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       g_spatial  - calculates the spatial distribution function (more control
       than g_sdf)

       VERSION 4.0.1


       g_spatial -f  traj.xtc  -s  topol.tpr  -n  index.ndx  -dm  rmsd.xpm  -o
       rmsd-clust.xpm  -g cluster.log -dist rmsd-dist.xvg -ev rmsd-eig.xvg -sz
       clust-size.xvg   -tr   clust-trans.xpm   -ntr   clust-trans.xvg   -clid
       clust-id.xvg -cl clusters.pdb -[no]h -nice int -b time -e time -dt time
       -tu enum -[no]w -[no]xvgr -[no]dista -nlevels int -cutoff real -[no]fit
       -max real -skip int -[no]av -wcl int -nst int -rmsmin real -method enum
       -minstruct int -[no]binary -M int -P int -seed int -niter int -kT real


       g_cluster  can  cluster  structures  with  several  different  methods.
       Distances  between  structures  can  be determined from a trajectory or
       read from an XPM matrix file with the  -dm option.  RMS deviation after
       fitting  or  RMS deviation of atom-pair distances can be used to define
       the distance between structures.

       single linkage: add a structure to a cluster when its distance  to  any
       element of the cluster is less than  cutoff.

       Jarvis  Patrick: add a structure to a cluster when this structure and a
       structure in the cluster have each other as neighbors and they  have  a
       least   P  neighbors  in common. The neighbors of a structure are the M
       closest structures or all structures within  cutoff.

       Monte Carlo: reorder the RMSD matrix using Monte Carlo.

       diagonalization: diagonalize the RMSD matrix.

       gromos: use algorithm as described in Daura  et  al.   (  Angew.  Chem.
       Int.  Ed.   1999,   38,  pp  236-240).  Count number of neighbors using
       cut-off, take structure with largest number of neighbors with  all  its
       neighbors as cluster and eleminate it from the pool of clusters. Repeat
       for remaining structures in pool.

       When the clustering algorithm assigns each  structure  to  exactly  one
       cluster  (single  linkage,  Jarvis Patrick and gromos) and a trajectory
       file is supplied, the structure with the smallest average  distance  to
       the  others or the average structure or all structures for each cluster
       will be written to a trajectory  file.  When  writing  all  structures,
       separate numbered files are made for each cluster.

       Two output files are always written:

         -o  writes the RMSD values in the upper left half of the matrix and a
       graphical depiction of the  clusters  in  the  lower  right  half  When
       -minstruct = 1 the graphical depiction is black when two structures are
       in the same cluster.  When  -minstruct  1 different colors will be used
       for each cluster.

         -g  writes information on the options used and a detailed list of all
       clusters and their members.

       Additionally, a number of optional output files can be written:

        -dist writes the RMSD distribution.

        -ev writes the eigenvectors of the RMSD matrix diagonalization.

        -sz writes the cluster sizes.

        -tr writes a matrix of the number transitions between cluster pairs.

        -ntr writes the total number of transitions to or from each cluster.

        -clid writes the cluster number as a function of time.

        -cl writes average (with option  -av) or  central  structure  of  each
       cluster  or  writes  numbered files with cluster members for a selected
       set of clusters (with option  -wcl, depends on  -nst and  -rmsmin).


       -f traj.xtc Input, Opt.
        Trajectory: xtc trr trj gro g96 pdb cpt

       -s topol.tpr Input, Opt.
        Structure+mass(db): tpr tpb tpa gro g96 pdb

       -n index.ndx Input, Opt.
        Index file

       -dm rmsd.xpm Input, Opt.
        X PixMap compatible matrix file

       -o rmsd-clust.xpm Output
        X PixMap compatible matrix file

       -g cluster.log Output
        Log file

       -dist rmsd-dist.xvg Output, Opt.
        xvgr/xmgr file

       -ev rmsd-eig.xvg Output, Opt.
        xvgr/xmgr file

       -sz clust-size.xvg Output, Opt.
        xvgr/xmgr file

       -tr clust-trans.xpm Output, Opt.
        X PixMap compatible matrix file

       -ntr clust-trans.xvg Output, Opt.
        xvgr/xmgr file

       -clid clust-id.xvg Output, Opt.
        xvgr/xmgr file

       -cl clusters.pdb Output, Opt.
        Trajectory: xtc trr trj gro g96 pdb cpt


        Print help info and quit

       -nice int 19
        Set the nicelevel

       -b time 0
        First frame (ps) to read from trajectory

       -e time 0
        Last frame (ps) to read from trajectory

       -dt time 0
        Only use frame when t MOD dt = first time (ps)

       -tu enum ps
        Time unit:  ps,  fs,  ns,  us,  ms or  s

        View output xvg, xpm, eps and pdb files

        Add specific codes (legends etc.) in the  output  xvg  files  for  the
       xmgrace program

        Use RMSD of distances instead of RMS deviation

       -nlevels int 40
        Discretize RMSD matrix in  levels

       -cutoff real 0.1
        RMSD cut-off (nm) for two structures to be neighbor

        Use least squares fitting before RMSD calculation

       -max real -1
        Maximum level in RMSD matrix

       -skip int 1
        Only analyze every nr-th frame

        Write average iso middle structure for each cluster

       -wcl int 0
        Write all structures for first  clusters to numbered files

       -nst int 1
        Only write all structures if more than  per cluster

       -rmsmin real 0
        minimum rms difference with rest of cluster for writing structures

       -method enum linkage
        Method   for   cluster   determination:    linkage,    jarvis-patrick,
       monte-carlo,  diagonalization or  gromos

       -minstruct int 1
        Minimum number of structures in cluster for coloring in the xpm file

        Treat the RMSD matrix as consisting of 0 and 1, where the  cut-off  is
       given by -cutoff

       -M int 10
        Number of nearest neighbors considered for Jarvis-Patrick algorithm, 0
       is use cutoff

       -P int 3
        Number of identical nearest neighbors required to form a cluster

       -seed int 1993
        Random number seed for Monte Carlo clustering algorithm

       -niter int 10000
        Number of iterations for MC

       -kT real 0.001
        Boltzmann weighting factor for Monte Carlo  optimization  (zero  turns
       off uphill steps)



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                                Thu 16 Oct 2008                   g_spatial(1)