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WFPC2 Drizzle Cookbooks

WFPC2 drizzling

See 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 simple 2-3 point dithers with the target placed on the WF chip/s

NOTE: You may use the c0m/c1m files you now receive from the archive 'as is', there is no need to convert them to GEIS format using STRFITS as mentioned below. If you have WFITS (c0f/c1f) data, then please follow the instructions for converting your data to GEIS (c0h/c1h) format. These instructions are intended for datasets where a simple 2 or 3 point dither line pattern was used, or a small mosaic pattern (with shifts less than 100 arcseconds).

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!)

If any part of your target falls on the WF4 chip, the data were taken after 1 March 2000, and you are working with c0h/c1h files, ensure that the WF4TFILE and WF4TCORR keywords are present and populated. The WF4TFILE keyword will be present (it should not be 'N/A') and WF4TCORR should be 'COMPLETE'. If this is not the case, you need to re-retrieve your data.

If your target falls on just one of the WF chips, for example WF3, you can set multidrizzle.group=3 to process only that chip. Similarly, if you wish to exclude the data from any chips (e.g. the PC data, or the WF4 data if it suffers too badly from the bias anomaly), you can mask out entire chips by flagging every pixel in the appropriate group (chip number) of the data quality file, for example:
> imreplace u*.c1h[1] 2048  # mask out all the PC1 chips
> imreplace u62p030ar.c1h[4] 2048  # mask out a WF4 chip with bad bias anomaly

If your data are not all from the same visit, or otherwise did not use the same guide stars, you need to refine the image registration.

MultiDrizzle parameters

The 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'  # input image list
multidrizzle.output = 'ngc999_f555w'   # a convenient filenaming convention
multidrizzle.group = ''  # leave blank to include all chips, or provide a chip number
multidrizzle.ra = 115.480667   # decimal NED coords to center the target in the output
multidrizzle.dec = -18.208472  # decimal NED coords to center the target in the output
multidrizzle.build = no  # produce single-extension FITS output
multidrizzle.shiftfile = 'shifts.txt'  # apply measured delta-shifts, if relevant
multidrizzle.static = yes
multidrizzle.skysub = no
multidrizzle.driz_separate = yes
multidrizzle.driz_sep_outnx = 1600
multidrizzle.driz_sep_outny = 1600
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 = INDEF  # don't rotate north up for CR-detection
multidrizzle.driz_sep_fillval = -9.9  # arbitrary low value; easy to exclude
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 = 2048
multidrizzle.final_outny = 2048
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 drop size
multidrizzle.final_rot = 0.0   # rotate north up
multidrizzle.final_fillval = 0.0
multidrizzle.final_bits = 0
* There is some potential for confusion in that drizzle.scale is a unitless ratio (output/input scale), but in PyDrizzle and MultiDrizzle the units became absolute arcsec a few years ago. So if input WF pixels are 0.10 arcsec, then 60% of that is 0.06 arcsec.

Inspect your output and iterate as needed

You 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).

Send any questions or concerns to the STScI Help Desk (help@stsci.edu).