WFPC2 drizzling cookbooks
Last updated 9 July 2007 by Max Mutchler
* 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, and the data was obtained
in 2004 or later, you might need to apply the
correction for the WF4 bias anomaly
- 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).
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