Man Linux: Main Page and Category List


       g_analyze - analyzes data sets

       VERSION 4.0.1


       g_analyze  -f  graph.xvg  -ac autocorr.xvg -msd msd.xvg -cc coscont.xvg
       -dist distr.xvg -av average.xvg -ee  errest.xvg  -g  fitlog.log  -[no]h
       -nice  int -[no]w -[no]xvgr -[no]time -b real -e real -n int -[no]d -bw
       real   -errbar   enum   -[no]integrate   -aver_start   real   -[no]xydy
       -[no]regression  -[no]luzar  -temp  real  -fitstart  real  -smooth real
       -filter   real   -[no]power   -[no]subav   -[no]oneacf   -acflen    int
       -[no]normalize  -P  enum -fitfn enum -ncskip int -beginfit real -endfit


       g_analyze reads an ascii file and analyzes data sets.  A  line  in  the
       input  file may start with a time (see option  -time) and any number of
       y values may follow.  Multiple sets can also  be  read  when  they  are
       seperated by & (option  -n), in this case only one y value is read from
       each line.  All lines starting with  and @ are skipped.   All  analyses
       can also be done for the derivative of a set (option  -d).

       All  options,  except  for   -av and  -power assume that the points are
       equidistant in time.

       g_analyze always shows the average and standard deviation of each  set.
       For  each  set  it  also  shows the relative deviation of the third and
       forth cumulant from those of a  Gaussian  distribution  with  the  same
       standard deviation.

       Option  -ac produces the autocorrelation function(s).

       Option   -cc  plots  the  resemblance  of  set  i  with a cosine of i/2
       periods. The formula is: 2 (int0-T y(t) cos(i pi t) dt)2 / int0-T  y(t)
       y(t) dt

       This  is  useful  for  principal  components  obtained  from covariance
       analysis, since the principal components of random diffusion  are  pure

       Option  -msd produces the mean square displacement(s).

       Option  -dist produces distribution plot(s).

       Option   -av  produces  the  average  over the sets.  Error bars can be
       added with the  option   -errbar.   The  errorbars  can  represent  the
       standard  deviation, the error (assuming the points are independent) or
       the interval containing 90% of the points,  by  discarding  5%  of  the
       points at the top and the bottom.

       Option   -ee  produces error estimates using block averaging.  A set is
       divided in a number of blocks and  averages  are  calculated  for  each
       block.  The error for the total average is calculated from the variance
       between averages of the m blocks B_i as follows: error2 =  Sum  (B_i  -
       B)2  /  (m*(m-1)).  These errors are plotted as a function of the block
       size.  Also an analytical block average curve is plotted, assuming that
       the autocorrelation is a sum of two exponentials.  The analytical curve
       for the block average is:

       f(t) = sigma sqrt(2/T (  a   (tau1 ((exp(-t/tau1) - 1) tau1/t + 1)) +

                              (1-a) (tau2 ((exp(-t/tau2) - 1) tau2/t +  1)))),
       where  T  is  the total time.  a, tau1 and tau2 are obtained by fitting
       f2(t) to error2.  When the actual block average is very  close  to  the
       analytical  curve,  the error is sigma*sqrt(2/T (a tau1 + (1-a) tau2)).
       The  complete  derivation  is  given  in  B.  Hess,  J.   Chem.   Phys.
       116:209-217, 2002.

       Option   -filter  prints the RMS high-frequency fluctuation of each set
       and over all sets with respect to a filtered average.   The  filter  is
       proportional to cos(pi t/len) where t goes from -len/2 to len/2. len is
       supplied with the option  -filter.  This  filter  reduces  oscillations
       with period len/2 and len by a factor of 0.79 and 0.33 respectively.

       Option  -g fits the data to the function given with option  -fitfn.

       Option   -power fits the data to b ta, which is accomplished by fitting
       to a t + b on log-log  scale.  All  points  after  the  first  zero  or
       negative value are ignored.

       Option   -luzar performs a Luzar & Chandler kinetics analysis on output
       from  g_hbond. The input file can be taken directly from  g_hbond  -ac,
       and then the same result should be produced.


       -f graph.xvg Input
        xvgr/xmgr file

       -ac autocorr.xvg Output, Opt.
        xvgr/xmgr file

       -msd msd.xvg Output, Opt.
        xvgr/xmgr file

       -cc coscont.xvg Output, Opt.
        xvgr/xmgr file

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

       -av average.xvg Output, Opt.
        xvgr/xmgr file

       -ee errest.xvg Output, Opt.
        xvgr/xmgr file

       -g fitlog.log Output, Opt.
        Log file


        Print help info and quit

       -nice int 0
        Set the nicelevel

        View output xvg, xpm, eps and pdb files

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

        Expect a time in the input

       -b real -1
        First time to read from set

       -e real -1
        Last time to read from set

       -n int 1
        Read  sets seperated by &

        Use the derivative

       -bw real 0.1
        Binwidth for the distribution

       -errbar enum none
        Error bars for -av:  none,  stddev,  error or  90

        Integrate data function(s) numerically using trapezium rule

       -aver_start real 0
        Start averaging the integral from here

        Interpret second data set as error in the y values for integrating

        Perform a linear regression analysis on the data

        Do a Luzar and Chandler analysis on a correlation function and related
       as  produced  by  g_hbond. When in addition the -xydy flag is given the
       second and fourth column will be interpreted  as  errors  in  c(t)  and

       -temp real 298.15
        Temperature for the Luzar hydrogen bonding kinetics analysis

       -fitstart real 1
        Time  (ps)  from  which  to start fitting the correlation functions in
       order to obtain the forward and backward rate constants for HB breaking
       and formation

       -smooth real -1
        If  =  0,  the  tail  of  the ACF will be smoothed by fitting it to an
       exponential function: y = A exp(-x/tau)

       -filter real 0
        Print the high-frequency fluctuation after  filtering  with  a  cosine
       filter of length

        Fit data to: b ta

        Subtract the average before autocorrelating

        Calculate one ACF over all sets

       -acflen int -1
        Length of the ACF, default is half the number of frames

        Normalize ACF

       -P enum 0
        Order of Legendre polynomial for ACF (0 indicates none):  0,  1,  2 or

       -fitfn enum none
        Fit function:  none,  exp,  aexp,   exp_exp,   vac,   exp5,   exp7  or

       -ncskip int 0
        Skip N points in the output file of correlation functions

       -beginfit real 0
        Time where to begin the exponential fit of the correlation function

       -endfit real -1
        Time  where to end the exponential fit of the correlation function, -1
       is till the end



       More     information     about     GROMACS     is     available      at

                                Thu 16 Oct 2008                   g_analyze(1)