g_analyze_d (1) - Linux Manuals

g_analyze_d: analyzes data sets


g_analyze - analyzes data sets



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 real


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 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: 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 n(t).

-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  3

-fitfn enum none
 Fit function:  none exp aexp exp_exp vac exp5 exp7 or  exp9

-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



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