|
Hi Karakura,
I think the following is a very nice example. You will need to use a combination of gaussian and a quadratic using GAUSSFIT(). I just modified the example code in the IDL help to use a sin() function instead, I hope this helps:
pro ex_gaussfit
; Define the independent variable.
n = 101
;x = (FINDGEN(n)-(n/2))/4
X = 2*!PI/100 * FINDGEN(100)
; Define the coefficients.
a = [4.0, 1.0, 2.0, 1.0, 0.25, 0.01]
print, 'Expected: ', a
z = (x - a[1])/a[2] ; Gaussian variable
!P.MULTI = [0,2,2] ; set up 2x2 plot window
seed = 123321 ; Pick a starting seed value
for nterms=3,6 do begin
; Define the dependent variables. Start with random noise.
;y = 0.4*RANDOMN(seed, n)
y = sin(x)
; Use a switch statement so we fall through to each term.
switch nterms of
6: y = y + a[5]*x^2
5: y = y + a[4]*x
4: y = y + a[3]
3: y = y + a[0]*exp(-z^2/2)
endswitch
; Fit the data to the function, storing coefficients in
; coeff:
yfit = GAUSSFIT(x, y, coeff, NTERMS=nterms)
print, 'Result: ', coeff[0:nterms-1]
; Plot the original data and the fitted curve:
PLOT, x, y, TITLE='nterms='+STRTRIM(nterms,2)
OPLOT, x, yfit, THICK=2
endfor
end
Cheers,
Fernando
|