/lcsim

Blazar light curve simulation package in python3

Primary LanguageJupyter NotebookBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

ascl:2310.002

lcsim

A python3 package to create artificial blazar light curves with a predefined power spectral density (PSD) and probability density function (PDF).

Requirements

The lcsim script uses the following standard python packages:

  • copy
  • math

The lcsim script uses the following python packages:

  • numpy
  • scipy
  • statsmodels

The simdb script uses the following standard python packages:

  • os
  • sys

The simdb script uses the following python packages:

  • numpy
  • sqlite3

Getting Started

Get the python scripts:

$ git clone https://github.com/skiehl/lcsim.git

Open the jupyter notebook SimulationDemo.ipynb for a demonstation of the code. Either use jupyter lab:

$ jupyter lab

Or jupyter notebook:

$ jupyter notebook SimulationDemo.ipynb

Modules

  • lcsim.py: The main module for simulating light curves.
  • simdb.py: A SQLite3-based database backend to store simulations.
  • run_lcsim.py: A wrapper script to run and save light curve simulations for multiple sources.

Usage

Usage of the package is demonstrated in the jupyter notebook SimulationDemo.ipynb

Note: Documentation of the simdb.py module is not yet included.

A complete code documentation is given in html/.

Citation

Bibtex:

@MISC{2023ascl.soft10002K,
       author = {{Kiehlmann}, Sebastian},
        title = "{lcsim: Light curve simulation code}",
     keywords = {Software},
 howpublished = {Astrophysics Source Code Library, record ascl:2310.002},
         year = 2023,
        month = oct,
          eid = {ascl:2310.002},
        pages = {ascl:2310.002},
archivePrefix = {ascl},
       eprint = {2310.002},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023ascl.soft10002K},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

This software package is indexed on ASCL and ADS.

License

lcsim is licensed under the BSD 3-Clause License - see the LICENSE file.

References

Timmer&Koenig, 1995. Emmanoulopoulos et al., 2013.

Alternatives

At least on other python implementations of the Emmanoulopoulos+ method is available: