.py` script.
        ```
        git clone https://github.com/DarkEnergySurvey/ugali.git
        cd ugali
        python setup.py install
        ```
        In addition to the code, if you plan on working with isochrones you probably want to install the ancillary isochrone information:
        ```
        python setup.py isochrones
        ```
        By default, the isochrone files (~100MB) will be installed in `$HOME/.ugali/isochrones`; however, this can be changed on the command line:
        ```
        python setup.py isochrones --isochrone-path <INSTALL_PATH>
        ```
        If you place the isochrones in a different directory be sure that ugali knows where to find them:
        ```
        export UGALIDIR=$<INSTALL_PATH>/isochrones
        ```
        
        USAGE EXAMPLES
        --------------
        Examples go here.
        
        CODE REPOSITORY
        ---------------
        * https://bitbucket.org/bechtol/ugali/
        * https://github.com/kadrlica/ugali/
        
        DEPENDENCIES
        ------------
        
        ### Python packages:
        * [numpy](http://www.numpy.org/)
        * [scipy](https://www.scipy.org/)
        * [matplotlib](http://matplotlib.org/)
        * [pyfits](http://www.stsci.edu/institute/software_hardware/pyfits)
        * [healpy](https://github.com/healpy/healpy)
        * [astropy](http://www.astropy.org/)
        * [emcee](http://dan.iel.fm/emcee/current/)
        * [pyyaml](http://pyyaml.org/)
        
        ### Mangle:
        Not a strict dependency. Used to interface with masks produced by
        the Dark Energy Survey Data Mangement group. Download and documentation 
        available at http://space.mit.edu/~molly/mangle/
        
        ### Isochrones:
        The ugali tools make use of a large library of stellar isochrones. These isochrones are derived from two different groups and are distributed as binary tarballs with releases of ugali.
        * Padova isochrones (http://stev.oapd.inaf.it/cgi-bin/cmd)
        * Dartmouth isochrones (http://stellar.dartmouth.edu/models/isolf_new.html)
        
        CONVENTIONS
        -----------
        
        ### Indexing:
        array[index_z][index_y][index_x]
        
        ### Naming:
        * package_name
        * module_name.py
        * ClassName
        * functionName
        * variable_name
        
        ABBREVIATIONS
        -------------
        * IMF: initial mass function
        * CMD: color-magnitude diagram
        * ROI: region of interest
        * PDF: probability distribution function
        * LUT: look-up table
        * LKHD: likelihood
        
Platform: any
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Natural Language :: English
Classifier: Topic :: Scientific/Engineering
