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.help newimage Jul84 noao.twodspec.multispec
.ih
NAME
newimage -- Create a new multi-spectra image
.ih
USAGE
newimage image output
.ih
PARAMETERS
.ls image
Image to be used to create the new image.
.le
.ls output
Filename for the new multi-spectra image.
.le
.ls lower = -10
Lower limit for model profiles. It is measured in pixels from the
spectra centers defined by the position functions in the database.
.le
.ls upper = -10
Upper limit for model profiles. It is measured in pixels from the
spectra centers defined by the position functions in the database.
.le
.ls lines = "*"
Image lines of the multi-spectra image to be in the new multi-spectra image.
.le
.ls ex_model = no
Create a model image?
.le
.ls clean = yes
Replace bad pixels with model values? The following parameters are used:
.ls nreplace = 1000.
Maximum number of pixels to be replaced per image line when cleaning with
model \fIgauss5\fR or maximum number of pixels to be replaced per spectrum when
cleaning with model \fIsmooth\fR.
.le
.ls sigma_cut = 4.
The cleaning threshold in terms of the predicted pixel sigma.
.le
.ls niterate = 1
Maximum number of cleaning iterations per line when cleaning with model
\fIgauss5\fR.
.le
.le
.ls model = "smooth"
Choice of \fIgauss5\fR or \fIsmooth\fR. Minimum match abbreviation is
allowed. This parameter is required only if \fIex_model\fR = yes
or \fIclean\fR = yes.
.le
.ls fit_type = 2
Model fitting algorithm for model \fIgauss5\fR.
.le
.ls naverage = 20
Number of lines to be averaged in model \fIsmooth\fR.
.le
.ls interpolator = "spline3"
Type of image interpolation function to be used.
The choices are "nearest", "linear", "poly3", "poly5", and "spline3".
Minimum match abbreviation is allowed.
.le
.ls verbose = no
Print verbose output?
.le
.ih
DESCRIPTION
A new multi-spectra image is created using the description of the
multi-spectra image in the MULTISPEC database associated with \fIimage\fR.
The user selects the image \fIlines\fR from the original image to be in
the new image. The options allow the creation of model images or images in
which the bad or deviant pixels are replaced by model profile values.
If \fIex_model\fR = yes or \fIclean\fR = yes model
spectra are fit to the spectra in the image. There are two models:
a five parameter Gaussian profile called \fIgauss5\fR and profiles obtained
by averaging \fInaverage\fR image lines surrounding the image line being
modeled called \fIsmooth\fR. The model is selected with the parameter
\fImodel\fR.
When \fIex_model\fR = yes an image containing model spectra is produced.
When \fIclean\fR = yes pixels with large residuals from the model are
detected and removed from the model fit. The selected model is
fit to the pixels which are not in the bad pixel list (not yet implemented)
and which have not been removed from the model fit. The sigma of the fit
is computed. Deviant pixels are detected by comparing them to the model
to determine if they differ by more than \fIsigma_cut\fR times the sigma.
The model fit is iterated, removing deviant pixels at each iteration, until
no more pixels are found deviant or \fInreplace\fR pixels have been found.
The pixels removed or in the bad pixel list are then replaced with
model values. (To clean and extract the spectra with this algorithm see
\fBmsextract\fR.)
There are some technical differences in the model fitting and cleaning
algorithms for the two models. In model \fIsmooth\fR
the fit for the profile scale factors is done independently for each spectrum
and automatically corrected when a bad pixel is detected. This fitting process
is fast and rigorous. The parameter \fInreplace\fR in this model refers to
the maximum number of pixels replaced \fIper spectrum\fR.
In model \fIgauss5\fR, however, the profile scale factors are fit
to the entire image line (hence its ability to fit blended spectra).
There are two fitting algorithms; a rigorous simultaneous fit
and an approximate method. The simultaneous fit is selected when
\fIfit_type\fR = 1. This step is relatively slow. The
alternative method of \fIfit_type\fR = 2 sets the scale factor for each
spectrum by taking the median scale, where scale = data / model profile,
for the three pixels nearest the center of the profile. The median
minimizes the chance of a large error due to a single bad pixel. This
scale may be greatly in error in the case of extreme blending but is also
quite fast; the extraction time is reduced by at least 40%.
The steps of profile fitting and deviant pixel detection are alternated
and the maximum number of iterations through these two steps is
set by \fIniterate\fR. The default of 1 means that the model fitting is not
repeated after detecting deviant pixels.
The option \fIverbose\fR can be used to print the image lines being extracted
and any pixels replaced by the cleaning process.
.ih
EXAMPLES
To create a cleaned version of the image using model \fIsmooth\fR for cleaning:
cl> newimage image newimage
To create an model image using model \fIgauss5\fR:
cl> newimage image newimage ex_model=yes model="gauss5"
.ih
SEE ALSO
msextract
.endhelp
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