A pure-python port of the dftools R package.
Project 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.
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.
