FLT.FILT_WLIM

Enables an experimental filtering method

Description:

If supplied, this will switch on an experimental method for handling missing or flagged data when filtering the time streams, based on replacing missing data with zero, and then normalising the smoothed data using a mask of good samples smoothed in the same way. The flt.filt_wlim value specifies the minimum fraction of good values that must contribute to a filtered value. For instance, if wlim is 0.9 then a filtered data value is flagged as bad unless at least 0.9 of the input values that contribute to it are good (i.e. have not been flagged as unusable for any reason). Thus a high filt_wlim value (i.e. close to 1.0) will cause more data to be rejected, and a low value (i.e. close to 0.0) will cause less data to be rejected. A value of <undef > causes the old filtering algorithm to be used that is based on filling gaps with artificial data. If the experimental algorithm is used the following additional settings can be made: " apod=0, fillgaps=0, noi.fillgaps=0" . [ <undef >]
Type:
real

SMURF Usage

MAKEMAP, CALCQU