Mac binaries

[jabaws.git] / website / archive / binaries / mac / src / disembl / Tisean_3.0.1 / source_f / lazy.f

diff --git a/website/archive/binaries/mac/src/disembl/Tisean_3.0.1/source_f/lazy.f b/website/archive/binaries/mac/src/disembl/Tisean_3.0.1/source_f/lazy.f
new file mode 100644 (file)
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+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   lazy.f
+c   simple nonlinear noise reduction
+c   see  H. Kantz, T. Schreiber, Nonlinear Time Series Analysis, Cambridge
+c      University Press (1997,2004)
+c   author T. Schreiber (1998)
+c===========================================================================
+      parameter(nx=1000000)
+      dimension x(nx), x0(nx), xc(nx)
+      character*72 file, fout
+      data eps/0./, frac/0./, imax/1/
+      data iverb/1/
+
+      call whatido("simple nonlinear noise reduction",iverb)
+      m=imust("m")
+      eps=fcan("r",eps)
+      frac=fcan("v",frac)
+      imax=ican("i",imax)
+      nmax=ican("l",nx)
+      nexcl=ican("x",0)
+      jcol=ican("c",0)
+      isout=igetout(fout,iverb)
+      if(eps.eq.0.and.frac.eq.0.) call usage()
+
+      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,"_l")
+      call rms(nmax,x,sc,sd)
+      if(frac.gt.0) eps=sd*frac
+      do 10 n=1,nmax
+ 10      x0(n)=x(n)
+      do 20 it=1,imax
+         call nrlazy(nmax,x,xc,m,eps)
+         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)
+         if(eps.eq.0.) then
+            if(iv_io(iverb).eq.1) write(istderr(),*) 
+     .      'Zero correction, finished'
+            stop
+         endif
+ 20      if(iv_io(iverb).eq.1) write(istderr(),*) 
+     .      'New diameter of neighbourhoods is ', eps
+      end
+
+      subroutine usage()
+c usage message
+
+      call whatineed(
+     .   "-m# [-r# | -v#] [-i# -o outfile -l# -x# -c# -V# -h] file")
+      call ptext("either -r or -v must be present")
+      call popt("m","embedding dimension")
+      call popt("r","absolut radius of neighbourhoods")
+      call popt("v","same as fraction of standard deviation")
+      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_lc,  file_lcc (etc.)")
+      call pall()
+      stop
+      end
+
+      subroutine nrlazy(nmax,y,yc,m,eps)
+      parameter(im=100,ii=100000000,nx=1000000) 
+      dimension y(nmax),yc(nmax),jh(0:im*im),jpntr(nx),nlist(nx)
+
+      if(nmax.gt.nx) stop "nrlazy: make nx larger."
+      call base(nmax,y,1,m,jh,jpntr,eps)
+      do 10 n=1,nmax
+ 10      yc(n)=y(n)   
+      do 20 n=m,nmax           
+         call neigh(nmax,y,y,n,nmax,1,m,jh,jpntr,eps,nlist,nfound)
+         av=0
+         do 30 nn=1,nfound            
+ 30         av=av+y(nlist(nn)-(m-1)/2)              ! average middle coordinate
+ 20      yc(n-(m-1)/2)=av/nfound
+      end