NAME¶
g_analyze - analyzes data sets
VERSION 4.5.4-dev-20110404-bc5695c
SYNOPSIS¶
g_analyze -f graph.xvg -ac autocorr.xvg
-msd msd.xvg -cc coscont.xvg -dist
distr.xvg -av average.xvg -ee errest.xvg
-bal ballisitc.xvg -g fitlog.log
-[no]h -[no]version -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
-smooth real -filter real
-[no]power -[no]subav -[no]oneacf
-acflen int -[no]normalize -P enum
-fitfn enum -ncskip int -beginfit real
-endfit real
DESCRIPTION¶
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
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.
g_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).
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 ( alpha (tau1 ((exp(-t/tau1) - 1) tau1/t + 1)) +
(1-alpha) (tau2 ((exp(-t/tau2) - 1) tau2/t + 1)))), where T is the total time.
alpha, 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
-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 component 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
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
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
-bal ballisitc.xvg Output, Opt.
xvgr/xmgr file
-g fitlog.log Output, Opt.
Log file
OTHER OPTIONS¶
-[no]hno
Print help info and quit
-[no]versionno
Print version info and quit
-nice int 0
Set the nicelevel
-[no]wno
View output
.xvg,
.xpm,
.eps and
.pdb files
-xvg enum xmgrace
xvg plot formatting:
xmgrace,
xmgr or
none
-[no]timeyes
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 separated by &
-[no]dno
Use the derivative
-bw real 0.1
Binwidth for the distribution
-errbar enum none
Error bars for
-av:
none,
stddev,
error or
90
-[no]integrateno
Integrate data function(s) numerically using trapezium rule
-aver_start real 0
Start averaging the integral from here
-[no]xydyno
Interpret second data set as error in the y values for integrating
-[no]regressionno
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 chi2 = (y - A0 x0 - A1 x1 - ... - AN
xN)2 where now Y is the first data set in the input file and xi the others. Do
read the information at the option
-time.
-[no]luzarno
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
-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
-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
-[no]powerno
Fit data to: b ta
-[no]subavyes
Subtract the average before autocorrelating
-[no]oneacfno
Calculate one ACF over all sets
-acflen int -1
Length of the ACF, default is half the number of frames
-[no]normalizeyes
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,
exp9 or
erffit
-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 until
the end
SEE ALSO¶
gromacs(7)
More information about
GROMACS is available at
<
http://www.gromacs.org/>.