Provided by: gromacs-data_4.6.5-1build1_all bug


       g_cluster - clusters structures

       VERSION 4.6.5


       g_cluster  -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 -[no]version -nice int -b
       time -e time -dt time -tu enum -[no]w -xvg  enum  -[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 -[no]pbc


        g_cluster can cluster structures using  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 eliminate 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

       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). The center of a cluster is the structure with the smallest
       average RMSD from all other structures of the cluster.


       -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

        Print version 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:  fs,  ps,  ns,  us,  ms or  s

        View output  .xvg,  .xpm,  .eps and  .pdb files

       -xvg enum xmgrace
        xvg plot formatting:  xmgrace,  xmgr or  none

        Use RMSD of distances instead of RMS deviation

       -nlevels int 40
        Discretize RMSD matrix in this number of 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 the structures for this number of clusters to numbered files

       -nst int 1
        Only write all structures if more than this number of structures 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)

        PBC check



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                                          Mon 2 Dec 2013                             g_cluster(1)