Returns

fits array: the fit for each region, back to back. Use nregions and regions parameters to unwrap this result.

Examples

simple example: one gaussian

 ; make a simple gauss
 x = lindgen(150)
 h = 400000.
 c = 75.
 w = 15.
 noise = 10000
 data=h*exp(-4.0*alog(2)*(x-c)^2/w^2)+(randomn(seed,n_elements(x))*noise)
 ; make an initial guess to this guassian
 h = 400000.
 c = 75.
 w = 15.
 inits = [h,c,w]
 nregions = 1
 regions = [[20,120]]
 ngauss = 1
 max_iters = 500
 yfit = gauss_fits(x,data,nregions,regions,inits,ngauss,max_iters,coefficients,errors,quiet=1)
 ; view the results
 plot, data
 gbtoplot, x[regions[0]:regions[1]], yfit, color=!red, /chan
 

complex examle: multiple gaussians in multiple regions

    ; create 5 gaussians in the same plot
    a1 = [400000.,35.,15.]
    a2 = [100000.,15,7.5]
    a3 = [200000.,110,8.0]
    a4 = [100000.,150,5.5]
    a5 = [100000.,170,5.5]
    a = [a1,a2,a3,a4,a5]
    x = lindgen(200)
    data = make_gauss(x,a,10000.)
    plot, data

    ; specify 3 regions
    nregions = 3
    regions = [[5,75],[90,130],[135,190]]
    inits = [[a1],[a2],[a3],[a4],[a5]]
    ngauss = 5
    max_iters = 500
    p = replicate({value:0.D, fixed:0, limited:[0,0], $
                       limits:[0.D,0]}, 15) 
                       ; 15 = 5 gauss * 3 parameter per guass
    p[*].value = a
    ; hold the first gaussians height fixed
    p[0].fixed = 1

    ; find all the fits at once
    yfit = gauss_fits(x,data,nregions,regions,inits,ngauss,max_iters,coefficients,errors,parinfo=p,quiet=1)

    ; unwrap the results and plot them
    ystart = 0
    for i=0,(nregions-1) do begin
        b = regions[0,i]
        e = regions[1,i]
        yend = ystart + (e-b)
        y = yfit[ystart:yend]
        ystart = yend + 1
        gbtoplot, x[b:e], y, color=!red, /chan
    endfor

 

Uses

mpcurvefit.pro

Version

$Id$

Parameters
xx in, required array	The x-values to use in the fit.
yy in, required array	The data to be fit at xx.
nregions in, required long	The number of regions in which to fit gaussians
regions in, required 2-D array	2-D array marking ends of each region.
inits in, required 2-D array	2-D array of the form [[h,c,w],[h,c,w],[h,c,w],...], where h = height, c = center, w = full width half maximum. Each [h,c,w] corresponds to a gaussian to fit.
ngauss in, required integer	The total number of gaussians to fit.
max_iters in, required long	max interations
coefficients out, required variable	2-D array of the form [[h,c,w],[h,c,w],[h,c,w],...], where h, c, w are the results for the fit of the height, center, and width. If one of these was specified as fixed, it will return identical to its value in the inits param.
errors out, required variable	2-D array of the form [[h,c,w],[h,c,w],[h,c,w],...], where h, c, w are the 1-sigma errors for the results returned in the coefficients parameter

Keywords
parinfo in, optional array	Array of structures for placing different constraints on each parameter: Each parameter is associated with one element of the array, in numerical order. The structure can have the following entries (none are required): .VALUE - the starting parameter value (but see the START_PARAMS parameter for more information). .FIXED - a boolean value, whether the parameter is to be held fixed or not. Fixed parameters are not varied by MPFIT, but are passed on to MYFUNCT for evaluation. .LIMITED - a two-element boolean array. If the first/second element is set, then the parameter is bounded on the lower/upper side. A parameter can be bounded on both sides. Both LIMITED and LIMITS must be given together. .LIMITS - a two-element float or double array. Gives the parameter limits on the lower and upper sides, respectively. Zero, one or two of these values can be set, depending on the values of LIMITED. Both LIMITED and LIMITS must be given together. .PARNAME - a string, giving the name of the parameter. The fitting code of MPFIT does not use this tag in any way. However, the default ITERPROC will print the parameter name if available. .STEP - the step size to be used in calculating the numerical derivatives. If set to zero, then the step size is computed automatically. Ignored when AUTODERIVATIVE=0. This value is superceded by the RELSTEP value. .RELSTEP - the *relative* step size to be used in calculating the numerical derivatives. This number is the fractional size of the step, compared to the parameter value. This value supercedes the STEP setting. If the parameter is zero, then a default step size is chosen. .MPSIDE - the sidedness of the finite difference when computing numerical derivatives. This field can take four values: 0 - one-sided derivative computed automatically 1 - one-sided derivative (f(x+h) - f(x) )/h -1 - one-sided derivative (f(x) - f(x-h))/h 2 - two-sided derivative (f(x+h) - f(x-h))/(2*h) Where H is the STEP parameter described above. The "automatic" one-sided derivative method will chose a direction for the finite difference which does not violate any constraints. The other methods do not perform this check. The two-sided method is in principle more precise, but requires twice as many function evaluations. Default: 0. .MPMAXSTEP - the maximum change to be made in the parameter value. During the fitting process, the parameter will never be changed by more than this value in one iteration. A value of 0 indicates no maximum. Default: 0. .TIED - a string expression which "ties" the parameter to other free or fixed parameters. Any expression involving constants and the parameter array P are permitted. Example: if parameter 2 is always to be twice parameter 1 then use the following: parinfo(2).tied = '2 * P(1)'. Since they are totally constrained, tied parameters are considered to be fixed; no errors are computed for them. [ NOTE: the PARNAME can't be used in expressions. ] .MPPRINT - if set to 1, then the default ITERPROC will print the parameter value. If set to 0, the parameter value will not be printed. This tag can be used to selectively print only a few parameter values out of many. Default: 1 (all parameters printed)
_EXTRA in, optional record	Extra keywords are passed to mpcurvefit.