STIS Instrument Science Report 97-08
The Spectrum Splicer Task
Steve Hulbert
June 1997
ABSTRACT
This report discusses the requirements for modification to existing tasks and creation of
new tasks in STSDAS to facilitate the splicing of multiple spectra into a single spectrum.
1. Introduction
The splicing or joining of spectra to produce a single spectrum can be operationally
defined by a small number of discrete reduction steps. The process begins with an implicit
requirement–we have a collection of spectra with calibrated wavelengths that overlap in
wavelength space that we wish to splice. The assignment of wavelengths is not within the
responsibilities of the splicer task. This does not mean that existing wavelengths cannot be
improved. The improvement, however, is restricted to identifying and removing zero-point
shifts between spectra overlapping in wavelength space. Removing small zero-point wavelength shifts from adjacent spectra represents the first step in the splicer task. This
processing is simply a matter of doing a cross-correlation in pixel space to identify the relative offsets. This step requires that the dispersions for the two spectra are close enough as
to be identical–otherwise the resolution will be degraded in the overlap regions as we mix
pixels of differing wavelengths.
Care must be taken to account for variation in the flux levels of overlapping spectra.
While we would expect that properly calibrated spectra would not should significant discrepancies in flux levels, experience has shown that this is not always the case. These
scaling effects can be removed prior to combining by simple image arithmetic. Alternately, the scaling can be handled during the splicing process with the input of the desired
additive and multiplicative constants.
1
To begin the splicing process we must select the reference wavelength scale onto
which the final spectrum will be placed. Ideally the type of scale would be unrestricted but
in practice is usually limited to an input spectrum (in the case of nonlinear scales) or to a
linearized scale as specified by a starting wavelength and dispersion. The splicing begins
by interpolating the individual spectra onto the output wavelength grid. In the case of more
than one input pixel contributing to an output pixel the splice task must perform some
combining process. Control over this process is critical to the splicing. Once combined,
the final spectrum is saved to disk.
It is necessary to be able to produce a plot of the spliced spectrum. The plot should
allow the overplotting of the input and output spectra. The resultant plots should be suitable for publication.
Figure 1 on page 6 summarizes the process of splicing spectra in the form of a flow
chart.
2. Wish List
Below we list (highly) desirable features of the spectrum splicing task(s). These items
are driven in large part by the nature of spectra taken with the spectrographs on HST.
1. Support for various input spectrum formats: input spectra as GEIS, FITS,
IRAF, and IRAF multispec images; and, input spectra as ASCII, FITS ASCII,
FITS binary (2-D and 3-D), and SDAS binary (2-D and 3-D) tables.
2. Support for various output spectrum formats: output spectra resampled to a
polynomial wavelength fit (which may be the same as one of the input spectra)
with the necessary WCS keywords populated in the case of GEIS or IRAF multispec formats; output spectra in GEIS format, i.e. wavelengths contained in a separate image from the flux info; output spectra with wavelength values identical to
the input values except in the overlapping region where the wavelengths have been
put on an arbitrary (but selectable) scale; and, output spectra written to tables (2-D
ASCII, 2-D Binary, 3-D binary).
3. Ability to produce “pretty pictures” of spliced spectra, including “histogram”
style plots.
4. Ability to boxcar smooth input spectra.
5. Ability to interactively determine wavelength offsets (zero-point shifts).
6. Ability to perform interactively spectrum arithmetic to normalize individual
spectra to common scale.
7. Ability to restrict the rebinning (resampling) of flux values to overlap regions
only.
8. No restriction on flux units.
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9. Ability to process a large number of input spectra (~50).
10. Resampling options, including linear, polynomial, and sinc function interpolation.
11. Ability to produce output with the necessary header information to properly
displayed by the splot task.
12. Ability to update header WCS keywords or to carry out the necessary arithmetic on the wavelengths to correct zero-point shifts.
3. Analysis of Current Capabilities
A review of the current set of IRAF/STSDAS tasks that can be used in the process of
splicing spectra is summarized in Table 1 on page 7. The important point to note is that the
basic features of the splicing process are covered by the existing tasks. Unfortunately, each
task has been written to support some but not all of the spectrum data formats. Table 2 on
page 8 lists the data formats that must be supported by the splicing process. In order for all
types of spectrum formats to be processed, there must be translation tasks to convert
among the various formats. Table 3 on page 9 lists the current set of translation tasks.
To demonstrate the current capabilities, consider how you would perform the
following three tasks.
How to splice FOS spectra with existing tasks (wavelength space)
•
Use poffsets to determine zero point shifts between overlapping spectra.
•
Update the WCS keywords using hedit to correct for the zero-point shifts.
•
Use mkmultispec to create multispec format data—this task interpolates the wavelengths onto a polynomial scale but does not modify the flux values.
•
Use splot to compare spectra and to determine if scaling of the flux is needed.
•
Use scombine to splice the spectra. If scaling is required use the scaling parameters
in scombine. Configure scombine to use the wavelength scale of the first input
spectrum as the output wavelength scale. Use the appropriate parameters to control
how the overlapping parts of the spectra are combined.
•
Use igi to make a pretty picture. The output of scombine is a multispec format spectrum. The igi task is able to read the WCS information from the header. If desired,
write the igi script to overplot the input spectra.
How to combine GHRS spectra of same object taken at different epochs (pixel space)
•
Use poffsets to determine zero point shifts between overlapping spectra.
•
Use specalign to combine the spectra. A polynomial fit to the wavelengths will
used to describe the output dispersion solution. This is no control over how overlapping regions are combined–a simple average is taken.
3
•
Use igi to make a pretty picture. The output of specalign is a multispec format
spectrum. The igi task will be able to read the WCS information from the header. If
desired, write the igi script to overplot the input spectra.
How to combine all echelle orders from a STIS MAMA observation (pixel space)
•
Use taextract to convert 3-D table data to 2-D table data.
•
Use tabim to convert 2-D table data to image data.
•
Use poffsets to determine zero point shifts between overlapping spectra.
•
Use imcalc to apply zero-point offsets to the wavelength images.
•
Use specalign to combine the spectra. A polynomial fit to the wavelengths will
used to describe the output dispersion solution. This is no control over how overlapping regions are combined–a simple average is taken.
•
Use igi to make a pretty picture. The output of specalign is a multispec format
spectrum. The igi task will be able to read the WCS information from the header. If
desired, write the igi script to overplot the input spectra.
Deficiencies of existing tasks
The current tasks have, in addition to being limited to a subset of the desired spectrum
formats, a number of deficiencies.
•
scombine does not let you specify a nonlinear wavelength scale as output. You can
use the nonlinear scale of the first input spectrum, but if you are using tabulated wavelengths (as opposed to a polynomial fit to the wavelengths) then the wavelength range
is limited and the output will be truncated to fit the input wavelengths regions. (I supposed you could created a zero-filled dummy spectrum with a collection of wavelengths that was whatever you wanted. I imagine you could even determine the overlap
region up front and then cut and past to build up a complete wavelength grid–as nonlinear as you might want. I think this sounds doable, although it also sounds like a bit
of work.
•
There is no simple, interactive task to determine the scaling parameters needed in the
splicing process. Since the splot task does not preserve the various spectra in memory, you are forced to keep reading the same pair of spectra in the course of the comparison. For example: you read in a spectrum; you divide by a second; if you are
unhappy with the comparison your read in a spectrum; you divide by a second; if you
are unhappy...
•
The current translation tasks are not packaged in a way to make their use straightforward. For example, I just can’t convert a 3-D FITS table spectrum to a GEIS spectrum.
Instead, I have to reconstruct the spectrum in the new format which requires that I
know the details of each of these formats. I think we should be able to hide many of
the details from the average user. In other words, I can’t just say “Convert this to GEIS
format”, I have to say “Create a .c1h image, create a .c0h image, create ...”.
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4. Recommendations
Below are my recommendations for improvements and additions to the existing tasks
that can be used to splice spectra. Notice that I don’t recommend adopting the approach of
creating the ultimate “black-box” for spectrum splicing. Instead I urge use of the approach
that has been extensively discussed among the STIS/NICMOS/STSDAS groups–namely,
to provide translation tasks that permit access to existing tasks.
Phase I
1. Create easy-to-use translation tools to permit use of existing tasks on data of
any format. The tasks should convert formats and not bother the user with the
details of how the formats are stored on disk. We need the ability to convert among
all of the data formats listed in the wish list: GEIS, FITS, IRAF, and IRAF multispec images; and, ASCII, FITS ASCII, FITS binary (2-D and 3-D), and SDAS
binary (2-D and 3-D) tables.
2. Create easy-to-use cl scripts to group routine processing steps. For example,
link poffsets and hedit to permit the updating of header WCS information
with wavelength zero-point shifts.
3. Create igi scripts for pretty pictures. This script (or scripts) should support
overplotting of spectra, plotting of multiple spectra in multiple boxes on one page,
and the style of the plot (i.e., provisions for histogram style plots). Written as a cl
script, this task could be easily customized by users. Think of this as paper products for spliced spectra.
Phase II
1. Add functionality to scombine to permit a wider variety of output wavelength scales, specifically for nonlinear scales. Permit the output scale to be a
merged scale generated from all input scales. Permit the output to be specified as a
separate input parameter instead of as one of the spectra to be spliced. I realize this
task is an IRAF/NOAO task–we would have to submit this request to IRAF.
2. Modify poffsets to display, in interactive mode, the two spectra being
cross-correlated as well as the cross-correlation values. Specifically, I would like
see the two spectra shifted by the calculated amount and then overplotted.
3. Modify specalign to permit more control over how overlapping pixels are
combined.
Phase III
1. Modify splot to maintain more than one spectrum in the buffer and to permit
spectrum arithmetic on individual spectra. Again, I realize this task is an IRAF/
NOAO task and that we would have to submit this request to IRAF.
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Figure 1: Flow chart of the spectrum splicing process
Spectra
Update
Wavelength
Scales?
YES
Determine
Zero-Point
Shifts
NO
Combine
into a
Single
Spectrum?
YES
YES
NO
Resample?
Resample
NO
YES
Scale?
Scale
NO
Combine
Spectrum
Plot
Plot
YES
Plot?
NO
6
7
Plot
Combine spectra
Scale flux values
imstat
Pixel
igi
scombine
Wavelength
Wavelength
specalign
Pixel
splot
tcalc
Pixel
Pixel
imcalc
trebin
Wavelength
Pixel
resample
Wavelength
crosscorr
Pixel
Resample to a linear
wavelength scale
poffsets
Pixel
Zero-point wavelength shift
Existing
Task
Parameter
Space
Processing
Step
Table 1. Existing capabilities in IRAF/STSDAS for splicing spectra
GEIS/
OIF/
2-D tables/
3-D tables
multispec
GEIS
OIF/
multispec
OIF/
multispec
2-D tables
GEIS/
OIF/
multispec
2-D tables
GEIS
OIF
GEIS
Required Format
for
Existing Task
yes
no
yes
yes
yes
no
yes
yes
no
no
yes
yes
Interactive
Mode
Desirable?
no
no
yes
no
no
no
no
no
no
yes
Existing Task
Interactive?
8
table column
table column
table cell
FITS ASCII 2-D tables
FITS binary 2-D tables
FITS binary 3-D tables
table column
ASCII 2-D tables
table cell
image
GEIS
SDAS binary 3-D tables
image
multispec
table column
image
OIF
SDAS binary 2-D tables
Flux stored as
Format name
Table 2. Spectrum formats to support
table cell
table column
table column
table cell
table column
table column
image
WCS keywords
WCS keywords
Wavelengths stored as
yes
yes
yes
yes
yes
yes
yes
yes
no
Supports
nonlinear
wavelength
scales
Since each pixel has an assigned wavelength nonlinear wavelength scales are supported.
Since each pixel has an assigned wavelength nonlinear wavelength scales are supported.
Since each pixel has an assigned wavelength nonlinear wavelength scales are supported.
Since each pixel has an assigned wavelength nonlinear wavelength scales are supported.
Since each pixel has an assigned wavelength nonlinear wavelength scales are supported.
Since each pixel has an assigned wavelength nonlinear wavelength scales are supported.
Multigroup format. Flux, wavelength information is stored in
separated files. Since each pixels has an assigned wavelength,
nonlinear wavelength scales are supported.
Special case in OIF. Supports nonlinear wavelength scales as
either coefficients to polynomial fit to wavelengths or as table
of wavelengths–one per pixel. Designed to store multiple
spectra in a single image.
IRAF image format. Supports linear wavelength scales using
WCS keywords.
Description
Table 3. Existing data format translation capabilities in IRAF/STSDAS
Existing Translator
Task
From
To
mkmultispec
GEIS
multispec image
taextract
3-D table cell
2-D binary able column
tainsert
2-D ASCII/binary
table column
3-D table cell
tabim
2-D ASCII/binary
table column
OIF image
imtab
OIF image
2-D table column
listpix
all images
ASCII table
9