De-trend light curves from Kepler, K2, and TESS missions, and search them for flares. Inject and recover synthetic flares to account for de-trending and noise loss in flare energy and determine energy-dependent recovery probability for every flare candidate. Uses the K2SC and lightkurve under the cover, as well as pandas, numpy, pytest, astropy and more.

Find the documentation at altaipony.readthedocs.io

Use pip to install AltaiPony

>>> pip install altaipony

Or install directly from the repository:

>>> git clone https://github.com/ekaterinailin/AltaiPony.git >>> cd AltaiPony >>> python setup.py install

See this notebook for an easy introduction, also docs.

Often, when something does not work in AltaiPony, and this documentation is useless, troubleshooting can be done by diving into the extensive lightkurve docs. Otherwise, you can always shoot Ekaterina an email or directly open an issue on GitHub. Many foreseeable problems will be due to bugs in AltaiPony or bad instructions on this website.

AltaiPony is under active development on Github. If you use AltaiPony in your research and find yourself missing a functionality, I recommend opening an issue on GitHub or shooting Ekaterina an email. Please do either of the two before you open a pull request. This may save you a lot of development time.

If you end up using this package for your science, please cite Ilin et al. (2021) [a] and Davenport (2016) [b].

Please also cite lightkurve as indicated in their docs¹.

Depending on the methods you use, you may also want to cite

• Maschberger and Kroupa (2009)² (MMLE power law fit)
• Wheatland (2004)³ (MCMC power law fit)
• Aigrain et al. (2016)⁴ and their softwar⁵ (K2SC de-trending)