If done from the command line, this setup must be done in the same window in which PyRAF will be started. Setting ‘iref’ from within
PyRAF will not work, even though subsequently typing ‘show iref’ would suggest it might. For convenience, this setup command can be added to your .setenv file, so that the iref environment variable will always be defined. The ‘iref’ environment variable is required whether you are reprocessing through python,
PyRAF or from your OS command line.
The MAST uses the latest available calibration reference files by default. In order to use non-default reference files, manual recalibration is required. The calibration reference file keywords will need to be updated manually in the raw data files with the desired file names before running calwf3. In addition, the user can choose to change which calibration steps are performed by
calwf3 by resetting the values of the calibration switch keywords. These keywords are listed in
Table 3.8 along with their default values as used in the STScI pipeline. To change the values of any of the keyword switches, use a FITS keyword editor, such as the IRAF hedit task or the python package pytfits:
|
|
python>pyfits.setval(‘myfile_raw.fits ’,extname= ‘sci ’,extver=1,keyword= ‘darkcorr’,value= ‘omit ’)
|
calwf3 does not alter the units of the pixels in the image when calculating photometric information. Instead it calculates and writes the inverse sensitivity conversion factors (
PHOTFLAM and
PHOTFNU) and the ST magnitude scale zero point (
PHOTZPT) into header keywords in the calibrated data files. Refer to subsections on
PHOTCORR in
Section 3.4.2 (UVIS) and
Section 3.4.3 (IR) for more information.
Timing tests for processing WFC3 datasets using calwf3 are given in
Table 3.9 Geometric correction or dither-combining using
AstroDrizzle will take extra time, because these are performed separately. The CPU usage column reports the amount of time the CPU was active and reflects the amount of time waiting for disk I/O. WFC3 observers should keep these requirements in mind when securing computing resources for data processing.
This section presents several examples of calwf3 reprocessing. The boxes show commands and output to the screen. The following examples indicate commands which are typed into
PyRAF (pyraf>) and pure python (python>). Pure python commands may also be used in a
PyRAF session.
The following example uses hypothetical UVIS observations of a stellar cluster, observed with the F814W filter. The exposures are CR-SPLIT into two exposures of 20 seconds each. The association table for this observation is i8bt07020_asn.fits. Typing ‘pyraf> tprint i8bt07020_asn.fits’ reveals the rootnames of the individual exposures: i8bt07oyq and i8bt07ozq.
|
|
pyraf and python> calwf3. calwf3( ‘i8bt07oyq_raw.fits ’)
|
|
|
python> pyfits.setval(‘i8e654c0q_raw.fits ’, ’darkfile ’, ’iref$mydark.fits ’)
|
|
|
pyraf and python> calwf3. calwf3( ‘i8e654010_asn.fits ’)
|
The following example is for a hypothetical IR exposure that has some number of individual readouts affected by an anomaly, such as scattered Earth light. In this example we reprocess the raw data using calwf3 after flagging all the pixels in the last 3 readouts of the exposure, so that the data from those readouts is not used in the ramp fitting process (
CRCORR step). A convenient data quality flag value to use is 256, which causes the ramp fitting step to ignore any flagged reads as if the data were saturated.
|
|
pyraf and python> calwf3. calwf3( ‘ia2k19k6q_raw.fits ’)
|
