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WFPC2 Drizzle Cookbooks

WFPC2 drizzling cookbooks
Last updated 18 September 2009 by Matt McMaster

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 in the cookbooks listed below. If you have WFITS (c0f/c1f) data, then please follow the instructions for converting your data to GEIS format as they appear in the cookbooks.
Target is on
PC chip only
Target is on
WF chip/s
Single images
(transient/moving targets)
Cookbook
Jupiter
Cookbook
No dithering
(CR-SPLITs)
Cookbook Cookbook
Simple 2-3 point dithers
and small mosaics
Cookbook Cookbook
Subsampling
4-point dither box
Cookbook
NGC 5315
Cookbook
NGC 2440*
Large mosaics or
complex datasets
N/A Cookbook

* See a visual comparison of the drizzled output from simple vs subsampling dithers. Links to more sample datasets will be added.

The HST calibration pipeline does not automatically drizzle (combine and clean) associated WFPC2 datasets, i.e. datasets employing a dither or mosaic pointing pattern (or a pattern defined with POS TARGs). The online cookbooks above provide reasonable first-pass parameters for quickly drizzling various types of WFPC2 datasets using PyDrizzle and MultiDrizzle. Select the cookbook that best represents your dataset, with regards to target placement and pointing strategy. For any given dataset, a few trial-and-error iterations are typically necessary to produce optimal results, so some guidance on inspecting your output and experimenting with parameters is included in each cookbook. Below some cookbooks are links to well-documented sample datasets, which illustrate the processing in greater detail (see their README files).

General tips

Specific applications of these tips appear in many of the cookbooks and sample datsets above.
  • Drizzle input files: request only the *c0f.fits (science) and corresponding *c1f.fits (data quality) files from the archive, and their associated best reference files. Note that the best bias and dark corrections are generally not available until several weeks after your observations. So either wait to retrieve your data, or be prepared to re-retrieve it (via on-the-fly-reprocessing or "OTFR") and re-drizzle it. You will need to convert these FITS files to GEIS format.
  • Create a uref directory and put your reference files in it. The drizzle software requires only the distortion correction files indicated in your image headers (keywords IDCTAB and OFFTAB), which can also be downloaded individually from the STScI uref directory.
  • If your dataset involves large mosaic shifts (greater than 100 arcsec), and/or data from different epochs, orientations, or observing programs (using different guide stars), then you will need to measure and apply delta shifts to refine the image registration before combining your data.
  • Output files: set build=no to generate separate files for science image (drz_sci.fits) and exposure weight map (drz_weight.fits). Set context=no to not generate the context image (drz_ctx.fits). Setting clean=yes leaves fewer intermediate files in your working directory, but you may need them for diagnostic purposes (see next bullet).
  • Intermediate files: set clean=no to keep the intermediate files that are helpful while verifying your output and iterating. In addition to inspecting your output files, the median image (*_med.fits) should look almost as good as your final drizzled output, or else it will not help reject cosmic rays and artifacts very well. The single-drizzled images (*_single_sci.fits) may also help verify good cleaning, and may also be needed for image registration.
  • Many types of detector artifacts are flagged in the calibration pipeline, and populate the data quality files (c1h). By default, the drizzle bits are set to zero, meaning any/all flagged pixels will be excluded from the processing. If you wish to include some of these pixels, set bits to include them (and sum the bits for multiple types). For example, to include saturated pixels and warm pixels, set bits = 8+1024 = 1032.
  • If the target is on both the PC and WF chips, the data should be drizzled as if it is all WF data. Or the PC data could be drizzled separately (specify group=1) following instructions for PC data.
  • 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.
  • To minimize CPU time and disk space usage (especially during early experimental iterations), you can specify the center (ra,dec) and dimensions (outnx, outny) of your output image to center your target in the output (e.g. give NED coordinates), and limit the output to the minimal region of interest (e.g. only the area essential for measuring shifts). This can greatly improve your efficiency when working with large datasets, especially mosaics. Further, if your target falls on only one chip, you can process that chip (group) alone.
  • Due to declining charge transfer efficiency (CTE), bright objects and artifacts (e.g. stars and cosmic rays) may have prominent comet-like tails of deferred charge in the anti-readout direction. You can use the driz_cr_ctegrow parameter to grow the cosmic ray rejections preferentially in the direction of these CTE tails.

Reference documents

Fruchter, A. S. & Hook, R. N. 2002, Drizzle: A Method for the Linear Reconstruction of Undersampled Images, PASP 114, 144 (astro-ph/9808087)

Koekemoer, A. M., Fruchter, A. S., Hook, R. N., & Hack, W. 2002, MultiDrizzle: An Integrated Pyraf Script for Registering, Cleaning and Combining Images, HST Calibration Workshop, Ed. S. Arribas, A. M. Koekemoer, B. Whitmore (STScI: Baltimore), p.337

Koekemoer, A. M., et al. 2002, HST Dither Handbook, Version 2.0 (Baltimore: STScI).