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.help fitgauss5 Jul84 noao.twodspec.multispec
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NAME
fitgauss5 -- Fit spectra profiles with five parameter Gaussian model
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USAGE
fitgauss5 image start
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PARAMETERS
.ls image
Image to be modeled.
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.ls start
Starting sample line containing the initial model parameters.
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.ls lower = -10
Lower limit for the profile fit relative to each spectrum position.
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.ls upper = 10
Upper limit for the profile fit relative to each spectrum position.
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.ls lines = "*"
Sample image lines to be fit.
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.ls spectra = "*"
Spectra to be fit.
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.ls naverage = 20
Number of data lines to be averaged about each sample image line before
model fitting.
.le
.ls factor = 0.05
The model fit to each line is iterated until the RMS error between the
model line and the data line improves by less than this factor.
.le
.ls track = yes
Track the model solution from the starting line to the other sample lines?
.le
.ls algorithm = 1
Parameter fitting algorithm to use. Legal values are 1 and 2.
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.ls fit_i0 = yes
Fit the profile scale parameters i0?
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.ls fit_x0 = yes
Fit the spectra position parameters x0?
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.ls fit_s0 = yes
Fit the spectra shape parameters s0?
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.ls fit_s1 = no
Fit the spectra shape parameters s1?
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.ls fit_s2 = no
Fit the spectra shape parameters s2?
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.ls smooth_s0 = yes
Fit a smoothing spline to the shape parameters s0 after each iteration?
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.ls smooth_s1 = yes
Fit a smoothing spline to the shape parameters s1 after each iteration?
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.ls smooth_s2 = yes
Fit a smoothing spline to the shape parameters s2 after each iteration?
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.ls spline_order = 4
Order of the smoothing spline to be fit to the shape parameters.
.le
.ls spline_pieces = 3
Number of polynomial pieces for the smoothing spline.
.le
.ls verbose = no
Print general information about the progress of the model fitting.
.le
.ih
DESCRIPTION
The spectra profiles in the interval (\fIlower, upper\fR) about each
spectrum position are fit with a five parameter Gaussian model for
the specified sample lines of the image. For a description of
the model see \fBgauss5\fR. The model fitting is performed using
simultaneous linearized least squares on the selected model profile
parameters as determined by the \fIalgorithm\fR for the specified
\fIspectra\fR. The parameter fitting technique computes correction
vectors for the parameters until the RMS error of the model image line
to the data image line, which is an average of \fInaverage\fR lines
about the sample line, improves by less than \fIfactor\fR.
A solution which increases the RMS error of the model is not allowed.
If the parameter \fItrack\fR is yes then the initial model parameters are
those given in the database for the sample line \fIstart_line\fR. From
this starting point the model parameters are iterated to a best fit at
each specified sample line and then the best fit is used as the starting
point at the next line. The tracking sequence is from the starting line
to the last line and then, starting again from the starting line, to
the first line. Note that the model parameters, including the starting
spectra positions, need be set only at the starting line.
If \fItrack\fR is no then each specified sample line is fitted independently
from the initial model parameters previously set for that line. This option
is used to add additional parameters to the model after an
initial solution has been obtained or to refit a new image whose database
was created as a copy of the database of a previously fit image.
The shape parameters s0, s1, and s2 can be smoothed by fitting a spline of
specified \fIorder\fR and number of spline pieces, \fInpp\fR to the
parameters as a function of spectra position.
The smoothing is performed after each iteration and before
computing the next RMS error. The smoothing is a form of local constraint
to keep neighboring spectra from having greatly different shapes.
The possibility of such erroneous solutions being obtained is present in
very blended data.
In \fIverbose\fR mode the RMS errors of each iteration are printed on the
standard output.
The selection of the parameters to be fit and the order in which they are
fit is determined by \fIalgorithm\fR. These algorithms are:
.ls 4 1
This algorithm fits the selected parameters (\fIfit_i0, fit_x0,
fit_s0, fit_s1, fit_s2\fR) for the selected \fIspectra\fR simultaneously.
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.ls 4 2
This algorithm begins by fitting the parameters i0, x0, and s0
simultaneously. Note that the values of s1 and s2 are used but are
kept fixed. Next the parameters s0 and s1 (the shape) are fit simultaneously
keeping i0, x0, and s2 fixed followed by fitting i0 and x0 while
keeping s0, s1, and s2 (the shape) fixed. If either of these fits
fails to improve the RMS then the algorithm terminates.
Also, if after the two steps (the fit of s0 and s1 followed by the fit
of i0 and x0), the RMS of the fit has not improved by more than the
user specified factor the algorithm also terminates. This algorithm has been
found to be the best way to fit highly blended spectra.
.le
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EXAMPLES
The default action is to fit Gaussian profiles to the spectra and trace
the fit from the starting line. An example of this is:
cl> fitgauss5 image 1
To fit heavily blended spectra with the four parameter model (i0, x0, s0, s1):
cl> fitgauss5 image 1 algorithm=2
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SEE ALSO
findspectra
.endhelp
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