Metadata-Version: 1.1
Name: pydftools
Version: 0.1.0
Summary: A pure-python port of the dftools R package.
Home-page: https://github.com/steven-murray/pydftools
Author: Steven Murray
Author-email: steven.murray@curtin.edu.au
License: MIT license
Description: =========
        pydftools
        =========
        
        
        .. image:: https://img.shields.io/pypi/v/pydftools.svg
                :target: https://pypi.python.org/pypi/pydftools
        
        .. image:: https://img.shields.io/travis/steven-murray/pydftools.svg
                :target: https://travis-ci.org/steven-murray/pydftools
        
        .. image:: https://readthedocs.org/projects/pydftools/badge/?version=latest
                :target: https://pydftools.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        
        A pure-python port of the ``dftools`` R package.
        
        This package attempts to imitate the ``dftools`` package (repo: https://github.com/obreschkow/dftools ) quite closely,
        while being as Pythonic as possible. Do note that 2D+ models are not yet implemented in this Python port, and neither
        are non-parametric models. Hopefully they will be along soon.
        
        From ``dftool``'s description:
        
            This package can find the most likely P parameters of a D-dimensional distribution function (DF) generating
            N objects, where each object is specified by D observables with measurement uncertainties. For instance, if the objects
            are galaxies, it can fit a MF (P=1), a mass-size distribution (P=2) or the mass-spin-morphology distribution (P=3).
            Unlike most common fitting approaches, this method accurately accounts for measurement is uncertainties and complex
            selection functions. A full description of the algorithm can be found in Obreschkow et al. (2017).
        
        In short, clean out Eddington bias from your fits:
        
        .. image:: https://user-images.githubusercontent.com/1272030/31757852-60cb6ebc-b4dd-11e7-8ce9-32b3232e8f94.png
           :scale: 30 %
        
        * Free software: MIT license
        * Documentation: https://pydftools.readthedocs.io.
        
        
        Features
        --------
        
        * Simple and fast parameter fitting for generative distribution functions
        * Several examples (with astronomical applications in mind)
        * Several plotting routines so that you can go from nothing to a plot in minutes
        * A ``mockdata()`` function which can produce data to fit.
        * Support for arbitrary 1D models, several kinds of selection functions, jackknife and bootstrap resampling, Gaussian
          error estimation and more.
        
        Credits
        ---------
        
        This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        
        
        
        =======
        History
        =======
        
        0.1.0 (2017-10-25)
        ------------------
        
        * First release on PyPI.
        * All basic examples working as expected
        * TravisCI, Readthedocs set up.
        * Does not have multi-dimension support, or non-parametric support.
        
Keywords: pydftools
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
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
