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Lightcurves

A package to analyze any kind of light curve/time series, e.g. with Bayesian Blocks, flare fitting (HOP), and a stochastic processe

Install / Use

/learn @swagner-astro/Lightcurves
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

lightcurves

This is the lightcurves repository. Check it out: Open In Colab <br>

See here for scientific application of this code: https://pos.sissa.it/395/868

lc.py

Initialize a LightCurve object based on time, flux and flux_error. Study its Bayesian block representation (based on Scargle et al. 2013 https://ui.adsabs.harvard.edu/abs/2013arXiv1304.2818S/abstract ).<br> Characterize flares (start, peak, end time) with the HOP algorithm (following Meyer et al. 2019 https://ui.adsabs.harvard.edu/abs/2019ApJ...877...39M/abstract ). There are four different methods to define flares (baseline, half, flip, sharp) as illustrated in the Jupyter Notebook.

hop.py

Initialize a Hopject to consider parameters of an individual flare.

lc_set

Initialize a (large) sample of light curves to study the distribution of flare parameters whithin that sample.<br>

Reference

If you use this code please cite: <br> Wagner, S. M., Burd, P., Dorner, D., et al. 2021, PoS, ICRC2021, 868 <url>https://ui.adsabs.harvard.edu/abs/2022icrc.confE.868W/abstract</url>

Related Skills

View on GitHub
GitHub Stars13
CategoryDevelopment
Updated7mo ago
Forks10

Languages

Jupyter Notebook

Security Score

87/100

Audited on Aug 27, 2025

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