- hselect > bias.lis
     inimages = *.fits[0]
     select = $I
     expr = obstype ?= 'BIAS'

   - emacs to get rid of [0]

- overscan subtract in this step as well, if you also choose to overscan subtract your data.
      => gbias @bias.lis bias.fits (fl_over+ fl_trim+ fl_int+)
 
       NOTE: the processed *_bias.fits is NOT overscan subtracted, be sure to overscan subtract 
             processes bias with gbias as well.

PARAMETERS:

PACKAGE = gmos
   TASK = gbias

inimages= gS20070913S0213_bias.fits  Input GMOS bias images or list
outbias = gbgS20070913S0213_bias.fits  Output bias (zero level) image
(logfile=                     ) Logfile
(rawpath=                     ) GPREPARE: Path for raw input images
(fl_over=                  yes) Subtract overscan level
(fl_trim=                  yes) Trim overscan section
(key_bia=              BIASSEC) Header keyword for overscan strip image section
(key_dat=              DATASEC) Header keyword for data section (excludes the ov
(key_ron=              RDNOISE) Header keyword for readout noise
(key_gai=                 GAIN) Header keyword for gain (e-/ADU
(ron    =                  3.5) Readout noise value to use if keyword not found
(gain   =                  2.2) Gain value to use if keyword not found
(gaindb =              default) Database with gain data
(fl_vard=                   no) Create variance and data quality frames?
(sci_ext=                  SCI) Name of science extension
(var_ext=                  VAR) Name of variance extension
(dq_ext =                   DQ) Name of data quality extension
(bpm    =                     ) Bad Pixel Mask filename
(sat    =                65000) Saturation level in raw images
(fl_inte=                  yes) Interactive overscan fitting
(median =                   no) Use median instead of average in column bias
(functio=            chebyshev) Overscan fitting function.
(order  =                    1) Order of overscan fitting function.
(low_rej=                   3.) Low sigma rejection factor.
(high_re=                   3.) High sigma rejection factor.
(niterat=                    3) Number of rejection iterations.
(combine=              average) Type of combination operation
(reject =            avsigclip) Type of rejection algorithm
(lthresh=                INDEF) Lower threshold for rejection before scaling
(hthresh=                INDEF) Upper threshold for rejection before scaling
(masktyp=            goodvalue) Mask type
(maskval=                   0.) Mask value
(scale  =                 none) Image scaling
(zero   =                 none) Image zero point offset
(weight =                 none) Image weights
(statsec=                [*,*]) Image region for computing statistics
(key_exp=              EXPTIME) Header keyword for exposure time
(nlow   =                    0) minmax: Number of low pixels to reject
(nhigh  =                    1) minmax: Number of high pixels to reject
(nkeep  =                    1) Minimum to keep or maximum to reject
(mclip  =                  yes) Use median in sigma clipping algorithms?
(lsigma =                   3.) Lower sigma clipping factor
(hsigma =                   3.) Upper sigma clipping factor
(snoise =                  0.0) ccdclip: Sensitivity noise (electrons)
(sigscal=                  0.1) Tolerance for sigma clipping scaling correction
(pclip  =                 -0.5) pclip: Percentile clipping parameter
(grow   =                   0.) Radius (pixels) for neighbor rejection
(verbose=                  yes) Verbose output?
(status =                    0) Exit status (0=good)
(scanfil=                     ) Internal use only
(mode   =                   ql)