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subroutine fitfun(mf, nx, xvar, fvec, iend)
c
c//////////////////////////////////////////////////////////////////////
c Copyright (c) 1997--2000 Matthew Newville, The University of Chicago
c Copyright (c) 1992--1996 Matthew Newville, University of Washington
c
c Permission to use and redistribute the source code or binary forms of
c this software and its documentation, with or without modification is
c hereby granted provided that the above notice of copyright, these
c terms of use, and the disclaimer of warranty below appear in the
c source code and documentation, and that none of the names of The
c University of Chicago, The University of Washington, or the authors
c appear in advertising or endorsement of works derived from this
c software without specific prior written permission from all parties.
c
c THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
c EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
c MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
c IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
c CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
c TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
c SOFTWARE OR THE USE OR OTHER DEALINGS IN THIS SOFTWARE.
c//////////////////////////////////////////////////////////////////////
c
c purpose: feffit fitting function for lmdif1 minimization
c
c arguments:
c mf number of points in ffit [in]
c nx number of points in xv [in]
c xv vector of variables [in]
c fvec output vector to be minimized [out]
c iend information tag [out]
implicit none
include 'consts.h'
include 'arrays.h'
include 'keywrd.h'
include 'fft.h'
include 'fefdat.h'
include 'pthpar.h'
include 'feffit.h'
c local variables
integer nx, mf, iend, i, id, isp, nx1, iter
integer jfit, nqdata, nfit1, nkpar, npaths
integer iupath(mpaths), inxx, ixx, joff, jqw
integer ier, iff_eval_dp
double precision xvar(nx), fvec(mf), xolow, xohigh, bvalue
double precision getsca, xspl(mtknot), bx, qx, sum, tmpval
double precision xsum, sumsqr
character arg*2
integer isarg, ilen, istrln, j, nmx
external bvalue, getsca, istrln, iff_eval_dp, sumsqr
save
c
nkpar = 0
id = 1
if (nx.ne.nvarys) iend = 1
if (mf.ne.mfit) iend = 2
nx1 = nx - nbkg(id)
cc print*, 'FITFUN N_vars:',nx,mf,nfdats
do 20 i = 1, nx
scalar(i) = xvar(i)
20 continue
call synvar
cc print*, 'VARS ', xvar(1), xvar(2), xvar(3), xvar(4)
if (final) rfact_total = zero
c
c sum function to minimize over data sets
c jfit is the counter (through all the data sets)
c for the total number of fitting points
jfit = 0
do 3000 id = 1, nfdats
nqdata = min(maxpts, max(2, nqfit(id)) + 10)
if (ifft(id).eq.1) then
xolow = rmin(id)
xohigh = rmax(id)
else
xolow = qmin(id)
xohigh = qmax(id)
endif
do 200 i = 1, nfit(id)*nqwfs(id)
thifit(i) = zero
200 continue
c re-initialize array for theoretical chi(k)
c by assigning this to the background function
do 300 i = 1, nqdata
thiq(i, id) = zero
thiqr(i, id) = zero
300 continue
c
c sum over paths for theory chi for this data set
npaths = 0
do 400 i = 1, mpaths
iupath(i) = 0
if (iulist(i,id).ne.0) then
npaths = npaths + 1
iupath(npaths) = iulist(i,id)
end if
400 continue
call sum_paths(id, iupath, npaths, nqdata,
$ thiqr(1,id), thiq(1,id))
c
c if refining background, include that here
if ( bkgfit(id)) then
do 450 isp = 1, nbkg(id)
write(tmpstr, '(a,i2.2,a,i2.2)') 'bkg',id,'_',isp
xspl(isp) = getsca(tmpstr,0)
cc print*, 'FITFUN i ' , isp, xspl(isp)
450 continue
do 470 i = 1, nqdata
qx = qgrid*(i-1)
thiq(i, id) = thiq(i, id) +
$ bvalue(qknot(1,id), xspl, nbkg(id),korder,qx,0)
470 continue
end if
c
c subtract data from theory
do 500 i = 1, nqdata
thiq(i,id) = thiq(i,id)-chiq(i,id)
500 continue
c
c loop over k-weights for multiple k-weights
c top down, so that the first k-weight listed is done last
c
do 1000 jqw = nqwfs(id), 1, -1
qweigh(id) = qwfs(jqw,id)
c take fft of theory(+bkg) - data
c note: imag part of theory chi(k) compares to real part of data!!!
c see ff2chi as well
call fitfft(thiq(1,id), maxpts, maxfft, wfftc, qgrid,
$ qwindo(1,id), qwfs(jqw,id), rwindo(1,id), one,
$ ifft(id),modeft(id),xolow,xohigh, nfit1, thifit)
if (nfit1.ne.nfit(id)) then
call warn(3,' fitfun fitfft failed internal test.')
iend = -10
end if
c
c evaluate the contribution to fvec for this data set. weight scales
c chi-square properly to the number of independent points. this is
c important for error analysis (if chi-square is to increase by one,
c it must be scaled correctly.), but only in the final pass, when
c chi-square and r-factors will be calculated.
c print*, 'fitfun ', jfit, jqw, id,
c $ weight(jqw, id), nfit(id), thifit(1), thifit(2)
do 700 i = 1, nfit(id)
fvec(jfit+i) = (thifit(i))/weight(jqw,id)
700 continue
jfit = jfit + nfit(id)
c construct r-factor
if (final) then
call fitfft(chiq(1,id), maxpts, maxfft, wfftc, qgrid,
$ qwindo(1,id), qwfs(jqw,id), rwindo(1,id), one,
$ ifft(id),modeft(id),xolow,xohigh, nfit1, chifit)
if (nfit1.ne.nfit(id)) then
call warn(3,' fitfun fitfft failed internal test.')
iend = -10
end if
sum = zero
rfactr(id) = zero
do 800 i = 1, nfit(id)
sum = sum + chifit(i)*chifit(i)
rfactr(id) = rfactr(id) + thifit(i)*thifit(i)
800 continue
if (sum.le.tiny) sum = tiny
rfactr(id) = rfactr(id) / (sum * nqwfs(id))
rfact_total = rfact_total + rfactr(id)
cc print*, ' final ', sum, rfactr(id), id, rfact_total
end if
c
c restraints
if (nrestraint(id).ge.1) then
cc print*, ' RESTRAINT ', nrestraint(id), id, jfit
do 900 i = 1, nrestraint(id)
if ( (restraint(i,id) .ne. undef).and.
$ (restraint(i,id) .ne. '')) then
ier = iff_eval_dp(restraint(i,id),tmpval)
if (ier.eq.0) then
jfit = jfit + 1
fvec(jfit) = tmpval
cc print*, ' t: ', tmpval, jfit, ier
endif
endif
900 continue
endif
1000 continue ! end loop over k-weights
cc print*, ' end of ndata loop ', id, nfit(id), jfit
3000 continue ! end loop over data sets
if (final) rfact_total = rfact_total / max(1,nfdats)
c
c execute user-defined macro
iter = int(getsca('&fit_iteration',0))
if (iter.gt.itera) then
itera = iter
xsum = sumsqr(fvec,jfit)
cc print*, ' iteration ', iter, mf,mfit,jfit,nfdats, xsum
if (ifit_mac.gt.0) then
call iff_macro_do(ifit_mac, fit_m_arg, 0,.false.)
end if
end if
return
c end subroutine fitfun
end
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