pyacs.gts.lib.filters.minimum_component module

Minimum component filter for Gts.

pyacs.gts.lib.filters.minimum_component.minimum_component(self, mask_period=[], p=1, fcut=None, Q=None, in_place=False, verbose=True)[source]

Minimum component filtering for Gts.

Minimum component filtering is useful for determining the background component of a signal in the presence of spikes

  • mask_periods – periods (list or list of lists) which should be ignored for smoothing

  • p – integer (optional). polynomial degree to be used for the fit (default = 1)

  • fcut – float (optional). the cutoff frequency for the low-pass filter. Default value is f_nyq / sqrt(N)

  • Q – float (optional). the strength of the low-pass filter. Larger Q means a steeper cutoff. default value is 0.1 * fcut

  • in_place – if True then replace the current time series

  • verbose – boolean, verbose mode


the filtered time series


This code follows the procedure explained in the book “Practical Statistics for Astronomers” by Wall & Jenkins book, as well as in Wall, J, A&A 122:371, 1997