--- /dev/null
+c===========================================================================
+c
+c This file is part of TISEAN
+c
+c Copyright (c) 1998-2007 Rainer Hegger, Holger Kantz, Thomas Schreiber
+c
+c TISEAN is free software; you can redistribute it and/or modify
+c it under the terms of the GNU General Public License as published by
+c the Free Software Foundation; either version 2 of the License, or
+c (at your option) any later version.
+c
+c TISEAN is distributed in the hope that it will be useful,
+c but WITHOUT ANY WARRANTY; without even the implied warranty of
+c MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+c GNU General Public License for more details.
+c
+c You should have received a copy of the GNU General Public License
+c along with TISEAN; if not, write to the Free Software
+c Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
+c
+c===========================================================================
+c nonlinear noise reduction
+c see H. Kantz, T. Schreiber, Nonlinear Time Series Analysis, Cambridge
+c University Press (1997,2004)
+c authors T. Schreiber, H. Kantz, R. Hegger (1998) based on earlier versions
+c===========================================================================
+ parameter(nx=100000)
+ dimension x(nx), x0(nx), xc(nx)
+ character*72 file, fout
+ data imax/1/
+ data iverb/1/
+
+ call whatido("nonlinear noise reduction (see also: noise)",iverb)
+ m=imust("m")
+ nq=m-imust("q")
+ eps=fmust("r",eps)
+ kmin=imust("k")
+ imax=ican("i",imax)
+ nmax=ican("l",nx)
+ nexcl=ican("x",0)
+ jcol=ican("c",0)
+ isout=igetout(fout,iverb)
+
+ call nthstring(1,file)
+ call readfile(nmax,x,nexcl,jcol,file,iverb)
+ if(file.eq."-") file="stdin"
+ if(isout.eq.1) call addsuff(fout,file,"_")
+
+ do 10 n=1,nmax
+ 10 x0(n)=x(n)
+ do 20 it=1,imax
+ call clean(nmax,x,xc,m,kmin,nq,eps,iverb)
+ if(fout.ne." ".or.isout.eq.1.or.it.eq.imax) then
+ if(isout.eq.1) call suffix(fout,"c")
+ call outfile(fout,iunit,iverb)
+ do 30 n=1,nmax
+ 30 write(iunit,*) xc(n), x0(n)-xc(n)
+ if(iunit.ne.istdout()) close(iunit)
+ if(iv_io(iverb).eq.1) call writereport(nmax,fout)
+ endif
+ eps=0
+ do 40 n=1,nmax
+ eps=eps+(xc(n)-x(n))**2
+ 40 x(n)=xc(n)
+ eps=sqrt(eps/nmax)
+ 20 if(iv_io(iverb).eq.1)
+ . write(istderr(),*) 'New diameter of neighbourhoods is ', eps
+ end
+
+ subroutine usage()
+c usage message
+
+ call whatineed(
+ . "-m# -q# -r# -k# [-i# -o outfile -l# -x# -c# -V# -h] file")
+ call popt("m","embedding dimension")
+ call popt("q","dimension of manifold")
+ call popt("r","radius of neighbourhoods")
+ call popt("k","minimal number of neighbours")
+ call popt("i","number of iterations (1)")
+ call popt("l","number of values to be read (all)")
+ call popt("x","number of values to be skipped (0)")
+ call popt("c","column to be read (1 or file,#)")
+ call pout("file_c, file_cc (etc.)")
+ call pall()
+ call ptext("Verbosity levels (add what you want):")
+ call ptext(" 1 = input/output" )
+ call ptext(" 2 = state of neighbour search")
+ write(istderr(),'()')
+ stop
+ end
+
+ subroutine clean(nmax,y,yc,m,kmin,nq,d,iverb)
+ parameter(im=100,ii=100000000,nx=100000,mm=15,small=0.0001)
+ dimension y(nmax),yc(nmax),r(mm),ju(nx),c(mm,mm),cm(mm),
+ . jh(0:im*im),jpntr(nx),nlist(nx), zcm(mm,nx)
+
+ if(nmax.gt.nx.or.m.gt.mm) stop "clean: make mm/nx larger."
+ sr=2*small+m-2 ! ${\rm tr}(1/r)=1$
+ do 10 i=1,m
+ r(i)=sr
+ 10 if(i.eq.m.or.i.eq.1) r(i)=sr/small
+ do 20 i=1,nmax
+ 20 yc(i)=y(i)
+ do 30 istep=1,2
+ eps=d
+ iu=nmax-m+1
+ do 40 i=1,iu
+ 40 ju(i)=i+m-1
+ 1 call base(nmax,y,1,m,jh,jpntr,eps)
+ iunp=0
+ do 50 nn=1,iu ! find neighbours
+ n=ju(nn)
+ call neigh(nmax,y,y,n,nmax,1,m,jh,jpntr,eps,nlist,nfound)
+ if(nfound.lt.kmin) then ! not enough neighbours found
+ iunp=iunp+1 ! mark for next sweep
+ ju(iunp)=n
+ else ! fine: enough neighbours
+ do 90 i=1,m ! centre of mass vector
+ s=0
+ do 100 np=1,nfound
+ 100 s=s+y(nlist(np)-m+i)
+ 90 cm(i)=s/nfound
+ if(istep.eq.1) then ! just store centre of mass
+ do 110 i=1,m
+ 110 zcm(i,n)=cm(i)
+ else
+ do 120 i=1,m ! corrected centre of mass vector
+ s=0
+ do 130 np=1,nfound
+ 130 s=s+zcm(i,nlist(np))
+ 120 cm(i)=2*cm(i)-s/nfound
+ do 140 i=1,m ! compute covariance matrix
+ do 140 j=i,m
+ s=0
+ do 150 np=1,nfound
+ jm=nlist(np)-m
+ 150 s=s+(y(jm+i)-cm(i))*(y(jm+j)-cm(j))
+ c(i,j)=r(i)*r(j)*s/nfound
+ 140 c(j,i)=c(i,j)
+ call eigen(c,m) ! find eigenvectors (decreasing)
+ do 160 i=1,m
+ s=0
+ do 170 iq=m-nq+1,m
+ do 170 j=1,m
+ 170 s=s+(y(n-m+j)-cm(j))*c(i,iq)*c(j,iq)*r(j)
+ 160 yc(n-m+i)=yc(n-m+i)-s/r(i)/r(i)
+ endif
+ endif
+ 50 continue
+ iu=iunp
+ if(iv_uncorr(iverb).eq.1)
+ . write(istderr(),*) "With ", eps, iunp, " uncorrected"
+ eps=eps*sqrt(2.)
+ 30 if(iunp.ne.0) goto 1
+ end
+
+c driver for diagonalisation routines
+c see H. Kantz, T. Schreiber, Nonlinear Time Series Analysis, Cambridge
+c University Press (1997)
+c Copyright (C) T. Schreiber (1997)
+
+ subroutine eigen(c,kk)
+ parameter(md=15)
+ dimension c(md,md),d(md),w1(md),w2(md),z(md,md)
+ if(kk.gt.md) stop "eigen: make md larger."
+
+ call rs(md,kk,c,d,1,z,w1,w2,ierr)
+ do 10 i=1,kk
+ do 10 j=1,kk
+ 10 c(i,j)=z(i,kk+1-j)
+ end
+
git://source.jalview.org / jabaws.git / blobdiff