g_analyze_d (1) - Linux Man Pages
g_analyze_d: analyzes data sets
NAMEg_analyze - analyzes data sets
SYNOPSISg_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
DESCRIPTIONg_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.
FILES-f graph.xvg Input
-nice int 0
-b real -1
-e real -1
-n int 1
-bw real 0.1
-errbar enum none
-aver_start real 0
-temp real 298.15
-fitstart real 1
-smooth real -1
-filter real 0
-acflen int -1
-P enum 0
-fitfn enum none
-ncskip int 0
-beginfit real 0
-endfit real -1
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