Software tools for NICMOS FITS files available in the STSDAS
have been designed to maintain compatibility with pre-existing analysis software. The tools have either been written in ANSI-C or are IRAF
CL scripts interfacing with pre-existing IRAF/STSDAS
These tasks include tools for mathematical and statistical operations on
science images and for analysis and display of reduced and raw data. In most cases, the utilities extend existing routines to include error and data quality propagation. These are the utilities of greatest interest to the user community. Under this category are several tasks described in Chapter 3 of the "HST Data Handbook
, along with a few other tasks we describe below, ndisplay
, mosdisplay, pstack, pstats, sampinfo, sampdiff, sampcum, CalTempFromBias, puffcorr, nicpipe, biaseq, pedsky, pedsub, saaclean, nic_rem_persist and rnlincor
. The first five are found in the package toolbox.imgtools.mstools
; the remaining ones reside in the package hst_calib.nicmos
The tasks in the toolbox.imgtools.mstools
package are particularly useful for working with individual STIS and NICMOS imsets. See “Working with STIS and NICMOS Imsets” in Chapter 3 of the "HST Data Handbook
" if you are not familiar with these tasks. Below we describe a few tasks of specific interest to NICMOS observers. For additional details and examples of these and other tools, please refer to the online help.
There used to be a package called nicproto
for new experimental software that was released between major STSDAS
releases. This package was removed in 2000 and was superseded by placing new tasks in the nicmos
package in regular STSDAS
releases only. All routines described in this edition of the handbook are available in STSDAS
versions v3.9 and later.
task reads the data quality (DQ) array from a NICMOS image and marks the DQ flags on the displayed image. Each flag value can be set independently to a different color or can be turned off. The ndisplay
task combines the capabilities of the IRAF
and the task markdq
: it displays a NICMOS image and overlays the DQ flags according to a user-specified color-code. Both tasks are useful for locating specific DQ values, for example, the cosmic rays rejected by calnica
in a MULTIACCUM image.
task provides a convenient way to display images from all IMSETS of a NICMOS MULTIACCUM image together as a mosaic in a single ximtool or saoimage window. The user may select which extension (e.g., SCI, ERR, TIME, SAMP
, or DQ
) to display, and can control the display threshold parameters or leave them to be automatically determined.
tasks plot all the samples of a specified pixel or image section, respectively, from a NICMOS MULTIACCUM image as a function of time. These tasks can be used to track the time behavior of an image on a pixel-by-pixel basis. For example, the temporal positions of cosmic ray hits or the onset of saturation during the course of an exposure can be located for a defined set of pixels. The pstats task can be particularly useful for identifying anomalous data behavior such as drifting bias levels or scattered light which may cause the background level to vary substantially during the course of an exposure.
The sampinfo task offers a convenient way to get readout-by-readout
information about a NICMOS MULTIACCUM image. It provides information about the overall readout sequence (SAMP_SEQ, NEXTEND, NSAMP, and EXPTIME), and then for each imset of the multi-extension fits file it lists the corresponding SAMPNUM, SAMPTIME and DELTATIME values. These can be useful bits of information when using non-standard processing techniques, such as the biaseq
routine. An example of the use of sampinfo
and its output is given in Section 4.2.4
task provides a convenient way to convert a MULTIACCUM image into a set of independent “first differences.” Normally, each IMSET (readout) of a MULTIACCUM image is the cumulative sum of the total exposure time prior to that readout. As such, the [sci,*]
images are not statistically independent. When analyzing NICMOS images, it is sometimes helpful to look at the data which was collected during each readout interval independent of that which was accumulated previously, i.e. by taking the difference of successive readouts. In this way, you can isolate readouts with problems (e.g., major cosmic ray hits or moving objects, sudden changes in bias, scattered light, etc.). The sampdiff
task automates this process. Note that, in general, this is only really a sensible thing to do if the image has not been converted from counts to count rate by the UNITCORR step of calnica!
task inverts this process, re-accumulating the first differences.
This routine calculates the temperature of the detector from the
measured bias levels. This routine works only on MULTIACCUM observations and runs on the *_raw files. This routine needs to be run before calnica such that an accurate temperature of the detector is determined that allows the right reference files to be used for dark subtraction, flat fielding, and photometric sensitivity correction. Details of this routine can be found in Section 3.3.1
task measures and removes an estimate of the "Mr. Staypuft" anomaly or quadrant crosstalk signal in a NICMOS image. This correction is only needed if there are bright sources in the image. The input image to the task must be a *_raw MULTIACCUM NICMOS dataset, with no calnica processing yet applied. The effect is described in more detail in Section 4.8.4
task provides a shortcut for partially processing NICMOS images through some but not all stages of calnica
. Normally, this is meant for use in preparing images for “bias equalization” using the biaseq
task (see Chapter 4
). Setting stage=biaseq
takes the processing through the steps ZSIGCORR, ZOFFCORR, MASKCORR, BIASCORR, NOISCALC, DARKCORR, NLINCORR, and BARSCORR. Setting stage=final
completes the processing. This can also be done by hand using chcalpar
to change the processing control switches in the image headers (see Section 3.5.2
) and running calnica
directly. Occasionally nicpipe
can come in handy at other times besides biaseq
processing: one example is given below.
The tasks biaseq
provide methods for dealing with the floating quadrant bias or “pedestal” effect in some (not all) data sets, and are described in Section 4.2.4
task measures and removes an estimate of the SAA persistence signal in a NICMOS image. Saaclean
runs on a single science image (i.e., not on all the separate readouts of a NICMOS MULTIACCUM file). It is generally recommended that the images have gone completely through calnica and have been processed by pedsub as well. The routine uses post-SAA darks, which were only taken automatically since Cycle 11 in the post-NCS era. Saaclean
is part of the runcalsaa
software and is included in the OTFR since March 2008. More details about this SAA persistence routine can be found in Section 3.3.3
and in the routine’s help file.
routine removes the persistence of the bright Earth that may have been falling on the detector during the previous occultation when operating NICMOS in parallel with another instrument. This will leave a large scale noise pattern that is slowly declining. nic_rem_persist
is part of the runcalsaa
software and is included in the OTFR since March 2008. Further information about this effect and this routine can be found in Section 3.3.3
task corrects for the countrate-dependent nonlinearity in a NICMOS image. The input image to the task must be a NICMOS science image in units of DN/s that has been processed through calnica
, and preferably has also been corrected for pedestal with either the pedsky or pedsub tasks. As the countrate nonlinearity is assumed to depend on incoming flux/countrate, the image should have instrumental effects removed as much as possible. Grism and polarimetry images are not supported by this task. Details about this countrate nonlinearity routine can be found in Section 4.5
and in the help file.
As an example, you might want to inspect NICMOS data for anomalies
which occur during individual readouts during a MULTIACCUM using a procedure like this:
In this example, the raw image is first partially processed through calnica
. By setting stage=biaseq
, the pipeline processing stops before flat fielding, conversion to count rate, and CRIDCALC cosmic ray processing. You then take first differences with sampdiff
, displays them as a mosaic with mosdisplay
to look for bars, bias jumps, monster cosmic rays, or other oddities in individual readouts, and then use pstats
to plot the median count rate (units=rate
) per sample time in the image quadrant [1:128,1:128]
In this case, you have corrected readout-to-readout bias drifts with biaseq
, also fitting for bias jumps in the process, and then carried out the final “pedestal” correction with pedsky
, solving for the background level and pedestal interactively. Please see Section 4.2.4
of this manual and the on-line help files for biaseq
for a detailed explanation of the parameters. The resulting end product image will be n4xj13jwq_ped.fits