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diff --git a/noao/twodspec/multispec/doc/fitgauss5.hlp b/noao/twodspec/multispec/doc/fitgauss5.hlp new file mode 100644 index 00000000..bcb37276 --- /dev/null +++ b/noao/twodspec/multispec/doc/fitgauss5.hlp @@ -0,0 +1,148 @@ +.help fitgauss5 Jul84 noao.twodspec.multispec +.ih +NAME +fitgauss5 -- Fit spectra profiles with five parameter Gaussian model +.ih +USAGE +fitgauss5 image start +.ih +PARAMETERS +.ls image +Image to be modeled. +.le +.ls start +Starting sample line containing the initial model parameters. +.le +.ls lower = -10 +Lower limit for the profile fit relative to each spectrum position. +.le +.ls upper = 10 +Upper limit for the profile fit relative to each spectrum position. +.le +.ls lines = "*" +Sample image lines to be fit. +.le +.ls spectra = "*" +Spectra to be fit. +.le +.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. +.le +.ls fit_i0 = yes +Fit the profile scale parameters i0? +.le +.ls fit_x0 = yes +Fit the spectra position parameters x0? +.le +.ls fit_s0 = yes +Fit the spectra shape parameters s0? +.le +.ls fit_s1 = no +Fit the spectra shape parameters s1? +.le +.ls fit_s2 = no +Fit the spectra shape parameters s2? +.le +.ls smooth_s0 = yes +Fit a smoothing spline to the shape parameters s0 after each iteration? +.le +.ls smooth_s1 = yes +Fit a smoothing spline to the shape parameters s1 after each iteration? +.le +.ls smooth_s2 = yes +Fit a smoothing spline to the shape parameters s2 after each iteration? +.le +.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. +.le +.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 +.ih +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 +.ih +SEE ALSO +findspectra +.endhelp |