Say I need to fit some data to a parabola, and then perform some calculations involving the correlation matrix elements of the fit parameters: is there a way to use these parameters directly in gnuplot after the fit converges? Are they stored in some variable like the error estimates?.
I quote the explicit problem I'm having. All of this is written to a plot.gp text file and ran with gnuplot plot.gp.
I include set fit errorbariables at the beginning, and then proceed with:
f(x)=a+b*x+c*x*x
fit f(x) 'file.dat' u 1:2:3 yerrors via a,b,c
Once the fit is done, I can use the values of a,b,c and their errors a_err, b_err and c_err directly in the plot.gp script; my question is: can I do the same with the correlation matrix of the parameters?
The problem is that the matrix is printed to terminal once the script finishes to run:
correlation matrix of the fit parameters:
a b e
a 1.000
b 0.910 1.000
c -0.956 -0.987 1.000
Are the entries of the matrix stores in some variable (like a_err, b_err) that I can access after the fit is done but before the script ends?
I think the command you are looking for is
set fit covariancevariables
If the `covariancevariables` option is turned on, the covariances between
final parameters will be saved to user-defined variables. The variable name
for a certain parameter combination is formed by prepending "FIT_COV_" to
the name of the first parameter and combining the two parameter names by
"_". For example given the parameters "a" and "b" the covariance variable is
named "FIT_COV_a_b".
Edit: I certainly missed gnuplot's intended way via option covariancevariables (apparently available since gnuplot 5.0). Ethan's answer is the way to go. I nevertheless leave my answer, with some modifications it might maybe be useful to extract something else from the fit output.
Maybe I missed it, but I am not aware that you can directly store the elements of the correlation matrix into variables, however, you can do it with some workaround.
You can set the output file for your fit results (check help set fit). The shortest output will be created with the option results. The results will be written to this file (actually, appended if the file already exists).
Example:
After 5 iterations the fit converged.
final sum of squares of residuals : 0.45
rel. change during last iteration : -3.96255e-10
degrees of freedom (FIT_NDF) : 1
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.67082
variance of residuals (reduced chisquare) = WSSR/ndf : 0.45
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = 1.75 +/- 0.3354 (19.17%)
b = -2.65 +/- 1.704 (64.29%)
c = 1.75 +/- 1.867 (106.7%)
correlation matrix of the fit parameters:
a b c
a 1.000
b -0.984 1.000
c 0.898 -0.955 1.000
Now, you can read this file back into a datablock (check gnuplot: load datafile 1:1 into datablock) and extract the values from the last lines (here: 3), check help word and check real.
Script:
### get fit correlation matrix into variables
reset session
$Data <<EOD
1 1
2 3
3 10
4 19
EOD
f(x) = a*x**2 + b*x + c
myFitFILE = "SO71788523_fit.dat"
set fit results logfile myFitFILE
fit f(x) $Data u 1:2 via a,b,c
set key top left
set grid x,y
# load file 1:1 into datablock
FileToDatablock(f,d) = GPVAL_SYSNAME[1:7] eq "Windows" ? \
sprintf('< echo %s ^<^<EOD & type "%s"',d,f) : \
sprintf('< echo "\%s <<EOD" & cat "%s"',d,f) # Linux/MacOS
load FileToDatablock(myFitFILE,'$FIT')
# extract parameters into variables
N = 3 # number of parameters
getValue(p1,p2) = real(word($FIT[|$FIT|-N+p1],p2+1)) # extract value as floating point number
aa = getValue(1,1)
ba = getValue(2,1)
bb = getValue(2,2)
ca = getValue(3,1)
cb = getValue(3,2)
cc = getValue(3,3)
set label 1 at graph 0.1,graph 0.8 \
sprintf("Correlation matrix:\naa: %g\nba: %g\nbb: %g\nca: %g\ncb: %g\ncc: %g",aa,ba,bb,ca,cb,cc)
plot $Data u 1:2 w lp pt 7 lc "red", \
f(x) w l lc "blue" title sprintf("fit: a=%g, b=%g, c=%g",a,b,c)
### end of script
Result:
Related
I am trying to use GNUplot to calculate the best-fit line for some time-series data. The data is just about linear already with a negative slope. The input data looks something like:
1615840396,138849,510249
1615840406,139011,511152
1615840416,137580,510330
1615840426,137493,510501
1615840436,137261,510186
1615840447,137435,511026
1615840456,137054,510252
1615840466,136955,510174
1615840476,136922,510540
1615840486,136970,510999
The first column is a Unix timestamp. A graph of column 2 vs. time looks like this:
I'm trying to produce a best-fit line like this:
gnuplot> set xdata time
gnuplot> set timefmt "%s"
gnuplot> set datafile separator comma
gnuplot> f(x) = m*x + b
gnuplot> fit f(x) 'data.csv' using 1:2 via m,b
Which produces:
Final set of parameters Asymptotic Standard Error
======================= ==========================
m = 8.08062e-05 +/- 1.633 (2.021e+06%)
b = 1 +/- 2.639e+09 (2.639e+11%)
The resulting best fit line has a positive slope, and doesn't really git the data at all:
What am I doing wrong?
This is a recurring question about fitting time data. I guess there should be similar questions here on SO, but I can't find them right now. I'm not sure if there is an example of fitting time data on the gnuplot homepage.
I guess the problem is the following: If you assume a linear function f(x) = a*x + b with time data, the origin will be at Jan, 1st 1970.
Typically, this will be pretty far from your actual data and furthermore, you only have a small range of data compared to the distance to your origin. So, I guess the fitting function cannot deliver really good values.
You better try to fit a function which is shifted by your start date.
You either set this start date manually, or you spend a few lines of code to find it automatically.
Additionally, it will help if you give some starting values for the fitting parameters.
Here, it seems that a will be found without giving a start value and if you set b=1 it will not give good result, but b=10 seems to be ok as starting value.
Code:
### fitting time data
reset session
# create some random test data
set print $Data
do for [i=1:100] {
print sprintf("%.0f,%g",time(0)+i*86400,i+rand(0)*10 )
}
set print
set datafile separator comma
# find out the StartDate
StartDate = 16158768671 # manually by setting a value
# or automatically by using stats
stats $Data u 1 index 0 every ::0:0:0:0 nooutput
StartDate = STATS_min
f(x) = a*(x-StartDate) + b
set fit brief nolog
b=10
fit f(x) $Data u 1:2 via a,b
set key top left
set format x "%b %d" timedate
plot $Data u 1:2 ti "Data", \
f(x) w l lc rgb "red" ti "Fit"
### end of code
Result:
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = 1.16005e-05 +/- 1.163e-07 (1.003%)
b = 6.1323 +/- 0.5759 (9.39%)
I calculated the eigenvalues of the Hamiltonian for the 1D-hydrogen atom in atomic units with the Fourier-Grid-Hamiltonian method in a nice little Fortran program.
All the eigenvalues found between -1 and 0 (the bound states) are saved into a file line by line like this:
-0.50016671392950229
-0.18026105614262633
-0.11485673263086937
-4.7309305955423042E-002
-4.7077108902158216E-002
As the number of found eigenvalues differs depends on the stepsize my program uses, the number of entries in the file can vary (in theory, there are infinite ones).
I now want to plot the values from the file as a line parallel to the x-axis with the offset given by the values read from file.
I also want to be able to plot the data only up to a certain line number, as the values get really close to each other the further you come to zero and they cannot be distinguished by eye anymore.
(Here e.g. it would make sence to plot the first four entries, the fifth is already too close to the previous one)
I know that one can plot lines parallel to the x axis with the command plot *offset* but I don't know how to tell gnuplot to use the data from the file. So far I had to manually plot the values.
As a second step I would like to plot the data only in a certain x range, more concrete between the points of intersection with the harmonic potential used for the numeric solution V(x) = -1/(1+abs(x))
The result should look like this:
scheme of the desired plot (lookalike)
The closest I got to, was with
plot -1/(1+abs(x)),-0.5 title 'E0',-0.18 title 'E1', -0.11 title 'E2'
which got me the following result:
my plot
Hope you guys can help me, and I'm really curios whether gnuplot actually can do the second step I described!
As for the first part of your question, you can for example use the xerrorbars plotting style as:
set terminal pngcairo
set output 'fig.png'
unset key
set xr [-1:1]
set yr [-1:0]
unset bars
plot '-' u (0):($1<-0.1?$1:1/0):(1) w xerrorbars pt 0 lc rgb 'red'
-0.50016671392950229
-0.18026105614262633
-0.11485673263086937
-4.7309305955423042E-002
-4.7077108902158216E-002
e
The idea here is to:
interpret the energies E as points with coordinates (0,E) and assign to each of them an x-errorbar of width 1 (via the third part of the specification (0):($1<-0.1?$1:1/0):(1))
"simulate" the horizontal lines with x-errorbars. To this end, unset bars and pt 0 ensure that Gnuplot displays just plain lines.
consider only energies E<-0.1, the expressions $1<-0.1?$1:1/0 evaluates otherwise to an undefined value 1/0 which has the consequence that nothing is plotted for such E.
plot '-' with explicit values can be of course replaced with, e.g., plot 'your_file.dat'
This produces:
For the second part, it mostly depends how complicated is your function V(x). In the particular case of V(x)=-1/(1+|x|), one could infer directly that it's symmetric around x=0 and calculate the turning points explicitly, e.g.,
set terminal pngcairo
set output 'fig.png'
fName = 'test.dat'
unset key
set xr [-10:10]
set yr [-1:0]
unset bars
f(x) = -1 / (1+abs(x))
g(y) = (-1/y - 1)
plot \
f(x) w l lc rgb 'black', \
fName u (0):($1<-0.1?$1:1/0):(g($1)) w xerrorbars pt 0 lc rgb 'red', \
fName u (0):($1<-0.1?$1:1/0):(sprintf("E%d", $0)) w labels offset 0, char 0.75
which yields
The idea is basically the same as before, just the width of the errorbar now depends on the y-coordinate (the energy). Also, the labels style is used in order to produce explicit labels.
Another approach may be to get data from "energy.dat" (as given in the question) with system and cat commands (so assuming a Un*x-like system...) and select V(x) and E at each x via max:
set key bottom right
set yr [-1:0.2]
set samples 1000
Edat = system( "cat energy.dat" )
max(a,b) = ( a > b ) ? a : b
V(x) = -1/(1+abs(x))
plot for [ E in Edat ] \
max(V(x),real(E)) title sprintf("E = %8.6f", real(E)) lw 2, \
V(x) title "V(x) = -1/(1+|x|)" lc rgb "red" lw 2
If we change the potential to V(x) = -abs(cos(x)), the plot looks pretty funny (and the energy levels are of course not correct!)
More details about the script:
max is not a built-in function in Gnuplot, but a user-defined function having two formal arguments. So for example, we may define it as
mymax( p, q ) = ( p > q ) ? p : q
with any other names (and use mymax in the plot command). Next, the ? symbol is a ternary operator that gives a short-hand notation for an if...else construct. In a pseudo-code, it works as
function max( a, b ) {
if ( a > b ) then
return a
else
return b
end
}
This way, max(V(x),real(E)) selects the greater value between V(x) and real(E) for any given x and E.
Next, Edat = system( "cat energy.dat" ) tells Gnuplot to run the shell command "cat energy.dat" and assign the output to a new variable Edat. In the above case, Edat becomes a string that contains a sequence of energy values read in from "energy.dat". You can check the contents of Edat by print( Edat ). For example, it may be something like
Edat = "-0.11 -0.22 ... -0.5002"
plot for [ E in Edat ] ... loops over words contained in a string Edat. In the above case, E takes a string "-0.11", "-0.22", ..., "-0.5002" one-by-one. real(E) converts this string to a floating-point value. It is used to pass E (a character string) to any mathematical function.
The basic idea is to draw a truncated potential above E, max(V(x),E), for each value of E. (You can check the shape of such potential by plot max(V(x),-0.5), for example). After plotting such curves, we redraw the potential V(x) to make it appear as a single potential curve with a different color.
set samples 1000 increases the resolution of the plot with 1000 points per curve. 1000 is arbitrary, but this seems to be sufficient to make the figure pretty smooth.
[Current]
I am importing a text file in which the first column has simulation time (0~150) the second column has the delay (0.01~0.02).
1.000000 0.010007
1.000000 0.010010
2.000000 0.010013
2.000000 0.010016
.
.
.
149.000000 0.010045
149.000000 0.010048
150.000000 0.010052
150.000000 0.010055
which gives me the plot:
[Desired]
I need to plot an average line on it like shown in the following image with red line:
Here is a gnuplot only solution with sample data:
set table "test.data"
set samples 1000
plot rand(0)+sin(x)
unset table
You should check the gnuplot demo page for a running average. I'm going to generalize this demo in terms of dynamically building the functions. This makes it much easier to change the number of points include in the average.
This is the script:
# number of points in moving average
n = 50
# initialize the variables
do for [i=1:n] {
eval(sprintf("back%d=0", i))
}
# build shift function (back_n = back_n-1, ..., back1=x)
shift = "("
do for [i=n:2:-1] {
shift = sprintf("%sback%d = back%d, ", shift, i, i-1)
}
shift = shift."back1 = x)"
# uncomment the next line for a check
# print shift
# build sum function (back1 + ... + backn)
sum = "(back1"
do for [i=2:n] {
sum = sprintf("%s+back%d", sum, i)
}
sum = sum.")"
# uncomment the next line for a check
# print sum
# define the functions like in the gnuplot demo
# use macro expansion for turning the strings into real functions
samples(x) = $0 > (n-1) ? n : ($0+1)
avg_n(x) = (shift_n(x), #sum/samples($0))
shift_n(x) = #shift
# the final plot command looks quite simple
set terminal pngcairo
set output "moving_average.png"
plot "test.data" using 1:2 w l notitle, \
"test.data" using 1:(avg_n($2)) w l lc rgb "red" lw 3 title "avg\\_".n
This is the result:
The average lags quite a bit behind the datapoints as expected from the algorithm. Maybe 50 points are too many. Alternatively, one could think about implementing a centered moving average, but this is beyond the scope of this question.
And, I also think that you are more flexible with an external program :)
Here's some replacement code for the top answer, which makes this also work for 1000+ points and much much faster. Only works in gnuplot 5.2 and later I guess
# number of points in moving average
n = 5000
array A[n]
samples(x) = $0 > (n-1) ? n : int($0+1)
mod(x) = int(x) % n
avg_n(x) = (A[mod($0)+1]=x, (sum [i=1:samples($0)] A[i]) / samples($0))
Edit
The updated question is about a moving average.
You can do this in a limited way with gnuplot alone, according to this demo.
But in my opinion, it would be more flexible to pre-process your data using a programming language like python or ruby and add an extra column for whatever kind of moving average you require.
The original answer is preserved below:
You can use fit. It seems you want to fit to a constant function. Like this:
f(x) = c
fit f(x) 'S1_delay_120_LT100_LU15_MU5.txt' using 1:2 every 5 via c
Then you can plot them both.
plot 'S1_delay_120_LT100_LU15_MU5.txt' using 1:2 every 5, \
f(x) with lines
Note that this is technique can be used with arbitrary functions, not just constant or lineair functions.
I wanted to comment on Franky_GT, but somehow stackoverflow didn't let me.
However, Franky_GT, your answer works great!
A note for people plotting .xvg files (e.g. after doing analysis of MD simulations), if you don't add the following line:
set datafile commentschars "##&"
Franky_GT's moving average code will result in this error:
unknown type in imag()
I hope this is of use to anyone.
For gnuplot >=5.2, probably the most efficient solution is using an array like #Franky_GT's solution.
However, it uses the pseudocolumn 0 (see help pseudocolumns). In case you have some empty lines in your data $0 will be reset to 0 which eventually might mess up your average.
This solution uses an index t to count up the datalines and a second array X[] in case a centered moving average is desired. Datapoints don't have to be equidistant in x.
At the beginning there will not be enough datapoints for a centered average of N points so for the x-value it will use every second point and the other will be NaN, that's why set datafile missing NaN is necessary to plot a connected line at the beginning.
Code:
### moving average over N points
reset session
# create some test data
set print $Data
y = 0
do for [i=1:5000] {
print sprintf("%g %g", i, y=y+rand(0)*2-1)
}
set print
# average over N values
N = 250
array Avg[N]
array X[N]
MovAvg(col) = (Avg[(t-1)%N+1]=column(col), n = t<N ? t : N, t=t+1, (sum [i=1:n] Avg[i])/n)
MovAvgCenterX(col) = (X[(t-1)%N+1]=column(col), n = t<N ? t%2 ? NaN : (t+1)/2 : ((t+1)-N/2)%N+1, n==n ? X[n] : NaN) # be aware: gnuplot does integer division here
set datafile missing NaN
plot $Data u 1:2 w l ti "Data", \
t=1 '' u 1:(MovAvg(2)) w l lc rgb "red" ti sprintf("Moving average over %d",N), \
t=1 '' u (MovAvgCenterX(1)):(MovAvg(2)) w l lw 2 lc rgb "green" ti sprintf("Moving average centered over %d",N)
### end of code
Result:
Is there a way to specify that input data is an expression that needs to be evaluated?
In my case the data is rational numbers encoded in the format "n/d". Is there a way to tell gnuplot to interpret "n/d" as "n divided by d"?
Example input data:
1/9 1
1/8 2
1/7 3
1/6 4
I tried plot "data" using ($1):2 but this truncates "n/d" to "n".
Update: After some digging in the manual, I found that in this case I can tell gnuplot to interpret "/" as a column separator and then divide the first number by the second as follows: plot "data" using ($1/$2):3 '%lf/%lf %lf'
I don't know a gnuplot only answer. But you can use the system command to let another program do the work. For example the bc program on linux. The following script works for me:
result(s) = system(sprintf('echo "%s" | bc -l ~/.bcrc', s)) + 0
set table "data.eval"
plot "data.dat" using 1:(result(strcol(2)))
unset table
This is the datafile:
1 1/2
2 1/2.0
3 4+4
4 4*5-1
5 4*(5-1)-(3-7)
6 sin(3.1415)
This is the output:
# Curve 0 of 1, 6 points
# Curve title: ""data.dat" using 1:(result(strcol(2)))"
# x y type
1 0.5 i
2 0.5 i
3 8 i
4 19 i
5 20 i
6 9.26536e-05 i
Notes:
The set table "data.eval" prints the values into a file, now it is easier to check the results.
strcol(2) reads the entries of the second column as a string. The expression must not contain white space.
The function result transfers the string to bc. The string itself must be quoted, else the shell would complain for example about brackets as in line 5 or 6 of the datafile.
The option -l on bc enables floating point evaluation of expressions like in the first line (1/2 = 0.5 instead of 1/2 = 0), and it defines functions like s(x) for sine and e(x) for exp(x).
~/.bcrc reads some function definitions
The system command returns a string. The string is promoted to a floating point number by adding 0.
My ~/.bcrc looks like this:
pi=4*a(1)
e=e(1)
define ln(x)
{return(l(x))}
define lg(x)
{return(l(x)/l(10))}
define exp(x)
{return(e(x))}
define sin(x)
{return(s(x))}
define fac(x)
{if (x<=1) return(1);
return(fac(x-1)*x)}
define ncr(n,r)
{return(fac(n)/(fac(r)*fac(n-r)))}
Tested with gnuplot 4.6 and bc 1.06.95 on Debian Jessie. On Windows you have the set command for integer calculations. It seems that Google knows some other commandline calculators.
It wouldn't be gnuplot if there wasn't a gnuplot-only solution.
Simply collect your expressions in a string by "mis"using stats and evaluate them via eval in a do for loop and write the results in a string and convert the values to a number via real and plot them.
Check help stats, help do, help eval, help real and the example below. Most of the data is taken from #maij's answer. The script works for gnuplot>=5.0 and with some adaptions probably with earlier versions.
Script: (works for gnuplot>=5.0, Jan 2015)
### evaluate expressions in input data
reset session
$Data <<EOD
1 1/2 # integer division
2 1/2.0 # float division
3 4+4
4 4*5-1
5 4*(5-1)-(3-7)
6 sin(3.1415/2)
7 2**3
8 sqrt(9)
EOD
myCol = 2
myExprs = ''
stats $Data u (myExprs=myExprs.sprintf(' "v=%s"',strcol(myCol))) nooutput
myValues = ''
do for [i=1:words(myExprs)] {
eval word(myExprs,i)
myValues = myValues.sprintf(" %g",v)
}
myValue(n) = real(word(myValues,int(column(n)+1)))
set offsets 0.5,0.5,2,0
plot $Data u 1:(myValue(0)) w lp pt 7 lc "red" ti "Expressions", \
'' u 1:(myValue(0)):2 w labels offset 0,1 notitle
### end of script
Result:
i'm having some problems with gnuplot
I have to draw a cdf function and i'm interested in the values of variable x when F(x) is equal to 0.1 and 0.9
How can I tell Gnuplot to show me on the x axis the value corresponding to a given value on the y value (in my example those values are 0.1 and 0.9)
thanks
You're basically asking gnuplot to solve an equation. In your particular case, actually two equations: F(x)=0.1 and F(x)=0.9. As far as I know this cannot be done, but I might be wrong. What you can do if you simply want a graphical solution, is make a conditional plot, and ask that when F(x) is very close to 0.1 0.9, gnuplot plots something other than the function.
For example, assume f(x)=x^2 and you want to know "graphically" for which x f(x)=0.1. Then you can request the value abs(f(x) - 0.1) be small, for example < 0.01. Then tell gnuplot to go to zero (just an example!) if this is the case, otherwise plot f(x)=x^2:
f(x)=x**2
set xrange [-2:2]
set samples 1000
plot abs(f(x)-1) < 0.01 ? 0 : f(x)
Which yields:
The two peaks that go to zero mark graphically on the x axis the solution to the equation f(x)=0.1. Of course, you need gnuplot to sample this point in order to see a peak. Thus you need to play with set samples and set xrange.
From your question it is not clear whether you have a function F(x) as expression or just a x,y-data file. I assume that your function is monotonic increasing in x and y.
Two solutions come to my mind:
via simple linear interpolation
via curve fitting
Let's create some test data. For this, let's assume your function is known (as expression) and something like this (check help norm): F(x) = a*norm(b*x + c)
Let's take a = 1; b = 0.8; c = -4. In the example below, sampling will be only 8, just for illustration purpose.
You can easily set samples 200 and you will get the same results as for the curve fitting method below. From gnuplot 5.0 on, you could write the data into a datablock instead of a file on disk.
Data: SO22276755.dat
0 3.16712e-05
1.42857 0.002137
2.85714 0.043238
4.28571 0.283855
5.71429 0.716145
7.14286 0.956762
8.57143 0.997863
10 0.999968
Script 1: (basically works for gnuplot 4.6.0, March 2012)
### interpolate x-values
reset
FILE = "SO22276755.dat"
yis = '0.10 0.90'
yi(n) = real(word(yis,n))
xis = ''
xi(n) = real(word(xis,n))
Interpolate(yi) = (x1-x0)/(y1-y0)*(yi-y0) + x0
getXis(xis) = xis.(n=words(xis), n<words(yis) ? yi=real(word(yis,n+1)) : 0, \
y0<=yi && y1>=yi ? sprintf(" %g",Interpolate(yi)) : '')
set key left top noautotitle
set grid x,y
plot x1=y1=NaN FILE u (x0=x1,x1=$1):(y0=y1,y1=$2,xis=getXis(xis),y1) \
w l lc rgb "blue" ti "data", \
'+' u (xi=xi(int($0+1))):(yi=yi(int($0+1))):\
(sprintf("(%.4g|%.4g)",xi,yi)) every ::0::1 \
w labels point pt 7 lc rgb "red" right offset -1,0 ti "interpolated"
### end of script
Result:
Script 2: (basically works for gnuplot>=4.6.0, March 2012)
With this approach you are fitting your known function F(x) to constant lines, i.e. your desired values 0.1 and 0.9. For this, a file will be created (could be a datablock for gnuplot>=5.0) and it will basically look like this SO22276755.fit:
0 0.1
1 0.1
0 0.9
1 0.9
### interpolate x-values
reset
F(x) = a*norm(b*x+c) # function
a = 1
b = 0.8
c = -4
yis = '0.10 0.90'
yi(n) = real(word(yis,n))
xis = ''
xi(n) = real(word(xis,n))
set key left top noautotitle
set grid x,y
# create fit levels file
LEVELS = "SO22276755.fit"
set table LEVELS
set samples 2
plot for [i=1:words(yis)] '+' u (yi(i))
unset table
xmin = 0
xmax = 10
set xrange[xmin:xmax]
set samples 100
xis = ''
do for [i=1:words(yis)] {
xi = (xmin+xmax)*0.5 # set start value
fit F(xi) LEVELS u 1:2 index i-1 via xi
xis = xis.sprintf(" %g",xi)
}
plot F(x) w l lc rgb "web-green" ti "F(x)", \
'+' u (xi=xi(int($0+1))):(yi=yi(int($0+1))):(sprintf("(%.4g|%.4g)",xi,yi)) \
every ::0::1 w labels point pt 7 lc rgb "red" righ offset -1,0 ti "fitted"
### end of script
Result: