Spectroscopic observations must also be linearized as described in §6. A convenient environment in which to conduct this and the remainder of the basic long-slit grism reduction is provided by the redgspec task. The redgspec task is the spectroscopic equivalent of the redimage task used for imaging data reduction. For example, to linearize a set of images obtained with a 180 sec exposure time, first form a list file of the image filenames using the csplist task by typing:
csplist time 180 images=@allfiles > 180files
redgspec overwrites the input images so first copy the input images to temporary files. This can be done by typing:
imcopy @180files @180files//t
Now use epar to set the redgspec parameters as listed below.
I R A F
Image Reduction and Analysis Facility
PACKAGE = caspir
TASK = redgspec
(images = @180files) List of CASPIR input images
(spectru= ) Base name of spectrum file
(linear = yes) Linearize data?
(combine= no) Combine individual 2D images?
(flatten= no) Divide by flatfield?
(fixbad = no) Fix known bad pixels?
(clean = no) Interactively clean additional pixels?
(transfo= no) Geometrically transform and subset?
(illumin= no) Correct non-uniform slit illumination?
(extract= no) Extract 1D spectra?
(fluxcal= no) Flux calibrate spectra?
(flatdiv= no) Divide by flat spectrum star?
(plot = no) Plot spectrum?
(bias = dark03) Bias frame to use
(dark = dark180) Dark frame to use
(obstype= abba) Type of observation made
(zerosec= [200:250,10:240]) Zero level image section
(flatfil= ) Flatfield frame to use
(badfile= caspirdir$grism) Bad pixel file
(badtype= interp) Type of bad pixel correction
(illfile= ) Illumination frame to use
(reffile= ) Comparison extraction reference file
(fluxspe= ) Flux calibration spectrum to use
(flatspe= ) Flat spectrum star file to use
(verbose= yes) Verbose output?
(imglist= )
(mode = ql)
The flags linear, combine, flatten, fixbad, clean, transform, illumination, extract, fluxcal, flatdiv, and plot define the reduction steps that will be performed. The remainder of the parameters are used in the execution of these basic functions.
The parameters relevant to the linearization of a particular dataset are bias and dark. These are used in the same way as described in §6 for redimage.