update on getting started

2019-08-13 23:10:37 +02:00
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+# ResamplingAnalysisScripts
+
+## Sample Scripts for data Analysis
+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__.
+
+## Usage
+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.