clean_signal – Signal cleaning (removal of strong periodic signals)

exception clean_signal.NoClustersError
exception clean_signal.NonIntegerClustersError
clean_signal.__do_cluster_plot(db, X)
clean_signal.__do_fit_box(p0, time, flux)

Fit the given data with a boxcar function, given guess parameters.

Parameters:
  • p0 (np.ndarray) – Array of [duration, depth, phase, period] to use as guess
  • time (np.ndarray) – Array of observation times
  • flux (np.ndarray) – Array of fluxes at each time
Return type:

np.ndarray
clean_signal.__do_fit_trapezoid(p0, time, flux, frac=0.25)

Fit the given data with a trapezoid model, given guess parameters. Note that since there is no guess for tau (ingress/egress duration), some fraction of the duration is used.

Parameters:
  • p0 (np.ndarray) – Array of [duration, depth, phase, period] to use as guess
  • time (np.ndarray) – Array of observation times
  • flux (np.ndarray) – Array of fluxes at each time
Return type:

np.ndarray
clean_signal.__do_period_search(X, time, mask, step=1, err_midtime=0.1, err_flux=0.01, max_period_err=0.1)
clean_signal.clean_signal(time, flux, dtime, dflux, dfluxerr, out, model='box')

Remove possible eclipsing binary signals from a light curve. This works best on deep, strongly periodic signals, so it is unlikely to clean transit signals (though it sometimes will). This should help BLS pulse find less prominent signals in the same data.

Parameters:
  • time (np.ndarray) – Raw time vector (no detrending or binning)
  • flux (np.ndarray) – Raw flux vector (no detrending or binning)
  • dtime (np.ndarray) – Binned and detrended time vector
  • dflux (np.ndarray) – Binned and detrended flux vector
  • dfluxerr (np.ndarray) – Binned and detrended flux error vector
  • out (dict) – Output from BLS pulse algorithm