https://github.com/adacs-australia/beans
2023A project lead by Adelle Goodwin
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: arxiv.org -
○Academic email domains
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (12.3%) to scientific vocabulary
Last synced: 10 months ago
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JSON representation
Repository
2023A project lead by Adelle Goodwin
Basic Info
- Host: GitHub
- Owner: ADACS-Australia
- License: mit
- Language: Python
- Default Branch: master
- Size: 6.6 MB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 1
- Open Issues: 0
- Releases: 0
Fork of adellej/beans
Created over 3 years ago
· Last pushed about 3 years ago
Metadata Files
Readme
Changelog
Contributing
License
README.rst
======
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 `_ as an example.)
Build and installation from this github repository
--------------------------------------------------
Please refer to `build instructions `_.
Owner
- Name: Astronomy Data and Computing Services
- Login: ADACS-Australia
- Kind: organization
- Location: Australia
- Repositories: 43
- Profile: https://github.com/ADACS-Australia
GitHub Events
Total
Last Year
Dependencies
requirements.txt
pypi
- astropy *
- chainconsumer *
- corner *
- emcee >=3.0
- h5py >=2.10.0
- matplotlib *
- numpy *
- pytest *
- scipy *
- tables *
requirements_dev.txt
pypi
- Sphinx ==1.8.5 development
- astropy * development
- bump2version ==0.5.11 development
- chainconsumer * development
- corner * development
- coverage ==4.5.4 development
- coveralls * development
- emcee * development
- flake8 ==3.7.8 development
- idlsave * development
- matplotlib * development
- numpy * development
- pip ==19.2.3 development
- pytest ==4.6.5 development
- pytest-runner ==5.1 development
- scipy * development
- tox ==3.14.0 development
- twine ==1.14.0 development
- watchdog ==0.9.0 development
- wheel ==0.33.6 development