An improved and lean version of Appaloosa w/o extensive I/O, but with de-trending but using K2SC
and lightkurve
.
The documenation (work in progress) are at altaipony.readthedocs.io
git clone https://github.com/ekaterinailin/AltaiPony.git cd AltaiPony python setup.py install
flarelc.py
A lightcurve class with its constructor that inherits from `k2sc`
and/or `lightkurve`
.
If raw LC is read in - run detrend.py
Convenience function: check if K2SC has de-trended LC already available.
detrend.py
Do K2SC detrending is stitched in here: use standalone.py
findflares.py
Split LC into continuous observation chunks. Apply thresholds to detect candidates.
fakeflares.py
Includes:
- semi-empirical flare model
- injection/recovery procedure for synthetic flares.
analysis.py
- calculates ED, duration, amplitude, uncertainties, observation times
- possibly other stats about the original flares
- (correlations with other astrophysical photometric varibility)
- flare energy correction factor
- flare recovery probability
altai.py
Main wrapper that
- 1a. takes a K2 (or TESS) ID or a path to a .fits or TPF.gz file
- 2a. creates a light curve using lightkurve.
- (2b. de-trends light curve using K2SC
- 3a. find flare candidates
- (3b. runs fake flare injection/recovery)
- (3c. Calculates flare parameters, corrects ED and returns recovery probability)