pspline now clips in both directions and on the first iteration

wotan/pspline.py

@@ -12,19 +12,19 @@ def pspline(time, flux):
 
  newflux = flux.copy()
  newtime = time.copy()
- detrended_flux = flux.copy()
+ detrended_flux = flux.copy()/numpy.nanmedian(newflux)
 
  for i in range(constants.PSPLINES_MAXITER):
  mask_outliers = numpy.ma.where(
- 1-detrended_flux < constants.PSPLINES_STDEV_CUT*numpy.std(detrended_flux))
+ numpy.abs(1-detrended_flux) < constants.PSPLINES_STDEV_CUT*numpy.std(detrended_flux))
  newtime, newflux = cleaned_array(newtime[mask_outliers], newflux[mask_outliers])
  gam = LinearGAM(s(0, n_splines=constants.PSPLINES_MAX_SPLINES))
  search_gam = gam.gridsearch(newtime[:, numpy.newaxis], newflux, progress=False)
  trend = search_gam.predict(newtime)
  detrended_flux = newflux / trend
  stdev = numpy.std(detrended_flux)
  mask_outliers = numpy.ma.where(
- 1-detrended_flux > constants.PSPLINES_STDEV_CUT*numpy.std(detrended_flux))
+ numpy.abs(1-detrended_flux) > constants.PSPLINES_STDEV_CUT*numpy.std(detrended_flux))
  print('Iteration:', i + 1, 'Rejected outliers:', len(mask_outliers[0]))
 
  # Check convergence
@@ -33,7 +33,7 @@ def pspline(time, flux):
  break
 
  # Final iteration, applied to unclipped time series (interpolated over clipped values)
- mask_outliers = numpy.ma.where(1-detrended_flux < constants.PSPLINES_STDEV_CUT*stdev)
+ mask_outliers = numpy.ma.where(numpy.abs(1-detrended_flux) < constants.PSPLINES_STDEV_CUT*stdev)
  newtime, newflux = cleaned_array(newtime[mask_outliers], newflux[mask_outliers])
  gam = LinearGAM(s(0, n_splines=constants.PSPLINES_MAX_SPLINES))
  search_gam = gam.gridsearch(newtime[:, numpy.newaxis], newflux, progress=False)