Provided by: gromacs-data_2018.1-1_all bug


       gmx-analyze - Analyze data sets


          gmx analyze [-f [<.xvg>]] [-ac [<.xvg>]] [-msd [<.xvg>]] [-cc [<.xvg>]]
                      [-dist [<.xvg>]] [-av [<.xvg>]] [-ee [<.xvg>]]
                      [-fitted [<.xvg>]] [-g [<.log>]] [-[no]w] [-xvg <enum>]
                      [-[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>]
                      [-fitend <real>] [-filter <real>] [-[no]power]
                      [-[no]subav] [-[no]oneacf] [-acflen <int>]
                      [-[no]normalize] [-P <enum>] [-fitfn <enum>]
                      [-beginfit <real>] [-endfit <real>]


       gmx  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 separated 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.

       gmx  analyze  always  shows the average and standard deviation of each set, as well as the
       relative deviation of the third and fourth cumulant from those of a Gaussian  distribution
       with the same standard deviation.

       Option  -ac  produces  the  autocorrelation  function(s).   Be sure that the time interval
       between data points is much shorter than the time scale of the autocorrelation.

       Option -cc plots the resemblance of set i with a cosine of i/2 periods. The formula is:

          2 (integral from 0 to T of y(t) cos(i pi t) dt)^2 / integral from 0 to T of y^2(t) dt

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

       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:  error^2  =
       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 (  alpha   (tau_1 ((exp(-t/tau_1) - 1) tau_1/t + 1)) +
                                 (1-alpha) (tau_2 ((exp(-t/tau_2) - 1) tau_2/t + 1)))),

       where  T  is  the  total  time.   alpha, tau_1 and tau_2 are obtained by fitting f^2(t) to
       error^2.  When the actual block average is very close to the analytical curve,  the  error
       is  sigma``*``sqrt(2/T  (a  tau_1 + (1-a) tau_2)).  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 t^a, which is accomplished by fitting to a t + b on
       log-log scale. All points after the first zero or with a negative value are ignored.

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

       Option -fitfn performs curve fitting to a number of different curves that  make  sense  in
       the  context  of molecular dynamics, mainly exponential curves. More information is in the
       manual. To check the output of the fitting procedure the option -fitted  will  print  both
       the  original  data and the fitted function to a new data file. The fitting parameters are
       stored as comment in the output file.


       Options to specify input files:

       -f [<.xvg>] (graph.xvg)
              xvgr/xmgr file

       Options to specify output files:

       -ac [<.xvg>] (autocorr.xvg) (Optional)
              xvgr/xmgr file

       -msd [<.xvg>] (msd.xvg) (Optional)
              xvgr/xmgr file

       -cc [<.xvg>] (coscont.xvg) (Optional)
              xvgr/xmgr file

       -dist [<.xvg>] (distr.xvg) (Optional)
              xvgr/xmgr file

       -av [<.xvg>] (average.xvg) (Optional)
              xvgr/xmgr file

       -ee [<.xvg>] (errest.xvg) (Optional)
              xvgr/xmgr file

       -fitted [<.xvg>] (fitted.xvg) (Optional)
              xvgr/xmgr file

       -g [<.log>] (fitlog.log) (Optional)
              Log file

       Other options:

       -[no]w (no)
              View output .xvg, .xpm, .eps and .pdb files

       -xvg <enum> (xmgrace)
              xvg plot formatting: xmgrace, xmgr, none

       -[no]time (yes)
              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 this number of sets separated by &

       -[no]d (no)
              Use the derivative

       -bw <real> (0.1)
              Binwidth for the distribution

       -errbar <enum> (none)
              Error bars for -av: none, stddev, error, 90

       -[no]integrate (no)
              Integrate data function(s) numerically using trapezium rule

       -aver_start <real> (0)
              Start averaging the integral from here

       -[no]xydy (no)
              Interpret second data set as error in the y values for integrating

       -[no]regression (no)
              Perform a linear regression analysis on the data. If -xydy is set a second set will
              be  interpreted  as  the error bar in the Y value. Otherwise, if multiple data sets
              are present a multilinear regression will be performed yielding the constant A that
              minimize  chi^2  = (y - A_0 x_0 - A_1 x_1 - … - A_N x_N)^2 where now Y is the first
              data set in the input file and x_i the others.  Do  read  the  information  at  the
              option -time.

       -[no]luzar (no)
              Do  a Luzar and Chandler analysis on a correlation function and related as produced
              by gmx hbond. When in addition the -xydy flag is given the second and fourth column
              will be interpreted as errors in c(t) and n(t).

       -temp <real> (298.15)
              Temperature for the Luzar hydrogen bonding kinetics analysis (K)

       -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

       -fitend <real> (60)
              Time (ps) where to stop fitting the correlation functions in order  to  obtain  the
              forward and backward rate constants for HB breaking and formation. Only with -gem

       -filter <real> (0)
              Print  the  high-frequency fluctuation after filtering with a cosine filter of this

       -[no]power (no)
              Fit data to: b t^a

       -[no]subav (yes)
              Subtract the average before autocorrelating

       -[no]oneacf (no)
              Calculate one ACF over all sets

       -acflen <int> (-1)
              Length of the ACF, default is half the number of frames

       -[no]normalize (yes)
              Normalize ACF

       -P <enum> (0)
              Order of Legendre polynomial for ACF (0 indicates none): 0, 1, 2, 3

       -fitfn <enum> (none)
              Fit function: none, exp, aexp, exp_exp, exp5, exp7, exp9

       -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 until  the



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