Metadata-Version: 1.2
Name: bexvar
Version: 1.0.1
Summary: Bayesian excess variance for Poisson data time series with backgrounds.
Home-page: https://github.com/JohannesBuchner/bexvar
Author: Johannes Buchner
Author-email: johannes.buchner.acad@gmx.com
License: Affero GNU General Public License v3
Description: bexvar
        ==================
        
        Bayesian excess variance for Poisson data time series with backgrounds.
        Excess variance is over-dispersion beyond the observational poisson noise,
        caused by an astrophysical source.
        
        * `Introduction <#introduction>`_
        * `Method <#method>`_
        * `Tutorial <#tutorial>`_
        * `Output plot <#visualising-the-results>`_ and files
        
        Introduction
        -------------------
        
        In high-energy astrophysics, the analysis of photon count time series
        is common. Examples include the detection of gamma-ray bursts,
        periodicity searches in pulsars, or the characterisation of
        damped random walk-like accretion in the X-ray emission of
        active galactic nuclei.
        
        Methods
        --------------
        
        This repository provides new statistical analysis methods for light curves.
        They can deal with
        
        * very low count statistics (0 or a few counts per time bin)
        * (potentially variable) instrument sensitivity
        * (potentially variable) backgrounds, measured simultaneously in an 'off' region.
        
        The tools can read eROSITA light curves. Contributions that can read other
        file formats are welcome.
        
        The `bexvar_ero.py` tool computes posterior distributions on the Bayesian excess variance,
        and source count rate.
        
        `quick_ero.py` computes simpler statistics, including Bayesian blocks,
        fraction variance, the normalised excess variance, and 
        the amplitude maximum deviation statistics.
        
        Licence
        --------
        AGPLv3 (see COPYING file). Contact me if you need a different licence.
        
        Install
        --------
        
        .. image:: https://img.shields.io/pypi/v/bexvar.svg
                :target: https://pypi.python.org/pypi/bexvar
        
        .. image:: https://github.com/JohannesBuchner/bexvar/actions/workflows/test.yml/badge.svg
            :target: https://github.com/JohannesBuchner/bexvar/actions/workflows/test.yml
        
        Install as usual::
        
        	$ pip3 install bexvar
        
        This also installs the required `ultranest <https://johannesbuchner.github.io/UltraNest/>`_
        python package.
        
        
        Example
        ----------
        
        Run with::
        
        	$ bexvar_ero.py 020_LightCurve_00001.fits
        
        Run simpler variability analyses with::
        
        	$ quick_ero.py 020_LightCurve_*.fits.gz
        
        
        Contributing
        --------------
        
        Contributions are welcome. Please open pull requests
        with code contributions, or issues for bugs and questions.
        
        Contributors include:
        
        * Johannes Buchner
        * David Bogensberger
        
        
        Changelog
        ----------
        
Keywords: bexvar
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.5.*
