Morten Hjorth-Jensen
20 lines
1.4 KiB
Markdown
20 lines
1.4 KiB
Markdown
# ResamplingAnalysisScripts
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## Sample Scripts for data Analysis
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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__.
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## Usage
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Simply run `python analysis.py FILENAME.xxx [NLINES]`
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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)
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Ouput is located into the `FILENAME/` folder.
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If more than 10⁵ lines are specified the autocorrelation function won't be computed, as it would take too long.
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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.
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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).
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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.
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