Metadata-Version: 1.1
Name: bxa
Version: 4.0.2
Summary: Bayesian X-ray spectral analysis
Home-page: https://github.com/JohannesBuchner/BXA/
Author: Johannes Buchner
Author-email: johannes.buchner.acad@gmx.com
License: UNKNOWN
Description: About Bayesian X-ray Analysis (BXA)
        ------------------------------------
        
        BXA connects the X-ray spectral analysis environments Xspec/Sherpa
        to the nested sampling algorithm UltraNest 
        for **Bayesian Parameter Estimation** and **Model comparison**.
        
        BXA provides the following features:
        
        * parameter estimation in arbitrary dimensions, which involves:
           * finding the best fit
           * computing error bars
           * computing marginal probability distributions
        * plotting of spectral model vs. the data:
           * for the best fit
           * for each of the solutions (posterior samples)
           * for each component
        * model selection:
           * computing the evidence for the considered model, 
             ready for use in Bayes factors
           * unlike likelihood-ratios, not limited to nested models 
        * model discovery:
           * visualize deviations between model and data with Quantile-Quantile (QQ) plots.
             QQ-plots do not require binning and are more comprehensive than residuals.
             This will give you ideas on when to introduce more complex models, which 
             may again be tested with model selection
        
        BXA shines especially
        
        * when systematically analysing a large data-set, or
        * when comparing multiple models, or
        * when analysing low counts data-set
        * when you don't want to babysit your fits
        * when you don't want to test MCMC chains for their convergence
        
        .. image:: https://img.shields.io/pypi/v/BXA.svg
                :target: https://pypi.python.org/pypi/BXA
        
        .. image:: https://coveralls.io/repos/github/JohannesBuchner/BXA/badge.svg
                :target: https://coveralls.io/github/JohannesBuchner/BXA
        
        .. image:: https://img.shields.io/badge/docs-published-ok.svg
                :target: https://johannesbuchner.github.io/BXA/
                :alt: Documentation Status
        
        .. image:: https://img.shields.io/badge/GitHub-JohannesBuchner%2FBXA-blue.svg?style=flat
                :target: https://github.com/JohannesBuchner/BXA/
                :alt: Github repository
        
        Who is using BXA?
        -------------------------------
        
        * Dr. Antonis Georgakakis, Dr. Angel Ruiz (NOA, Athens)
        * Dr. Mike Anderson (MPA, Munich)
        * Dr. Franz Bauer, Charlotte Simmonds (PUC, Jonathan Quirola Vásquez, Santiago)
        * Dr. Stéphane Paltani, Dr. Carlo Ferrigno (ISDC, Geneva)
        * Dr. Zhu Liu (NAO, Beijing)
        * Dr. Georgios Vasilopoulos (Yale, New Haven)
        * Dr. Francesca Civano, Dr. Aneta Siemiginowska (CfA/SAO, Cambridge)
        * Dr. Teng Liu, Adam Malyali, Riccardo Arcodia, Sophia Waddell, ... (MPE, Garching)
        * Dr. Sibasish Laha, Dr. Alex Markowitz (UCSD, San Diego)
        * Dr. Arash Bahramian (MSU, East Lansing)
        * 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>`_?
        
        Documentation
        ----------------
        
        BXA's `documentation <http://johannesbuchner.github.io/BXA/>`_ is hosted at http://johannesbuchner.github.io/BXA/
        
        Installation
        -------------
        
        First, you need to have `Sherpa`_ or `Xspec`_ installed and its environment loaded.
        
        BXA itself can installed easily using pip or conda::
        
        	$ pip install bxa
        
        If you want to install in your home directory, install with::
        
        	$ pip install bxa --user
        
        The following commands should not yield any error message::
        
        	$ python -c 'import ultranest'
        	$ python -c 'import xspec'
        	$ sherpa
        
        You may need to install required python packages through your package manager. For example::
        
        	$ yum install ipython python-matplotlib scipy numpy matplotlib
        	$ apt-get install python-numpy python-scipy python-matplotlib ipython
        
        The source code is available from https://github.com/JohannesBuchner/BXA,
        so alternatively you can download and install it::
        	
        	$ git clone https://github.com/JohannesBuchner/BXA
        	$ cd BXA
        	$ python setup.py install
        
        Or if you only want to install it for the current user::
        
        	$ python setup.py install --user
        
        Running
        --------------
        
        In *Sherpa*, load the package::
        
        	jbuchner@ds42 ~ $ sherpa
        	-----------------------------------------------------
        	Welcome to Sherpa: CXC's Modeling and Fitting Package
        	-----------------------------------------------------
        	CIAO 4.4 Sherpa version 2 Tuesday, June 5, 2012
        
        	sherpa-1> import bxa.sherpa as bxa
        	sherpa-2> bxa.BXASolver?
        
        For *Xspec*, start python or ipython::
        	
        	jbuchner@ds42 ~ $ ipython
        	In [1]: import xspec
        	
        	In [2]: import bxa.xspec as bxa
        	
        	In [3]:	bxa.BXASolver?
        
        Now you can use BXA.
        
        .. _ultranest: http://johannesbuchner.github.io/UltraNest/
        
        .. _Sherpa: http://cxc.harvard.edu/sherpa/
        
        .. _Xspec: http://heasarc.gsfc.nasa.gov/docs/xanadu/xspec/
        
        Code
        -------------------------------
        
        See the `code repository page <https://github.com/JohannesBuchner/BXA>`_ 
        
        .. _cite:
        
        Citing BXA correctly
        ---------------------
        
        Refer 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 
        detailed discussion on the methodology and its statistical footing.
        
        We suggest giving credit to the developers of Sherpa/Xspec, UltraNest and of this software.
        As an example::
        
        	For analysing X-ray spectra, we use the analysis software BXA (\ref{Buchner2014}),
        	which connects the nested sampling algorithm UltraNest (\ref{ultranest})
        	with the fitting environment CIAO/Sherpa (\ref{Fruscione2006}).
        
        Where the BibTex entries are:
        
        * for BXA and the contributions to X-ray spectral analysis methodology (model comparison, model discovery, Experiment design, Model discovery through QQ-plots):
        
        	- Buchner et al. (2014) A&A
        	- The paper is available at `arXiv:1402.0004 <http://arxiv.org/abs/arXiv:1402.0004>`_
        	- `bibtex entry <https://ui.adsabs.harvard.edu/abs/2014A%26A...564A.125B/exportcitation>`_
        
        * for UltraNest: see https://johannesbuchner.github.io/UltraNest/issues.html#how-should-i-cite-ultranest
        * for Sherpa: see `Sherpa`_
        * for Xspec: see `Xspec`_
        
Platform: UNKNOWN
Requires: ultranest
Requires: numpy
Requires: tqdm
Requires: corner
Requires: h5py
Requires: matplotlib
