apfit input output fittype
input
List of input images to be fit.
output =
List of output images to be created with the fitting results. If the null
string is given or the end of the output list is reached before the end
of the input list then the input image name is used and an extension
of ".fit", ".diff", or ".ratio" is added based on the type of fit.
fittype = difference
Type of fitted output. The choices are:
fit
The fitted spectra are output.
difference
The difference (or residuals) of the data and the fit (data - fit).
ratio
The ratio of the data to the fit. If a fitted pixel goes below a specified
threshold the ratio is set to 1.
references =
List of reference images to be used to define apertures for the input
images. When a reference image is given it supersedes apertures
previously defined for the input image. The list may be null, "", or
any number of images less than or equal to the list of input images.
There are three special words which may be used in place of an image
name. The word "last" refers to the last set of apertures written to
the database. The word "OLD" requires that an entry exist
and the word "NEW" requires that the entry not exist for each input image.
interactive = yes
Run this task interactively? If the task is not run interactively then
all user queries are suppressed and interactive aperture editing and trace
fitting are disabled.
find = yes
Find the spectra and define apertures automatically? In order for
spectra to be found automatically there must be no apertures for the
input image or reference image defined in the database.
recenter = yes
Recenter the apertures?
resize = yes
Resize the apertures?
edit = yes
Edit the apertures? The interactive parameter must also be yes.
trace = yes
Trace the apertures?
fittrace = yes
Interactively fit the traced positions by a function? The interactive
parameter must also be yes.
fit = yes
Fit the spectra and produce a fitted output image?
The following two parameters are used in the finding, recentering, resizing,
editing, and tracing operations.
line = INDEF
The starting dispersion line (line or column perpendicular to the dispersion
axis) for the tracing. A value of INDEF starts at the middle of the image.
nsum = 1
Number of dispersion lines to be summed or medianed at each step along
the dispersion. For tracing only summing is done and the sign is
ignored.
threshold = 10.
Division threshold for ratio fit type. If a pixel in the fitted spectrum
is less than this value then a ratio of 1 is output.
The following parameters control the profile and spectrum fitting.
background = none
Type of background subtraction. The choices are "none" for no
background subtraction, "average" to average the background within the
background regions, or "fit" to fit across the dispersion using the
background within the background regions. Note that the "average"
option does not do any medianing or bad pixel checking; it is faster
than fitting however. Background subtraction also requires that the
background fitting parameters are properly defined. For the "average"
option only the background sample regions parameter is used.
pfit = fit1d (fit1d|fit2d)
Profile fitting algorithm to use with variance weighting or cleaning.
When determining a profile the two dimensional spectrum is divided by
an estimate of the one dimensional spectrum to form a normalized two
dimensional spectrum profile. This profile is then smoothed by fitting
one dimensional functions, "fit1d", along the lines or columns most closely
corresponding to the dispersion axis or a special two dimensional
function, "fit2d", described by Marsh (see approfile).
clean = no
Detect and replace deviant pixels?
skybox = 1
Box car smoothing length for sky background when using background
subtraction. Since the background noise is often the limiting factor
for good extraction one may box car smooth the sky to improve the
statistics in smooth background regions at the expense of distorting
the subtraction near spectral features. This is most appropriate when
the sky regions are limited due to a small slit length.
saturation = INDEF
Saturation or nonlinearity level. During variance weighted extractions
wavelength points having any pixels above this value are excluded from the
profile determination.
readnoise = 0.
Read out noise in photons. This parameter defines the minimum noise
sigma. It is defined in terms of photons (or electrons) and scales
to the data values through the gain parameter. A image header keyword
(case insensitive) may be specified to get the value from the image.
gain = 1
Detector gain or conversion factor between photons/electrons and
data values. It is specified as the number of photons per data value.
A image header keyword (case insensitive) may be specified to get the value
from the image.
lsigma = 3., usigma = 3.
Lower and upper rejection thresholds, given as a number of times the
estimated sigma of a pixel, for cleaning.
I/O parameters and the default dispersion axis are taken from the package parameters, the default aperture parameters from apdefault, automatic aperture finding parameters from apfind, recentering parameters from aprecenter, resizing parameters from apresize, parameters used for centering and editing the apertures from apedit, and tracing parameters from aptrace.
The two dimensional spectra within the defined apertures of the input images are fit by a model and new output images are created with either the model spectra, the difference between the input and model spectra, or the ratio of input and model spectra. The type of output is selected by the parameter fittype which may have one of the values "fit", "difference", or "ratio".
Aperture definitions may be inherited from those of other images by specifying a reference image with the references parameter. Images in the reference list are matched with those in the input list in order. If the reference image list is shorter than the number of input images, the last reference image is used for all remaining input images. Thus, a single reference image may be given for all the input images or different reference images may be given for each input image. The special reference name "last" may be used to select the last set apertures used in any of the apextract tasks.
If an aperture reference image is not specified or no apertures are found for the specified reference image, previously defined apertures for the input image are sought in the aperture database. Note that reference apertures supersede apertures for the input image. If no apertures are defined they may be created automatically, the find option, or interactively in the aperture editor, if the interactive and edit options are set.
The functions performed by the task are selected by a set of flag parameters. The functions are an automatic spectrum finding and aperture defining algorithm (see apfind) which is ignored if apertures are already defined, automatic recentering and resizing algorithms (see aprecenter and apresize), an interactive aperture editing function (see apedit), a spectrum position tracing and trace function fit (see aptrace), and the main function of this task, two dimensional model fitting.
Each function selection will produce a query for each input spectrum if the interactive parameter is set. The queries are answered by "yes", "no", "YES", or "NO", where the upper case responses suppress the query for following images. There are other queries associated with tracing which first ask whether the operation is to be done interactively and, if yes, lead to queries for each aperture. If the interactive parameter is not set then aperture editing and interactive trace fitting are ignored.
The two dimensional spectrum model consists of a smooth two dimensional normalized profile multiplied by the variance weighted one dimensional spectrum. The profile is computed by dividing the data within the aperture by the one dimensional spectrum, smoothing with either low order function fits parallel to the dispersion axis or a special two dimensional function as selected by the pfit parameter. The smooth profile is then used to improve the spectrum estimate using variance weighting and to eliminate deviant or cosmic ray pixels by sigma tests. The profile algorithm is described in detail in approfiles and the variance weighted spectrum is described in apvariance.
The process of determining the profile and variance weighted spectrum, and hence the two dimensional spectrum model, is identical to that used for variance weighted extraction of the one dimensional spectra in the tasks apall or apsum. Most of the parameters of in this task are the same as those in the extraction tasks and so further information about them may be found in the descriptions of those tasks.
Because of the connection with variance weighted extraction and cleaning of one dimensional spectra, this task is useful as a diagnostic tool for understanding and evaluating the variance weighting algorithm. For example the "difference" image provides the residuals in a two dimensional visual form.
The "fit" output image does not include any background determination; i.e the fit is background subtracted. Pixels outside the modeled spectra are set to zero.
The "difference" output image is simply the difference between the background subtracted "fit" and the data. Thus the difference within the apertures should approximate the background and outside the apertures the difference will be identical with the input image.
The "ratio" output image does include any background in the model before taking the ratio of the data and model. If a model pixel is less than the given threshold parameter the output ratio is set to one. This is used to avoid division by zero and set a limit to noise in ratio image. Outside of the apertures the ratio output pixels are set to one.
1. To compute the residuals of a model fit where the image already has aperture defined:
cl> apfit ls1 inter- rec- res- trace- read=3 gain=1 back=fit
apbackground, approfile, apvariance, , apdefault, apfind, aprecenter, apresize, apedit, aptrace, apsum, apall,