fitfunction image
image
Image in which the parameter values are to be fitted.
parameter = x0
Parameter to be fit. The legal minimum match abbreviated parameters
are x0, s0, s1, s2.
lines = *
Sample image lines to be used in the function fit.
spectra = *
Spectra for which the parameters are to be fit.
function = interpolation spline
Fitting function to be used. The function is specified as a string
which may be minimum match abbreviated. The functions currently available
are:
interpolation spline
Interpolation spline of specified order.
smoothing spline
Smoothing spline of specified order and number of polynomial pieces.
spline_order = 4
Order of the fitting spline. The order must be even.
The minimum value is 2 and maximum value is determined from the number of
sample lines in the fit.
spline_pieces = 1
The number of polynomial pieces in a smoothing spline.
The minimum value is 1 and the maximum value is determined from the number of
sample lines in the fit.
A function is fit to the parameter values previously determined at the sample lines for each spectrum. The function coefficients are stored in the database and the fitted values replace the original values at all the sample lines (not just the sample lines used in the fit). The type of function, the parameter to be fitted, the sample lines used in the fit, and the spectra to be fitted are all selected by the user. The function is extrapolated to cover all image lines.
The values of the function fit at arbitrary image lines may be listed with mslist.
The extraction of the spectra requires that a fitting function be determined for the spectra positions. This is done by:
cl> fitfunction image
To smooth the parameter "s0" in model gauss5 with a cubic spline and leave out a bad point at sample line 7:
cl> fitfunction image parmeter=s0 function=smooth >>> lines="1-6,8-"