gmx-analyze(1) GROMACS Manual gmx-analyze(1)NAMEgmx-analyze - Analyze data sets
SYNOPSIS
gmx analyze [-f [<.xvg>]] [-ac [<.xvg>]] [-msd [<.xvg>]]
[-cc [<.xvg>]] [-dist [<.xvg>]] [-av [<.xvg>]]
[-ee [<.xvg>]] [-bal [<.xvg>]] [-g [<.log>]]
[-nice <int>] [-[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>]
DESCRIPTION
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 sepa‐
rated 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 y2(t) dt This is useful for principal compo‐
nents 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 devi‐
ation, 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 ( 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 f2(t) to error2. 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 -bal finds and subtracts the ultrafast "ballistic" component
from a hydrogen bond autocorrelation function by the fitting of a sum
of exponentials, as described in e.g. O. Markovitch, J. Chem. Phys.
129:084505, 2008. The fastest term is the one with the most negative
coefficient in the exponential, or with -d, the one with most negative
time derivative at time 0. -nbalexp sets the number of exponentials to
fit.
Option -gem fits bimolecular rate constants ka and kb (and optionally
kD) to the hydrogen bond autocorrelation function according to the
reversible geminate recombination model. Removal of the ballistic com‐
ponent first is strongly advised. The model is presented in O.
Markovitch, J. Chem. Phys. 129:084505, 2008.
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 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 produced.
OPTIONS
Options to specify input and output files:
-f [<.xvg>] (graph.xvg) (Input)
xvgr/xmgr file
-ac [<.xvg>] (autocorr.xvg) (Output, Optional)
xvgr/xmgr file
-msd [<.xvg>] (msd.xvg) (Output, Optional)
xvgr/xmgr file
-cc [<.xvg>] (coscont.xvg) (Output, Optional)
xvgr/xmgr file
-dist [<.xvg>] (distr.xvg) (Output, Optional)
xvgr/xmgr file
-av [<.xvg>] (average.xvg) (Output, Optional)
xvgr/xmgr file
-ee [<.xvg>] (errest.xvg) (Output, Optional)
xvgr/xmgr file
-bal [<.xvg>] (ballisitc.xvg) (Output, Optional)
xvgr/xmgr file
-g [<.log>] (fitlog.log) (Output, Optional)
Log file
Other options:
-nice <int> (0)
Set the nicelevel
-[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. Other‐
wise, if multiple data sets are present a multilinear regression will
be performed yielding the constant A that minimize chi2 = (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 length
-[no]power (no)
Fit data to: b ta
-[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, vac, exp5, exp7, exp9, erf‐
fit
-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 end
SEE ALSOgromacs(7)
More information about GROMACS is available at <http://www.gro‐
macs.org/>.
VERSION 5.0.6gmx-analyze(1)
