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writing calling program for a subroutine

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Tony1984

Tony1984

Technical User
Jul 2, 2012
20
US
Hello
any one knows how to write a calling program for the following subroutine for this kind of data
1800
1.00E-11 3.74E+07
1.06E-11 3.63E+07
1.13E-11 3.53E+07
1.19E-11 3.43E+07
1.25E-11 3.35E+07
1.38E-11 3.19E+07
1.50E-11 3.06E+07
1.63E-11 2.94E+07
1.75E-11 2.83E+07
1.88E-11 2.73E+07
2.00E-11 2.65E+07
2.15E-11 2.55E+07
2.29E-11 2.47E+07
2.44E-11 2.40E+07
2.58E-11 2.33E+07
2.73E-11 2.27E+07
2.87E-11 2.21E+07
3.16E-11 2.10E+07
3.46E-11 2.01E+07
3.75E-11 1.93E+07
4.04E-11 1.86E+07
4.33E-11 1.80E+07
4.66E-11 1.73E+07
5.00E-11 1.67E+07
5.31E-11 1.62E+07
5.63E-11 1.58E+07
5.94E-11 1.54E+07
6.25E-11 1.50E+07
6.88E-11 1.43E+07
7.50E-11 1.37E+07
8.13E-11 1.31E+07
8.75E-11 1.26E+07
9.38E-11 1.22E+07
1.00E-10 1.18E+07
1.06E-10 1.15E+07
1.13E-10 1.12E+07
1.19E-10 1.09E+07
1.25E-10 1.06E+07
1.38E-10 1.01E+07
1.50E-10 9.66E+06
1.63E-10 9.28E+06
1.75E-10 8.94E+06
1.88E-10 8.64E+06
2.00E-10 8.37E+06
2.19E-10 8.00E+06
2.38E-10 7.68E+06
2.56E-10 7.39E+06
2.75E-10 7.13E+06
2.94E-10 6.90E+06
3.13E-10 6.69E+06
3.31E-10 6.50E+06
3.50E-10 6.32E+06
3.88E-10 6.01E+06
4.25E-10 5.74E+06
4.63E-10 5.50E+06
5.00E-10 5.29E+06
5.31E-10 5.13E+06
5.63E-10 4.99E+06
5.94E-10 4.85E+06
6.25E-10 4.73E+06
6.88E-10 4.51E+06
7.50E-10 4.32E+06
8.13E-10 4.15E+06
8.75E-10 3.99E+06
9.38E-10 3.86E+06
1.00E-09 3.74E+06
1.06E-09 3.62E+06
1.13E-09 3.52E+06
1.19E-09 3.43E+06
1.25E-09 3.34E+06
1.38E-09 3.18E+06
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1.63E-09 2.93E+06
1.75E-09 2.82E+06
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4.25E-09 1.79E+06
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6.25E-09 1.47E+06
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8.75E-09 1.23E+06
9.38E-09 1.19E+06
1.00E-08 1.15E+06
1.06E-08 1.11E+06
1.13E-08 1.08E+06
1.19E-08 1.05E+06
1.25E-08 1.02E+06
1.31E-08 9.97E+05
1.38E-08 9.72E+05
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1.63E-08 8.89E+05
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4.42E-08 5.09E+05
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5.00E-08 4.73E+05
5.31E-08 4.56E+05
5.63E-08 4.41E+05
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1.00E-07 3.08E+05
1.08E-07 2.94E+05
1.16E-07 2.82E+05
1.24E-07 2.71E+05
1.32E-07 2.62E+05
1.39E-07 2.53E+05
1.47E-07 2.46E+05
1.55E-07 2.41E+05
1.63E-07 2.36E+05
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3.62E-07 1.82E+05
3.68E-07 1.76E+05
3.75E-07 1.70E+05
3.81E-07 1.64E+05
3.87E-07 1.59E+05
3.93E-07 1.54E+05
3.99E-07 1.50E+05
4.05E-07 1.46E+05
4.10E-07 1.43E+05
4.16E-07 1.40E+05
4.21E-07 1.37E+05
4.27E-07 1.34E+05
4.32E-07 1.31E+05
4.38E-07 1.28E+05
4.44E-07 1.26E+05
4.49E-07 1.23E+05
4.55E-07 1.21E+05
4.66E-07 1.17E+05
4.77E-07 1.13E+05
5.00E-07 1.07E+05
5.31E-07 9.96E+04
5.63E-07 9.38E+04
5.94E-07 8.92E+04
6.25E-07 8.53E+04
6.56E-07 8.22E+04
6.88E-07 7.97E+04
7.19E-07 7.77E+04
7.50E-07 7.62E+04
7.81E-07 7.52E+04
8.13E-07 7.47E+04
8.44E-07 7.47E+04
8.75E-07 7.55E+04
9.06E-07 7.71E+04
9.38E-07 7.99E+04
9.53E-07 8.18E+04
9.69E-07 8.43E+04
9.84E-07 8.74E+04
1.00E-06 9.12E+04
1.02E-06 9.70E+04
1.03E-06 1.01E+05
1.04E-06 1.04E+05
1.05E-06 1.10E+05
1.06E-06 1.17E+05
1.07E-06 1.24E+05
1.09E-06 1.31E+05
1.11E-06 1.45E+05
1.12E-06 1.50E+05
1.13E-06 1.51E+05
1.14E-06 1.49E+05
1.16E-06 1.44E+05
1.17E-06 1.34E+05
1.18E-06 1.21E+05
1.19E-06 1.06E+05
1.21E-06 9.29E+04
1.22E-06 8.30E+04
1.23E-06 7.44E+04
1.24E-06 6.71E+04
1.25E-06 6.11E+04
1.26E-06 5.63E+04
1.27E-06 5.25E+04
1.28E-06 4.98E+04
1.29E-06 4.79E+04
1.31E-06 4.63E+04
1.31E-06 4.56E+04
1.32E-06 4.48E+04
1.33E-06 4.31E+04
1.35E-06 4.12E+04
1.36E-06 3.95E+04
1.38E-06 3.81E+04
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1.40E-06 3.61E+04
1.43E-06 3.46E+04
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1.67E-06 3.02E+04
1.71E-06 2.99E+04
1.75E-06 2.97E+04
1.79E-06 2.96E+04
1.81E-06 2.96E+04
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1.85E-06 2.99E+04
1.86E-06 3.01E+04
1.88E-06 3.03E+04
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1.93E-06 3.32E+04
1.94E-06 3.41E+04
1.94E-06 3.49E+04
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1.95E-06 3.70E+04
1.96E-06 3.84E+04
1.96E-06 4.02E+04
1.97E-06 4.24E+04
1.97E-06 4.50E+04
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1.98E-06 5.22E+04
1.99E-06 5.68E+04
1.99E-06 6.22E+04
2.00E-06 6.81E+04
2.02E-06 8.92E+04
2.02E-06 9.35E+04
2.03E-06 9.69E+04
2.03E-06 9.91E+04
2.03E-06 1.00E+05
2.04E-06 9.99E+04
2.04E-06 9.87E+04
2.05E-06 9.68E+04
2.05E-06 9.40E+04
2.06E-06 8.68E+04
2.06E-06 7.82E+04
2.08E-06 6.31E+04
2.08E-06 5.66E+04
2.09E-06 5.10E+04
2.09E-06 4.86E+04
2.09E-06 4.65E+04
2.10E-06 4.45E+04
2.10E-06 4.28E+04
2.10E-06 4.13E+04
2.11E-06 3.99E+04
2.11E-06 3.77E+04
2.12E-06 3.63E+04
2.12E-06 3.52E+04
2.13E-06 3.43E+04
2.13E-06 3.35E+04
2.14E-06 3.22E+04
2.15E-06 3.13E+04
2.16E-06 3.05E+04
2.17E-06 2.98E+04
2.17E-06 2.93E+04
2.18E-06 2.89E+04
2.19E-06 2.85E+04
2.21E-06 2.79E+04
2.23E-06 2.74E+04
2.24E-06 2.70E+04
2.26E-06 2.67E+04
2.28E-06 2.63E+04
2.31E-06 2.60E+04
2.33E-06 2.57E+04
2.36E-06 2.55E+04
2.41E-06 2.50E+04
2.46E-06 2.46E+04
2.52E-06 2.40E+04
2.54E-06 2.38E+04
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2.63E-06 2.30E+04
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3.29E-06 3.91E+04
3.31E-06 3.76E+04
3.32E-06 3.67E+04
3.34E-06 3.62E+04
3.35E-06 3.61E+04
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2.56E-05 9.86E+04
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2.57E-05 9.11E+04
2.57E-05 8.57E+04
2.58E-05 7.94E+04
2.58E-05 6.54E+04
2.59E-05 5.21E+04
2.59E-05 4.61E+04
2.59E-05 4.08E+04
2.60E-05 3.62E+04
2.60E-05 3.42E+04
2.60E-05 3.25E+04
2.60E-05 3.10E+04
2.60E-05 2.96E+04
2.61E-05 2.86E+04
2.61E-05 2.77E+04
2.61E-05 2.72E+04
2.61E-05 2.68E+04
2.61E-05 2.66E+04
2.61E-05 2.66E+04
2.61E-05 2.68E+04
2.61E-05 2.72E+04
2.62E-05 2.78E+04
2.62E-05 2.87E+04
2.62E-05 2.98E+04
2.62E-05 3.12E+04
2.62E-05 3.29E+04
2.62E-05 3.49E+04
2.62E-05 3.81E+04
2.63E-05 4.20E+04
2.63E-05 4.66E+04
2.63E-05 5.19E+04
2.63E-05 5.79E+04
2.63E-05 6.44E+04
2.64E-05 7.88E+04
2.64E-05 9.04E+04
2.64E-05 1.00E+05
2.64E-05 1.04E+05
2.65E-05 1.07E+05
2.65E-05 1.09E+05
2.65E-05 1.10E+05
2.65E-05 1.10E+05
2.65E-05 1.09E+05
2.65E-05 1.04E+05
2.66E-05 9.65E+04
2.66E-05 8.79E+04
2.66E-05 7.85E+04
2.67E-05 6.98E+04
2.67E-05 6.23E+04
2.67E-05 5.91E+04
2.67E-05 5.61E+04
2.67E-05 5.34E+04
2.67E-05 5.10E+04
2.68E-05 4.69E+04
2.68E-05 4.35E+04
2.68E-05 4.07E+04
2.69E-05 3.92E+04
2.69E-05 3.79E+04
2.69E-05 3.68E+04
2.69E-05 3.59E+04
2.69E-05 3.52E+04
2.69E-05 3.48E+04
2.70E-05 3.45E+04
Code: 
	subroutine nonlinearInt(coefit,ndata,chisq)

	parameter (maxlink=8)
	dimension coefit(4+maxlink),lista(4+maxlink)

	common /maxiter/maxiter
    common /mreftol/rmreftol
	common /ihklnbr/k,ihkl(3,maxlink+1)
	logical*1 bad
	common /bad/bad

	do 11 j=1, 4+maxlink
	   lista(j)=0
11	continue
	lista(1)=1
	lista(4)=1
	do 10 j=2,k
	   lista(j+3)=1
10	continue

	if (coefit(1).gt.0) then
	   coefit(1)=sqrt(coefit(1))
	else
	   coefit(1)=1.
	endif
	if (coefit(4).le.0) coefit(4)=1.
	do 12 j=2,k
	   coefit(j+3)=coefit(1)
12	continue
	do 16 j=k+4,maxlink+4
	   coefit(j)=0.
16	continue
	coefit1Rem=coefit(1)
	coefit4Rem=coefit(4)

	chi2last=1.e30
        iter=0
1120    alamda=-1.
        iter=iter+1
        if (bad) goto 13

        call mrqmin92int(ndata,coefit,lista,12,12,chisq,alamda)
        if (bad) goto 13

120     chisqlast=chisq
140     alamdalast=alamda
        if (iter.ge.maxiter) goto 130
        iter=iter+1
        call mrqmin92int(ndata,coefit,lista,12,12,chisq,alamda)
        if (bad) goto 13

        if (alamda.ge.alamdalast) goto 140
        if (2.*abs(chisq-chisqlast).gt.rmreftol*(abs(chisq)+abs(chisqlast))) goto 120

130     continue
        alamda=0.0
        call mrqmin92int(ndata,coefit,lista,12,12,chisq,alamda)
	if (bad) goto 13
        if (iter.ge.maxiter) then
           write(6,*)'nonlinearInt: maximum iteration reached'
           bad=.TRUE.
        endif
	goto 14

13	coefit(1)=coefit1Rem
	coefit(4)=coefit4Rem

14	coefit(1)=coefit(1)*coefit(1)
	do 15 j=2,k
	   coefit(j+3)=coefit(j+3)*coefit(j+3)
15	continue

	return
	end

	subroutine mrqmin92int(ndata,a,ia,ma,nca,chisq,alamda)

! set to largest number of fit parameters.

	parameter (mmax=12)
	integer	ma,nca,ndata,ia(ma)
	real	alamda,chisq,funcs,a(ma),alpha(mmax,mmax),covar(mmax,mmax),covarrem(mmax,mmax)

! Levenberg-Marquardt method, attempting to reduce the value che-square of a 
! fit between a set of data points x(1:ndata), y(1:ndata) with individual 
! standard deviations sig(1:ndata), and a nonlinear function dependent on ma 
! coefficients a(1:ma).  The input array ia(1:ma) indicated by nonzero entries 
! those components of a that should be fitted for, and by zero entries those 
! components that should be held fixed at their input values. The program 
! returns current best-fit values for the parameters a(1:ma), and chi-square
! = chisq.  The arrays covar(1:nca,1:nca), alpha(1:nca,1:nca) with physical 
! dimension nca (> or =the number of fitted parameters) are used as working 
! space during most iterations.  Supply a subroutine funcs(x,a,yfit,dyda,ma) 
! that evaluates the fitting functions yfit, and its derivatives dyda with 
! respect to the fitting parameters a at x.  On the first call provide an 
! initial guess for the parameters a, and set alamda < 0 for initialization 
! (which then sets alamda = 0.001).  If a step succeeds chisq becomes smaller 
! and alamda decreases by a factor of 10.  If a step fails alamda grows by a 
! factor of 10.  You must call this routine repeatedly until convergence is 
! achieved.  Then, make one final call with alamda = 0, so that covar(1:ma,
! 1:ma) returns the covarience matrix, and alpha the curvature matrix. 
! (Parameters held fixed will return zero covariences.)

	integer j,k,l,m,mfit
	real	ochisq,atry(mmax),beta(mmax),da(mmax),darem(mmax)
	save	ochisq,atry,beta,da,mfit
	logical*1 bad
	common /bad/bad
	common /lmspeed/speed

! initialization

	if (alamda.lt.0.) then
	   mfit=0
	   do 11 j=1,ma
              if (ia(j).ne.0) mfit=mfit+1
11	   continue
	   alamda=0.001
	   call mrqcof92int(ndata,a,ia,ma,alpha,beta,nca,chisq)
	   if (bad) return
	   ochisq=chisq
	   do 12 j=1,ma
	      atry(j)=a(j)
12	   continue
	endif
	j=0

! alter linearized fitting matrix, by augmenting diagnal elements.

	do 14 l=1,ma
	   if (ia(l).ne.0) then
	      j=j+1
	      k=0
	      do 13 m=1,ma
	         if (ia(m).ne.0) then
	            k=k+1
	            covar(j,k)=alpha(j,k)
	         endif
13	      continue
	      covar(j,j)=alpha(j,j)*(1.+alamda)
	      da(j)=beta(j)
	   endif
14	continue

! matrix solution

	do 101 i=1,ma
	   darem(i)=da(i)
	   do 101 j=1,ma
	      covarrem(i,j)=covar(i,j)
101	continue
	call gaussjpf(covar,mfit,nca,da,1,1)
	if (bad) then
	   bad = .FALSE.
	   do 102 i=1,ma
              da(i)=darem(i)
              do 102 j=1,ma
                 covar(i,j)=covarrem(i,j)
102        continue
	   call usesvdpf(covar,ma,ma,mmax,mmax,da)
	endif
	if (bad) return

! once converged, evaluate covariance matrix.

	if (alamda.eq.0.) then
	   call covsrt92(covar,nca,ma,ia,mfit)
	   return
	endif
	j=0

! did the trial succeed?

	do 15 l=1,ma
	   if (ia(l).ne.0) then
	      j=j+1
	      atry(l)=a(l)+da(j)
	   endif
15	continue
	call mrqcof92int(ndata,atry,ia,ma,covar,da,nca,chisq)
	if (bad) return

! success, accept the new solution.

	if (chisq.lt.ochisq) then
	   if (alamda.gt.1.e-35) alamda=alamda/speed
	   ochisq=chisq
	   j=0
	   do 17 l=1,ma
	      if (ia(l).ne.0) then
	         j=j+1
	         k=0
	         do 16 m=1,ma
	            if (ia(m).ne.0) then
	               k=k+1
	               alpha(j,k)=covar(j,k)
	            endif
16	         continue
	         beta(j)=da(j)
	         a(l)=atry(l)
	      endif
17	   continue

! failure, increase alamda and return.

	else
	   if (alamda.lt.1.e35) alamda=alamda*speed
	   chisq=ochisq
	endif

	return
	end




	subroutine mrqcof92int(ndata,a,ia,ma,alpha,beta,nalp,chisq)

	integer	ma,nalp,ndata,ia(ma),mmax
	real	chisq,a(ma),alpha(nalp,nalp),beta(ma)
	parameter (mmax=12)
	logical*1 bad
        common /bad/bad

! used by mrqmin to evaluate the linearized fitting matrix alpha, and vector 
! beta as in (15.5.8), and calculate chi-square.

	integer mfit,i,j,k,l,m
	real	dy,sig2i,wt,ymod,dyda(mmax)

	mfit=0
	do 11 j=1,ma
	   if (ia(j).ne.0) mfit=mfit+1
11	continue

! initialize (symmetric) alpha, beta

	do 13 j=1,mfit
	   do 12 k=1,j
	      alpha(j,k)=0.
12	   continue
	   beta(j)=0.
13	continue
	chisq=0.

! summation loop over all data.

	do 16 i=1,ndata
	   t=timgnew(i)
	   call nonlinearSO(a,ymod,dyda,ma)
	   if (bad) return
	   if (t.lt.0..or.t.ge.32767.) then
              sig2i=0.
           else
	      sig2i=1.
           endif
	   dy=t-ymod
	   j=0
	   do 15 l=1,ma
	      if (ia(l).ne.0) then
	         j=j+1
	         wt=dyda(l)*sig2i
	         k=0
	         do 14 m=1,l
	            if (ia(m).ne.0) then
	               k=k+1
	               alpha(j,k)=alpha(j,k)+wt*dyda(m)
	            endif
14	         continue
	         beta(j)=beta(j)+dy*wt
	      endif
15	   continue

! and find xhi-square

	   chisq=chisq+dy*dy*sig2i
16	continue

! fill in the symmetric side.

	do 18 j=2,mfit
	   do 17 k=1,j-1
	      alpha(k,j)=alpha(j,k)
17	   continue
18	continue

	return
	end




	subroutine nonlinearSO(a,ymod,dyda,ma)

	parameter (maxlink=8)
	dimension afunc(12),a(ma),dyda(ma)
	common /ihklnbr/k,ihkl(3,maxlink+1)

	call mosmodel(afunc,ma)
	ymod=a(1)*a(1)*afunc(1)+a(2)*afunc(2)+a(3)*afunc(3)+a(4)
	do 10 j=2,k
	   ymod=ymod+a(j+3)*a(j+3)*afunc(j+3)
10	continue

	dyda(1)=2*a(1)*afunc(1)
	dyda(2)=afunc(2)
	dyda(3)=afunc(3)
	dyda(4)=1.
	do 20 j=2,k
	   dyda(j+3)=2*a(j+3)*afunc(j+3)
20	continue
	do 30 j=k+4,ma
	   dyda(j)=0.
30	continue

	return
	end
 
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You need other subroutines called from nonlinearInt, i.e.:
gaussjpf
usesvdpf
covsrt92
timgnew
mosmodel
 
Upvote 0 Downvote
Hi Tony,
Please do not post next time 1800 lines of data. It's not necessary - for testing purposes, 10-20 lines of data were adequate.
To bring your subroutine to work you have to provide other subroutines I listed above.
 
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