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            "Development Status :: 5 - Production/Stable",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
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        "description": "About Bayesian X-ray Analysis (BXA)\n------------------------------------\n\nBXA connects the X-ray spectral analysis environments Xspec/Sherpa\nto the nested sampling algorithm UltraNest \nfor **Bayesian Parameter Estimation** and **Model comparison**.\n\nBXA provides the following features:\n\n* parameter estimation in arbitrary dimensions, which involves:\n   * finding the best fit\n   * computing error bars\n   * computing marginal probability distributions\n   * parallelisation with MPI\n* plotting of spectral model vs. the data:\n   * for the best fit\n   * for each of the solutions (posterior samples)\n   * for each component\n* model selection:\n   * computing the evidence for the considered model, \n     ready for use in Bayes factors\n   * unlike likelihood-ratios, not limited to nested models \n* model discovery:\n   * visualize deviations between model and data with Quantile-Quantile (QQ) plots.\n     QQ-plots do not require binning and are more comprehensive than residuals.\n     This will give you ideas on when to introduce more complex models, which \n     may again be tested with model selection\n\nBXA shines especially\n\n* when systematically analysing a large data-set, or\n* when comparing multiple models\n* when analysing low counts data-set with realistic models\n\nbecause its robust and unsupervised fitting algorithm explores\neven complicated parameter spaces in an automated fashion.\nThe user does not need to initialise to good starting points.\nThe `algorithm <https://johannesbuchner.github.io/UltraNest/method.html>`_ automatically runs until convergence, and slows down to sample\ncarefully if complicated parameter spaces are encountered. This allows building automated analysis pipelines.\n\n.. image:: https://img.shields.io/pypi/v/BXA.svg\n        :target: https://pypi.python.org/pypi/BXA\n\n.. image:: https://coveralls.io/repos/github/JohannesBuchner/BXA/badge.svg\n        :target: https://coveralls.io/github/JohannesBuchner/BXA\n\n.. image:: https://img.shields.io/badge/docs-published-ok.svg\n        :target: https://johannesbuchner.github.io/BXA/\n        :alt: Documentation Status\n\n.. image:: https://img.shields.io/badge/GitHub-JohannesBuchner%2FBXA-blue.svg?style=flat\n        :target: https://github.com/JohannesBuchner/BXA/\n        :alt: Github repository\n\nWho is using BXA?\n-------------------------------\n\n* Dr. Antonis Georgakakis, Dr. Angel Ruiz (NOA, Athens)\n* Dr. Mike Anderson (MPA, Munich)\n* Dr. Franz Bauer, Charlotte Simmonds (PUC, Jonathan Quirola V\u00e1squez, Santiago)\n* Dr. St\u00e9phane Paltani, Dr. Carlo Ferrigno (ISDC, Geneva)\n* Dr. Zhu Liu (NAO, Beijing)\n* Dr. Georgios Vasilopoulos (Yale, New Haven)\n* Dr. Francesca Civano, Dr. Aneta Siemiginowska (CfA/SAO, Cambridge)\n* Dr. Teng Liu, Adam Malyali, Riccardo Arcodia, Sophia Waddell, Torben Simm, ... (MPE, Garching)\n* Dr. Sibasish Laha, Dr. Alex Markowitz (UCSD, San Diego)\n* Dr. Arash Bahramian (Curtin University, Perth)\n* Dr. Peter Boorman (U of Southampton, Southampton; ASU, Prague)\n* and `you <https://ui.adsabs.harvard.edu/search/q=citations(bibcode%3A2014A%26A...564A.125B)%20full%3A%22BXA%22&sort=date%20desc%2C%20bibcode%20desc&p_=0>`_?\n\nDocumentation\n----------------\n\nBXA's `documentation <http://johannesbuchner.github.io/BXA/>`_ is hosted at http://johannesbuchner.github.io/BXA/\n\nInstallation\n-------------\n\nFirst, you need to have either `Sherpa`_ or `Xspec`_ installed and its environment loaded.\n\nBXA itself can installed easily using pip or conda::\n\n\t$ pip install bxa\n\nIf you want to install in your home directory, install with::\n\n\t$ pip install bxa --user\n\nThe following commands should not yield any error message::\n\n\t$ python -c 'import ultranest'\n\t$ python -c 'import xspec'\n\t$ sherpa\n\nYou may need to install python and some basic packages through your package manager. For example::\n\n\t$ yum install ipython python-matplotlib scipy numpy matplotlib\n\t$ apt-get install python-numpy python-scipy python-matplotlib ipython\n\nBXA requires the following python packages: requests corner astropy h5py cython scipy tqdm.\nThey should be downloaded automatically. If they are not, install them\nalso with pip/conda.\n\nThe source code is available from https://github.com/JohannesBuchner/BXA,\nso alternatively you can download and install it::\n\n\t$ git clone https://github.com/JohannesBuchner/BXA\n\t$ cd BXA\n\t$ python setup.py install\n\nOr if you only want to install it for the current user::\n\n\t$ python setup.py install --user\n\n**Supported operating systems**: \nBXA runs on all operating systems supported by \n`ciao/sherpa <https://cxc.cfa.harvard.edu/ciao/watchout.html#install>`_ or \n`heasoft/xspec <https://heasarc.gsfc.nasa.gov/lheasoft/issues.html>`_.\nThe support is systematically tested for every BXA release by \n`Travis CI <https://travis-ci.com/github/JohannesBuchner/BXA>`_, but only for Ubuntu Linux.\n\n\nRunning\n--------------\n\nIn *Sherpa*, load the package::\n\n\tjbuchner@ds42 ~ $ sherpa\n\t-----------------------------------------------------\n\tWelcome to Sherpa: CXC's Modeling and Fitting Package\n\t-----------------------------------------------------\n\tCIAO 4.4 Sherpa version 2 Tuesday, June 5, 2012\n\n\tsherpa-1> import bxa.sherpa as bxa\n\tsherpa-2> bxa.BXASolver?\n\nFor *Xspec*, start python or ipython::\n\n\tjbuchner@ds42 ~ $ ipython\n\tIn [1]: import xspec\n\n\tIn [2]: import bxa.xspec as bxa\n\n\tIn [3]:\tbxa.BXASolver?\n\nNow you can use BXA. See the documentation pages for how\nto perform analyses. Several examples are included.\n\n.. _ultranest: http://johannesbuchner.github.io/UltraNest/\n\n.. _Sherpa: http://cxc.harvard.edu/sherpa/\n\n.. _Xspec: http://heasarc.gsfc.nasa.gov/docs/xanadu/xspec/\n\nCode\n-------------------------------\n\nSee the `code repository page <https://github.com/JohannesBuchner/BXA>`_ \n\n.. _cite:\n\nCiting BXA correctly\n---------------------\n\nRefer to the `accompaning paper Buchner et al. (2014) <http://www.aanda.org/articles/aa/abs/2014/04/aa22971-13/aa22971-13.html>`_ which gives introduction and \ndetailed discussion on the methodology and its statistical footing.\n\nWe suggest giving credit to the developers of Sherpa/Xspec, UltraNest and of this software.\nAs an example::\n\n\tFor analysing X-ray spectra, we use the analysis software BXA (\\ref{Buchner2014}),\n\twhich connects the nested sampling algorithm UltraNest (\\ref{ultranest})\n\twith the fitting environment CIAO/Sherpa (\\ref{Fruscione2006}).\n\nWhere the BibTex entries are:\n\n* for BXA and the contributions to X-ray spectral analysis methodology (model comparison, model discovery, Experiment design, Model discovery through QQ-plots):\n\n\t- Buchner et al. (2014) A&A\n\t- The paper is available at `arXiv:1402.0004 <http://arxiv.org/abs/arXiv:1402.0004>`_\n\t- `bibtex entry <https://ui.adsabs.harvard.edu/abs/2014A%26A...564A.125B/exportcitation>`_\n\n* for UltraNest: see https://johannesbuchner.github.io/UltraNest/issues.html#how-should-i-cite-ultranest\n* for Sherpa: see `Sherpa`_\n* for Xspec: see `Xspec`_\n\n\n",
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