Package for modelling and removing correlated noise in lightcurves, specifically pointing drift systematics, and stellar variability. Uses a Gaussian process regression to model the noise, hyperparameters aimed specifically for detrending K2 and TESS lightcurves. Intended for noise removal prior to performing a transit search.
python 3, uses the george package (https://george.readthedocs.io/en/latest/)
Interface is not fully developed yet. /lcnm/k2gp.py contains the main functions that perform the detrending automatically (not only for K2 data). For a lightcurve in the form of a pandas.DataFrame with the columns 't', 'x', 'y', 'f' referring to the time, x-position, y-position and total flux/brightness of a star respectively:
from lcnm import k2gp
from lcnm import lc_preparation
lcf = lc_preparation.initialise_lcf(lcf, f_col='f')
lcf_detrended = k2gp.detrend_lcf_classic(lcf)lcf_detrended will contain the same columns as lcf, plus: 'f_temporal', 'f_spatial', 'f_detrended', 'o_flag'. 'f_detrended' contains the "flattened" lightcurve, minus time-correlated noise (long timescales) and x,y-correlated noise.
This hasn't been tested on other computers yet.