# fill in missing data on 1-axis by minimizing power out of a given filter.
#
subroutine miss1(     na, a, np, p, copy, niter)
integer	iter, ip, nr, na,    np,          niter
real	p(np)		# in: known data with zeros for missing values.
			# out: known plus missing data.
real	copy(np)	# in: copy(ip) vanishes where p(ip) is a missing value.
real	a(na)		# in: roughening filter
temporary real	dp(np),sp(np),  r(np+na-1),dr(np+na-1),sr(np+na-1)
nr = np+na-1
	call contran( 0, 0, na,a, np, p, r)	# r = a*p   convolution
	call scale  (        -1., nr,    r)	# r = -r
do iter= 0, niter {				# niter= number missing or less
	call contran( 1, 0, na,a, np,dp, r)	# dp(a,r)   correlation
	do ip= 1, np
		if( copy(ip) != 0.)		# missing data where copy(ip)==0
			dp(ip) = 0.		# can't change known data
	call contran( 0, 0, na,a, np,dp, dr)	# dr=a*dp   convolution
	call cgstep( iter, np,p,dp,sp, nr,r,dr,sr)	# p=p+dp; r=r-dr
	}
return; end