Metadata-Version: 1.1
Name: CosmoPhotoz
Version: 0.1
Summary: 
Photometric redshift library that implements Generalised Linear Models.

Home-page: http://github.com/COINtoolbox/COSMOPhotoz/CosmoPy
Author: J. Elliott, R. S. de Souza, A. Krone-Martins
Author-email: jonnynelliott@googlemail.com
License: GNU Public License
Description: CosmoPhotoz - GLM PhotoZ estimation
        ====================================
        
        .. image:: https://readthedocs.org/projects/cosmophotoz/badge/?version=latest
            :target: http://cosmophotoz.readthedocs.org/en/latest/
            :alt: Latest version
        
        .. image:: https://badge.waffle.io/COINtoolbox/CosmoPhotoz.svg?label=ready&title=Ready
            :target: https://waffle.io/COINtoolbox/CosmoPhotoz
            :alt: 'Stories in Ready'
        
        Homepage: `GitHub Repository <https://github.com/COINtoolbox/CosmoPhotoz/tree/master/Python>`_
        
        `CosmoPhotoz` is a package that determines photometric redshifts from galaxies utilising their magnitudes. The method utilises Generalized Linear Models which reproduce the physical aspects of the output distribution. The rest of the methodology and testing of the technique is described in the associated Astronomy and Computing publication (link TBC).
        
        
        Features
        --------
        
        - Principle Component Anylsis and decomposition of input photometric catalogue
        - Generalized Linear Model family and link choice
        - Seaborn publication quality plots
        
        
        Get it now
        ----------
        
        The package can be installed using the PyPI and pip.
        
        ::
        
            $ pip install -U CosmoPhotoz
        
        Or if the tarball or repository is downloaded, distutils can be
        
        ::
        
            $ python setup.py install
        
        Examples
        --------
        
        Run from the command line.
        
        :: 
        
            $ run_glm.py --dataset sample.csv --num_components 3 --train_size 10000 --family Gamma --link log
        
        
        Or import the library into python.
        
        .. code-block:: python  
        
            from CosmoPhotoz.photoz import PhotoSample # import the library
            import numpy as np
        
            import os
            import CosmoPhotoz.photoz as phz
        
            # Instantiate the class
            UserCatalogue = PhotoSample(filename="PHAT0", family="Gamma", link="log")
        
            # Make a training size array to loop through
            train_size_arr = np.arange(500,10000,500)
            catastrophic_error = []
        
            # Select your number of components
            UserCatalogue.num_components = 4
        
            for i in range(len(train_size_arr)):
                UserCatalogue.do_PCA()
                UserCatalogue.test_size = train_size_arr[i]
                UserCatalogue.split_sample(random=True)
                UserCatalogue.do_GLM()
                catastrophic_error.append(UserCatalogue.catastrophic_error)
        
            min_indx = np.array(catastrophic_error) < 5.937
            optimum_train_size = train_size_arr[min_indx]
            print optimum_train_size
        
        
        See more examples within the `Documentation`_.
        
        .. _`Documentation`: http://cosmophotoz.readthedocs.org/
        
        
        Documentation
        -------------
        
        -  The library documentation can be accessed at `Read the Docs <http://cosmophotoz.readthedocs.org/en/latest/>`_
        
        -  The git repository can be accessed at `GitHub <http://github.com/COINtoolbox/COSMOPhotoz>`_
        
        -  The PyPI package page can be accessed at `PyPI <https://pypi.python.org/pypi?name=CosmoPhotoz&version=0.1>`_
        
        Requirements
        ------------
        
        - Python >= 2.7 or >= 3.3
        
        
        License
        -------
        
        - GNU General Public License (GPL>=3)
        
        .. _pattern: http://www.clips.ua.ac.be/pattern
        .. _NLTK: http://nltk.org/
        
Platform: any
Classifier: Programming Language :: Python
Classifier: Development Status :: 3 - Alpha
Classifier: Natural Language :: English
Classifier: Environment :: X11 Applications
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Astronomy
