beansp 1.0.1

Bayesian Estimation of Accreting Neutron Stars parameters

BEANSp

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:

pip install beansp

from beansp.beans import Beans

(Please refer to this simple test script as an example.)

Build and installation from this github repository

Please refer to build instructions.

History

0.1.0 (2019-09-19)

• First release on PyPI.