So far this is a simple python script (should be made parallel...) to perform resampling of a data set. Methods used are Bootstrapping, Jackknife and Blocking.
Simply run python analysis.py FILENAME.xxx [NLINES]
Where FILENAME is expected to have a 3 charachter extension NLINES (optional) is the number of lines in the file to read and process (default is the whole file, but it gets very slow above 2-3 hundred thousand entries)
Ouput is located into the FILENAME/ folder.
If more than 10⁵ lines are specified the autocorrelation function won't be computed, as it would take too long.
The gaussian.dat dataset has been generated with numpy, as a proof of concept. It represents a normally distributed set of 5x10⁵ elements with std = 0.05. One will notice that the estimate on the error of the central value is greatly improved by all resampling methods.
energy.dat is an autocorrelated data set, with autocorrelation time of roughly 200. It is useful to see the use of blocking on this dataset as a convenient method to estimate the autocorrelation time (compare the elapsed time on the different methods).
In the plaquette.dat file there is a small data set (just 1000 samples) and it shows the strenght of using resampling methods to better estimate the error on the central value as opposed to the standard deviation.