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drzprep -- generate DPP's for a list of images and object lists
drzprep inlist configs
- List to give input images and object lists.
- Name of aXe configuration file(s) to be used.
- (opt_extr = no) [yes|no]
- Also create extension for optimal extraction.
- (back = no) [yes|no]
- Create also background DPP's?.
The task produces a set of Dirzzle PrePare (DPP) files for the grism images specified in "inlist". The PET's and the BAF's for those grism images must have been prepared, e.g. by a previous run of "axecore".
A DPP is a multi extension fits file. For each first order beam in the corresponding PET the DPP has three extensions, [beam_??A], [err_??a] and [cont_??a]. The three extensions are the stamp images for the data values, the error values and the contamination values of the beam with the ID number "??". All extensions have important keywords which later are extracted and used in "axedrizzle". Among those keywords are the coefficients that allow "axedrizzle" to combine the stamp images with the same ID from different DPP's onto a deep 2D grism image with identical dispersion and pixel scale in cross dispersion direction.
To do that "drzprep" defines a common frame for all identical objects in the all grism images given in "inlist". "axedrizzle" will give ONLY consistent results if its input DPP's were generated within the same "drzprep" run. It is also not possible to run "drzprep" separately on the two science extensions of the WFC images. The usual dithers done for ACS image sets let an object sometimes fall on chip 1 and in some instances on chip 2. To establish the common frame needed in "axedrizzle" requires also in this case that the DPP's were generated in only one "drzprep" run. For WFC images with its two chips you will specify two configuration files in a comma separated list. Of course the two PET's, one for each extension number, must both exist.
With opt_extr="yes" also two additional extensions necessary for optimal weighting are created and saved. Those extensions are named [mod_??A] and [var_??A] and contain the emission model as calculated in quantitative contamination and the theoretical inverse variance, respectively.
Setting "back=yes" also generates background DPP's, which are then based on the background PET's. With "axedrizzle" those background DPP's can be drizzled to deep 2D backgrounds for the individual object ID's.
The format of the image list given as inlist is:
grim_image1 object_cat11[,object_cat12] [direct_image1] [dmag1] grim_image2 object_cat21[,object_cat22] [direct_image2] [dmag2] grim_image3 object_cat31[,object_cat32] [direct_image3] [dmag3]
For the task "drzprep" only the first column with the name of the grism image is extracted and used. All further columns are neglected. It is possible to use the same inlist as in "axeprep" and "axecore" (also later in axedrizzle).
aXe tasks use default names for input and output files based on the given name of the "grism" image. If the input grism image would be called <grism-rootname>.fits, the task creates the file
<grism-rootname>_<science extension>.DPP.fits, and if "back='YES'" <grism-rootname>_<science extension>.BCK.DPP.fitsAll files are stored in the directory pointed by $AXE_OUTPUT_ROOT.
1) For ACS HRC images:
ax> drzprep inlist="axeprep.lis" configs="HUDF.HRC.conf" back="YES"
Reduce both, object DPP's and background DPP's from the images and object lists given in "axeprep.lis".
2) For ACS WFC images:
ax> drzprep inlist="axeprep.lis" opt_extr="yes" configs="ACS.WFC.CHIP1.conf,ACS.WFC.CHIP2.conf" back="NO"
Generate only the object DPP's for the grism images in "axeprep.lis". Work on the PET's specified by the extensions specified in ACS.WFC.CHIP1.conf and ACS.WFC.CHIP2.conf (usin the usual file naming convention. Create also the extensions for optimal weighting.
Refer to manual for more detailed information about using this aXe task: