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WFPC2 drizzlingSee the WFPC2 drizzling overview and general tips, to find a cookbook and/or sample dataset that best represents your dataset, with regards to target placement and pointing strategy.
Cookbook for large WFPC2 mosaics or complex datasetsThese instructions are intended for mosaic datasets involving large shifts of over 100 arcseconds, or complex datasets involving observations from many epochs or observing programs.De-archive the science and data quality FITS files to your working directory. Make a uref directory and download the distortion reference files (IDCTAB and OFFTAB in your image headers) into it, and define your uref directory (set uref). Convert the files to GEIS format (with strfits), and make a list of input images (list_c0h): > set uref = "/data/mymachine/uref/" > strfits *c0f.fits "" "" > strfits *c1f.fits "" "" > ls u*c0h > list_c0h (no blank lines!)Apply the correction for the WF4 bias anomaly. Datasets including shifts greater than ~100 arcseconds, or data from multiple epochs or observing programs, involve the use of multiple guide star pairs. The errors in guide star positions require you to refine the image registration. MultiDrizzle parametersThe following are suggested parameters (with some rationale in the comments) for applying the geometric distortion correction, combining, and cleaning your dataset. Some of the default parameters are not listed here.> unlearn multidrizzle # reset all parameters to default values first multidrizzle.input = '@list_c0h_f555w' # input image list multidrizzle.output = 'ngc999_f555w' # convenient target_filter filenaming convention multidrizzle.group = '' # blank to include all chips multidrizzle.ra = 115.480667 # center the target in the output image, e.g. NED coords multidrizzle.dec = -18.208472 # center the target in the output image, e.g. NED coords multidrizzle.build = no # produce single-extension FITS output multidrizzle.shiftfile = 'shifts.txt' # apply measured delta-shifts multidrizzle.static = yes multidrizzle.skysub = no multidrizzle.driz_separate = yes multidrizzle.driz_sep_outnx = 4096 # large dimensions for mosaics; adjust as needed* multidrizzle.driz_sep_outny = 4096 # large dimensions for mosaics; adjust as needed* multidrizzle.driz_sep_kernel = 'turbo' multidrizzle.driz_sep_wt_scl = 'exptime' multidrizzle.driz_sep_scale = 0.0996 # WF detector pixel scale multidrizzle.driz_sep_pixfrac = 1.0 multidrizzle.driz_sep_rot = 0.0 # North up; for sum image comparison multidrizzle.driz_sep_fillval = -9.9 # arbitrary low value; easy to exclude from median multidrizzle.driz_sep_bits = 0 # exclude all flagged pixels multidrizzle.median = yes multidrizzle.combine_type = 'median' multidrizzle.combine_lthresh = '-8.8' # exclude fill values from median multidrizzle.blot = yes multidrizzle.driz_cr = yes multidrizzle.driz_cr_snr = '4.0 3.5' multidrizzle.driz_combine = yes multidrizzle.final_wht_type = 'EXP' multidrizzle.final_outnx = 4096 # large dimensions for mosaics; adjust as needed* multidrizzle.final_outny = 4096 # large dimensions for mosaics; adjust as needed* multidrizzle.final_kernel = 'square' multidrizzle.final_wt_scl = 'exptime' multidrizzle.final_scale = 0.0996 # output pixel = input pixel scale multidrizzle.final_pixfrac = 1.0 # don't shrink the drops multidrizzle.final_rot = 0.0 # rotate north up multidrizzle.final_fillval = 0.0 # INDEF or zero multidrizzle.final_bits = 0 multidrizzle.crbit = 0 Inspect your output and iterate as neededYou might need to make your output dimensions (outnx, outny) larger to include all the pixels, or wish to make them smaller to truncate unneeded outer parts of the data (i.e. if your target is small). Look for under-rejection of cosmic rays, or over-rejection of real features (e.g. stellar cores) by blink-comparing the output drizzled image and weight map (drz_sci.fits and drz_weight.fits). You may need to adjust your rejection thresholds (driz_cr_snr), or grow the rejections and/or reject CTE tails (driz_cr_grow and driz_cr_ctegrow).* Set your central RA,DEC and mosaic dimensions to include the desired data. For the initial iterations of very large mosaics (and especially when you set clean=no), CPU time and disk space can be greatly minimized by finding the minimum dimensions that allow you to register the mosaic (includes key overlap areas), and verify the cleaning. Then for the final drizzling (and with clean=yes) you can set larger output dimensions that includes all the data. Send any questions or concerns to the STScI Help Desk (help@stsci.edu). |
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