stingray 0.2

Time Series Methods For Astronomical X-ray Data

Master

X-Ray Spectral Timing Made Easy

Stingray is an in-development spectral-timing software package for astrophysical X-ray (and more) data. Stingray merges existing efforts for a (spectral-)timing package in Python, and is structured with the best guidelines for modern open-source programming, following the example of Astropy.

It is composed of:

1. a library of time series methods, including power spectra, cross spectra, covariance spectra, lags, and so on;

2. a set of scripts to load FITS data files from different missions;

3. a simulator of light curves and event lists, that includes different kinds of variability and more complicated phenomena based on the impulse response of given physical events (e.g. reverberation);

4. finally, an in-development GUI to ease the learning curve for new users.

There are a number of official software packages for X-ray spectral fitting (Xspec, ISIS, Sherpa, …). Such a widely used and standard software package does not exist for X-ray timing, that remains for now mostly done with custom software. Stingray aims not only at becoming a standard timing package, but at extending the implementation to the most advanced spectral timing techniques available in the literature. The ultimate goal of this project is to provide the community with a package that eases the learning curve for the advanced spectral timing techniques with a correct statistical framework.

Note to Users

We welcome contributions and we need your help! If you have your own code duplicating any part of the methods implemented in Stingray, please try out Stingray and compare to your own results.

We do welcome any sort of feedback: if something breaks, please report it via the issues page. Similarly, please open an issue if any functionality is missing, the API is not intuitive or if you have suggestions for additional functionality that would be useful to have.

If you have code you might want to contribute, we’d love to hear from you, either via a pull request or via an issue.

Citing Stingray

Please cite Huppenkothen et al. (2019) if you find this package useful in your research.

The BibTeX entry for the paper is:

@ARTICLE{2019arXiv190107681H,
       author = {{Huppenkothen}, D. and {Bachetti}, M. and {Stevens}, A.~L. and
         {Migliari}, S. and {Balm}, P. and {Hammad}, O. and {Khan}, U.~M. and
         {Mishra}, H. and {Rashid}, H. and {Sharma}, S.},
        title = "{Stingray: A Modern Python Library For Spectral Timing}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena},
         year = "2019",
        month = "Jan",
          eid = {arXiv:1901.07681},
        pages = {arXiv:1901.07681},
archivePrefix = {arXiv},
       eprint = {1901.07681},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190107681H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Contents

• make a light curve from event data

• make periodograms in Leahy and r.m.s. normalization

• average periodograms

• maximum likelihood fitting of periodograms/parametric models

• coherence

• cross spectra, r.m.s. spectra and lags (time vs energy, time vs frequency)

• covariance spectra

• bispectra

• Bayesian quasi-periodic oscillation searches

• simulate a light curve with a given power spectrum

• simulate a light curve from another light curve and a 1-d (time) or 2-d (time-energy) impulse response

• simulate an event list from a given light curve _and_ with a given energy spectrum

• load event lists from fits files of a few missions (RXTE/PCA, NuSTAR/FPM, XMM-Newton/EPIC)

• cross correlation functions

• pulsar searches with Epoch Folding, $Z^2_n$ test

Future Additions

• bicoherence

• phase-resolved spectroscopy of quasi-periodic oscillations

• Fourier-frequency-resolved spectroscopy

• power colours

• full HEASARC-compatible mission support

• pulsar searches with $H$-test

• binary pulsar searches

• (…) Feel free to propose! Use the Issues page!

Installation

You can find install Stingray via conda, pip or from the source repository itself. More details on how to install Stingray can be found on the Installations page.

Documentation

Is hosted at https://stingray.readthedocs.io/

And is generated using Sphinx. Try:

$ sphinx-build docs docs/_build

Then open ./docs/_build/index.html in the browser of your choice.

Test suite

Stingray uses py.test for testing. To run the tests, try:

$ python setup.py test

If you have installed Stingray via pip or conda, the source directory might not be easily accessible. Once installed, you can also run the tests using:

$ python -c 'import stingray; stingray.test()'

or from within a python interpreter:

>>> import stingray
>>> stingray.test()

Copyright

All content © 2019 the authors. The code is distributed under the MIT license.

Pull requests are welcome! If you are interested in the further development of this project, please get in touch via the issues!