Metadata-Version: 2.1
Name: beansp
Version: 0.9.2
Summary: Bayesian parameter Estimation of Accreting Neutron Stars
Home-page: https://github.com/adellej/beans
Author: Adelle Goodwin
Author-email: adelle.goodwin@monash.edu
License: MIT license
Keywords: beans
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.6.0
License-File: LICENSE
Requires-Dist: numpy (>=1.16)
Requires-Dist: matplotlib
Requires-Dist: emcee (>=3.0)
Requires-Dist: corner
Requires-Dist: astropy
Requires-Dist: scipy
Requires-Dist: tables
Requires-Dist: chainconsumer
Requires-Dist: h5py (>=2.10.0)
Requires-Dist: pySettle

======
BEANSp
======


.. .. image:: https://img.shields.io/pypi/v/beans.svg
..         :target: https://pypi.python.org/pypi/beans

.. .. image:: https://img.shields.io/travis/adellej/beans.svg
..         :target: https://travis-ci.org/adellej/beans

.. .. image:: https://readthedocs.org/projects/beans/badge/?version=latest
..         :target: https://beans.readthedocs.io/en/latest/?badge=latest
..         :alt: Documentation Status



Bayesian Estimation of Accreting Neutron Star parameters
-----------------------------------------------------------------

* Free software: MIT license
* Documentation: https://beans-7.readthedocs.io/en/latest/
* Repo: https://github.com/adellej/beans


Features
--------

This software uses a Markov Chain Monte Carlo approach to match observations of an accreting neutron star in outburst with a simple ignition model to predict unobservable parameters such as neutron star mass, radius, surface gravity, distance and inclination of the source, and accreted fuel composition. The code is all written in Python 3, except for settle which is a c++ code with a python wrapper. It makes use of Dan Foreman-Mackey's python implementation of MCMC, emcee, available here - https://github.com/dfm/emcee.

Credits
-------

Software written by Adelle Goodwin. See Goodwin et al. (2019) - https://arxiv.org/pdf/1907.00996.

This softwate (BEANSp) was based on code written by Duncan Galloway, and uses Dan Foreman-Mackey's python implementation of MCMC, emcee. It depends on pySettle (https://github.com/adellej/pysettle), which was forked from the original settle written by Andrew Cumming.

Package installation and usage
------------------------------
BEANSp is on pyPI (https://pypi.org/project/beansp/) so installation is easy - either straight or in virtual environment:

   .. code-block::
   
      pip install beansp
  
   .. ::
   
   .. code-block::
   
      from beansp.beans import Beans 

(Please refer to `this simple test script <https://github.com/adellej/beans/blob/master/tests/test_sft_beans.py>`_ as an example.)

Build and installation from this github repository
--------------------------------------------------

Please refer to `build instructions <https://github.com/adellej/beans/blob/master/BUILD.rst>`_.




=======
History
=======

0.1.0 (2019-09-19)
------------------

* First release on PyPI.
