Metadata-Version: 1.1
Name: frankenz
Version: 0.1.5
Summary: A photometric redshift monstrosity
Home-page: https://github.com/joshspeagle/frankenz
Author: Joshua S Speagle
Author-email: jspeagle@cfa.harvard.edu
License: MIT
Description: frankenz
        =========
        #### A photometric redshift monstrosity.
        
        **WARNING: This project is under active development and not yet stable.**
        
        `frankenz` is a Pure Python implementation of a variety of methods to quickly
        yet robustly perform (hierarchical) Bayesian inference using large
        (but discrete) sets of (possibly noisy) models with (noisy) photometric data.
        The code also contains a number of additional utilities, including:
        - a module for generating quick mocks (along with filter curves and SEDs), 
        - several manifold-learning algorithms,
        - a flexible set of photometric likelihoods,
        - fast kernel density estimation, and
        - PDF-oriented plotting utilities.
        
        Paper forthcoming.
        
        ### Documentation
        **Currently nonexistent.** See the demos for examples.
        
        ### Installation
        `frankenz` can be installed via
        ```
        pip install frankenz
        ```
        Alternately, it can also be installed by running
        ```
        python setup.py install
        ```
        from inside the repository.
        
        ### Demos
        Several Jupyter notebooks that demonstrate most of the available features
        can be found [here](https://github.com/joshspeagle/frankenz/tree/master/demos).
        
Keywords: photo-z,photometric redshift,bayesian,template fitting,machine learning,nearest neighbors,self-organizing map,growing neural gas
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Intended Audience :: Science/Research
